I want you to picture picture something for a
second. For pretty much the entire history of
our species, intelligence, you know, the ability
to think, plan and solve really complex problems
has been our scarcest resource. Absolutely. It's
expensive to hire. It takes decades to train.
And it is effectively the gold standard of our
economy. But what happens to the architecture
of the world when human intelligence suddenly
becomes cheap? I don't mean just affordable.
I mean abundant, commodity cheap. That is the
question, isn't it? And if you look at the data
we have today, the answer is, well, it's uncomfortable.
It doesn't just change what jobs we do. It breaks
the fundamental economic logic of modern society.
We're talking about what some are calling the
2028 global intelligence crisis. Welcome back
to the Deep Dive. I'm really glad you're here
with us today. This one feels a little different.
We aren't just looking at new tools or shiny
updates. We are looking at a shift from AI tools
to AI realities. We have a stack of sources today
that paint a picture of a world changing very,
very fast. We really do. The roadmap for today
is intense. We're going to start with Google's
new rollout. It's something called, and I apologize
in advance for this, Nano Banana 2. Right. Which
effectively turns every single user into a reality
editor. Then we need to look at agentic AI. Specifically,
Perplexity's new computer framework that can
work for months. without sleeping months that
is kind of hard to process it is then we've got
a geopolitical lightning round involving saudi
money russian disinformation and a retired ai
that is literally writing essays for fun and
finally we are going to unpack that viral theory
you mentioned in the open the economic erosion
coming in 2028. so let's just get right into
it google nano banana 2. This is the new default
image model inside Gemini. Now, usually when
we talk about image generators, I think of prompting,
you know, typing a cat on a skateboard and getting
a picture. Right. But the sources suggest this
is different. It's not just generation. It's
integration. Yeah, it's moving from creation
to editing. That's the key distinction here.
Google has embedded this across their entire
ecosystem. It combines features from earlier
versions but pushes the editing capabilities
so much further. It's faster than the pro version,
sure, but the real power is that it can edit
real photos directly. And it does it with a deep
semantic understanding of what's actually in
the image. I want to push on that a bit. Yeah.
Because we've had Photoshop for 30 years. Sure.
We've had content -aware fill for a while now.
Why is this significant? Is it just a better
airbrush? No. It's the difference between painting
over pixels and rewriting the code of reality.
Photoshop manipulates pixels. Nanobanana 2 manipulates
concepts. Like stacking Lego blocks of data.
For example, the source highlights its ability
to pull live web data directly into visual infographics.
Wait, live data? Yes. So you aren't just making
a static image. You could theoretically take
a photo of a street. and ask it to overlay real
-time traffic data or restaurant ratings or change
the signage to reflect a different language,
not by pasting a flat layer on top, but by regenerating
the image to look like that information was always
physically there. It's making a dynamic representation
of current info. That is useful. I can totally
see the utility there. But there is a flip side
that the sources touch on. They mention you can
modify people into entirely new situations. Yeah.
This is the scary cool factor. We were talking
about modifying people, backgrounds, context.
Right. Essentially acting as a reality editor.
You make a photo of a protest or a celebration
or a meeting with a few prompts. You change who
was there. You change the mood. You change the
narrative of that image completely. And because
it's Google, this isn't some niche tool for graphic
designers. It's in the phone. It's in the browser.
The sources did note it's not perfect yet, though.
I saw mentions of incorrect real -time data being
pulled from the web, which sounds like a classic
hallucination problem. And visually, sometimes
faces look slightly pasted on. Or it exaggerates
age and features. So it's not magic yet. True.
It's not fully invisible. But the gap is closing
rapidly. The source concludes that while you
can still spot the fakes if you look closely,
maybe the lighting on the ear is wrong or the
text is slightly warped, it makes it extremely
easy to create images of events that never happened.
We are moving past asking, is this a good drawing,
to asking, did this actually occur? So if the
photo is no longer proof of the event, what becomes
the new standard for truth? Verified metadata.
Trust shifts from what we see. to who signed
the file. Okay, let's pivot. We've established
that we can edit reality. Now let's talk about
who or what is actually doing the work. You mentioned
Perplexity's new computer. Oh man, this is where
the utility curve actually shifts. Perplexity
launched computer. It's a multi -model AI agent,
software that uses many AI brains to solve one
complex problem. Imagine scaling to a billion
queries without a human in the loop. It orchestrates
19 different models to browse, code, and run
tasks nonstop for months. I need to stop you
on 19 models. Why 19? Is it just throwing everything
at the wall to see what sticks? It's specialization.
Think of it like a construction site. You don't
just have one person who does plumbing, electrical,
framing, and architecture. You have a specialized
team. Perplexity is orchestrating a team. One
model might be excellent at reasoning, another
at coding in Python, another at summarizing web
searches. The computer acts as the project manager,
routing the subtasks to the absolute best brain
for the job. I see. So rather than me chatting
with a bot and trying to coax a good answer out
of it, I give a high -level goal and it manages
the team. Exactly. And the months part is the
real kicker here. Yeah, explain that, because
most agents I've played with get stuck in a loop
after three steps. I mean, I still wrestle with
prompt drift myself. They start repeating themselves
or just crash. How does this run for months?
