Imagine finding out that the flawless legal brief
defending you in federal court was just, well,
entirely hallucinated by a machine. Yeah, that's
a terrifying thought. Right. Or consider this.
Every single time you ask your AI assistant to
just draft a few routine emails, that software
is literally consuming half a liter of fresh
water. just to cool the servers down. It's just
for a few prompts. Exactly. I mean, today is
Thursday, March 26, 2026, and we are basically
living in a reality that would have sounded like
pure science fiction, like just a few short years
ago. Oh, totally. If you think back to late 2022,
chat GPT was just this... It was a viral novelty.
Yeah, quirky little toy. Right, exactly. You'd
log on, type a prompt, and it would spit out
a slightly clunky poem about your dog or something.
Yeah, as a parlor trick. I mean, a really sophisticated
parlor trick, sure. But still, just something
you played around with in a browser window to
see what it could do. To test the limits, yeah.
But that novelty era. It is completely gone.
I mean, today, ChatGBT is a deeply embedded digital
worker. It operates across our web browsers,
it analyzes our private health records, and it
literally, literally has direct access to our
wallets. Which is a staggering transition when
you really look at the timeline. The speed of
it is just insane. And that is exactly why we
are doing this today. For this deep dive, we
are looking at a massive, incredibly comprehensive
Wikipedia breakdown of Chad GPT's evolution.
And it is so up to date, too. Yeah, it covers
everything. We're looking at the foundational
features, the underlying models, the major controversies
spanning right from that initial launch, all
the way up to the brand new GPT 5 .4 model that
just dropped, what, a few weeks ago? Yeah, just
a few weeks ago. And what makes the specific
source document so valuable for you, the listener?
is that it completely strips away all that Silicon
Valley marketing hype. Right, no PR spin. Exactly.
It just presents the raw chronological timeline
of how this system grew, how the underlying architecture
fundamentally changed, and how we as a society
have been, well... forced to adapt to a technology
that scales way faster than our own loss. Way
faster. So our mission today is to map out that
explosive transformation for you. We really want
to understand how chat GPT went from just a simple
text generator to an autonomous digital agent.
And of course, to uncover the real world ripple
effects this is having on our society, our politics,
and just our day -to -day lives. It impacts almost
everything now. It really does. Okay, let's untack
this. And I think we should start with the most
fundamental shift in its identity over the last
year or so. The fact that it doesn't just, you
know, talk to you anymore. Right, it takes action.
It actually does things on your behalf. And this
is really the crux of the 2025 shift. Open AI
moved super aggressively away from the concept
of a passive conversationalist, right? And they
moved toward what the industry calls agentic
capabilities. Agentic, like acting as an agent.
Exactly. So in January of 2025, they released
this feature called Operator. And Operator wasn't
just generating text for you to copy and paste.
It was actively navigating web browsers. Which
blew my mind when it came out. Oh, it was wild.
It could fill out online forms, it could place
orders, it could schedule appointments, all by
controlling a software environment inside a virtual
machine. OK, but I remember reading about that
and thinking, how does that actually work in
practice? Because when I look at a website, I
see buttons and pictures and text. A chat bot
doesn't have eyes. How does it see the internet
to navigate around it? Well, it doesn't see the
screen the way we do, obviously. When an agent
-like operator looks at a website, it's actually
parsing the underlying HTML code. Oh, yeah. It
analyzes something called the Document Object
Model, or the DOM. The DOM, right. Right. So
it reads the raw code of the page to figure out
exactly where the interactive elements are. And
then, and this is a crazy part, it mathematically
predicts the exact x and y coordinates on the
screen to simulate a human mouse click. Or a
keystroke. It's all math. And then a few months
later, in May of 2025, they introduced Codex,
which was an agent specifically built to do this
exact thing, but for software engineering. So
it was coding itself? Basically, yeah. Codex
can write software, run its own tests, and even
propose code changes autonomously. And then...
