Right now, AI models can actually boot up and
beat Pokemon autonomously. Yeah, it totally sounds
like a really entertaining, harmless party trick.
But then you read today's MIT Future Tech Research
Report. Right. Those experts warn this exact
level of autonomy brings severe dangers. They
see a 10 % chance of triggering global societal
catastrophe. I mean, we're talking about $100
billion damages by 2030. Beat! Welcome to today's
deep dive. I am incredibly glad you're joining
us. We truly have a lot of Complex, fascinating
ground to cover today. So we are unpacking an
avalanche of massive AI capability jumps right
now. Anthropic just dropped their highly anticipated
Claude Fable 5 model today. And we really need
to examine the structural collision course this
creates. Yeah, these blistering capabilities
are hitting global security and hardware supply
chains. They're also crashing right into our
necessary safety guardrails. So let's start with
the sheer raw power hitting the market right
now. The newest models arriving today are just
absolutely wild. Anthropic released Claude Fable
5 directly to the public recently. It is the
first major model from their new mythos family.
Fable 5 is absolutely crushing the benchmark
charts across the board. It shows truly exceptional
results in complex knowledge and vision tasks.
It is kind of like stacking Lego blocks of data.
That is a perfect way to visualize its multimodal
architecture. You just snap a visual screenshot
onto a basic text prompt. And the AI just builds
the entire functional code right from there.
It does not see images and text as separate languages.
It blends them together into a unified understanding
of the problem. Which perfectly explains how
it can rebuild entire web apps from scratch.
Yeah, and it also explains the Pokemon Fire Red
achievement. The AI completed the entire game
without any human intervention at all. Just think
about this. deep long term planning required
for that. You have to manage inventory and navigate
highly complex spatial maps. The agent handles
all of those sequential decisions completely
autonomously. It proves these agents can handle
long term enterprise software workflows now.
But this incredible autonomous capability definitely
comes with a steep price. It costs $10 per million
input tokens right now. Tokens are just the fundamental
puzzle pieces of language that AI processes.
And output tokens will actually cost you $50
per million. That is exactly double the cost
of their previous Opus model. Opus 4 .8 was already
considered a fairly pricey option. Startup founders
building on Fable 5 will burn cash much faster.
They really have to route simple tasks to much
cheaper models. Fable 5 is currently available
via their developer API system. API is a software
bridge letting different computer programs talk.
If you maintain a Pro, Max, or Team subscription,
though, you actually get it included completely
free until June 22nd. That gives power users
a fantastic window to test the capabilities.
And this rapid capability leap is mirrored across
the whole ecosystem. We are fully entering the
agentic era of artificial intelligence. Look
at this fascinating new tool called KimiWork,
for example. It deploys up to 300 local AI agents
simultaneously. Local agents are AI programs
running directly on your personal hardware. They
automate complex web tasks and analyze dense
financial data sets. Then we have another tool
called Macatron, making major waves today. It
controls native applications using very simple
conversational English commands. We're also seeing
Google aggressively push Gemini 3 .5. Their live
translate feature handles real -time conversational
translation. Almost perfectly now. You can translate
complex, nuanced conversations exactly as they
happen globally. The business scale behind these
tools is getting incredibly hard to fathom. A
company called Lovable just crossed $500 million
in annualized revenue. They have seen over 50
million user projects created already. And their
usage is accelerating to 1 million new projects
weekly. Whoa, imagine scaling to a billion queries.
It completely breaks my brain just trying to
comprehend that volume. It is a totally un -
unprecedented level of software creation speed.
You no longer need to hire an entire team of
software engineers. You just describe what you
want in simple, plain English. And Lovable spins
up the entire working application for you instantly.
Two sec silence. It is genuinely magical to watch.
With Fable 5 dominating, where does that leave
Claude Opus for a user trying to get work done?
We actually heard directly from Tariq on the
Claude Code team about this. Oh, wow. What did
he specifically say about Opus? Well, he says
Opus remains the absolute strongest model for
extended work. So it is better for those really
long, complex tasks. Right. Opus still handles
multi -day, sustained, autonomous execution much
better today. So Opus remains the marathon runner
while Fable sprints through complex reasoning.
Exactly. You always need to pick the right tool
for the job. Which logically brings us to the
physical reality underneath all this software.
You cannot magically scale to a million new coding
projects weekly. It takes immense hardware, massive
funding, and incredible physical infrastructure
networks. And we are seeing massive structural
shifts in the financial markets. The old FAA
acronym is completely dead at this point. Financial
analysts are heavily using the new acronym MANGOES
instead today. That stands for Meta, Anthropic,
NVIDIA, Google, OpenAI, and SpaceX. It perfectly
captures the massive financial shift toward artificial
intelligence dominance. And the sheer volume
of money flowing into these companies is staggering.
