I mean, you ask an AI a highly complex question.
You hope for profound clarity, right? Yeah, you
want to answer that actually connects the dots.
Exactly. But instead, you just get this massive,
impenetrable wall of text beat, and then the
real work begins. Oh, totally. You have to manually
extract that data line by line. You build the
presentation yourself. You rebuild the spreadsheet
yourself. It takes hours of your time. And honestly,
it's incredibly frustrating. Two secs silence.
But I want you to imagine a totally different
scenario today. OK, I'm listening. You ask that
exact same question, but instead of text, a fully
editable PowerPoint file just appears. A populated
Excel spreadsheet materializes right there in
the chat. I mean, that changes everything about
how we work. You stop fighting the interface
entirely, and you start actually collaborating.
Welcome to the deep dive. I'm very glad you're
here with us. Our primary mission today is exploring
that exact shift. We are looking really closely
at Google's notebook, LM 2 .0, because the source
material reveals a fascinating transition in
what this technology is actually doing. Right.
This tool was previously just a simple document
reader. It would summarize what you gave it.
Yeah, but now it operates much closer to a junior
data analyst. To really trust an AI, to build
our spreadsheets and parse our data, we first
have to understand how it suddenly learned to
do real math. And that requires looking closely
at the engine powering this whole operation.
Right, because to understand how it creates these
tangible files, we need context. We kind of have
to look at the brain first. Notebook LM has officially
moved to the Gemini 3 .5 engine, and that brings
some heavy implications for you. The benchmarks
in the research are pretty staggering. It's showing
a 78 .2 % win rate in web research. And when
it comes to analyzing massive, complex documents,
it is hitting nearly 70%. But, you know, raw
numbers only tell part of the story. Yeah. The
fascinating part is why it is suddenly so much
more capable. Exactly. Historically, large language
models are terrible at math. They don't actually
calculate numbers. No, they just predict the
next most likely word in a sequence based on
their training data. Right. It is basically highly
educated guessing. Which is why you could never
truly cross them with your accounting or your
precise data sets. But the architecture here
has fundamentally changed. Every single notebook
now gets its own sandbox. Right. Which is a safe,
isolated space for running code. Right. And that
is the actual game changer here. Before, the
AI just estimated math from text. Now... It writes
and runs its own code. It relies on over 100
curated software skills to compute absolute answers.
Whoa. I mean, imagine it just spinning up its
own secure cloud computer to calculate your invoices.
That is wild. You upload three years of complex
billing records. Instead of summarizing what
might be in them, it takes physical action. It
writes a unique script. It runs it in that isolated
sandbox. And it hands back a mathematically perfect
finished report. Beat. Okay, let's untack this.
It stops being a guessing machine and becomes
a computation engine. But wait, if it is writing
code to achieve this, am I expected to know how
to prompt in Python or manually trigger these
specific skills? Not at all. That is the beauty
of the system design. The AI automatically evaluates
your prompt, realizes it needs hard math instead
of text, and silently writes and executes the
necessary scripts in the background. So it silently
picks the right tools without you lifting a finger.
Precisely. It completely abstracts the coding
layer away from you. But an engine that powerful
is useless without the right fuel. So how do
we actually feed this new system? The fundamental
rule of data analysis has not changed at all.
The quality of your sources still absolutely
matters. Yeah, the old adage applies perfectly
here. Barbage in, garbage out. Notebook LM simply
amplifies the foundation you handed. Let's make
this concrete for you. Imagine you're building
a new course on freelance copywriting. OK, you
need a very solid, multifaceted knowledge base
for that. Exactly. So you upload competitor course
pages and their pricing tiers. You drop in lengthy
Reddit threads where real freelancers are asking
questions. You add your own scattered notes from
past client work. You include broad industry
reports on freelance income trends. It actively
connects all of those disparate pieces together.
It treats all of it. as one connected mind. Yeah,
rather than separate files sitting in different
folders. But, you know, human research is rarely
ever perfect. There are almost always missing
pieces in our logic. Right, and this is where
the self -directed research feature comes in.
