Welcome back to the Deep Dive. Today we are looking
at a website that, and I'm willing to bet on
this, almost every single person listening has
on their phone right now. Oh, for sure. You probably
have a tab open for it on your work computer.
And if you're like me, you have this. Very specific,
slightly dreaded relationship with it. I know
exactly what you're talking about. It's that
place you go when you need a job or when you
feel like you should be networking, whatever
that actually means in practice. Right. It's
a professional obligation. Exactly. You go there,
you update your digital resume, you humbly brag
about a promotion, and you try your best to ignore
the 15 messages from recruiters who clearly haven't
read a single word of your profile. You are talking
about the blue app. LinkedIn. I am talking about
LinkedIn. And look, I know what everyone is thinking.
Great, we're going to talk about a job board
for an hour. How thrilling. Yeah, riveting stuff.
But here's the thing that really surprised me
when we started digging into the stack of sources
for this. We aren't just looking at a place to
upload a PDF of your CV. No. Not at all. When
you actually peel back the layers and we're looking
at Wikipedia archives, financial reports from
Microsoft, academic sociology papers, it turns
out we're looking at something much, much bigger.
It's not a job board. That is, I think, the first
and most important misconception we need to clear
up. right away. Right. It is not a job board.
It's what might be the most comprehensive and
honestly slightly terrifying digital map of the
global economy that has ever been built. It's
the perfect way to frame it. And that right there,
that is the central tension of this entire deep
dive. How so? Well. On the surface, to you and
me, it's a utility. It's a tool. It's a place
to keep in touch with old colleagues, maybe find
a new gig. But if you look under the hood, as
we're going to do today, it is a massive data
extraction engine. And the scale is just, it's
hard to process. As of November 2023, we're talking
about over 1 billion members. 1 billion. Let
that number sink in. That number is hard to wrap
your head around. 1 billion human beings. 1 billion
members across 200 countries. That is... a staggering,
almost unimaginable amount of human capital data,
all concentrated in a single database. So the
question we should be asking isn't how do I optimize
my profile with the right keywords? Exactly.
The real question is, what is this machine doing
with all of that information? Is it a social
network? Is it a public utility? Or is it what
they internally call the economic graph? The
economic graph. It sounds like something out
of a sci -fi novel. Or a very, very dry economics
textbook. Right. But as we found in the research,
that concept is the key. It's the skeleton key
to understanding everything about this company.
It really is. So here's our mission for this
deep dive. We want to understand how this machine
actually makes money because, spoiler alert,
it is not just from those little ads for project
management software you scroll past. Not by a
long shot. We need to look at the sociology of
it, how it fundamentally changes the way we hire
people and the way we get hired. And we have
to talk about the dark side. Oh, there's definitely
a dark side. The privacy breaches, the data scraping
wars, and the geopolitical mess of trying to
run a global database in a world that is more
fractured than ever. And we can't ignore the
security aspect. I mean, think about it. When
you hold the professional identities of a billion
people, you become a massive target. A target
for who? For everyone. A target for hackers,
for competitors, and even for spies. Spies on
LinkedIn. Okay, we are definitely getting to
that. But let's start at the beginning because
LinkedIn feels like it has been around forever,
right? Like it's just part of the internet's
furniture. It does feel that way. But it actually
has a very specific origin story that explains
a lot about its corporate DNA. And it all goes
back to a group of people we've talked about
before on this show, the PayPal mafia. It is
fascinating, isn't it? How all roads in Silicon
Valley seem to lead back to PayPal. You had Elon
Musk, Peter Thiel, Max Levchin. All the big names?
All of them. But the key figure for LinkedIn
is Reid Hoffman. Reid Hoffman. He was the executive
VP at PayPal, right? That was his role. Correct.
And before that, he had actually started a dating
site. called socialnet .com. I did not know that.
Yeah, it failed, but it taught him a lot about
matching algorithms and social networks. So in
December 2002, right after eBay bought PayPal
and everyone got very, very rich, Hoffman gathered
a team. And it wasn't just him? No, it was a
team of people from PayPal and from that old
social net company. People like Alan Blue, Eric
Lee, Jean -Luc Fignon. They sat down to build
something that was intentionally not about social.
In the fun sense. So not Friendster or Myspace.
Exactly. It was about social in the utility sense.