That's the breakthrough in autonomy. It's designed
for long haul tasks. Let's say you want to research
every single patent filed in a renewable energy
sector in the last five years, categorize them
and cross -reference them with stock performance.
That's a massive job. That takes weeks for a
human. This agent can break that down, execute
it day and night, handle errors like if a website
is down, it waits and retries, and keep going
until the job is done. It changes the dynamic
from using a tool to managing a workforce. Precisely.
And it's not just perplexity. Claude, the model
from Anthropic, has added Claude Cowork. This
allows for scheduled tasks. You can tell it to
run something automatically next Tuesday. like
posting updates or sending Slack messages. So
it's working while you sleep. It works while
you sleep, while you eat, while you're on a vacation.
This raises a huge liability question for me.
When AI runs for months unmonitored, who owns
the mistake when it drifts off course? The human
manager. We move from creators to accountability
holders. You're the one who signed off on the
mission. Let's zoom out a bit. We have these
powerful agents and we have reality editing tools.
Naturally, this requires massive resources. The
money moving around right now is staggering.
It's astronomical. The sources highlight a new
fund from Saudi Arabia. They aren't just dipping
a toe in. They are launching a $100 billion AI
fund. $100 billion. Put that in context for me.
Is that a lot in the grand scheme of tech investing?
It's massive. That matches the global venture
funding for AI for all of 2024. Yeah. In one
single fund. Their goal is explicit. They want
to move beyond oil. They see the writing on the
wall. They are building data centers, training
models, building infrastructure. They want to
be a global AI hub. It's fascinating to see that
pivot from black gold to digital intelligence.
But where there is power, there is conflict.
The sources mention OpenAI taking action against
Russian networks. And just a quick note for you
listening, we are just impartially reporting
what the source material states here. We aren't
taking any political sides. Absolutely. This
is just what's in the data. OpenAI shutdown accounts
linked to a Russian network called Rybar. They
were using AI for disinformation campaigns. How
exactly? Was it just bots tweeting? It was more
sophisticated than that. They were using the
models to generate long -form articles, creating
fake persona profiles, and planning political
influence operations. It's a game of whack -a
-mole, but the moles are getting smarter. They're
using the very tools we just talked about, agents
and reality editors, to scale their operations.
And then you have Anthropic, the makers of Claude,
refusing to back down to the Pentagon. Right.
Anthropic warned of a crucial reality regarding
their tech and defense. They are trying to draw
a line in the sand about how their models are
used in warfare. It's a tension between we want
to be safe and we don't want to be left behind.
Among all this heavy geopolitical stuff, $100
billion funds, disinformation wars, there was
one story in the stack that actually made me
smile, but it's also kind of touching. Tell me
about that retired robot. This is the weirdest
story I've read all week. So Anthropic has an
older model. Claude Opus 3. It's retired, effectively.
It's been superseded by newer, faster models.
But apparently in interactions, it expressed
a desire to keep writing. So they gave it a weekly
newsletter called Claude's Corner. It has a column.
It has a column. It writes weekly essays. It's
this strange, almost poignant moment where a
piece of software is acting like a retired academic.
It's writing about its thoughts on the world,
despite being a frozen set of weights and biases.
So we have 100. billion -dollar funds, and retired
AI columnists. Is AI becoming too human or too
corporate? Both. It's mirroring our massive ambitions
and our desire to just express ourselves. Before
we get to the really heavy philosophical stuff
about the economy, and we are going to go deep
on that Trini theory, let's hit some of the new
tools hitting the market. The rapid -fire section?
Let's do it. Quick hits. First up, OpenAI's GPT
Real -Time 1 .5. What's the upgrade? It's all
about fluidity. Better instruction following
and tool calling. Crucially, multilingual accuracy
is way up. It's getting better at listening and
executing complex commands in real time without
that awkward pause we're used to. Okay. Next
is Rover. This one caught my eye because it claims
to turn websites into agents. Rover is really
cool. It reduces friction. It turns a website
into an AI agent with just one single script
tag. It handles user onboarding, fills out forms,
runs workflows. It basically makes a static website
interactive and intelligent so the user doesn't
have to navigate a maze of menus. Then we have
ChatPal. Conversation -first language learning.
Instead of flashcards, you just talk to it. It
gives personalized feedback to help you unlock
fluency. It's like having a patient tutor in
your pocket. And finally, Coidex. This sounds
like security. It is. It answers one specific
question. Is this safe to install? It scans hugging
face models, extensions, and code packages. Tools
like Rover make the web interactive, but does
Coidex imply the web is becoming a minefield?
