Speaker 1: Welcome to thrilling threads. So today we are pulling on
a thread that I mean, it feels like it was
ripped straight out of a cyberpunk novel or.
Speaker 2: A high stake spy thriller exactly.
Speaker 1: And I have to say this right at the top,
every single bit of this is real. We're talking about
a company that was born, specifically from the ashes of
nine to eleven, a company funded by the CIA, designed
with one singular, terrifying and well frankly onspiring purpose to
see everything, to see everything.
Speaker 2: And when we say everything, we really aren't exaggerating for effect. Here.
We're talking about an artificial intelligence ecosystem that tracks wars
in real time, It monitors national borders, manages global pandemics,
and it predicts corporate profits before they even happen. It's
a tool that governments rely on for national security, right,
and that massive corporations, you know, the big ones, depend
on just to survive the fiscal year.
Speaker 1: The crazy part is that most people just walking down
the street have absolutely no idea this entity even exist,
none at all. But if you've been on a plane,
or opened a bank account, or received a vaccine, I mean,
even if you've just lived through the last few years
of geopolitical chaos. Your life has been touched by their algorithms.
We are, of course talking about pal and Teer Technologies.
Speaker 2: Arguably the most controversial, secretive and significant AI company operating today.
And to be honest, just calling it a tech story
kind of sells it short.
Speaker 1: It really does.
Speaker 2: This is a story about the tradeoff between security and privacy.
It's a case study on the ethics of what happens
when you hand over the god view of the world
to a private entity.
Speaker 1: So I hope you have your coffee ready or maybe
a piece of tape over your webcam. I don't know,
because this is going to be a wild ride. It is.
We've got a stack of sources here, everything from Peter
Thiel's biography, financial disclosures, investigative reports from New York magazine,
military case studies, you name it. A lot to unpack
a ton. So the mission for this episode of Thrill
Threads is to figure out exactly what this company is,
where it came from, and why it matters to you.
Speaker 2: It's the billion dollar question, isn't it? And to answer it,
we can't start with the tech. We have to start
with the trauma. Right, we have to go back to
September eleventh, two thousand and.
Speaker 1: One, right, because the narrative we usually hear about nine
to eleven is all about the ideology or the geopolitics.
But inside the intelligence community, the post mortem was it
was very.
Speaker 2: Different, completely different.
Speaker 1: It wasn't just seen as an attack, it was seen
as a catastrophic data failure.
Speaker 2: Precisely. The nine to eleven Commission report is I mean,
it's essentially a six hundred page document just screaming about
information silos. The United States actually had the data to
stock the attacks.
Speaker 1: That's the part that's so hard to believe.
Speaker 2: It is the CIA, the FBI, the NSA, they all
held different pieces of the puzzle. But the systems were
so archaic. We're talking about an era where agents were
and this is not a joke, physically walking floppy disks
between offices because the secure networks couldn't talk to each other.
Speaker 1: That is just it's mind blowing to think about. Now,
you just assume that government sees everything, but back then
they were completely separate.
Speaker 2: Totally separate.
Speaker 1: Like one agency knew a guy was taking flight lessons
in Florida, another agency knew that exact same guy was
on a watch list in Malaysia, right, But those two
little sticky notes never made it onto the same whiteboard exactly.
Speaker 2: It was a format problem, a database problem. The FBI
files were literally in paper boxes or these antiquated mainframes
that couldn't talk to the CIA's databases. There was no
single pane of glass to use the industry.
Speaker 1: Terms single pane of glass, yeah.
Speaker 2: Where an analyst could just look at the world and
see all the connections at once. The question that just
burned in every intelligence report for the next decade was
how the hell did we not connect the dots?
Speaker 1: And this is where Silicon Valley steps in, and specifically
a group of people who had been solving a very
very different kind of problem. The solution, I mean, it
sounds almost too simple in retrospect. The idea was, what
if we built software that could just sift through these
oceans of data, emails, surveillance footage, passport records, bank transfers,
and find the next Osama bin Laden before he even
booked a plane ticket.
Speaker 2: And the person driving this idea was Peter Teal.
Speaker 1: The PayPal guy. Which always feels like such a weird pivot.
It does you know, from I want to make it
easier to email money to my friend to I want
to hunt global terrorists. It feels like two completely different universes.
Speaker 2: It really seems like a leap, but if you look
at the underlying math, it's it's actually the same problem.