They wanted to map professional relationships.
They launched on May 5, 2003. And I think it's
really important to contextualize this. This
is before Facebook. A year before Facebook. Before
YouTube. This is the very, very early days of
what we now call Web 2 .0. But unlike the other
social networks that just exploded overnight,
LinkedIn was kind of a slow burn, wasn't it?
Incredibly slow. I mean, in the startup world
today, if you don't have a million users in a
month, you're considered dead on arrival. Yeah,
you're a failure. It took LinkedIn over a year
until August 2004 just to reach one million users.
They weren't chasing that viral loop of teenagers
posting party photos. They were trying to get
serious, busy professionals to sit down and upload
their entire resume. That is a much harder ask.
It's work. It's a huge ask. But because they
were targeting professionals from day one, the
economics were completely different. They weren't
just burning through venture capital cash, hoping
to figure out a business model later. Which was
the model for almost everyone else at the time.
It was. This is a critical distinction. They
hit their first month of profitability in March
2006. Wait, 2006? That's only three years after
they launched. Three years. Think about that.
Twitter, or X, took over a decade to turn a consistent
profit. Uber is another one. It took forever.
LinkedIn was in the black almost immediately.
They realized very early on that if you have
high -quality data on high -quality professionals,
other people, recruiters, salespeople, will pay
a premium for access to it. So while everyone
else was trying to sell ads for sneakers and
soda, LinkedIn was methodically building this
incredibly valuable database of the global workforce.
Precisely. Fast forward a few years. They are
growing. They're opening offices in India, Ireland,
Australia. It's becoming a global thing. And
then we get to 2011, the IPO. The IPO moment.
This seems like the moment the rest of the world,
especially Wall Street. woke up and realized,
oh, this isn't just a boring resume site. It
was a massive wake -up call. They filed in January
2011 and started trading in May under the ticker
LNKD. And on the very first day of trading, the
stock rose 171%. 171 % in a single day. That
is unheard of. It closed at $94 .25, more than
double the initial offering price. It was the
biggest tech IPO since Google. Wow. And the reason
investors went absolutely crazy for it wasn't
because the website was fun to use or had amazing
engagement metrics. No, it's pretty dry. It was
because they saw the moat. They saw that LinkedIn
had convinced millions and millions of professionals
to voluntarily maintain and update their own
data for free. They outsourced their data entry
to the entire world. Essentially, yes. And by
2011, they were actually earning more from advertising
revenue than Twitter did, which shocks most people.
That is shocking. I always think of Twitter as
the ad machine from that era. But I guess high
value targets, you know, CEOs, VPs, decision
makers are worth a lot more to advertisers than
random teenagers. An order of magnitude more.
But that independent era didn't last forever.
We have to talk about the biggest shift in the
company's entire history. The moment they got
swallowed by a giant. The Microsoft acquisition.
I remember when this happened in 2016 and the
price tag was just, it was eye -watering. June
2016, Microsoft announces they're acquiring LinkedIn
for $196 per share. The total deal was valued
at $26 .2 billion. $26 .2 billion in cash. All
cash. Even for a company the size of Microsoft,
that is a massive check to write. Why? What was
the strategic logic there? At the time, it was
Microsoft's largest acquisition ever. It's only
been surpassed recently by the Activision Blizzard
deal. Right. To understand the why, you have
to understand Satya Nadella's strategy for Microsoft.
Microsoft already owned the professional cloud.
They have Office, Word, Excel, Outlook. That's
the tool set of work. It's what you use to do
your job. Exactly. But they didn't own the network
of work. They had the documents, but they didn't
have the people. Oh, okay. That makes sense.
Imagine the synergy they were pitching to their
board. You're in Outlook. You're writing an email
to a potential client. Microsoft wanted a world
where you didn't just see an email address. You
saw their entire LinkedIn profile right there
in the sidebar. You'd see where they went to
school, who you know in common, their recent
posts. All of it. It was about integrating that
social graph directly into the productivity suite.
It connects the human context to the otherwise
dry document or email. That was the vision. And
surprisingly, for a deal this big, Microsoft
took a very hands -off approach. That's not usually
how these things go. No. Usually when a big company
buys a smaller one, they gut it, they integrate
it, and they completely destroy the culture.
They absorb it. Right. Microsoft did the opposite.
