Just two years ago, human beings wrote software
code manually. We typed every single logical
instruction by hand. Yeah, line by tedious line.
Right. Today, we are building autonomous AI managers
instead. Those managers hire other AI workers
to do it. Welcome to our latest deep dive. I
am truly glad you are here with us. Okay, let's
unpack this. Today, we explore a profound technological
shift. We are moving from human prompts to continuous
agentic loops. It is a massive leap forward.
It really is. We will trace the staggering hardware
costs of this autonomy. We will see how it enters
Hollywood and the beauty industry. Finally, we
examine a plan to shield the Internet's foundation.
We are crossing into completely uncharted territory
today. The fundamental architecture of digital
creation is transforming. It is happening much
faster than anyone originally predicted. We should
start with how work actually gets done now. AI
is operating autonomously in continuous, invisible
loops. Beat? At Meta's At Scale conference, we
heard a fascinating announcement. Right, from
Boris Cherny. He created Cloud Code. Exactly.
He officially declared the era of agents prompting
other agents. This changes the baseline of software
development entirely. The evolution of software
engineering is actually quite striking. First,
we relied on humans writing handwritten code.
Which, you know, took thousands of hours. Then
we transitioned to asking AI agents to write
code. That required complex prompt engineering
and constant human oversight. Prompt engineering
is rapidly becoming an obsolete skill. Yeah.
It really is. We used to spend hours perfectly
phrasing our technical requests. Now the AI agent
optimizes its own internal prompt structure.
Human engineers just set up managers to handle
tasks. Those managers then prompt other sub -agents
to complete specific jobs. These autonomous processes
are formally known as agentic loops. They rely
entirely on complex, non -deterministic logic.
Let me define that specific technical term for
you quickly. An AI making its own choices instead
of following fixed rules. Right. It doesn't follow
rigid tracks like a train anymore. It is more
like a self -driving car encountering a detour.
It calculates a brand new route dynamically in
real time. It evaluates the physical obstacles.
Right. And adjusts its path continuously. Czerny
noted this is already happening professionally.
He has agents constantly running in his system's
background. Just constantly working. Exactly.
They scan his entire code base for outdated legacy
code. They unify duplicated abstractions and
improve code architecture endlessly. They do
this tedious maintenance work without any human
prompting. I still wrestle with prompt drift
myself. My AI forgets instructions during a long
chat session. Goal. It loses the original context
after 20 or 30 messages. How exactly do they
keep these advanced models on track? Well, developers
now use clever engineering tricks like the Ralph
Loop. It is a brilliant mechanism for maintaining
digital focus. How does it work? It forces the
AI to output its reasoning aloud first. By reading
its own logic step by step, the model self -corrects.
It feeds its previous output back as the next
input. So it prevents the autonomous model from
hallucinating a wrong turn. It constantly grounds
the AI in its own verified logic. Exactly. That
is crucial for what engineers call hill -climbing
problems. Meaning an AI making incremental software
improvements endlessly. Right. It optimizes complex
architecture as long as your budget allows. Anthropic
recently showed a truly powerful demo of this
concept. Their engineers used AI to build software
entirely from scratch. Not just asking it to
build a whole app lazily. Exactly. They guide
the agents through the complex development process
iteratively. There is an emerging tool called
SkyBridge doing exactly this. It handles the
complex development loop entirely on its own.
Meaning it spins up a virtual network and tests
the written code? Yeah, it establishes secure
testing tunnels to evaluate external webhooks
safely. It manages view rendering and client
compatibility automatically behind scenes. So
human engineers just focus on designing the core
product features. It's like stacking Lego blocks
of data. That is a great analogy. But how does
a non -deterministic loop actually know when
to stop? If it runs in the background endlessly,
what kills it? It seems like it would just optimize
code forever. Well, developers program a separate
independent subagent to monitor the process.
This subagent compares mathematical confidence
scores against a predefined threshold. Ah, I
see. Yeah, it constantly evaluates the current
output against the primary goal. When the score
passes the success metric, it cuts power. It
acts as an objective independent auditor for
the working agent. So an AI manager simply decides
when the job is finally done. Yeah, it becomes
a completely self -regulating digital ecosystem.
The human becomes a supervisor rather than a
direct creator. Infinite loops are undeniably
amazing in theory, but they require infinite
guardrails to remain perfectly safe. Two secs
silence. They also require practically unlimited
computing power to function properly. This brings
us to the immense financial cost of autonomy.
These continuous loops burn through processing
tokens insanely fast. Every single loop cycle
costs real money to execute. There is practically
no theoretical compute ceiling here at all. This
raises an urgent question about evaluation and
software testing. Before you run infinite loops,
you need strong safety nets. You cannot afford
to let an agent hallucinate endlessly. There
is a new tool called AgentX for this. It evaluates
these agents and creates comprehensive test suites.