Teal had just made millions of dollars by stopping credit
card fraud at PayPal. Okay, now think about what fraud
detection really is. You are looking for a tiny, tiny
signal in a massive amount of noise. You have millions
of legitimate transactions, people buying coffee, paying their rent, and
you need to find the one transaction that is, you know,
a Russian mobster testing a stolen card number.
Speaker 1: Okay, I see the parallel that at PayPal they use
pattern matching. They looks for networks like this IP address
is linked to this bank account, which is linked to
this other suspicious purchase.
Speaker 2: That's it. It's graph theory, nodes and edges. At PayPal,
the nodes were bank accounts and computers. In counter terrorism,
the nodes are people and phone numbers and flight manifests.
So Thial's logic was His logic was, if our anti
fraud tools can detect a stolen credit card. By mapping
these networks, why can't we build tools to detect a
terrorist cell.
Speaker 1: Why can't we apply that same algorithmic logic to national security.
Speaker 2: That was the genesis of the whole thing.
Speaker 1: Because he sets out to build this thing, he recruits
some of the smartest engineers from Stanford and PayPal, including
Alex KRP and we definitely get to him in a minute,
and they start Pollantier And for all the fantasy nerds listening,
and I say that with love as one of them. Yes,
it is named after the Palantary from the.
Speaker 2: Lord of the Rings, which is such a telling choice
of name, isn't it. And honestly a bit of a
red flag, if you know your.
Speaker 1: Tolkien, a huge red flag.
Speaker 2: And the lore the Palenteer are the all seeing orbs.
They're these powerful magical stones that let you see across
vast distances and uncover secrets. But and this is the
crucial part, they are dangerous, very dangerous. They're tools used
by wizards and villains alike. And when you look into
the Pallenteer, the dark Lord Souron can look back at you.
It corrupts the user.
Speaker 1: It's an incredibly bold move to name your company after
a cursed object used by the bad guys in a
fantasy epic. It just signals this obsession with visibility, secrecy,
and power right from day one.
Speaker 2: It's like naming your security company the.
Speaker 1: Death Star exactly. It's like, we know what this is.
But here's where the origin story gets really spy novel esque.
Pallenteer didn't start like a normal startup. They didn't go
to the usual Silicon Valley venture capitalists.
Speaker 2: No Sequoia, no and recent Horowitz.
Speaker 1: They didn't go begging for seed money on sand Hill Road.
Speaker 2: No. Their first major outside investor, the entity that kept
the lights on when nobody else really understood the product,
was a firm called in q tel.
Speaker 1: And for those of you who don't spend your free
time reading government budget reports, what is in qtel.
Speaker 2: In qtel is the CIA's very own venture capital firm.
Speaker 1: I still can't get over that. Yeah, the CIA has
a venture capital firm. It sounds like a joke from
a movie.
Speaker 2: It's very real. It was founded in the late nineties
because the CIA realized they were falling way behind the
government moves so slow, tech moves so fast. If the
CIA wanted the best spyware or the best data analysis tools.
They couldn't wait for a government contractor to build it
over ten years. They needed to buy it from startups.
So in Qtel exists to fund startups that build things
useful to spies.
Speaker 1: So this funding is just it's so critical to the story.
It signaled immediately that Pallenteer wasn't just another tech startup
trying to sell ads or you know, cloud storage. Right
Their client from the very beginning.
Speaker 2: Was the state, and that shaped the entire product. The
goal was to build this godview, to blend all this messy,
siloed government data, passport logs, drone footage, field reports into
one single interface.
Speaker 1: But from what I've read in the early histories of
the company, the first few years weren't exactly smooth sales
so far from it.
Speaker 2: The reports say the early software was buggy and way
over engineered.
Speaker 1: I saw a great quote from a government official who
said using early Palenteer was like trying to fly a
space shuttle just to find your car keys.
Speaker 2: That's perfect. Bureaucrats hated it. The NSA apparently thought it
was cute, which is about the most devastating insult you
can get in the Intel world ouch and the FBI
apparently found it terrifying because it made accessing data too easy.
It broke down their silos.
Speaker 1: But they just kept building. They were obsessive. And the
culture they built, yeah, wow, it wasn't the Google campus
let's ride bikes and eat free sushi kind of vibe.
Speaker 2: Not at all. It was more like a bunker.
Speaker 1: A bunker, that's a great way to put it. Let's
talk about that culture because it all centers around the CEO,
Alex Karp. If you haven't seen a picture of Alex Krp,
you need to google him right now. He does not
look like a tech ceo, not even close.
