Welcome to the deep dive. We've got a... A massive
stack of your sources today covering the whole
sprawling, fast -moving world of generative AI.
Yeah, we're looking at a really extensive, totally
up -to -date Wikipedia article for 2026. Right.
And I mean, if you're listening to this, you've
probably typed a prompt into a box recently and
just watched a magic trick happen. Oh, absolutely.
Everyone has. You ask for like a photorealistic
image of a cat riding a skateboard on Mars, and
poof, it just appears on your screen. And usually
when we encounter a technological shift like
we just sort of clap at the magic. Exactly. We
tend to focus entirely on the rabbit and we just
completely ignore the trapdoor. Yeah. But if
you really want to understand the trick, you
have to look under the stage, you know, you have
to look at the pulleys, the hidden compartments,
and just the really messy reality of how the
illusion actually works. Which is exactly what
we're doing with you today. Right. Because the
goal here is not to just rattle off a list of
new chatbots or software updates. We want to
cut through the incredible amount of noise out
there. We're going to explore the underlying
mechanics. the sweeping global hype cycle, the
really complicated legal battles, and the surprising
real -world consequences we are seeing unfold
right now. OK, let's untack this. To understand
what generative AI is doing today, we really
have to look at where it came from. Because I
think a lot of people assume this technology
was born in a Silicon Valley garage maybe a decade
ago. Oh, no. The timeline goes back way further
than that, over a century, actually. A century?
Wait, really? Yeah, it really does. The foundational
concept of algorithmically generating media dates
all the way back to 1906. Wow. There was this
Russian mathematician named Andrei Markov and
he was studying Alexander Prushkin's poem Eugenianogen.
And obviously he didn't have a computer in 1906.
Alright, no computers. He sat there and manually
analyzed the text. He was counting the patterns
of vowels and consonants to calculate the mathematical
probability of one letter following another.
Which is just incredible to think about. I mean
he was essentially doing probabilistic text generation
entirely by hand. Like if you have a consonant
What are the exact odds a vowel comes next? That
core idea, just predicting what comes next based
on established patterns, is the absolute bedrock
of the systems we use today. It's essentially
the great grandfather of your phone's autocomplete
feature. He created what mathematicians still
call a Markov chain. And it didn't just stay
as math on paper, right? No, it evolved. If we
jump forward, by the 1970s, an artist named Harold
Cohen built a computer program called ARIN, and
ARIN could actually autonomously create physical
paintings. Which is wild for the 70s. Yeah. And
then by the 1980s and 90s, the military and aerospace
sectors were using something called generative
planning. They used it to map out crisis action
plans or even prototype autonomous spacecraft.
But the real turning point, the big shift, that
happens in the 20 pens with the rise of learning,
because for a really long time, artificial neural
networks were primarily discriminative. Yes.
They categorize things. Right. That means they
were used for solding data. You feed a computer
a million pictures, and it learns to discriminate
between them. It can confidently tell you, yes,
this is a dog, or no, this is a blueberry muffin.
which was a big deal at the time, but training
a model to actually generate a brand new, completely
original picture of a dog from scratch, that
was an entirely different. much harder problem.
Right, until 2014. That's when we see the introduction
of JANs, Generative Adversarial Networks. Yeah,
that was a brilliant conceptual leap in how we
train artificial intelligence. The way I always
like to visualize a JAN is to imagine like a
master art forger and a brilliant art detective
locked in a room together. Oh, that's a great
way to look at it. Right. So the forger's job
is to paint fake masterpieces and the detective's
job is to spot the fakes. And in the beginning,
you know, the forger is terrible. Just awful
at it. Yeah, and the detective easily flags the
fakes. But they do this millions of times. The
forger learns from every single mistake, constantly
improving, until eventually the forger's work
is completely indistinguishable from the real
thing. And that is essentially what those two
neural networks are doing inside the computer.
What's fascinating here is how that adversarial
setup forced the technology to improve exponentially.
It pushed the boundaries. But JANs were mostly
a revelation for generating images, right? Right.
The real earthquake for language processing happened
a few years later. In 2017, researchers invented
the transformer architecture. This is the big
one. It is. To understand why this was revolutionary,
you have to look at how AI read text before 2017.
Older models processed text sequentially. just
word by word from left to right. Which is incredibly
slow. So slow. And more importantly, it means
the model struggles with memory. Like if you
feed it a really long paragraph by the time it
gets to the last sentence, it has mathematically
forgotten the context of the very first sentence.
Exactly. And the transformer changed that completely.
It processes entire sequences of tokens, which
are essentially just chunks of words or syllables
in parallel. Right. It uses a mechanism called
self -attention. This allows the model to look
at a whole sentence at once and determine the
contextual importance of every single word relative
to every other word. So it actually understands
context. Yes. For example, it instantly understands
the difference between a riverbank and a bank
account based on the surrounding words. Oh, wow.