They let LinkedIn run as a distinct, independent
entity. Jeff Wehner, the CEO at the time, stayed
on and reported directly to Satya Nadella. They
kept their brand, their culture, their offices.
It was a very smart move. Speaking of Jeff Weiner,
he seems like a really pivotal character here.
He ran the company for 11 years before stepping
down in 2020. He did. He became executive chairman
and Ryan Roslansky stepped up as CEO. And in
the research, it's Weiner who really pushed this
concept of the economic graph. We dropped that
term in the intro, but we need to really dig
into it. What is the economic graph, practically
speaking? So in 2012, long before the Microsoft
deal. Jeff Weiner articulated his vision. He
said he didn't want to just build a social network.
He wanted to create a digital map of the entire
global economy. A map of the economy? What does
that even mean? He defined the nodes of this
map very specifically. Nodes. OK, we're getting
into graph theory here. It's simpler than it
sounds. A node is just a connection point. Weiner
said they needed to digitally represent and map
every single job in the world. Every job. Every
skill required for those jobs, every company,
every professional, and every single educational
institution. Okay, so it's not just John Smith
works at Google. No, it's much deeper. It's John
Smith, who is Node A, has Python skills, Node
G, works at Google Node C, which is located in
Mountain View, Node D, and is currently hiring
for a product manager, Node E. which requires
Python skills node B again. And all those nodes
are interconnected. Exactly. And when you map
all of those relationships at a scale of a billion
people, you can start to do things that were
previously impossible, science fiction even.
Like what? The stated goal was to make the labor
market more efficient by making it transparent,
to remove friction. Imagine if a government or
a company could see in real time that there is
a shortage of 5 ,000 nurses in London. but a
surplus of 5 ,000 nurses in Manchester. If you
have that data, you can theoretically fix that
inefficiency. You can target training programs
or relocation incentives. That's the ambition.
It's like they're trying to play SimCity with
the actual global labor market. But to do that,
you need incredible technology. You can't just
run a standard database search on that level
of complexity and scale. No, you can't. Which
is why in 2014, they unveiled a new custom -built
search architecture they called Killeen. Killeen.
What makes that special? Well, think about a
normal Google search. You type in a word, it
finds documents with that word. That's a relatively
easy problem to solve at scale. All right. But
a graph search is fundamentally different. You're
asking much more complex questions. You're asking
things like, find me all of my second degree
connections who live in the Bay Area, have worked
at Apple in the past, no C++ art, and went to
Stanford. That's a lot of different variables.
It requires traversing this massive web of relationships
almost instantly. Galeen is the custom built
engine that parses that billion node web to find
the needle in the haystack in milliseconds. So
when I go to the LinkedIn search bar and start
adding filters for people, company, and location,
that's Galeen in the background sprinting through
a billion connections to give me a list. That's
Galeen. And that technology is what enables the
entire business model. But, and here's where
we need to get critical, building the map is
one thing. How human beings actually behave on
that map is something else entirely. Right, the
human element. We have to look at the sociology
of LinkedIn. This was the part of the research
that I found most fascinating. Because we tend
to think of getting a job as a meritocracy. You
know, I have the right skills, therefore I get
the job. But the academic research suggests it's
a lot messier than that. It's incredibly messy.
The source material brings up the weak ties theory.
This is classic, isn't it? It's one of the most
famous concepts in sociology, developed by Mark
Granovetter way back in the 1970s. And LinkedIn
is basically the industrialization of this theory.
So what's the core idea? The idea is this. Your
strong ties, your best friends, your family,
your closest co -workers, they all inhabit the
same world you do. They know the same people.
They read the same news. They hear about the
same job openings. So if I'm looking for a new
opportunity. Asking my best friend is actually
kind of inefficient because he probably only
knows what I already know. Exactly. The information
is redundant. But your weak ties, that's the
key. The guy you met at a conference three years
ago, the woman you worked with on one short project
at a previous job. People on the periphery of
your network. Right. They move in different circles.
They have access to new information, information
that you do not have. And research has proven
this out on LinkedIn specifically. Yes. A study
published in Research Policy in 2017 analyzed
PhD holders on the platform. It confirmed that
those who had larger, more diverse networks of
weak ties were significantly more likely to transition
successfully from academia into industry jobs.