Speaker 2: He has a PhD in philosophy from Frankfurt University. He
studied under Jurgen Habermas. He has this wild einstein esque hare.
He wears bright orange tracksuits or puffy vests and these
wireframe glasses.
Speaker 1: He looks like he wandered out of a Nietzsche book club,
not a boardrome.
Speaker 2: That's the perfect description.
Speaker 1: And he acts like it too. I read he's often
referred to as the monk inside the company. He's a bachelor,
obsessive about fitness. He practices martial arts in the office.
Speaker 2: But more importantly, he would give these lectures to the
engineers on the ethics of power. His famous line, the
one he always repeats, is we're not in the business
of surveillance. We're in the business of understanding.
Speaker 1: And that distinction is so key to their internal mythology.
They saw themselves as the anti Google.
Speaker 2: Absolutely, if Google was.
Speaker 1: This data mining theme park that sold your attention to advertisers,
Pallenteer was a fortress designed to protect the West.
Speaker 2: And they didn't hire salespeople for years, not a single one.
They hired forward deployed engineers.
Speaker 1: That term forward deployed is so interesting. It sounds military.
Speaker 2: It is. It's totally military. Unlike other software companies that
send a salesman in a suit to take you to lunch,
Palenteer sends engineers to live with the client.
Speaker 1: So the client is the US Army in Afghanistan.
Speaker 2: The Palenteer engineer goes to Afghanistan. They sit in the
tent with the soldiers and they code the software in
real time to fix problems as they're happening on the battlefield.
Speaker 1: Wow, that level of embedding creates a stickiness that you
just don't get with Microsoft excelf.
Speaker 2: You can't compete with that.
Speaker 1: So let's look at what they were actually building. The
product that made their name. It was called Gotham.
Speaker 2: Gotham again with the superhero branding, right. Gotham is the
platform designed for intelligence and defense.
Speaker 1: So what makes Gotham different from just a really really
good search engine.
Speaker 2: It's about ontology. That's a word you hear a lot
with Pellenteer. It' the philosophy. Okay, most databases are just
rows and columns of text and numbers. Gotham maps the
world as objects and relationships. So an object could be
a person, a car, a building, or a bank account.
Gotham switches together all these disparate data streams to show
you how those objects interact.
Speaker 1: So instead of having one spreadsheet of phone calls and
a separate map of bomb explosions, Gotham just layers them
on top of each other exactly.
Speaker 2: Let's take a real world example and ied a roadside
bomb goes off in Bagdad. That's an event. Okay, Gotham
pulls in the report, but then it automatically overlays the
cell phone tower logs from that exact time and location.
It notices a specific burner phone pinged tower right before
the blast. Then it checks that phone number against a
database of captured pocket litters, you know, scraps of paper
found in a raid three months ago.
Speaker 1: And suddenly you have a name.
Speaker 2: You have a name, you have a location, you have
the guy who paid him, you have the entire kill chain.
It wasn't just here's where a bomb went off. It
was here is the network that made it happen.
Speaker 1: And the soldiers on the ground loved it. I mean
from all the accounts, they absolutely loved it. For the
first time, they weren't drowning in data, they were surfing it.
Speaker 2: But there was a major hurdle. Palenteer was an outsider
in Washington, DC. Defense contracting is a club, it's the
Iron Triangle. You need lobbyists, you need golf retreats, you
need retired generals on your board of directors. Pollenteer had
none of that.
Speaker 1: They just had better software, and.
Speaker 2: Usually that's not enough. In DC. The army actually tried
to block Palenteer. They did, oh yeah, they wanted to
build their own system called DCGSA. It cost billions of
dollars and by all accounts, it barely worked. Soldiers were
literally leaving the official Army computers in their boxes and
buying Palenteer laptops with their units petty cash because they
knew it would keep them alive.
Speaker 1: So when the Army tried to give this massive, like
two hundred million dollars intelligence contract to a legacy contractor
without even holding a competition, Pallenteer did something completely unthinkable.
Speaker 2: Mm hmm.
Speaker 1: They sued the United States Army, a.
Speaker 2: Tiny Silicon Valley startup suing the Army. It's just unheard of.
Its career suicide.
Speaker 1: It should be.
Speaker 2: But they had a secret weapon. It was this neglected
law from nineteen ninety four called the Federal Acquisition Streamlining Act.