And this meant researchers could scale up training
exponentially. It moved AI from being a neat
parlor trick to actually possessing a profound,
almost eerie level of linguistic understanding.
And because of that transformer architecture,
the timeline just suddenly accelerates. I mean,
we went from spending 110 years in the theoretical
lab to a complete cultural explosion in about
36 months. It was incredibly fast. In 2020, an
anonymous MIT researcher releases 15 .ai, which
was an early, highly viral audio deep fake tool.
Then in 2021, Delay showed the general public
what text to image generation actually looked
like. And then 2022 was just the watershed year.
You had mid -journey, stable diffusion. and of
course, chat GPT. And what the sources make very
clear is that the hardware running all this software
is an entire spectrum unto itself. Right, it's
not all just supercomputers? No. On one extreme,
yes, you have the massive foundation models like
GPT -4. These require vast arrays of advanced
graphics processing units, or GPUs, running in
enormous, really energy -hungry data centers.
But then on the other end of the spectrum, you
have these smaller, highly efficient models.
The sources mention a 7 billion parameter version
of LAMA. Which is remarkably capable. Yeah, and
just to clarify for everyone, parameters are
essentially the artificial synapses in the network,
the connections that help it make decisions.
7 billion sounds like a lot. But the biggest
models have well over a trillion. But because
it's smaller, this version of Llama can run locally
on a $40 Raspberry Pi computer. People are even
running models like stable diffusion entirely
on older iPhones. And the reason a user would
want to run AI locally on their own device isn't
just to avoid subscription fees. It's fundamentally
about privacy. Yeah, that makes sense. If you're
processing sensitive corporate data, or if you
simply want to avoid corporate censorship and
rate limits, running a local model gives you
complete control. And that hardware reality ties
directly into the geopolitical landscape, too.
Because those advanced data center chips, specifically
NVIDIA's H100s, are so critical to building the
biggest models, the U .S. government actually
imposed strict export controls in 2022 to restrict
them from being sold to China. Right. But the
sources detail how Chinese companies immediately
develop work Yeah, the Byron chip, right? Exactly,
the Byron BR -104 chip. The U .S. sanctions specifically
targeted a metric called interconnect speed,
which is basically how fast multiple chips can
talk to each other to share a workload. So the
Chinese manufacturer simply designed the BR -104
to physically throttle its own interconnect speed.
By doing that, they technically complied with
the exact letter of the U .S. sanctions, legally
allowing them to be manufactured, while still
maintaining high enough processing power to fuel
their domestic AI ambitions. That is wild. And
culturally, the reaction to all this technology
has been entirely fractured, depending on where
you look. Oh, completely. Like, in the West,
there's been a significant amount of hand wringing
and anxiety about the future. But in the Asia
-Pacific region, the sentiment is overwhelmingly
optimistic. Very much so. A 2024 survey showed
68 % of people in the Asia -Pacific believed
AI was having a positive impact compared to only
57 % globally. And in China, as of 2023, 83 %
of respondents were actively using generative
AI. They also lead the world in generative AI
patents by a staggering margin. Right. Yet, despite
that enthusiasm on the consumer side, the enterprise
reality in 2025 has been a bit of a reality check.
Analysts at Gertner noted that the business world
has entered what they call the trough of disillusionment.
The trough of disillusionment. Yeah. Many major
companies are quietly abandoning their generative
AI pilot projects entirely. They're reporting
poor data quality, integration nightmares, and
a distinct lack of return on their really massive
investments. Wait, I have to push back on that
a little. Sure. If the underlying technology
is as revolutionary as the transformer architecture
suggests, and if over 80 % of Chinese respondents
are finding ways to use it daily, why are Western
corporations suddenly bailing on their pilot
projects? Like, is the technology itself failing
to deliver, or are businesses just deploying
it incorrectly? It really comes down to the difference
between having an engine and having a working
car. The transformer model is a remarkably powerful
engine. But having a powerful engine sitting
on your driveway does not mean you can drive
to the grocery store. Right. Integrating generative
AI into a business requires clean, organized
data. But real -world corporate data is usually
siloed, it's messy, it's full of errors. Oh,
I'm sure. Integrating AI into that mess is a
staggering engineering challenge. businesses
are discovering that if their internal databases
are full of garbage, the AI doesn't magically
fix it. It just synthesizes that garbage faster
and more confidently. That makes perfect sense.
And honestly, that integration friction in the
enterprise world is quiet compared to what happens
when this technology collides with the legal
system. Oh, yeah. Because when you drop a revolutionary
data synthesis machine into a society that is
fundamentally built on intellectual property,
Things start to break. They break very publicly.