So the platform's real value isn't actually keeping
in touch with your friends. It's hoarding acquaintances.
It is about maintaining a low -maintenance digital
Rolodex of people you barely know, because statistically,
one of them is going to be your bridge to your
next opportunity. But there's a darker side to
this digital map. It's not a neutral map, is
it? The research from sociologist Ofer Sharon
talks about something called the filtration effect.
This made me really rethink how we present ourselves
online. Ofer Sharon's work is absolutely essential
here. He didn't just look at the data. He interviewed
unemployed workers and recruiters to understand
how the hiring process actually works in the
LinkedIn era. And what did he find? He found
that platforms like this create all these new
structural barriers that have almost nothing
to do with your actual... ability to do the job.
What kind of barriers? Visuals, for one. The
profile picture. In the old days of paper resumes,
at least in the U .S., you never included a photo.
It was considered a way to avoid bias based on
race, age, gender. Sure. On LinkedIn, a photo
is basically mandatory if you want to be taken
seriously. Sharon found that people get filtered
out consciously or subconsciously, based on the
perceived professionalism of their headshot.
And also based on the personal narrative, the
about section. Yes, how you construct your personal
brand. So if you are an amazing engineer, but
you're bad at taking selfies, and you're not
great at writing marketing copy about yourself,
you lose. You become invisible. You're invisible.
The algorithm and the recruiters filter you out
before they even look at your GitHub or your
actual credentials. It exerts this massive pressure
on every worker to become their own personal
brand manager. You have to perform your professional
identity. Constantly. It's a performance of employability.
That explains all those posts we see, right?
The I'm humbled and honored to announce. It's
a performance because we all know. We're being
watched and judged by the algorithm and by a
hundred potential future employers. And another
study from 2019 showed that this creates something
called a bifurcated market. Bifurcated, meaning
split in two. Yes. How does that work? The researchers
found a stark class divide in how the platform
is used. For high -skilled, high -demand workers,
think AI engineers, senior executives, recruiters
use LinkedIn to actively poach them. These are
the passive candidates. Exactly. These people
aren't even looking for jobs. The jobs come to
them through in -mail. For them, the platform
is a red carpet. Must be nice. But for everyone
else, lower skilled workers, generalists, people
in the administrative roles, LinkedIn works completely
differently. Recruiters don't hunt for them.
They just post an ad and let thousands of people
apply and fight over it. So for that group, it's
not a red carpet. It's a gladiator arena. That
is a depressing but very accurate way to put
it. And there was one more piece of research
that totally contradicted the advice everyone
gives you. We always hear, get a referral. A
referral is the golden ticket. But the data says,
maybe not. This was a fascinating study from
the Foster School of Business. They found that
job seekers were actually less likely to get
a referral from a connection if they were in
the same field. Wait, that seems completely backwards.
If I'm a graphic designer, wouldn't I want to
help another graphic designer get a job at my
company? You would think so. But the researchers
called it competition self -protection. Competition
self -protection. If I refer you... and you do
exactly what I do, subconsciously, I might be
worried you'll replace me or you might compete
with me for the best projects or for the next
promotion. So people tend to hoard opportunities
when it comes to their direct peers. So much
for community. It's really, I'll help you as
long as you're not a direct threat to my paycheck.
You and nature doesn't magically change just
because you put it on a website with a blue logo.
Okay, so we've established the sociology, the
human behavior on the map. Now let's talk about
the actual product features and how they monetize
this. Because I think most people don't realize
that we, the users, are not the customers. That
is rule number one of the internet. If you aren't
paying for the product, you are the product.
So who is the customer? LinkedIn has a few revenue
streams, but the biggest one historically has
always been talent solutions. Which is just a
nice corporate phrase for selling our data to
recruiters. That's exactly what it is. If you're
a corporate recruiter, you pay thousands, sometimes
tens of thousands of dollars a year for a special,
super -powered version of LinkedIn called Recruiter.
And what does that get you? It unlocks the full
power of the economic graph. It allows you to
see everyone's full profile, search with all
those advanced filters we talked about thanks
to Galeen, and crucially, message people who
aren't in your network using InMail. That is
the cash cow. They are selling the map back to
the companies that are on the map. That's the
business model. Then you have premium subscriptions
for job seekers and sales professionals. And
of course, advertising like sponsored updates.
