We are witnessing the death of a prophecy. Beat.
The idea of one AI tool to rule them all is dying.
Meta wants to delete the computer operating system
entirely. Welcome to the deep dive. Thanks. It
is genuinely great to be here today. Today's
stack of sources reveals a really massive shift.
We are moving far away from single AI tools.
We're heading into complex, customized tool chains.
We're scaling to... billions of autonomous agents,
and we are completely rethinking how AI interacts
with our computers. What's fascinating here is
how fast this is moving. We are jumping from
the current coding trenches today, right straight
into the future of neural operating systems.
Yeah, let's start down in those coding trenches.
Developers are feeling this massive shift right
now. The dream of a monolithic coding AI is practically
dead. It's totally fragmenting. I mean, the AI
coding market is now a three -layer stack. Devs
are actively composing their own tool chains.
They're basically combining cursor, quad, and
codex together. Right. It's like stacking Lego
blocks of data. You don't want a pre -built house
anymore. You want specialized blocks to build
your own custom toolchains. So you want Claude
as your head chef. He handles the complex architectural
recipes. And you want Codex just chopping vegetables
in the background. Yeah, and it fundamentally
changes the whole workflow. Just look at Cursor
3, which they're calling Glass. It's actively
distancing itself from its VS Code roots. It
focuses on its agents window and agent tabs.
They call that the manager surface, right? Exactly.
They officially call it the manager surface.
A manager surface implies active delegation.
You know, you're no longer just typing code yourself.
You're managing digital workers who write it
for you. Precisely. You're orchestrating the
build process. And then you have quad code entering
the mix. It holds 46 % of the most loved market
share. It uses an MCP -based plug -in system
to run things. Let's define that really quickly
for you listening. An MCP -based plugin is a
universal plug letting AI talk directly to local
files. Spot on. That system acts as the essential
glue. It's the primary execution layer for complex
architectural refactoring. It basically does
the heavy intellectual lifting. But looking at
OpenAI's placement in this stack, it feels like
a downgrade. Are they really just the review
layer now? Yeah, they repositioned Codex to fit
exactly that niche. They pragmatically embedded
it directly into Cloud Code. Using a plugin,
right? They did, yeah. It's called the Codex
Plugin CC. They are actively fighting what developers
call agent fatigue. You use Claude for the big
picture architectural stuff, but then you swap
models for the smaller tasks. You hand low complexity
tasks to Codex or Kimi K2 .5. Just to clarify,
Kimi K2 .5 for everyone, it's a lightweight model
for simple background tasks. You do that to manage
your token burn. Right, because token burn is
a huge issue. Wasting expensive compute on simple
tasks will bankrupt you. It's a highly pragmatic
token economy. I still wrestle with prompt drift
myself. Oh, we all do. It's exhausting. Yeah,
the friction of managing these different tools
is incredibly real. Keeping context perfectly
aligned across platforms is tough. So let me
ask you this. Is OpenAI's pragmatic plug -in
strategy a sign of surrender? Or is it pure genius?
I think it's pure genius. They willingly surrendered
the terminal to win the workflow. They embedded
themselves exactly where the work actually happened.
So they abandoned the interface to capture the
actual process. That's the perfect way to phrase
it. But to run these multilayered tool chains,
you need serious infrastructure. You need massive
computing power to handle that load. That brings
scale, but also very serious risks. Risks are
getting incredibly massive. And the infrastructure
is scaling up right now. Cloudflare just announced
Agents Week. They're rolling out edge -based
infrastructure designed to run billions of agents
simultaneously. Billions of active digital workers.
Imagine scaling to a billion queries. The physical
compute power required is staggering. to think
about. And consumer adoption is totally matching
that scale. CloudCore just moved to full release
for paid plans. Pro, Max, Team, Enterprise tiers.
The demand is off the charts. At the Humanex
AI conference last week, one chatbot came up
in every panel. Industry leaders dubbed CloudCode
the absolute must -have tool. But this environment
is getting genuinely risky. The sheer scale is
unprecedented. Sam Altman recently broke his
silence in a New Yorker profile. Yeah, right
after that frightening attack on his home. Right.
He admitted to some past leadership mistakes,
but he also issued a really stark warning. He
warned that the AGI race is pushing into highly
risky behaviors. The chaos on the ground is real.
TechCrunch is even updating their official glossary
of terms just to help normal people navigate
the madness. Which you desperately need in this
space. Take the term hallucination, for example.
Hallucination. When an AI confidently makes up
fake information. Exactly. We need simple definitions
for these complex problems. The vocabulary has
to keep up. But let me push back here for a second.
We are scaling up to billions of fully autonomous
agents. Even Altman is explicitly warning about
risky behavior. Are we basically building the
massive engine without installing any brakes?
It's a super valid concern right now. The infrastructure
expansion is totally outpacing the safety guardrails.
We're deploying agents before we understand their
emergent behaviors. So how do we actually resolve
this tension between massive digital scale and
fundamental human safety? Well, we have to build
robust guardrails at the infrastructure layer
itself. We just can't rely on the models to police
themselves anymore. We have to hardwire those
safety brakes directly into the infrastructure.
That is the only viable path forward. Okay, let's
untack this. Beat, we spent the entire last decade
putting glowing screens on everything. We moved
all our private files to the distant cloud. And
now Apple wants to remove screens entirely. LM
Studio wants to take everything off the cloud.
It's a massive philosophical pivot. It really
is. It's a direct reaction to the friction of
modern computing. People are tired of the costs
and risks of cloud AI. We're seeing a massive
pivot toward offline, highly localized tools.