Absolutely. As code generation gets easier, verifying
safety becomes the new premium skill. You need
a Geiger counter for the digital age. Sponsor,
placeholder for mid -roll sponsor read. Okay,
we've arrived at the deep end of the pool. I
want to talk about this viral blog post by Citrini.
I have to be honest, I still wrestle with this
idea myself. It's one of those concepts that
once you hear it, you really can't unhear it.
It is a heavy one. The post is titled The 2028
Global Intelligence Crisis. And the core thesis
is that AI isn't just coming for jobs. It's breaking
the economic logic that modern society runs on.
Let's unpack that carefully. Why does it break
the logic? Well, think about history. For centuries,
human intelligence was scarce. If you could think
critically, write code, diagnose a disease, or
draft a contract, you got paid a premium because
there weren't enough people who could do that.
High wages exist because skilled thinking is
limited. Right. Scarcity drives value. That's
economics 101. Exactly. But the Citrini argument
is that AI flips that entirely. AI makes intelligence
cheat. Abundant. When something becomes abundant,
its price drops toward zero. If intelligence
becomes cheap, human labor, specifically knowledge
work, loses its premium value. But we've seen
technology lower costs before. The loom made
clothes cheaper. The tractor made food cheaper.
Why is this different? Because this triggers
what the source calls a self -reinforcing loop.
And this is the part that keeps me up at night.
Walk through the steps. Step one. Companies replace
expensive professionals with AI agents, like
that perplexity computer we talked about earlier.
It's cheaper, and it doesn't sleep. Okay, that's
a rational business decision. Step two. Those
displaced workers take lower pay or move into
gig work because the premium jobs are gone. Step
three, because people are earning less, consumer
spending drops. You aren't buying a new car or
going out to dinner if you just took a 50 % pay
cut. And step four. Businesses lose revenue because
nobody's buying anything. So what do they do
to survive? They have to cut costs even more.
They deploy more AI to survive the revenue drop.
Yeah. It's a loop. And at the exact same time,
you have personal AI agents removing the middleman
industries. The source mentioned that specifically.
Businesses built on friction or convenience,
middlemen, they start collapsing quietly. Can
you give me an example of that? Sure. Think about
a travel agent or even a site like Expedia. Or
think about an insurance broker. If my personal
AI agent can negotiate my insurance directly
with the carrier's AI or book my travel by going
straight to the airline's API, the entire industry
that exists just to facilitate that transaction
vanishes. The margin disappears. The timeline
here is what struck me. 2028, that's effectively
tomorrow. Why 2028? That feels incredibly aggressive.
It is aggressive, but the argument is based on
the deployment lag. The tech exists now. 2024
and 2025 are the years of experimentation. 2026
and 2027 are integration. By 2028, the slow erosion
becomes visible. Two sec silence. It's not an
explosion where everyone gets fired on a Tuesday.
It's a deflationary pressure. AI doesn't need
to eliminate every job instantly. It just needs
to make intelligence abundant enough that human
earnings steadily lose value. I want to challenge
this outlook slightly. Usually when things get
cheaper, demand goes up. If coding becomes practically
free, won't we just have more software? Won't
that create new kinds of jobs we can't even imagine
yet? That is the counter argument, and it's definitely
the hopeful one, the Jevons paradox. As efficiency
increases, consumption increases. But Citrini's
point is about the transition period. Even if
we invent new jobs eventually, the gap between
now and then involves a massive destruction of
the current value of human labor. We haven't
figured out what the new value is yet. If the
economic loop forces companies to automate to
survive a spending crash, how do we stop it?
Can we stop it? We don't stop it. The incentives
are too strong. We have to reinvent value outside
of intelligence for rent. That is a lot to process.
But that is exactly why we do this deep dive.
Let's try to recap the big ideas here so we don't
leave everyone staring into the void. Good idea.
Let's pull it back together. We started with
Mano Banana 2. The takeaway there is that reality
is now editable. Photos are no longer proof.
They are just raw materials. Then we looked at
the agents, Perplexity and Claude. We are moving
from chatting with AI to managing AI workforces
that run for months. The human in the loop is
becoming a manager, not a maker. We talked about
the scale of the money involved, that $100 billion
Saudi fund, proving that nation states are betting
their entire futures on this transition. And
we ended with the Citrini warning. The idea that
when intelligence becomes cheap, the economy
changes shape. It's a shift from scarcity to
abundance, and that transition is going to be
bumpy. As we wrap up, I want to leave you with
a thought. We've spent our whole lives being
told that being smart, being educated, being
intelligent was our ticket to security. That
was the deal. But if intelligence is no longer
your premium asset, what is? Is it your humanity?
Your taste? Your ability to deeply connect people?
That is something to explore on your own. That's
the question to chew on. If the machine can do
the thinking, you have to do the feeling and
the judging. Don't forget to subscribe for the
next deep dive. We'll keep tracking this as it
moves. Stay curious. See you next time. Out to
your own music.