You know, for the most powerful computers we
have, the absolute limit, it used to be just
13 seconds. 13 seconds, yeah. That's how long
they could stay running before, well, physics
basically shut them down. It sounds like a long
time in maybe pure computation terms, but in
reality, nothing. Imagine trying to run anything
serious knowing the whole thing's going to collapse.
Exactly. That limit, atom loss, it was the ceiling
for quantum, but... the sources we looked at
today they show this huge harvard breakthrough
right a physics team built a system a quantum
system that actually repairs itself mid -calculation
whoa okay that changes everything seriously we're
shifting from these like super delicate lab demos
like can you miss it yeah to a real possibility
of like always on quantum servers that's the
pivot For the real world, for commercial use.
Huge. Welcome to the Deep Dive. We're going to
pull on three big threads from the sources you
shared with us. And the theme that kind of ties
them together is this hunt for reliability, whether
that's securing AI for businesses or keeping
these quantum machines actually running. Yeah,
and our roadmap really shows how deep that goes.
First up, we're going to unpack... IBM's big
move, very strategic one, I think, into open
source AI with Granite 4 .0. Okay. Then second,
we'll look at this boom in AI agents and agentic
systems and what they're actually becoming. You
know, the practical tools for businesses. And
finally, yeah, we dig into that quantum breakthrough,
the self -healing machine, the one that gets
rid of atom loss, making these really long computations
possible, like truly possible for the first time.
Right. OK, let's start with Granite. So IBM rolled
out this Granite 4 .0 family and their positioning
seems really clear. It's hybrid, it's enterprise
ready and crucially open source. They don't seem
to be chasing the, you know, biggest model trophy.
more like the most deployable one. That distinction,
it's everything here. This isn't just another,
hey, look, a big model announcement. It's a strategic
play designed for the messy reality of corporate
IT. They even made it light enough to run on
pretty cheap GPUs, which for companies wanting
to use it internally, maybe on the edge, that's
critical. And the performance numbers are...
Honestly, kind of surprising for the size. Like
the 3 billion parameter granite model, it actually
beats their own older 8 billion parameter model
in some key areas. Yeah, beats its big brother.
Right. That's pure efficiency. And it matters
because, okay, get this, 70 % less memory needed
to run. 70%. Wow. So businesses can run this
on hardware they maybe already have or cheaper
stuff. Right now. And here's something really
interesting for anyone dealing with lots of data.
Okay. The core design has linear scaling, meaning
if you feed it more input, like a huge document,
massive context, it actually gets faster, not
slower. Wait, faster with more input. That's
counterintuitive. I know. But it's a huge win
for companies just drowning in documents and
data they need to process. Beat. Big deal. And
beyond just the efficiency, the sources really
hammered the security and compliance side. It's
licensed under Apache 2 .0, which means it's
properly open. None of those weird restrictive
clauses we've seen pop up elsewhere. Right. But
maybe the real kicker for big regulated companies.
This is the first major open model that has governance
baked right in from the start. It's ISO 42001
certified. ISO 42001. Yeah. Think of it like
a giant pre -approved security blanket. For companies
where compliance is everything. Okay, hold on,
though. That ISO certification, that sounds like
a lot of hoops to jump through, a lot of overhead.
Is that really the most critical feature here,
or is it maybe more marketing? No, I think it's
genuinely critical. I mean, yes, the efficiency
for edge stuff is also vital, absolutely. But
for a lot of enterprises, their legal team will
just shut down any open model project if those
governance guardrails aren't there. Period. So
IBM is basically saying, look, we did the really
hard compliance paperwork. So your lawyers don't
kill your AI plans. They're jumping into that
gap, that uncertainty left by, say, Meta's licensing.
So the goal isn't really to be the most powerful
model, but the most trustworthy, the vetted,
secure, efficient option compared to maybe Lama
or Quinn. So companies can build agents, but
still keep control. Exactly. Keep control of
the whole stack. That makes a lot of sense. Okay,
let's shift gears then. Let's talk about those
agents. Right, agents. We keep saying the word.
So a simple definition. An agentic system is
AI that's designed to do things. to perform actions,
not just answer a question you type in. They're
proactive. And this whole trend is moving so
fast. I mean, just to show how serious this is,
one of the sources mentioned a senior Google
engineer just dropped this huge 424 -page document.
Wow. Free to download called Agentic Design Patterns.