We are currently watching a huge wave of high
-stakes lawsuits unfold. The New York Times,
Getty Images, the Authors Guild, they are all
suing tech giants like Microsoft and OpenAI.
And the defense there is fair use. Exactly. The
core defense from the AI developers is that training
their models on publicly available copyrighted
data falls under fair use. They argue that the
AI is learning concepts, not copying text, making
it a highly transformative process. But the copyright
holders are countering with a very different
perspective. They're essentially arguing, you
ingested our entire life's work without permission
or compensation to build a machine that now directly
competes with us in the marketplace. Yes. And
beyond just the training data, there is the deeply
confusing flip side. Who actually owns the output?
Right. The outputs are a huge legal gray area.
Initially, the U .S. Copyright Office ruled that
works created entirely by AI lack human authorship
and therefore cannot be copyrighted at all. They
even referenced that famous 2018 legal case where
a monkey took a selfie with a photographer's
camera and the court ruled that non -humans cannot
hold copyrights. But the law is really struggling
to keep up and it's shifting constantly. By early
2025, the Copyright Office had to release new,
highly nuanced guidance. Which changed everything.
It did. They stated that if a human user exerts
a significant amount of control over the selection
and placement of the creative elements, the resulting
output might actually be copyrightable on a strict
case -by -case basis. Which leads to one of the
most surreal details on this entire deep dive.
the 2025 American Cheese update. Yes. The U .S.
Copyright Office officially registered a visual
artwork titled, A Single Piece of American Cheese.
It became the very first visual artwork composed
entirely of AI -generated materials to actually
receive a copyright. And it only happened because
the user extensively documented their process.
Right. They showed that they spent hours tweaking
specific prompts, adjusting sliders, and dictating
the precise placement of the pixels. If I casually
type draw a dog into mid -journey, I don't own
the result. Right. But if I spend three hours
micromanaging the generation of a picture of
American cheese, suddenly I am the legal author.
Like where exactly is the legal line between
using a tool and being an author? If we connect
this to the bigger picture, you can see how our
legal frameworks are currently doing complete
gymnastics. We are trying to apply 20th century
copyright law. which is built around printing
press, cameras, and human illustrators, to 21st
century statistical probability models. It just
doesn't fit. It's a profound mismatch. And we
are seeing that exact same tension play out in
the labor market. Yeah, the labor impact data
is incredibly contradictory. On one side, you
have very real localized pain. Reports from China
in 2023 showed that 70 % of video game illustrators
lost their jobs to image generation AI. 70%.
That's massive. It is. And we all remember the
2023 Hollywood strikes, where the Writers Guild
and the Screen Actors Guild essentially went
to war over AI protections, calling it an existential
threat to creative professions. Yet, if you zoom
out and look at the macroeconomic data, the narrative
shifts entirely. A 2025 study concluded that
the overall U .S. labor market had experienced
absolutely no discernible disruption from generative
AI. None at all. None. Another study followed
Danish workers who were given access to chat
bots. It found they saved about 2 .8 % of their
time on average, with no significant change in
their earnings or hours worked. So it's not replacing
them. Right. The discrepancy exists because,
right now, AI is generally replacing specific
tasks rather than entire James. Its disruption
is heavily isolated to highly specific micro
-industries like concept illustration. The macro
-level job apocalypse just hasn't materialized
yet. OK, but if the legal and labor worlds are
struggling to handle who owns the content and
who makes the content, the internet itself is
struggling to survive the sheer volume of what
is being produced. Here's where it gets really
interesting. And honestly, a little dark. We
need to talk about SLOP. SLOP is the perfect
term for it. It is the Generative AI Equivalent
of Spam. yeah it refers to the shoddy unwanted
AI -generated text and images that are currently
flooding search engine results, social media
feeds, and even published scientific papers.
The scale of the slop is just staggering. A research
paper from Amazon Web Services found that over
57 % of sentences on a vast sample of the web
had been machine translated. And usually poorly
translated at that. Right. It has gotten so pervasive
that in 2024, a highly respected open source
database called WordFreak had to shut down entirely.
That was a huge loss for researchers. It was.
This database calculated word frequencies across
the internet to help researchers understand human
language. But the creator shut it down because
generative AI had permanently polluted the data.
Words like delve were suddenly everywhere because
chat GPT overuses them, which completely skewed
our measurement of authentic human language.