You can simply ask the chat what is missing from
your own brain trust. It reviews your uploaded
sources alongside your conversation. For our
copywriting course example, it easily spots two
clear structural gaps. First, it identifies completely
missing financial data. Right. You lack customer
acquisition cost numbers and you lack average
conversion rates for sales pages. It finds the
financial weaknesses in your business plan. It
really does. Then it finds a blind spot in your
focus. Your research perfectly explains how students
will acquire freelance clients. But it entirely
fails to explain how you, the creator, will acquire
those students in the first place. I genuinely
love that level of critical pushback from a tool.
It's not just agreeing with me. No, it's finding
the holes in your thinking. And you can tell
it to actively chase down one of those gaps.
Let's say you pick customer acquisition costs.
It searches the live web for those exact missing
metrics. It comes back with a tailored report
and suggested external sources to fill the hole.
Two -sec silence. I have to admit something vulnerable
here. I still wrestle with the fear of AI hallucinating
data, so approving sources manually is huge for
me. If it's searching the open web to fill these
gaps, how do I know it's not quietly slipping
unverified junk in my course outline? Because
of the hard gatekeeping built into the interface,
it will suggest those web sources, yes, but it
physically cannot incorporate that data into
your Notebook's core knowledge base until you
explicitly click approve. You remain the strict
gatekeeper for every single piece of data. You
do. Every answer stays completely grounded in
material you have personally reviewed. This deep
dive is completely supported by our listener
community. Your backing keeps this conversation
independent, ad -free, and deeply curious. We
appreciate you being part of this journey with
us. And we are back. Let's keep exploring this
architecture. We now have a perfectly curated
and vetted brain of sources. Right. But how do
we actually extract the work from it? Right.
downloadable files. What's fascinating here is
the physical reality of the files it generates.
This all happens within the studio panel on the
right side of your screen. Let's look at the
first major example. Say you want an Excel spreadsheet.
Okay. You need a 12 -month revenue projection
broken down by specific marketing campaigns.
You just select the approved sources holding
your figures and you give the instructions in
the chat. It pulls those scattered numbers into
one unified Excel sheet. And importantly, the
formulas and the numbers are real data. They
automatically update when you edit them later
in Excel. Yeah, it performs the tedious structural
formatting work for you. Saves hours of cell
linking. And then there's the PowerPoint generation,
which is equally impressive. You need a 10 slide
pitch deck for investors. You tell it to focus
heavily on the business model. You instruct it
to skip the company history section entirely
because investors don't care. A real formatted
file shows up a few minutes later. But there
is a significant cache we must discuss here.
It is a known limitation that many users eventually
run into. There is. It involves how the AI handles
the PowerPoint text boxes. They often generate
as flat images packed inside a PowerPoint frame
rather than editable text fields. It's like stacking
Lego blocks of data. You can't just mold the
plastic, you have to swap the blog entirely.
Because large language models struggle with spatial
bounding boxes, they often just render the text
as a picture. Exactly. You cannot simply click
and type directly on the slide to fix a typo.
You must use the revise button located in the
chat interface. You ask the chat to change the
specific slide. And it regenerates the whole
block. The revise button becomes your primary
tool for edits. It rewrites the slide instead
of you typing on it. If I am staring at a newly
generated deck right before a meeting, how do
I quickly figure out if I am looking at editable
text or just a baked -in image block? It is a
very simple manual test. You download the file
and just click any line of text. If your cursor
does not let you type directly into the paragraph,
it is an image block. So a quick click... instantly
reveals if it is an image block. Exactly right.
That need for structural revision is a good segue
into how it handles truly dense contradictory
information. Because sometimes even a perfectly
formatted spreadsheet is still too dense. Right.
We need to turn that density into a cohesive
story we can understand at a single glance. Let's
look at a very specific data comparison example
from the research. You upload two distinct files
into your notebook. First, your monthly Facebook
ad spend broken down by campaign. Second, your
daily course enrollments exported from your hosting
platform. Two completely different formats of
data. Right. And you ask it to compare those
two specific data sets. The resulting analysis
is incredibly sharp. It calculates the cost per
enrollment for each month by synthesizing the
two files. It discovers the cost drops from roughly
$79 .59 in January, all the way down to $31 .91
by June. The advertising is clearly getting much
more efficient over time. But here is the truly
critical moment in this interaction. It's the
moment that proves how the architecture has changed.