Speaker 1: Okay, what did that do?
Speaker 2: This law basically says, if a commercial product exists that
can do the job, the government must consider buying it
before it goes and builds its own custom version from scratch.
Speaker 1: And they won.
Speaker 2: They won. A federal judge ruled that the Army had
broken the law by refusing to even consider Palenteer's innovation.
And this was a watershed.
Speaker 1: Moment, a huge moment.
Speaker 2: It didn't just help Palenteer, it forced the door open
for all of Silicon Valley and defense tech. It sent
a message that the Pentagon couldn't just keep feeding the
old defense giants if better tech existed somewhere else.
Speaker 1: So they conquered the battlefield. They proved Gotham worked. They
won the legal battle. But then well, the war on
Terror started to wind down, least in Iraq and Afghanistan.
Speaker 2: Troops came home, and Pallenteer came home with them.
Speaker 1: And this is where the story gets well a lot stickier.
Speaker 2: This is the pivot that caused the most controversy for sure.
Pallenteer began working with domestic agencies like IC, like IC
Immigration and Customs Enforcement and the Department of Homeland Security.
Speaker 1: So the exact same software those designed to track terrorists
in the mountains of Afghanistan was now being used to
track undocumented immigrants in the United States.
Speaker 2: Correct, they utilized a system called Falcon, and it allowed
IC agents to access all these disparate databases, employment records,
address histories, family connections, all in that same single pane
of glass.
Speaker 1: And we have specific examples of how this changed enforcement. Right.
It wasn't just theoretical, we do.
Speaker 2: There are reports detailing how the software could identify, say
a suspect's cousin simply because they rented a tr rock
near a known safe house or shared an address five
years ago. The system would just map these social networks
and effectively flag individuals for investigation or detention.
Speaker 1: It makes the whole needle in a haystack problem easy.
But when the needle is a family living in the suburbs,
the moral implications just feel very, very different than when
the needle is an insurgent.
Speaker 2: Sell That's the absolute crux of the backlash. Civil rights
groups like the ACLU were incredibly alarmed. They argued that
Pollenteer turned deportation into an assembly.
Speaker 1: Line did remove the friction, exactly.
Speaker 2: Remove the friction. When you make it incredibly easy to
find people, you inevitably increase the number of people who
get found.
Speaker 1: And it wasn't just external critics pointing this out inside Polantatier,
the employees started revolting.
Speaker 2: Yeah, this was during the Trump administration, when immigration policy
was I mean it was incredibly heated. You had engineers
at Pallenteer writing letters saying I signed up to stop terrorists,
not to deport families. It really highlights the problem of
dual use technology right.
Speaker 1: Dual use a hammer can build a house, or it
can crack a skull.
Speaker 2: Exactly the underlying mechanism. The math of connecting data points
is neutral, but when you apply that math to domestic
immigration enforcement, the moral calculus changes significantly.
Speaker 1: So how to Pollaneer respond?
Speaker 2: Cooluntier held the line. Alex Karp argued that tech companies
shouldn't be the ones deciding policy. He basically said, if
the democratic process alects a government that wants to enforce
immigration laws, we don't have the right to say no.
Speaker 1: Which is a pretty convenient stance when the contracts are
worth millions and millions of dollars, of course, but despite
the protests and the resignations, the contracts just kept coming.
Pallenteer wasn't just a counter terrorism company anymore. It had
become a kind of state sponsored crystal ball m HM.
And once they had the government locked down, they looked
at the only battlefield left untouched, corporate America.
Speaker 2: And this was the launch of Foundry.
Speaker 1: Okay, so Gotham is for spies, Foundry is for who business.
Speaker 2: Think of Foundry as Gotham, but wearing a suit.
Speaker 1: And tie Gotham in a suit. I like that.
Speaker 2: The realization Palenteer had was that while governments have basically
infinite money, they move incredibly slowly. CEOs, on the other hand,
sign checks very fast. True, but big corporations have the
exact same problem as the CIA did in two thousand
and one. They have data silos. Sales doesn't talk to logistics,
HR doesn't talk to finance. It's all separate.
Speaker 1: So they pitched Foundry to the Fortune five hundred. But
what does it actually do? I mean, is it just
a bunch of fancy charts because every company has a
dashboard these days.
Speaker 2: No, and that is the key difference. A dashboard shows
you what happened yesterday, sales were down five percent. Foundry
integrates all the enterprise data to create what they call
a digital twin of the company. It simulates the entire
business and then it.