And that data pollution leads to a severe technical
vulnerability known as model collapse. Model
collapse. Yeah. When an AI model is trained on
authentic human data, it works wonderfully. But
as the internet sills with AI slop, the next
generation of AI models ends up unknowingly training
on the output of previous AI models. Like an
echo chamber. Exactly. It's like taking a photocopy
of a photocopy of a photocopy. The model gradually
loses the quirks, the nuances, and the specific
outliers of human behavior. If you repeat that
cycle recursively, the model degrades until it
collapses into generic garbled mush. Are we just
building a digital Auroboros, you know, a snake
eating its own tail where AI churns out slop,
trains on its own slop, and burns down the planet's
energy grid just to give us a lower quality version
of the Internet? This raises an important question,
especially regarding the energy grid. Because
the environmental toll of keeping these massive
systems running is immense. The research institute
EPOC AI noted that a single chat GPT query uses
about 0 .3 watt hours. Which doesn't sound like
a lot. It might not sound like much in isolation,
but it is roughly 10 times the energy required
for a standard Google search. When you multiply
that by hundreds of millions of users daily,
the scale of energy consumption becomes a serious
liability. And the projections in the sources
are sobering. It's estimated that by 2035 the
global carbon footprint of generative AI could
reach up to 245 million tons of CO2 annually.
Yeah. To put that abstract number into perspective,
that rivals the entire carbon footprint of the
United States beef industry. Which is exactly
why researchers are urgently pushing for mitigation
strategies. They're advocating for strict audits
and environmental impacts and exploring ways
to use synthetic data safely without triggering
that model collapse we talked about. There's
also a major push for detection tools, right?
Yes. Google, for instance, deployed a tool called
Synthed in 2025. It subtly watermarks AI -generated
text, images, and video directly at the source,
aiming to help platforms identify and label synthetic
content before it spreads. And reliable detection
is critical because the cybercrime and disinformation
applications are escalating rapidly. We aren't
just talking about teenagers using chatbots to
write high school essays anymore. Not at all.
Cybercriminals are building their own dedicated
malicious models. Tools with names like Worm
GPT and Fraud GPT exist specifically to automate
phishing attacks and social engineering scams
at an industrial scale. And at the state level,
the technology is being leveraged for information
laundering. Generative AI allows state -sponsored
actors to disguise the origin of propaganda by
rapidly rewriting it and distributing it across
thousands of fake social media accounts. The
sources actually highlight two very specific
examples of this. In 2025, a research group called
the American Sunlight Project reported on a network
known as Pravda. This network was publishing
up to 10 ,000 articles a day to push Russian
narratives, specifically aiming to get that text
absorbed into the training data of large language
models. Right. Similarly, in 2025, it was reported
that Israel signed a $6 million contract with
a U .S. firm called Clock Tower X. The stated
goal was to flood social platforms with specific
information to intentionally influence models
like chat GPT through a process called a rag
To understand RAG poisoning, we have to look
at the mechanics. RAG stands for Retrieval Augmented
Generation. OK. Normally, an AI only knows the
data it was initially trained on. But our key
allows the chatbot to search the live internet
to answer your questions in real time. So it
pulls in fresh info. Exactly. But if a state
actor successfully flugs the internet with thousands
of fabricated articles, the AI chatbot searches
the web, unknowingly retrieves those fake articles,
and summarizes them as objective truth for the
user. It highlights how the new battleground
is not just about generating text. It's about
manipulating the underlying databases of knowledge
that the world relies on for answers. OK, let's
take a breath. We have covered a vast amount
of ground today. We traced the mathematical roots
all the way back to Andrei Markov calculating
vowel probabilities in 1906. We explored the
2017 transformer breakthrough that allowed AI
to understand context by reading words in parallel.
We really went through it all. We navigated the
incredibly complex 2026 landscape where a picture
of American cheese can secure a copyright. Concept
artists face significant job losses and the internet
is slowly filling with automated slop while consuming
the energy equivalent of the U .S. beef industry.
So what does this all mean? It means we are navigating
a profound societal transition. The technology
is undeniably capable, but our legal, social,
and technical infrastructures simply were not
built to handle a machine that can synthesize
human output at this scale. The trough of disillusionment
the corporate world is experiencing right now
is really just society trying to build the guardrails
to safely contain and utilize this engine. I
keep coming back to that model collapse problem,
the snake eating its own tail. If AI models actively
degrade when they train on their own synthetic
slop, and if 57 % of the web is already machine
translated, what happens when the AI companies
simply run out of fresh human -created data?
It fundamentally changes the value of human creation.
Exactly. Will pre -2022 verifiably human -made
text, art, and data suddenly become the most
valuable scarce commodity on earth? Will we one
day reach a point where we have to pay a premium
subscription just to read an article or look
at an illustration that we know for an absolute
fact a human being actually spent the time to
create? It's entirely possible. When you look
past the magic trick and past the trap doors,
maybe the real revelation is that the messy slow
human mind was the most valuable component all
along.