It lays out how to actually build these things
properly. That tells you a lot about where the
focus is shifting. Structure. And the competition
is fierce. OpenAI, they're releasing something
called an agent builder, which looks like a direct
challenge. to tools people already use, like
Zapier or N8n for workflow automation. Ah, interesting.
Yeah, I mean, people have been building these
sorts of wrappers around LLMs for a bit now.
But when the big platform providers start building
integrated tools themselves... That's a major
shakeup for those existing companies, a real
threat. It does feel like a big leap, though,
going from just, you know, typing a prompt into
a box to setting up these complex multi -step
agent things. I bet a lot of people, even if
they use AI daily, find that sequence part tricky.
That's fair. And it points to the challenge right
now. I'll admit it's still complicated even for
me sometimes. I still wrestle with prompt drift
myself sometimes, you know, making those really
complex multi -step prompts work reliably. Yeah.
Yeah. It's not always smooth sailing yet. Right.
Which is why we've got to focus on the practical
wins first. Exactly. And we found some good stuff
in the sources on that, like how to use single
prompts, just one command, in tools like Google's
Notebook LM to get really specific structured
stuff out quickly. Cuts through that complexity.
Yeah. That's where the immediate value is, I
think. Like imagine getting a full meeting summary
from a 90 -minute recording with one prompt.
Or creating a study guide that pulls info from
three different documents. Or drafting a whole
30 -day content plan. Boom. Done. These are real,
actionable things you can do with one command
that save, like, actual hours. And we're seeing
these tools pop up right in the daily workflow
now. Google's apparently put its AI coding agent.
jewels, they call it, right into terminals and
slack. Yeah. So it's moving out of the lab into
the places you actually work. And the money's
following the practical use cases, too. Like
Inspiron raised $100 million for an AI platform
that helps improve care and efficiency in senior
living facilities. Wow. $100 million. Yeah. Agents
are going straight to where the real operational
headaches are, solving actual problems like staffing,
task management. concrete stuff. So it's really
that shift from the model answering us to the
model doing stuff for us. That difference feels
like the core of this next wave in business automation.
How fast do you think we'll see this agent tech
move? Yeah. You know, from just being rappers
to actually handling complex tasks without a
human watching every step. Hmm. That's the big
question. But given the speed things are moving,
I think automating specific complex business
tasks feels, well, imminent. It's coming fast.
OK, let's pull back a bit now. Look at some of
the big industry headlines that kind of give
us the vibe of the whole market, the push and
pull happening. Well, the biggest drama, obviously,
is still the open AI and Musk thing. Open AI
really fired back at his lawsuit over trade secrets.
Oh, yeah. What'd they say? Called it basically
harassment, a tactic just meant to slow them
down. So, yeah, that feud is definitely still
simmering. Hotly. Meanwhile, despite all that
noise, the money just keeps pouring in. OpenAI
apparently just officially became the world's
most valuable private company. Yeah, which just
underscores the insane level of investment happening
right now. It is this really intense mix, isn't
it? Like, massive valuations on one side, and
on the other you've got Sam Altman himself warning
the whole AI industry might be heading for a,
quote, spectacular implosion. That's a pretty
stark warning. Especially coming from the guy
leading the most valuable company in the space.
It's... Yeah. High stakes. What it tells me,
looking at these top stories, is that everything's
moving at once. The deep infrastructure stuff
and the user experience stuff. Like, look at
NVIDIA. They launched this AI aerial tool, uses
their GPUs to actively boost 5G and even 6G networks.
Yeah, they're basically plugging AI straight
into the core global communication grid. making
sure they stay dominant at that hardware level.
And then on the user side, you see Apple making
big moves, apparently looking outside the company
for a new AI chief. Which signals they know they
need fresh talent, maybe need to play catch up
fast in some areas. These are huge structural
shifts. And then there's just some interesting
little details too for flavor. Google pushed
nano banana into full production. Nano banana.
Yeah. Apparently it now supports 10 new aspect
ratios. And this is big for creators. You can
get just the image out. No need for text prompts
mixed in. Image only output. Ah, OK. That's useful.
And you can't ignore the cultural side either.
The sources mentioned that viral trend recreating
famous movie scenes. Oh, with Pikachu. Yeah.
Like Batman or The Godfather, but starring Pikachu.