And it's a big business. The numbers are huge.
Oh, yeah. In 2022, revenue hit $13 .8 billion.
That was up from $10 .3 billion just the year
before in 2021. The growth under Microsoft has
been astronomical. And what about the features
for us regular mortals? I feel like the whole
definition of a connection has changed over the
years. It has shifted dramatically. In the beginning,
LinkedIn was what they called a gated network.
Gated access. The philosophy was that you should
only connect with people you actually know and
trust. It was about replicating your real world
trusted professional network online. I remember
that. You used to have to know someone's email
address to even send them an invitation. You
couldn't just click a connect button. Exactly.
But somewhere around 2017, they clearly realized
that trusted networks don't grow very fast and
social media is a game of scale. So they threw
open the gates. And now you can have up to 30
,000 connections. 30 ,000, which completely changes
the dynamic. It has moved from being a private
Rolodex to a public broadcasting platform. That's
why you see so many influencers and content creators
on there now. And the endorsements feature. I
have to laugh about this one. My profile says
I'm endorsed for skills I haven't used since
college from people I haven't spoken to in a
decade. The endorsement feature is a perfect
case study in gamification gone wrong. It was
supposed to be a lightweight way to verify skills.
But it just became a game. It became a game of...
I'll endorse you for strategic planning if you
endorse me for public speaking. It's reciprocal
backscratching. It's meaningless. Pretty much.
In 2016, LinkedIn tried to fix it by highlighting
endorsements from mutual connections or colleagues
from the same company, trying to add some social
proof, some weight to it. But honestly, most
recruiters I've spoken to say they completely
ignore them. They're just vanity metrics. They
also made a big push to become a media company
with the Influencers Program. Yes, bringing in
people like Bill Gates, Richard Branson, Arianna
Huffington. This was a really smart play to increase
time on site. Right, to keep you on the app longer.
If you just go to LinkedIn to update your resume,
you might visit once every six months. But if
you go there to read Bill Gates' latest thoughts
on climate change, you might visit every day.
It turned the platform from a static profile
page into a dynamic content feed. And then there's
the whole education piece, LinkedIn Learning.
Which was born from the acquisition of lynda
.com back in 2015 for $1 .5 billion. A huge purchase
at the time. It was, and it completes the loop
for them. You look for a job on LinkedIn, you
see you're missing a required skill, you pay
LinkedIn to learn that skill through LinkedIn
Learning, and then you apply for the job on LinkedIn.
They want to own the entire life cycle of your
career. From education to application to networking.
The whole thing. And recently, in October 2025,
they launched the Career Hub, which seems to
be doubling down on this all -in -one ecosystem
approach. But as they gather more and more of
our data to power these features, they run into
massive legal and ethical walls. And this brings
us to the part of the story that makes me the
most nervous. The privacy part. Yeah, let's get
into the dark side. Because for a company that
relies so heavily on the idea of professional
trust, they have done some pretty shady things
to grow. We have to talk about the Perkins lawsuit.
This is a classic example of growth hacking crossing
the line into illegality. In the early days,
LinkedIn had that feature. To find friends, you'd
grant them access to your email address book,
and they would see which of your contacts were
already on the platform. Which sounds standard.
A lot of apps do that. The first part was standard.
But what users didn't realize, because it was
buried deep in the terms of service, was that
LinkedIn would then take the email addresses
of everyone in your contacts who was not on LinkedIn.
Oh, no. And send them an invite email. And if
they didn't join after the first email, they
would send another one. And then a third one.
And the worst part was that the emails looked
like they came directly from me personally. Yes.
The subject line was literally, host speaker
has invited you to join LinkedIn. It felt like
you were personally nagging your friends and
colleagues. People were furious. They felt humiliated.
I can imagine. So a class action lawsuit was
filed. Perkins versus LinkedIn core. That's right.
And the court argument was, I agreed to connect
with my friends. I did not agree to spam my entire
address book, including my grandma. And how did
the court rule? The court agreed with the users.
They ruled that while a user might have consented
to that first invitation email, they absolutely
did not consent to the follow -up reminder emails.