A startup called Moonshot AI is currently raising
$2 billion. That represents a massive 50 % jump
in their overall valuation. They were valued
at $20 billion just one short month. It is a
level of rapid capital accumulation we rarely
ever see. But all that investment money has to
buy actual physical hardware. They need these
highly specific AI processing chips for 2028.
And there is a massive shift happening in global
chip manufacturing. Google and Nvidia are reportedly
turning to Intel foundries right now. They are
actively using Intel as a critical backup to
TSMC. TSMC has historically dominated the entire
global semiconductor chip manufacturing market.
Why are Google and Nvidia suddenly looking at
Intel for their 2028 chips instead of just sticking
with TSMC? It largely comes down to intense geopolitical
realities and sheer volume. Meaning they just
cannot rely on one single factory anymore. Exactly.
Relying entirely on a single foundry in Taiwan
is incredibly risky. Right. Diversifying away
from TSMC is basically a massive supply chain
insurance policy. That is exactly what it is
for these massive tech giants. Building a semiconductor
foundry. It takes many billions of dollars in
years. You absolutely cannot just spin up a new
chip factory overnight. It requires massive clean
rooms and incredibly complex photolithography
machines today. But Taiwan sits in an incredibly
precarious geopolitical hotspot right now. If
those supply chains get disrupted, the entire
AI industry halts completely. Google and Nvidia
absolutely cannot afford that massive level of
existential risk. So they are pouring massive
resources into Intel's new domestic foundries.
But the infrastructure... friction is not just
about the physical semiconductor chips. We also
have to deeply examine the massive software security
vulnerabilities. Microsoft just had to surgically
remove over 70 compromised GitHub repositories.
Hackers actively slipped highly dangerous malware
into these specific coding projects. These compromised
projects were tied directly to automated AI coding
tools. They specifically targeted automated agent
platforms like Cloud Code and Gemini CLI. It
shows how deeply vulnerable these automated development
environments truly are. When AI agents write
code, they often pull from open source libraries.
If a hacker poisons that library, the AI blindly
pulls the malware. And human developers are trusting
these AI tools a bit too blindly. It is a massive
structural security flaw in the modern coding
ecosystem. This severe friction extends to the
actual raw coding capabilities as well. A company
called Cognition just released their new frontier
code benchmark system. The absolute top frontier
models currently score just 13 out of 100. That
explicitly shows real -world enterprise coding
still hits massive walls of friction. I still
wrestle with prompt drift myself. Yeah, prompt
drift is when an AI gradually loses focus on
your original instructions. You ask the agent
to fix a specific tiny front -end button issue.
And suddenly it tries to rewrite your entire
back -end database architecture instead. We are
still very far from having completely autonomous
software engineers. The agentic era is clearly
here, but it is deeply. deeply messy. We have
brilliant flashes of magic mixed with fundamental
structural failures. It creates a very precarious
foundation for our critical digital infrastructure
today. And the mounting geopolitical tensions
over chips just adds significantly more pressure.
We are moving incredibly fast on deeply fragile
underlying foundation systems. Which brings us
to the biggest structural problem we collectively
face. The catastrophic risks of this technology
scaling without proper safety guardrails. Be
sponsored. Before the break, we analyzed intense
geopolitical hardware chip supply tensions. And
we looked at those massive automated GitHub malware
security breaches. These structural software
issues lead directly into the broader macro risk
landscape. We have blistering capability growth
actively colliding with terrible structural dangers.
MIT FutureTech just released a deeply unsettling
new AI blueprint today. They comprehensively
surveyed 272 leading international AI experts.
The detailed findings are honestly enough to
keep you awake tonight. They carefully analyzed
24 major AI risks currently threatening our modern
society. Experts critically believe 18 of those
risks have catastrophic outcome potential. They
assign a greater than 10 % chance to those catastrophic
events. That explicitly means... hundred billion
dollar damages or massive global societal disruption.
And they predict these massive structural failures
could easily happen by 2030. That is only a few
short, highly volatile years away from us today.
The top catastrophic risks they identified are
incredibly specific and deeply concerning. They
deeply worry about dangerous autonomous capabilities
emerging in these frontier models. They highlight
severe geopolitical competitive dynamics aggressively
accelerating the global arms race. And they specifically
warn about highly weaponized cyber attack capabilities
rapidly expanding globally. Which links perfectly
back to that recent Microsoft GitHub malware
hack. The software vulnerabilities are already
being actively exploited in the wild today. But
the MIT study also revealed a massive, terrifying
societal vulnerability gap. The people most deeply
vulnerable to these catastrophic failures are
everyday users. The general public absorbs the
massive financial damage when vital... systems
break. But the general purpose AI developers
are the ones structurally responsible today.
Government regulators also hold massive inherent
responsibility for preventing these specific
catastrophes. The critical crosshairs are currently
aimed at information, finance and national security.
Think about autonomous AI getting deeply embedded
in critical financial decision making systems.