Speaker 1: Tells you what to do, like a digital boss.
Speaker 2: A digital concilier is the term that's often used. Wow,
let's look at a real example. Palenteer works with Airbus,
the air plane manufacturer. They built a system called Skywise.
It pulls data from every single sensor on thousands of
planes in the air, temperature, vibration, pressure, everything, and.
Speaker 1: So it predicts when a part is going to break.
Speaker 2: Before it breaks, it might send an alert to an
airline that says, the fuel pump on plane four H
two is vibrating at a frequency that suggests failure in
the next fifty flight hours. Replace it tonight in London
where you have a spare part, rather than letting it
break down and dubuy where you don't.
Speaker 1: That's incredibly valuable. I mean that says millions in delays
and cancelations.
Speaker 2: It does, but it goes deeper in other industries. It
might tell an oil company to shut down plant three
because market prices are dropping. It might tell a logistics
firm to reroute a container ship to avoid a coming storm.
Speaker 1: Or it might tell a manager to fire Dave.
Speaker 2: It might tell a manager to fire Dave.
Speaker 1: Poor Dave. But seriously, it's making personnel decisions.
Speaker 2: It's providing the data that leads to those decisions, often
with such algorithmic certainty that the humans just they just
follow the instruction. It replaced intuition with data.
Speaker 1: I read the hitge funds use it.
Speaker 2: Oh yeah, they use it to predict markets based on
satellite imagery of Walmart parking lots. If the lots are
full earnings will probably be good if they're empty.
Speaker 1: Short the stock exactly. This leads us straight to the
black box problem, doesn't it. Companies and governments are making
huge decisions based on the output of this software, but
they often don't fully understand how the software reached that conclusion.
Speaker 2: That is the danger. It is a black box with
massive influence. You're just trusting the algorithm, You're trusting the
ontology that the engineers built. And this trust was pushed
to its absolute limit during the COVID nineteen pandemic.
Speaker 1: Oh right, Palenteer was everywhere during COVID, and I feel
like that really flew under the radar for a lot
of people.
Speaker 2: It totally did, but they were the underlying operating system
for the pandemic response in both the US and the UK.
The project in the US was called Tiberius.
Speaker 1: Tiberius another Roman empurn name. They really stick to the.
Speaker 2: Brand they do. Tiberi's tracked everything. It tracked where the
outbreaks were happening, where hospital beds were full, where the
ppe was needed, and crucially, when the vaccines came out.
Palenteer's code helped determine where every single vial was sent.
Speaker 1: So wait, if you got a vaccine in twenty twenty one,
Palateer likely had a hand in deciding which pharmacy it
went to.
Speaker 2: In many cases, yes, they managed the supply chain logic. Now,
on one hand, this is incredibly impressive. The government's own systems,
you know, faxes and spreadsheets had completely collapsed under the pressure.
Right Palteer mobilized in weeks and almost certainly saved lives
by getting shots in arms more efficiently.
Speaker 1: But on the other hand, we gave a completely unaccountable
private company total control over our national healthcare logistics.
Speaker 2: Critics argued exactly that, They said, we were handing over
the keys to the kingdom. Because the interface looked cool
and you know, it worked. It deepened the reliance of
the state on this one private entity. If Palenteer turned
off the servers, the government would have been completely blind.
Speaker 1: That is a terrifying amount of leverage for one company
to have. And speaking of leverage, eventually Palenteer had to
go public. They couldn't stay in the shadows forever if
they wanted to grow, And in true Palenteer fashion, they
didn't do it normally.
Speaker 2: No traditional IPO roadshow for them. They did a direct listing,
and when they finally opened their books to the public,
investors were really confused. I remember that the company wasn't profitable,
the margins were weird. They spent an astronomical amount on
stock based compensation for their employees. The numbers didn't make sense.
Speaker 1: In a traditional way, but people bought it anyway. Yeah,
the retail investor hype was just insane. Why what were
they buying.
Speaker 2: They weren't buying the financials, they were buying the mythology.
They were buying the mission. They were buying a piece
of the operating system of power. Palenteer had positioned itself
as the company that knows more than the generals and
the central banks, and.
Speaker 1: That reputation has only grown with the war in Ukraine.
This is where we get into the realm of modern
warfare that really feels like science fiction.
Speaker 2: Volunteer has been deeply, deeply involved in Ukraine since the
Russian invasion. Alex Karp was actually the first Western CEO
to meet with Zelenski in Kiev after the war started.