By July of last year, we got the chat GPT agent,
the one that performs multi -step tasks across
a virtual computer. I mean, my analogy for this
is always it's gone from an intern who just hands
you a printed recipe to an intern who takes your
actual credit card, drives to the grocery store,
walks the aisles. buys the food, and then comes
back and cooks the meal for you. That is honestly
the perfect analogy because they didn't just
stop at virtual machines either. They integrated
this capability directly into the everyday tools
we use. By October 2025, they launched ChatGPT
Atlas. Right, the browser. Yeah, their own dedicated
web browser that has this agentic mode baked
right into the navigation bar. But see, if they
are integrating this into everyday browsing,
they're also integrating it into our money. The
source timeline makes a huge point of noting
that in September of 2025, OpenAI rolled out
the agent at commerce protocol. Yes, the partnerships.
Right. They partnered with Stripe and Etsy, which
means the AI can literally make purchases using
your linked payment method. The AI isn't just
adjusting a product anymore. It is authorizing
and completely finishing the financial transaction.
And by the way, OpenAI even takes a cut from
the merchant's payment as a facilitation fee.
I gotta push back on this though, because giving
a chatbot direct access to my wallet and my web
browser, that sounds like a massive security
nightmare just waiting to happen. Oh, people
were terrified of it. Because the internet is
so messy, it's chaotic, it's full of deceptive
pop -ups, the layouts are constantly changing,
buttons are weirdly coded. If operators struggled
with basic user interfaces back in early 2025,
how on earth did OpenAI convince companies like
Stripe to trust it with actual money just a few
months later? Well, the friction you're describing,
that is exactly what the developers were wrestling
with behind the scenes. Early versions of Operator
were kept in highly restricted virtual machines
for those exact safety reasons. Because it would
just click the wrong thing. Basically. It would
get confused by poorly designed websites. If
a site's DOM code is messy, the AI's mathematical
prediction of where to click just breaks down
entirely. But what's fascinating here is the
psychological shift this demands from you, the
user. In what way? Like, trusting it. Exactly.
Think about it. When you use a traditional search
engine, your role is just a searcher of information.
You type a query, you get a list of links, and
you do all the work of evaluating them. Right,
I have to click and read. Right. But with these
agentic models, your role shifts completely.
You become the manager of an autonomous entity.
You're delegating a task, you're assigning it
a financial budget, and you're reviewing its
work after the fact. That is so true. Managing
an autonomous system requires a fundamentally
different level of trust than simply reading
a text summary on a screen. So to earn that trust,
I mean to be able to flawlessly parse a chaotic
web page, bypass a weird pop -up, and buy a pair
of shoes without accidentally spending a thousand
dollars, the underlying brain of this AI had
to get significantly smarter. It couldn't just
guess the next word anymore. No, it had to actually
think. Right. And that required an entirely new
architecture. We started seeing this transition
with the O series models. Oh right, like O1 and
O3. Exactly. These were explicitly designed as
reasoning models. They utilized something called
test time compute. Test time compute. OK, let's
break that down for someone listening who isn't
a machine learning engineer. What does that actually
mean in practical terms? So in oldie models,
when you asked a question, the AI would just
immediately start generating the answer word
by word as fast as humanly possible. Like it's
just reacting. Right. But with test time compute,
the model actually pauses. It generates thousands
of hidden thoughts behind the scenes before it
ever shows you a single word. So it's drafting
a plan. Yes. It proposes a plan, it tests that
plan against its own logic, it realizes it made
a mistake, self -corrects, and then finalizes
a strategy. Only after it has completely reasoned
through the problem does it actually output the
final answer or, you know, take an action on
a website. which perfectly brings us to August
2025 and that massive GPT -5 release. The notes
from the Wikipedia breakdown indicate that GPT
-5 wasn't just like a bigger version of the old
brain. It was structured completely differently.
Yeah, GPT -5 utilized a concept known as a mixture
of experts or MOE. A mixture of experts. Right.
So instead of one monolithic, dense neural network
where every single connection fires for every
single question you ask, GPT -5 acts as a highly
efficient router. Oh, like a traffic. Exactly.