If these AI tools get embedded in the financial
sector and fail, who is actually left holding
the bag? Well, the MIT study clearly shows the
general public takes the massive hit. Wait, really?
The public takes the hit? Yeah. While developers
and regulators are responsible, they lack financial
incentives to slow down. So every day people
suffer. But tech developers and government regulators
hold the actual responsibility. That is the tragic,
undeniable reality of this massive, glaring accountability
gap. The underlying structural dilemma here is
incredibly difficult to solve quickly. Any tech
company that deliberately slows down for safety
takes a massive hit. They immediately lose massive
competitive ground in a violently red hot market.
Absent strict external government rules, slowing
down is essentially financial suicide today.
Developers have deep structural and financial
reasons not to prioritize vital risk mitigation.
This perfectly explains Anthropic's highly specific
approach to the recent Fable release. Ampropic
purposefully put a massive safety guardrail system
on Claude Fable 5. They have loudly warned everyone
about severe recursive self -improvement dangers
very recently. An AI system continuously making
itself smarter without human help. So if you
prompt Fable 5 with highly sensitive or dangerous
topics. The model will actually actively hard
block your specific request right away. It automatically
routes your session straight back to the safer
Opus model. To legally use Fable 5, you also
face a mandatory data retention policy. You must
formally agree to a strict 30 -day data retention
window. Anthropic explicitly promises not to
train future models on that retained user data.
But they absolutely need the logs to actively
monitor for dangerous emergent behavior. Meanwhile,
the completely unrestricted, deeply powerful
version is called Claude Mythos 5. But Anthropic
intentionally locked that specific model away
behind heavily closed doors. It is currently
only available for highly approved, heavily vetted
security partners now. They are actively taking
a massive, deliberate financial hit to ensure
global safety. They essentially built the fastest
car but permanently locked the top gear. Because
they clearly know the regulatory brakes on the
broader industry are failing. It is a fascinating,
rare, real -world example of prioritizing structural
safety first. But they are largely acting completely
alone in this highly specific, cautious approach.
Most corporate competitors are just blindly racing
toward maximum capability and profit. And Fropic
has always actively branded themselves as the
premier safety -first AI lab. They famously split
off from OpenAI years ago over fundamental core
safety disagreements. So releasing a highly capable
autonomous model like Fable 5 is deeply risky.
They truly had to thread a nearly impossible
corporate and ethical needle here. The mandatory
30 -day enterprise data retention policy is a
truly perfect example. Most massive enterprise
users absolutely hate corporate data retention
policies today. They desperately want complete
privacy and zero data logging for internal...
corporate security. But Anthropic stubbornly
insists on keeping those specific user logs for
30 days. They need to actively monitor for extremely
dangerous emergent capabilities developing online.
They are specifically looking for terrifying
signs of biological weapons research queries
happening. Or highly automated, deeply destructive
cyber warfare scripts being actively generated
by their models. Think about the sheer terrifying
scale of $100 billion in damages. That isn't
just a tiny, easily ignored blip in the global
financial market. That represents massive global
job losses and entirely decimated corporate pension
funds. And a 10 % chance is practically playing
Russian roulette with the economy. You would
never voluntarily board an airplane with a 10
% crash rate. But we are currently strapping
our entire global economy to this exact rocket.
And the ambitious people building the massive
rocket currently have no real brakes. Two secs
silence. If we logically pull back and look at
the broader big picture today. We are living
in a deeply profound jarring split screen reality
right now. On one side we have. deeply magical,
fully autonomous software coding agents. They
are completely rebuilding complex web applications
entirely from visual scratch today. We have a
violently red -hot mangoes economy that is basically
printing infinite money. But on the other darker
side, we face a deeply terrifying structural
gap. We have a massive, currently unaddressed
structural safety and global accountability gap.
Actively slowing down to implement crucial safety
guardrails takes immense corporate courage today.
Anthropic, courageously restricted Mythos 5,
but willingly took a massive financial hit. This
dangerous competitive dynamic leaves our most
critical financial and national security sectors
vulnerable. Completely vulnerable to massive,
highly catastrophic global risks by the year
2030. The fundamental corporate incentive structures
are completely misaligned with long -term human
survival today. Which ultimately leaves you with
a deeply unsettling, highly provocative question
to ponder. Taking a severe competitive financial
hit is currently the only reward for safety.
And external federal government regulations are
historically moving far too slowly today. Will
it necessarily take the first $100 billion global
financial market crash? Or perhaps a massive,
deeply weaponized global political disinformation
event soon? To finally force the entire global
AI industry's brake pedal straight to the floor.
It is a truly terrifying, sobering thought to
leave hanging in the air. Thank you so much for
taking this fascinating deep dive with us today.
We always deeply appreciate you thoughtfully
exploring these complex technical topics with
us. Stay intensely curious, and we will definitely
see you on the next one.