It just shows how quickly this high quality generative
AI stuff is hitting. like mainstream culture.
It's accelerating. And speaking of accelerating,
Meta's moving on hardware too. Users are apparently
testing live navigation features right now on
their AI Ray -Bans. Whoa. Yeah, imagine getting
directions prompts right there in your glasses
as you walk around. So you've got this crazy
tension, right? Unbelievable valuations, serious
warnings about a bubble, but... Underneath it
all, the actual infrastructure is being rebuilt
super fast. It's wild. Yeah. Does that title,
most valuable private company, even mean that
much when the founder is warning about a market
implosion? Well, it really highlights the conflict,
doesn't it? Massive investment hype running headlong
into these deep worries about whether the industry's
growth is sustainable or just moving too fast.
Medroll sponsor, Reed Placeholder. Okay, this
next piece, this quantum breakthrough, it feels
genuinely like... Profound. We mentioned earlier
how fragile quantum computers are. Right. The
13 second limit. Yeah. For years, atom loss just
meant even the best machines tapped out around
13 seconds, which is why you couldn't do long,
complex, continuous calculations. It was scientifically
just impossible. Physics itself imposed that
limit. But this Harvard team, it seems like they
beat it by focusing on continuous operation,
their solution, actively replacing the atoms
that get lost in real time. mid -calculation,
without messing up the quantum state. The way
they do it is pretty brilliant. They use something
called an optical lattice, basically. Super -controlled
light beams holding atoms and combine it with
optical tweezers. Tweezers made of light. Yeah.
They grab fresh atoms and just sloth them into
the empty spots left by the lost ones. And they
can do it incredibly fast, injecting something
like 300 ,000 atoms per second back into the
system. Wow. So it's like a self -healing machine
but at the atomic level? Exactly. It holds 3
,000 qubits, which is already impressive, and
keeps that delicate quantum state going even
while this atom refresh is happening constantly
in the background. And that one change, it just
unlocks everything that was blocked before, doesn't
it? Totally. Suddenly. Long, complex calculations.
They're actually possible. It gives us a clear
path toward real quantum programs, programs that
run like, you know, actual robust apps, not just
these fragile 13 second demos. Yeah. Whoa. I
mean, just imagine scaling that a billion queries
maybe. Yeah. When you don't have to constantly
restart the machine every 13 seconds, that continuous
power, it just rewrites the whole commercial
roadmap for quantum. Which is why people are
calling it. a potential iPhone moment for quantum
computing. You know, AWS is Harvard's partner
on this, and you can bet Microsoft, Google, they're
all watching this incredibly closely. Because
if you solve the stability problem, you basically
solve the commercialization problem. Yeah, given
how fragile things have been. Is this mid -calculation
repair system, is that the real key? The thing
that finally unlocks commercial quantum computing?
It feels like it has to be, right? Continuous
operation seems like the absolute prerequisite
for having always -on quantum servers that businesses
can actually rely on. use widely. It moves it
from physics lab curiosity to potential enterprise
tool. Hashtag tag tag outro. OK, so let's pull
this down. Three key nuggets we pulled out for
you today. Go for it. First, enterprise ready,
secure, open source AI is definitely here. IBM's
Granite 4 .0 is leading that charge with its
focus on efficiency and crucially, those compliance
guardrails. Right. Second takeaway, AI agents
are moving fast. They're automating professional
work, going beyond simple chat into real tools
like OpenAI's Agent Builder and those super useful
single prompt commands for specific tasks. And
third, quantum computing just took this absolutely
massive leap. They solved the core instability
problem that creates a real viable path towards
continuous, always -on quantum power. Finally.
It's such an interesting contrast when you put
it all together from the sources, isn't it? On
one hand, this huge necessary push for control,
like the ISO certification for granite, making
it safe to deploy. And on the other hand, this
giant leap into almost uncontrolled power with
these always -on self -healing quantum machines.
But both roads are really aiming for the same
thing, ultimate reliability. Yeah, it's that
moment where fragility finally starts giving
way to actual functionality. You can see it happening.
It leaves us with maybe one last thought for
you to chew on. If quantum machines can now repair
themselves in real time, constantly to keep running,
what processes, what systems maybe in your own
work life are due for that kind of constant self
-healing upgrade? That's good food for thought.
Well, thanks for joining us for this deep dive
into your sources. Thanks, everyone. We'll catch
you next time.