You ask the AI which specific campaign drove
the most signups. And the system admits outright
that it cannot answer that question. The uploaded
data only shows total daily signups. It does
not explicitly link those individual signups
to specific ad campaigns. Beat, the AI does not
try to guess. Yeah, that algorithmic honesty
is absolutely vital. Historically, AI models
are people pleasers. They are designed to give
you an answer, even if they have to hallucinate
to do it. But this system is programmed to hit
a wall when the uploaded data stops. doesn't
invent a bridge to cross it. Exactly. Which is
why I'm so used to these models trying to impress
me with an answer no matter what. Why is the
AI refusing to answer the campaign question actually
a massive feature, not a bug? Because its primary
directive is grounded analysis, not conversation.
It is designed to build profound trust. You know
it will not invent convenient answers when your
data falls short. honesty about its limits proves
it will never invent fake data. Exactly. And
you can visualize this data density easily, too.
You just ask it to turn this comparison into
a visual chart. The chart appears within a minute,
ready to download. And it shows the ad spend
climbing from $3 ,900 to $9 ,000. but the enrollments
rocket from 49 to 282. That is a massive 475
% increase. One glance proves the fundamental
efficiency of the business. You see both lines
rise, but the enrollments grow significantly
faster. Yeah. You can run other incredibly valuable
comparisons too. Weekly website traffic versus
newsletter signups. Customer support ticket volume
against average response times. Or department
budgets against actual quarterly spending. It
turns a dense written analytical answer into
actionable clarity. Trust and clarity are great
in theory. But we need to ground this entirely
in practical reality before we wrap up. Who can
actually use this system right now? And what
is the best mental framework to approach it with?
There is a paywall we need to acknowledge up
front. This specific update is not available
to everyone just yet. It requires Google AI Ultra.
Or you need to be a workspace business customer
with AI Ultra or Expanded Access. Broad access
is planned, but it's currently limited. Let's
quickly recap the ideal workflow you can copy
today if you have access. Say you run a small
online store. you want to decide whether to expand
your product line. You start a notebook with
your existing documents, supplier catalogs, historical
sales reports, customer reviews, competitor pricing
matrices. Then you actively ask the tool what
strategic gaps exist. You carefully review and
approve any new web sources before adding them
to the brain. Right. Then you generate a slide
deck for a supplier pitch. You build a data comparison
table comparing your past sales against ad spend.
Finally, you turn that comparison into a visual
chart for your team. But you must remember the
absolute golden rule here. You must treat everything
this system produces as a powerful first draft.
You cannot treat this as a perfectly finished,
infallible product. You always verify the Excel
math against your raw sources. You always test
those PowerPoint editing capabilities before
walking into a presentation. Treating it as a
strong first draft keeps the entire workflow
trustworthy. It saves immense time, but it doesn't
replace careful human verification. With all
this capability, from coding in sandboxes to
building pitch decks, does a tool like this effectively
replace a human data analyst on a small team?
Not at all. It just closes the massive gap between
having a business problem and holding a workable
draft solution. The human still completely owns
the final strategic decisions. It does the grunt
work, but you make the final strategic decisions.
Perfectly said. The critical thinking remains
entirely yours. Let's take a measured calm moment
here at the end. Let's reflect on the overarching
theme of this deep dive. We have witnessed a
profound architectural shift in consumer technology
today. We really have. We have officially moved
from AI as a simple conversational partner, a
glorified chatbot, to AI as an active synthesizer
and real file creator. It uses isolated secure
computation to do real verifiable math. It spots
the logical gaps in our own human research. And
it outputs highly tangible tools like bitch decks
and complex charts. to sex silence. But all of
this extraordinary capability comes with one
fundamental condition. We must give it a solid
factual foundation to stand on. I want to leave
you with a final thought to ponder as you go
about your day. If an AI can now perfectly connect
the dots between your scattered notes and turn
them into a beautifully polished investor pitch
in seconds, it raises a profound question. Yeah.
Will the true future skill be less about knowing
how to format a presentation and entirely about
how deeply you understand the original raw ideas
you feed into the machine? Keep questioning your
assumptions. Keep exploring these new frontiers.
Thank you for joining us on this deep dive.