LinkedIn settled in 2015 for $13 million. Which
for them is a rounding error. A slap on the wrist
financially. But it was a huge reputational hit,
and it forced them to stop that specific aggressive
behavior. But that was about how they misused
internal data. There's also this huge war over
who owns the data once it's made public, the
HiQ Labs versus LinkedIn case. This one feels
like it sets a massive precedent for the entire
Internet. It really does. This is all about the
practice of data scraping. So HiQ Labs was a
data analytics startup. And what did they do?
They built a business model around scraping public
LinkedIn profiles to analyze workforce trends.
So they could tell their clients things like,
engineers at this company are likely to quit
soon, based on changes to their profiles. And
to be clear, they weren't hacking into LinkedIn's
servers. No, not at all. They were just writing
automated scripts to visit the public facing
pages and copy the information that anyone could
see. They were doing what a human could do, but
at the speed of light. LinkedIn didn't like that.
LinkedIn hated it. They sent a cease and desist
letter citing the Computer Fraud and Abuse Act,
the CFAA, which is an anti -hacking law. They
basically said, this is our data. It's on our
servers. You are breaking into our house. And
the court said. The court sided with Haikyuu.
They granted a preliminary injunction that forced
LinkedIn to allow them to continue scraping.
The reasoning was profound for the future of
the web. What was the reasoning? The court argued.
That if a user chooses to make their profile
public visible to the entire world without a
password, then the company, in this case LinkedIn,
cannot use anti -hacking laws to stop someone
else from looking at it. That is huge. It basically
says if you put something in the public square,
you can't be mad when someone takes a picture
of it. That's the perfect analogy. Thank you.
But this creates a security nightmare. If scraping
public data is legal, how do you distinguish
between a startup like HiQ and a bad actor from
Russia harvesting data for identity theft or
phishing campaigns? And we know the bad actors
are there. We absolutely have to talk about the
2012 hack. The 2012 hack was the moment. LinkedIn,
I think, lost its innocence in the public eye.
Hackers stole 6 .4 million passwords. But the
real scandal wasn't just the theft. It was the
sheer incompetence of their security practices.
They were storing passwords as unsalted SHA -1
hashes. They were. Okay, we need to pause and
explain that. For the listener who isn't a cryptographer,
what is unsalted SHA -1 and why is it so bad?
Okay, think of hashing a password like putting
through a shredder. You can't tape it back together
to read the original password, but if you have
the shredded remains, you can check if they match
another shredded document. SHA -1 was the type
of shredder they were using, and by 2012, it
was already known to be a weak, outdated, and
insecure type of shredder. And what about the
salt? Salting is like throwing a handful of unique,
random glitter into the shredder along with each
piece of paper. It makes the resulting shredded
mess unique to that specific piece of paper.
So even if two people have the same password,
the shredded result will be different. Exactly.
If you don't salt the passwords, then the password
123 always shreds into the exact same string
of characters. Hackers have these massive lists
of pre -shredded common passwords. They're called
rainbow tables. Because LinkedIn didn't salt
them, the hackers could just look up the hashes
in a table and crack millions of them almost
instantly. That is a security 101 failure. It's
basic stuff. It was grossly negligent. And the
fallout from that one breach lasted for years.
In 2016, a new data dump appeared online. 117
million login credentials, email and password
combinations, likely from that original 2012
breach, appeared for sale on the dark web. For
how much? For about $2 ,200. $2 ,200 for 117
million accounts. That is terrifyingly cheap.
It shows how commoditized our personal data has
become. And the security issues didn't stop there.
In 2021, you might remember reports surfacing
of 500 to 700 million user records being leaked.
I do remember that. It was all over the news.
Now, LinkedIn's defense to this was very interesting.
They said, we weren't hacked. This was just aggregated
scraped data. So they were falling back on the
high Q ruling saying that someone just scraped
what was already public and put it all in one
big convenient database for criminals. That was
their argument. But to you, the user, does it
really matter if your phone number, your email,
your entire work history and your location are
for sale on a hacker forum? Do you care if the
data was technically obtained via a hack or just
a scrape? The end result is the same. My privacy
is compromised. The result is identical. And
there was one other feature that really creeped
me out when I was reading through the source
material. The intro feature from 2013. Oh, this
was wild. This is peak Silicon Valley hubris.
What did it do? So LinkedIn wanted to integrate
more deeply with the iPhone mail app. They wanted
to show you the LinkedIn profiles of people who
were emailing you right inside the app. Okay,
that sounds potentially useful. How do they do
it? To do that, they rerouted all of your incoming
and outgoing emails through LinkedIn's own servers
first so they could inject the necessary HTML
code. Wait, they were intercepting your email?