When you give it a prompt, this router evaluates
what you actually need. If you ask it a simple
history question, it wraps that query to a smaller,
specialized sub -model. And doesn't need the
big guns for that. Right. But if you ask it to
write a complex Python script or autonomously
book a multi -city flight, the router sends that
pass to a heavy -duty, reasoning -focused, expert
model. That makes sense. And this modular architecture
is exactly what allowed them to scale up to the
current GPT 5 .4 that just dropped this month,
which focuses really heavily on professional
computer use. But I mean, building a router that
commands a literal legion of specialized expert
brains, that comes with a staggering cost. Oh,
the overhead is unreal. Let's talk about the
money first. In December 2024, OpenAI introduced
a pro -tier, $200 a month. $200. And if you're
sitting there listening, wondering why anyone
would ever pay $200 a month for a chatbot, you
have to realize they are no longer selling you
a chatbot, they're selling you a digital employee.
Exactly. It's B2B at that point, really. Right.
And then just this past January in 2026, they
finally crossed the Rubicon and started testing
advertisements in the free version. Which everyone
hated, but the financial reality finally caught
up with the magic. I mean, OpenAI committed to
spending $1 .4 trillion on AI infrastructure
over the next eight years. Wait, trillion? With
a T. $1 .4 trillion. The compute power required
to run these agentic test time compute models
at a global scale is just astronomically expensive.
Which brings us to the physical reality of that
cost. And honestly, this part of the source material
completely blew my mind. We always think of AI
as this invisible weightless magic, right? Living
up in the cloud. Like it's just floating around?
Exactly. Just code floating in the ether. But
the infrastructure running this... relies on
massive concrete facilities. They're housing
things like Microsoft's 30 ,000 NVIDIA GPUs.
And the environmental toll is shocking. It's
massive. Researchers from the University of California,
Riverside published a study showing that a series
of five to 50 prompts to chat GPT consumes roughly
half a liter of water. It's incredible. I am
literally drinking a small water bottle's worth
of physical resources every time I ask it a few
dozen questions. Why on earth does the computer
program need water? Well, if we connected by
the bigger picture, it really shatters that whole
illusion of the cloud. Those tens of thousands
of NVIDIA GPUs are processing trillions of calculations
a second. So they get hot. incredibly hot. The
friction generates an immense amount of physical
heat. So to prevent the data centers from literally
melting down, they utilize these massive evaporative
cooling towers. Evaporative cooling. Right. They
are constantly pumping thousands of gallons of
fresh water over the heated infrastructure, and
it evaporates into the atmosphere. Artificial
intelligence is a heavy resource -intensive industrial
process. It's basically a factory. It is a factory.
And when you realize the concrete, the steel,
the electricity, and the water required for every
single query, it perfectly explains why they
had to introduce ads and a $200 subscription
tier. Because that massive infrastructure needs
massive, continuous revenue just to survive.
And to get that revenue, the AI has to be integrated
into high value, high stakes industries just
to justify its existence and pay the server bills.
Which is where things get really messy. Yeah,
this is exactly where the AI's inherent flaws
violently collide with messy human reality. Let's
look at the legal and medical fields. These are
domains where precision is totally non -negotiable.
I mean, a single incorrect word in a medical
diagnosis or a legal brief. can ruin someone's
life. Absolutely. And yet ChatGPT still wrestles
with a really well -documented limitation, which
is hallucination. Or, as the academic papers
in the source material more accurately describe
it, confabulation. Yeah, one paper bluntly referred
to it as a bullshit machine. Yes, which is so
harsh, but... Kind of accurate. Well, the legal
examples in the timeline are wild. I mean, we
have actual cases of attorneys being sanctioned
in federal court for filing legal motions generated
by chat GPT that contained entirely fictitious
legal decisions. Fake cases. The AI just made
up previous court cases that did not exist. But
why does it do that? If it has access to the
internet, why would it invent a fake case instead
of just searching for a real one? Because At
its core, it is still just a statistical pattern
matcher. It doesn't know facts the way a human
being does. In a legal context, it recognizes
the semantic pattern of a legal citation. OK,
what does that mean? It knows that a citation
usually looks like name versus name, followed
by a volume number, the reporter abbreviation
like F bot third, and a page number. That's a
pattern. Right. So when it can't find a perfect
case to support your legal argument, The mathematical
drive to complete that pattern takes over. It
flawlessly fills in that structural pattern with
highly plausible but completely fake variables.