Before they fail in production, right? Exactly.
It runs intense evaluations before agents go
live. It simulates thousands of random user interactions
simultaneously. This helps pinpoint catastrophic
edge case failures. incredibly early. But better
software testing does not solve the hardware
bottleneck. If loops just burn tokens indefinitely,
what happens next? Aren't we just building a
machine designed to bankrupt developers? It sounds
like a severe financial drain for smaller teams.
Whoa, imagine scaling to a billion queries. The
hardware compute costs become absolutely astronomical
very quickly. This is precisely why Grok recently
raised $650 million. They were challenging the
current graphical processing paradigms directly
now. NVIDIA also completed a $20 billion deal
recently. Right. A massive not -acquire -hire
deal. They absorbed key engineering talent without
triggering antitrust monopoly laws. That deal
also absorbed highly specific chip architecture
intellectual property. Traditional AI models
rely heavily on graphics processing units. Those
chips process thousands of math problems simultaneously
in parallel, but they suffer from severe memory
bandwidth limitations during generation. Why
is Grok's alternative hardware so crucial for
infinite loops? They engineered what they call
language processing units instead. These specific
chips process sequential data without traditional
memory bottlenecks. So they eliminate the memory
bandwidth limitations that plague traditional
graphics cards. Exactly. They skip the heavy
graphics processing overhead entirely during
operation. This makes running text -based AI
models significantly faster and cheaper. Grok
is spreading this specific architecture across
13 data centers. They clearly see an enormous,
unending demand for continuous compute. Will
these severe hardware bottlenecks eventually
kill the infinite loop dream? Are we hitting
the physical limits of our digital ambitions?
It certainly will, unless alternative clouds
challenge the chip monopolies. Companies like
Grok must successfully provide affordable, scalable
computing power. They need to drive the cost
of inference down dramatically. Otherwise, only
tech giants can afford to run these models. Basically,
endless AI needs endless chips or the whole thing
stalls. Physical reality firmly limits our theoretical
digital potential. To fuel these highly capable
models, they need enormous memory. They also
require constant ongoing observation of complex
human behavior. beat. This collision directly
impacts our foundational expectations of digital
privacy. Google's Gemini 3 .5 Pro nears a June
launch. It features deeply advanced reasoning
capabilities for complex tasks. It also features
a staggering 2 million token context window.
Let me define that specific context window for
you quickly. The amount of text the AI can remember
in one conversation. A 2 million token window
is practically an entire personal archive. Yeah,
and pro and ultra tier users get this advanced
access first. To capture proper human context,
new software tracks everything constantly. A
tool called ReadyWhen monitors your daily workplace
decisions continuously. It indexes your daily
Slack messages, your email, and meetings. Right.
It builds a comprehensive vector database of
your professional life. It drafts your necessary
next steps automatically. based on behavior.
Here's where it gets really interesting. Meta
paused an internal AI training program recently.
Employee personal data was exposed during the
initial training process. The internal program
tracked daily keystrokes and mouse movements
continuously. They wanted to train models on
how employees actually work. They wanted the
AI to learn our natural human hesitations. Anthropic
updated Claude's official privacy policy in a
similar vein. They now strictly require identity
checks for specific flagged users. This verification
process includes scanning passports and official
driver's licenses. It even requires uploading
selfies and complex face geometry data. They
map the physical topology of your face for security.
If we connect this to the bigger picture, it
feels invasive. Models clearly need deep, intimate
access to be truly helpful. But that deep access
creates enormous, terrifying new privacy vulnerabilities.
There is a playful new tool online called In
the Weights. It lets you check if large models
learned your actual name. You can literally see
where you rank in their training data. How does
that tool actually work mathematically behind
the scenes? Well, it queries the model's latent
space for specific token associations. It measures
how strongly your name... connects to specific
data vectors. If your name appears frequently,
the mathematical connection is stronger. Are
we trading our digital anonymity just for better
autocomplete? Why do they need something as intimate
as face geometry? As AI capabilities grow dangerously
powerful, accountability becomes a break. Companies
cannot allow anonymous users to run autonomous
infinite loops. Because a malicious infinite
loop could launch devastating cyber attacks continuously.
Exactly. They need to know exactly who deployed
the destructive code. Verified human accountability
is the ultimate emergency stop button. Bigger
memory means bigger risks. Forcing companies
to demand your actual face. Right. And the glorious
anonymity of the early Internet disappears. All
this highly personalized data isn't staying confined
to dashboards. Beat. It is actively moving into
our creative and physical realities. It is rapidly
moving from the back office to the movie set.
That is great analogy. Google is currently investing
$75 million into A24. That is the brilliant studio
behind everything everywhere all at once. Right.