They were putting themselves in the middle of
your private email conversations. Basically,
yes. That's it. Security firms went absolutely
nuclear. Uh -huh. Bishop Fox, a well -respected
security consultancy, described it as being architecturally
similar to a man -in -the -middle attack. That's
a hacking term. It's a classic hacking term for
when a third party secretly relays and possibly
alters communication between two parties who
believe they're communicating directly. LinkedIn
was performing a man in the middle attack on
its own users and calling it a feature. Unbelievable.
They eventually killed the feature after the
backlash, but it just showed this mindset of
we will break the fundamental security model
of email just to show you a profile picture.
It's that constant tension, isn't it? Convenience
versus privacy and privacy almost always loses.
Almost always. Now, I want to zoom out to the
biggest picture possible. We've talked about
users. We've talked about the company. But LinkedIn
is a global entity, which means it has to deal
with global politics. And this is where the lofty
mission of connecting the world's professionals
crashes into the hard reality of authoritarian
regimes. Specifically. China. The China conundrum.
This is a fascinating case study in corporate
compromise. For a long time, LinkedIn was the
only major American social network allowed to
operate in China. That's right. Facebook was
banned. Twitter was banned. Google was banned.
But LinkedIn survived. How? Did they have some
kind of special deal with the government? They
made a deal. They compromised. When they launched
their localized Chinese version called Nying
back in 2014, the CEO at the time, Jeff Weiner,
was very transparent about it. He wrote a blog
post saying effectively, we will have to censor
some content to comply with local Chinese law.
But we believe providing some access is better
than providing no access at all. That is a heavy
utilitarian calculation to make. We will agree
to silence some people in order to help millions
of others get jobs and connect with the world.
It's a massive ethical tightrope. And for a while,
it seemed to work. But the demands of the Chinese
government are never static. They always want
more control. When what happened? In 2021, the
crackdown intensified. LinkedIn started blocking
the profiles of U .S. journalists, people like
Bethany Allen Abrahamian and Melissa Chan, blocking
them from being viewed inside China because their
profiles mentioned sensitive topics like their
coverage of human rights abuses. So an American
company was actively censoring American citizens
on an American -owned platform to appease the
government. in Beijing. That was the breaking
point. The bad press was overwhelming. The operational
compliance was becoming impossible. And so late
in 2021, Microsoft finally pulled the plug. They
shut it down. They shut down the social networking
service in China. They replaced it with a bare
-bones app called InJobs, which had no social
feed, no ability to share posts or articles.
It was just a sterile job board. And even that
didn't last, did it? No. In 2023, they discontinued
the local job apps entirely. It was the end of
the experiment. And it proved, I think, that
you cannot run a Western -style social network,
even a professional one, inside a regime that
demands total information control. The two models
are fundamentally incompatible. And Russia was
even stricter, right? They didn't even get to
the censorship phase. They just got banned outright.
Russia kicked them out in 2016. No. Russia passed
a data sovereignty law that requires any company
holding the personal data of Russian citizens
to store that data on servers physically located
within Russia. And LinkedIn refused. LinkedIn
refused to move the data. So Putin pulled the
plug. LinkedIn is blocked in Russia to this day.
It was removed from the App Store and Google
Play there. If you're a professional in Moscow
and you want to update your resume, You have
to use a VPN. It really shows that the economic
graph has hard borders. It is not truly global.
But where there are borders and where there's
valuable data, there are spies. And this part
of the outline reads like it's straight out of
a spy novel. LinkedIn is apparently a playground
for espionage. It makes perfect sense if you
think about it. If you're an intelligence officer
and your mission is to recruit a source inside,
say, the U .S. Department of Defense, or inside
a major European telecom company, where do you
go to find your targets? You go to the global
directory where everyone conveniently lists their
job title, their security clearance level, and
the specific sensitive projects they've worked
on. It's a menu for spies, a targeting database.
And it's not just the bad guys, so to speak.
There was a famous case revealed by the Snowden
documents called Operation Socialist. And who
was behind that? This was GCHQ British Intelligence.
They set up fake LinkedIn pages to target employees
of Belgacom, a major Belgian telecom company.