People want specialized personal AI on their
own hardware. And LM Studio just made a big strategic
move there. They certainly did. They acquired
a fascinating company called Locally AI. Adrienne
Grondin is joining to lead native AI experiences.
They're bringing open source models natively
to your personal devices. iPhone, iPad, Mac integration.
With no cloud server connection required at all.
Zero cloud connectivity. No sign -up friction.
No monthly fee. It's just localized processing
utilizing your own silicon. Apple is moving in
a remarkably similar direction too. They're testing
four distinct smart glasses styles for 2027.
Crucially, these glasses feature absolutely no
displays. None. Zero visual displays. It's a
huge departure from their previous AR headsets.
It's just a camera, spatial music, phone calls,
and Siri. It's a much simpler approach to ambient
computing. The tools are becoming hyper -specialized
and local. Let's look at a few examples from
our sources. There's a fascinating new tool called
Ray. Oh, Ray is incredibly interesting. It acts
as a terminal -based CFO. It reads your real
computer transactions locally. It tells you what
to do, helps plan budgets, and it runs entirely
on your own private machine. Then there's R0Y,
spelled with a zero, a natural language financial
studio. It builds full investing dashboards in
seconds, tailored to you. And Google's Gemini
just introduced interactive visual simulations.
You don't just read text. You ask Gemini to show
me or help me visualize. You can actually play
with abstract concepts locally. We also have
the Eleven Labs music marketplace. Creators generate
tracks and publish them seamlessly. They earn
real money on downloads. Eleven Labs has already
paid out $11 million to voice creators. It's
birthing a totally new creator economy. The localized
empowerment gives individuals studio -level production
capabilities. Two sec silence. So looking at
all this screenless, highly specific tech, is
the true future of AI actually completely invisible
and entirely offline? History shows the best
technology always fades away. It disappears completely
into the ambient background of our daily lives.
So the best technology simply becomes an invisible
part of our environment. It just becomes the
silent air we breathe. We talked about powerful
local tools. We discussed the shift toward invisible,
display -free hardware. Here's where it gets
really interesting. Beat, Meta is trying to leapfrog
this entire localized paradigm. They want the
AI to literally become the operating system.
Yeah, they're trying to fundamentally delete
the traditional OS entirely. Meta is moving away
from models that simply use computers. They're
developing models that actually act as the computers
themselves. It's a monumental shift in architecture.
Their new neural computer prototype executes
desktop tasks directly via learned behavior.
There are no APIs involved, no complex orchestration
layers to translate commands. This is radically
different from traditional AI tool use, isn't
it? Completely different. When ChatGPT calls
a Python interpreter, that's basic tool use.
It uses a programmatic bridge. Meta's prototype
is a unified, end -to -end, learned system. It's
like teaching someone to drive by showing them
thousands of photos of a dashboard. You never
actually explain what a steering wheel mechanically
does. You just show the visual results of turning
it. That's a perfect visual metaphor. It was
trained on thousands of hours of raw screen recordings.
It passively watched human cursor movements and
watched complex terminal sessions unfold pixel
by pixel. So the logic isn't programmed with
standard code. It's encoded directly into the
model's mathematical weights. Yeah. And that
changes how it actually operates. It predicts
the next screen state exactly like GPT predicts
the next word. It treats human UI interaction
as raw visual. So if it needs to move a file,
what does it actually do? It doesn't call a computer
command or an API. It literally thinks. the specific
cursor movements required it generates the exact
keyboard inputs needed to drag and drop that
file it essentially hallucinates the physical
mouse moving across the screen mechanically yeah
it predicts the pixel shifting but uh there are
some glaring limitations right now it's just
an early prototype not a finished product what
happens when it tries a complex multi -step task
it really struggles with sustained context it
might remember to open a system folder but three
steps later it completely forgets why it was
there it loses the thread it lacks basic digital
object permanence. That's exactly the problem.
It hasn't learned the concept of a button. It
has only learned the visual pattern of a button.
It recognizes the pattern of an X icon in the
corner. Right. It sees the X and knows to click
it. But until the model learns that an X icon
always means close, it remains limited. It has
to understand that concept regardless of the
application. Until then it's basically a highly
sophisticated macro recorder. Kate, let's dig
into that limitation. Is the barrier between
recognizing visual patterns and understanding
underlying concepts the hardest problem in machine
learning today? I firmly believe it is. Bridging
that massive cognitive gap is basically the definition
of true artificial general intelligence. It's
the difference between mimicking intelligence
and possessing actual comprehension. So scaling
up visual patterns might not organically lead
to true conceptual understanding. That remains
the absolute biggest unanswered question in AI
research. Scaling visual patterns just doesn't
guarantee true cognitive reasoning. You're finding
that out the hard way, yeah. Let's zoom out for
a second. We've covered a massive amount of technical
ground today. If we connect this to the bigger
picture. We're witnessing a total restructuring
of how humanity computes. Devs are hacking together
specialized tool chains today. We're scaling
infrastructure to support billions of agents
tomorrow. We're bringing open source models locally
to our pockets. And Meta is aggressively trying
to bypass all of it by turning the AI... into
the computer itself it is a massive unprecedented
transition thank you for joining us on this deep
dive take a moment today to look at your own
digital workflow try to find where you're getting
your own agent fatigue and see where you can
specialize your daily tools the landscape is
shifting incredibly fast beneath our feet it
really is and it leaves me with one final fascinating
question to consider beat if meta's neural computer
eventually succeeds in operating our graphic
interfaces will the software of the future be
designed to look good to human eyes Or will it
be optimized entirely for AI vision models to
easily read? Out to your own music.