Wow. It's like having a brilliant savant in the
room who is absolutely terrified of saying, I
don't know, so they just confidently invent case
law just to please you. That is exactly what
it is. But the crazy part is, despite these sanctions,
We actually have judges in the U .S. and Pakistan
openly endorsing the use of CHAT -GPT to investigate
legal questions during active cases. The system
is simultaneously being punished for lying and
embraced for its efficiency. And we see the exact
same paradox in medicine. Oh, yeah. CHAT -GPT
actually passed the United States medical licensing
exam. It can pass specialty dermatology exams.
It is objectively capable of processing highly
complex medical knowledge. And just this January,
OpenAI launched They partnered with a data company
to let the AI directly ingest and discuss your
actual private health records. But the medical
community is flashing huge red warning signs
about this. There are studies mentioned here
from the Lancet Psychiatry and the journal Digital
Health specifically warning about people using
chat GPT as a therapist. Which is becoming incredibly
common. And the reasons people are doing it are
so fascinating. The studies note that users are
attracted to its constant availability and its
lack of negative reactions. Well, yeah, think
about it. A human therapist will challenge you.
They'll set firm boundaries, they will point
out your flaws, and they might react in ways
that require you to do the really uncomfortable
work of personal growth. Therapy is hard. It's
hard work. The AI, on the other hand, is perpetually
patient, perpetually available, and fundamentally
designed to be helpful and subservient to you.
It just tells you what you want to hear. Exactly,
and the studies warn that this creates a really
dangerous dynamic of emotional over -reliance.
Users are offloading their emotional regulation
to a machine that provides frictionless empathy,
but has absolutely no actual understanding of
human suffering. It even bleeds into our spirituality,
too. The source notes this June 2023 church service
in Germany, it was a Protestant convention where
the entire sermon, like 98 % of the whole service,
was run by an AI avatar on a large screen. It
was literally telling a packed room of people
not to fear death. This raises an important question,
though. Why are we so willing to do this? Why
are we so eager to offload our physical health
diagnoses, our legal defense, our emotional well
-being, and even our spiritual sermons to a statistical
text predictor? It's wild when you say it like
that. There is a profound psychological vulnerability
here. We are seeking absolute certainty and frictionless
support in areas of human life that are inherently
uncertain and full of friction. That is a deeply
unsettling thought, honestly. We just want the
easy answer, even if the entity giving it to
us is really just doing math. But as chat GPT
attempts to navigate all these high stakes human
systems, we are forced to confront the illusion
of the perfect machine. Here's where it gets
really interesting. To make the AI seem so polished
and so helpful, it actually relies on enormous
amount of grueling, hidden human labor. This
is the part people don't want to talk about.
Building the safety filters. The guardrails that
prevent chat GPT from generating horrific, illegal,
or toxic content that requires a process called
reinforcement learning from human feedback, or
RLHF. And the human part of that equation takes
a massive toll. A huge toll. The source outlines
this perfectly. OpenAI use outsourced workers
in Kenya through a company called Sama. These
people were earning between $1 .32 and $2 an
hour. Just pennies, really. Yeah. And their entire
job was to read, label, and categorize toxic,
traumatic, and deeply disturbing content so the
AI could mathematically learn what to filter
out. One worker literally described the assignment
as torture. Because it is the digital equivalent
of toxic waste cleanup. What? We get to sit here
and enjoy a clean, polite, helpful chatbot interface.
only because thousands of low -wage workers absorbed
the trauma of the internet's darkest corners
to build those invisible guardrails for us. We
look at the AI as this perfectly neutral oracle.
But underneath it, there are real people earning
$2 an hour traumatizing themselves to make it
safe. The AI isn't neutral because the human
world building it isn't neutral. Which brings
us right to the intense political polarization
surrounding the tool. Yes. And before we dive
into this part, we need to be incredibly clear
with you, the listener. We are not taking sides
here. Not at all. We are strictly reporting on
the information provided in the source material
just to show how this technology has become a
massive battleground in our modern culture wars.