Wait, the British were hacking the Belgians,
aren't they allies? In the world of signals intelligence,
everyone is a potential target. GCHQ used these
fake profiles to lure Belgacom engineers to a
malware -infected page so they could infiltrate
their network. They used LinkedIn as the honeypot.
And I assume the Chinese and Russians and Iranians
are doing the same thing? At an industrial scale.
The French intelligence services have issued
public warnings that Chinese spies use LinkedIn
to contact thousands of French officials. There
was an Iranian hacking group, Threat Group 2889,
that created these incredibly detailed fake personas.
What did they look like? Fully fleshed out profiles,
professional headshots, endorsements from real
people, fake work histories at real companies.
They look like legitimate recruiters. They would
connect with you, build trust over a few weeks,
and then send you a job description document
that was actually a malware payload designed
to steal your... It really makes you think twice
about accepting that random connection request
from the recruiter with the vague profile picture.
You absolutely should be skeptical. Professional
networking as a concept relies on trust on the
heuristic that a friend of a friend is probably
safe. Espionage operations are designed to exploit
that exact heuristic. Before we wrap up, we need
to look at the present day, or at least the very
near future. The source material mentions some
pretty significant changes in 2025 that seem
to reflect the shifting political landscape,
particularly here in the U .S. Yes, these big
tech platforms are often barometers of the broader
political climate. They're not neutral. In January
2025, during the second Trump administration,
LinkedIn quietly deleted its entire diversity,
equity, and inclusion, or DEI, webpage. Just
scrubbed it from the site. Gone. And then in
July 2025, they removed specific protections
against misgendering or deadnaming transgender
users from their professional community policies.
That's a huge rollback. It's a significant reversal.
For years, corporate America, especially big
tech, was moving in a very specific direction
on these social issues. Now, these sources suggest
they are pivoting rapidly to align with a new
political reality. It shows that even these neutral
professional platforms are deeply political in
how they choose to govern their communities.
And finally, we have to talk about the AI elephant
in the room. We are all feeding the machine.
We are all the training data. In 2024, there
was a controversy in the UK because it was revealed
that LinkedIn had quietly opted in all users
by default to have their public profile data.
used for AI training. So my profile, my posts,
my work history, my skills, it's all being fed
into their large language models. Unless you
explicitly go deep into your settings and find
the button to turn it off. Yes. The UK regulators
pushed back and forced them to pause it there.
But for most of the world, we are the fuel for
their AI ambitions. The economic graph is becoming
the brain of an AI. So we've covered the history,
the sociology, the money, the lawsuits, the spies
and the politics. It is a massive, complex and
powerful beast. It is so much more than a digital
resume. So let's try to synthesize this for the
listener. When they open that blue app tomorrow
morning to scroll through the feed, how should
they see it differently? I think you have to
see it as a trade, a very explicit trade. You
are trading your data, your entire professional
life history, updated in real time for access
to the network. and the opportunities it provides.
And that trade can have real value. It can. It
can get you a job. It can help you build your
career. But you cannot be naive about the cost
of that trade. You are a node in a graph that
is being sold to recruiters, scraped by startups,
and monitored by governments. Oh, be intentional
about it. Be very intentional. Curate what information
you choose to make public. Understand that your
weak ties are powerful, but the platform itself
is not your friend. It is a marketplace, and
you are the inventory. I want to leave us with
a final provocative thought based on where this
is all going. We mentioned the new career hub
and the AI agents that LinkedIn is rolling out
in 2024 and 2025. If LinkedIn has all the data,
they know who I am, they know my skills, they
know what jobs are open, and they know who the
recruiters are. I see exactly where you're going
with this. Are we moving toward a future where
I don't even apply for jobs anymore? Where the
LinkedIn AI just... talks to the recruiters,
they negotiate in the background and they just
tell me where to show up for work on Monday.
That is the logical conclusion of the economic
graph, a completely frictionless labor allocation
system. Yeah. But if we ever get there, if the
machine does the networking and the applying
for us, does the platform actually build community
or have we just built a massive, silent, automated
vending machine for human capital? A vending
machine for human capital. That is a haunting
image and definitely something to mull over the
next time you get an email asking you to endorse
someone for strategic planning. Thanks for listening
to The Deep Dive. We'll see you next time. Goodbye.