Speaker 1: The first before all the defense contractors, before Boeing and Raytheon.
Speaker 2: Before everyone. He went into an active war zone to
pitch his product and they branded their involvement as AI
for war Fighting.
Speaker 1: Right.
Speaker 2: They aren't just analyzing past data anymore. They are helping
Ukraine map Russian supply lines, optimize artillery targeting, and predict
drone strikes in real time.
Speaker 1: I read a quote with one of the Source articles
that described it perfectly said this software was like algorithms
whispering aim here.
Speaker 2: That is the reality of it. It's a digital war
room that updates faster than CNN. It speaks five languages,
It processes satellite imagery almost instantly. It is helping a
much smaller force punch way way above its weight class.
By using information dominance.
Speaker 1: They're creating something they call a metica.
Speaker 2: Installation, a meta constantly, which doesn't even mean it means
linking space to mud. Satellites in orbit are talking to
drones in the air, which are talking to tablets in
the hands of soldiers in a trench. Paalenteer's software is
the glue that's holding that entire network together.
Speaker 1: So it reduces the sensor to shooter time massively, which
is the time between seeing a tank and actually blowing
it up.
Speaker 2: Exactly in the old days that could take an hour
of radio calls and approvals. Now it can take minutes
or even seconds.
Speaker 1: But the bit brings is right back to the black
box problem. If the algorithm says aim here, and you
aim there and it turns out to be a civilian building,
who salt is that? Is it the soldier who pushed
the button or the engineer in Palo Alto who wrote
the code.
Speaker 2: Volunteer has a defense for this, of course. It's often
called the hammer defense. The hammer defense, they say, we
build the hammer. If someone uses it to build a house,
or if they use it to break a window, that's
not on us.
Speaker 1: We just build the hammer. Yeah, that feels, I don't know,
a little convenient.
Speaker 2: It's a very common defense in the arms industry, which
is essentially what big tech is becoming. They claim the
moral high ground by saying they refuse to work with
China or other authoritarian regimes. They pick a side.
Speaker 1: They are very explicitly Team West.
Speaker 2: Yes, they are in their own words, Western chauvinists.
Speaker 1: Which is such a stark contrast at companies like Apple
or Google who try to be these neutral global entities
at work everywhere. Right Coolunteer says, no, we are Team America.
Speaker 2: And Team NATA. But the line between defense and domination
is it's pretty blurry, and they aren't stopping at war.
They're moving into what they call the meta constellation of
prediction for everything. For everything, they want to simulate economic collapses,
disease outbreaks, civil unrest. The goal isn't just to react
to the future anymore, it's to shape it. They are
positioning themselves as the operating system for the modern state.
Speaker 1: So if we zoom all the way out here, we
have a single company that knows more than the cabinet,
more than the generals, more than the central bank. It
started with this, you know, arguably noble goal of stopping
the next nine to eleven, but it has evolved into
a tool that essentially runs the world's most critical infrastructure.
Speaker 2: It raises what might be the most provocative question of
our time, when we move from human intuition to algorithmic certainty,
who is actually in charge? Is it the elected officials
we vote for, or is it the engineers maintaining the
code that tells those officials exactly what to do?
Speaker 1: Because if the computer screen says detain this person to
stop a terror attack, what politician is brave enough to
say no? I trust my.
Speaker 2: Gut Instead, no one you can't. The data becomes the dictator.
Speaker 1: And that is the chilling thought to leave you with.
We now live in a world where an all seeing
eye actually exists. It might keep us safe, it might
catch the bad guys, but the price of that safety
is a level of surveillance and centralized power that we
have never ever seen before in human history.
Speaker 2: And unlike the Palanteerian Lord of the Rings, we can't
just throw a cloth over this orb and hide it
away in a tower. It's woven into the very fabric
of our society. Now. It's running the trains, the banks,
and the armies.
Speaker 1: So I want to turn this over to you listening
right now. Are you comfortable with an all seeing eye
if it keeps you safe? Is the loss of privacy
a fair trade for security? Or is the idea of
a digital conciliere running the state just too dystopian for you.
Speaker 2: It's a question we all need to start answering, because
the technology isn't waiting for our permission, it's already here.
Speaker 1: Leave a comment let us know what you think. This
has been thrilling threads. Thanks for listening and stay curious.
But maybe you know, keep that webcam covered. Catch you
next time.