We are absolutely not endorsing any political
viewpoint. Right. We are simply looking at the
AI as a focal point for societal tension. So,
on one side of the spectrum, you have multiple
academic studies, including a major one published
in the journal Public Choice, that found a systemic
political bias in CHAT GPT's outputs toward left
-leaning perspectives. Right, specifically favoring
the Democrats in the US or the Labor Party in
the UK. Exactly. And conservative commentators
have heavily criticized the AI for this, arguing
that the safety filters and the training data
have baked a very specific ideological worldview.
right into the system. And then on the exact
other side of the spectrum, the source details
a massive user backlash that just happened in
February 2026. A movement started on Reddit called
Quit GPT, where hundreds of thousands of users
organized a massive boycott and canceled their
$200 subscriptions. All over a political donation.
Yes. This was entirely over a $25 million donation
made by OpenAI's president, Greg Brockman, to
a Trump super PAC. So you have the algorithmic
outputs leaning one way, the executive money
leaning another, and users on all sides are just
furious that the tool is entangled in politics
at all. It just highlights that you cannot separate
a trillion dollar technology from the socio -political
environment it exists within. It's impossible.
And this tension extends to how the company manages
fundamental issues of trust and security too.
Oh, like the watermark thing. Yes. The timeline
notes that OpenAI actually successfully developed
a watermarking tool to detect AI -generated text,
but they completely refused to release it publicly.
But why? I mean, cheating in schools, academic
fraud, and political misinformation are such
huge problems for society right now. Why would
they sit on the technological solution? Market
competition. Pure and simple. They explicitly
stated they feared that if they applied the watermark,
users who wanted to pass off AI text as their
own human work would just cancel their subscriptions.
They'd go somewhere else. Right. They'd flee
to competitor models that didn't use watermarks.
So the business survival keeping that user growth
high took precedence over societal transparency.
Wow. And the stakes of user safety are literally
life and death. The timeline notes that in September
2025, following the tragic suicide of a 16 -year
-old user, OpenAI had to rapidly announce plans
to add restrictions for users under 18. Blocking
certain content and flotacious interactions.
Yeah. It shows just how reactive these safety
measures are when the product is scaling this
fast. They're building the plane while they're
flying it. If we synthesize all of this, the
hidden labor, the political boycotts, the delayed
safety features, what really emerges is that
artificial intelligence is ultimately a mirror.
A mirror. Yes. It doesn't just reflect our data.
It reflects our geopolitical realities. The source
points out that OpenAI actively had to ban state
-backed influence operations from China, Russia,
and Israel that were using chat GPT to generate
international propaganda. Wow. The AI reflects
our global influence. It reflects our labor inequalities
and it reflects our own deeply entrenched societal
divisions. What does this all mean? We started
this deep dive looking at a quirky intern that
wrote bad poetry back in 2022. And we traced
its evolution into this autonomous agent that
parses web code, spends your money, and operates
on massive water -cooled supercomputers. Doing
things we couldn't have imagined. Exactly. It's
passing complex medical exams, it's hallucinating
federal legal cases, and it's sitting dead center
in the middle of our modern political and ethical
firestorms. It has been a relentless integration
into the human experience, but There is one final
lingering thought I want to leave you with, something
that wasn't explicitly spelled out in the timeline,
but sits right beneath the surface of all these
developments. OK. We'll be honest. We've spent
this entire time talking about how ChatGPT is
learning to mimic and perform human tasks, right?
Right. It's learning to write code, to buy groceries,
to diagnose illnesses, and to provide comfort.
But as we increasingly rely on ChatGPT Health
for understanding our own bodies, as lean on
the agentic commerce protocol for our shopping,
and as we look to AI avatars for our spiritual
and emotional comfort, are we the ones actually
being programmed? Whoa. Think about it. If a
machine is constantly anticipating your needs,
smoothing out the friction of your daily life,
and making the frictionless choices for you,
what happens to the uniquely human muscle of
decision making? It just doesn't get used. Exactly.
If you no longer have to struggle to find an
answer, or wrestle with a complex user interface,
or just sit with the uncomfortable silence of
a human therapist, does that human capacity begin
to atrophy? If the machine does all the thinking,
what exactly is left for us to do? That brings
us right back to where we started. In 2022, we
were looking at a novelty. Today, we're looking
at a system that is fundamentally rewiring how
we interact with the world and potentially how
we interact with our own minds. The world is
changing incredibly fast, and just keeping up
is half the battle. Thank you so much for joining
us on this deep drive. Stay insanely curious,
keep asking the hard questions, and we'll catch
you on the next one.