They are famous for their incredibly specific,
surreal visual aesthetics. Google wants to build
powerful new AI filmmaking tools together. Because
algorithms are excellent in generation, but they
lack actual taste. Yeah, they tend to create
sterile, plastic -looking visual content by default.
So Google is using direct feedback from real
Hollywood artists. They want to ensure the tools
serve the true creative process. The beauty industry
has historically relied entirely on physical
products. Now they are transforming into massive
digital technology companies, essentially. L
'Oreal recently partnered with OpenAI for a highly
visual project. They revealed an incredible Maybelline
virtual makeup. try -on tool recently. Which
they showcased at VivaTech 2026, right? Yeah.
You will soon use this tool directly inside the
chat GPT interface. They understand that digital
appearance is becoming increasingly important
today. The Maybelline tool analyzes the unique
geometry of your face perfectly. It applies digital
makeup that reacts perfectly to virtual lighting.
You can test makeup virtually before buying the
physical product. There's also a marketing tool
called Align 2 .0. It captures exact micro -brand
details for modern targeted marketing campaign.
It learns the specific typography and hex codes
of a brand. It generates perfectly on -brand
social assets and digital advertisements easily.
It is kind of incredibly efficient for large
creative teams. Does Google paying A24 mean AI
officially replaces human artists? Or does it
prove algorithms desperately need our aesthetic
judgment? It seems like they're admitting their
models lack artistic soul. Developers finally
realize algorithms lack true human aesthetic
taste entirely. They're essentially buying access
to artists to train aesthetics. They need human
creativity to guide the enormous computing power.
Right. Without human taste, the generated output
remains completely sterile. They're paying artists
to help build the exact tools they'll use, sponsor.
If we are going to rely on AI for our movies
and makeup, we desperately need to secure the
open source code running underneath. Two sec
silence. Open source software projects are the
fundamental bedrock of commercial tech. They
run our servers, our phones, and our global banking
networks. But they are mostly run by unpaid,
deeply passionate volunteers. This widespread
decentralization creates huge, terrifying security
gaps worldwide. OpenAI just announced a major
new program called Patch the Planet. Which is
a geeky nod to the 1995 movie hackers. OpenAI
officially teamed up with a cybersecurity firm
called Trail of Bits. They are actively defending
major open source projects using codec security.
This is a fascinating approach to a deeply decentralized
problem. The AI scans vast open source repositories
for deeply hidden vulnerabilities. It understands
the underlying logic of the complex software
architecture. It flags potential security flaws
that human reviewers easily miss. Do you remember
the massive log4j security crisis a few years
ago? A tiny unnoticed piece of open source code
broke half the internet. So what does this all
mean? That is exactly the kind of nightmare scenario
Trail of Bits targets. Human reviewers cannot
possibly audit millions of lines of volunteer
code. The AI acts as an untiring, incredibly
thorough security guard. But the human engineers
at Trail of Bits carefully review the AI findings.
Right. They handle all the difficult heavy lifting
for the community. They translate the AI's complex
math into human readable security patches. They
actively work with volunteer projects to deploy
these essential fixes. important software tests
and build reusable workflows securely. And they
do all this before project maintainers ever see
the bug. It is an incredible technological irony
when you really think about it. We deploy autonomous
AI systems to shield our heavily decentralized
internet. We use it to fix dangerous vulnerabilities
we created ourselves. It is a wild cycle for
sure. Does relying on open AI to find bugs create
a single point of failure? Is it bad for internet
security to rely on one corporate entity? Human
engineers at Trail of Bits act as a vital buffer.
They carefully review abstract syntax trees before
pushing patches live. They run the suggested
fixes in isolated sandbox environments first.
This ensures the AI model does not accidentally
introduce new backdoors. We cannot trust a machine
to rewrite our security protocols blindly. Exactly.
The machine acts as an incredibly powerful digital
magnifying glass. But the human engineer must
remain the ultimate decision maker. Humans still
verify the AI's math before the fix goes live.
Right. And that expert human oversight is absolutely
critical currently. We have journeyed through
an incredible, profound technological shift today.
We moved from manually writing simple code to
swarms of agents. Now, autonomous digital managers
build our complex software and our art. But this
incredible autonomy requires absolutely unimaginable
computing power to sustain. It demands a profound,
unprecedented surrender of our personal biometric
privacy. And it requires a completely new security
paradigm for protecting the Internet. We are
trading fundamental control for unprecedented
convenience on an enormous scale. Thank you so
much for joining this deep dive today. We always
appreciate you spending your valuable time with
us. I want to leave you with one final lingering
thought. Two years ago, humans literally wrote
our basic software manually. Now imagine a powerful
AI loop running endlessly in the background.
It subtly rewrites its own complex architecture,
continuously and silently. Human developers just
sit back and lazily review the final patch. Beat.
At what point does the software officially stop
belonging to us and start belonging entirely
to the continuously evolving autonomous swarm?