Imagine someone offers you this piece of land,
right? Quiet Valley. And then just before you
decide, they reveal the blueprints for this huge
city they're going to build there. That's Palo.
Well, that's sort of where we are with a new
AI technology today poised to unlock, they say,
a four hundred billion dollar opportunity. Welcome
back to Deep Dive. This is where we try to unpack
these big complex ideas for you. And today we
are diving deep into Google's Notebook LM, specifically
its video generation feature. It's powerful,
but actually remarkably simple to use. Exactly.
We're going to look at what makes this tool genuinely
different from other AI, how these new video
capabilities actually function and maybe most
importantly for you, five really concrete step
-by -step ways you could start generating income
with it, like right now. It's about moving from
just watching this stuff happen to actually being
part of it, to, you know, earning. OK, so let's
just set the stage here a bit. We're really in
the early, early days of a massive technological
shift. Think back to the internet or smartphones.
This feels like it's on that scale. Experts are
projecting this huge $400 billion opportunity
in applied AI services. And this tool we're discussing,
Notebook LM, it seems to be right at the heart
of that potential. Exactly. It really does feel
that way. But before we jump into the money side,
we really need to get a handle on what Notebook
LM actually is. Because it's not just another
chatbot like ChatGPT or Jim. and understanding
that difference is absolutely key to seeing its
real value. Right. Most of us have probably played
around with those large language models, LLMs.
They're trained on just this enormous amount
of data from the public internet, which makes
them incredibly knowledgeable about, well, almost
anything. But that huge data set also leads to
a pretty critical flaw, hallucination. Yeah,
hallucination. That's the AI term for when it
just... make stuff up. It sounds confident and
sounds plausible, but the information is actually
wrong. Invented details. And that's a massive
problem if you need reliable information for,
say, professional work. Oh, absolutely. You can't
build a business strategy or write an accurate
report based on fabricated facts. The trust just
isn't there. So Notebook LM approaches this completely
differently. It uses what's called a restricted
knowledge base. Sometimes people call it grounded
AI. And this is where it gets really interesting
because you become the source of truth. How does
that work exactly? Well, instead of relying on
the whole internet, you feed it specific trusted
documents. Think PDFs, Google Docs, notes you've
taken, even transcripts from videos or meetings.
The AI then operates only within those sources
you provided. Ah, okay. So it doesn't pull in
random stuff from outside. Nope. It's like you've
given it its own private curated library and
locked the door to everything else. And the really
big deal here, the game changer, is that every
single piece of information it generates, it's
directly tied back to a specific passage in your
documents. It actually provides citations right
there in the output. Exactly. built -in citations.
So you never have to guess if the AI just invented
something. You know it's 100 % faithful to the
material you gave it. Which is a huge leap in
terms of reliability and accountability. Makes
it incredibly valuable for anyone needing precision.
Researchers, legal teams, students, and as we're
about to dig into, content creators like you.
So the core difference is that total reliance
on user -provided information, right? That's
its superpower. Yes, absolutely. It ensures accuracy
by sticking strictly to only your specific sources.
And Notebook LM had already got some attention
for doing something pretty neat before this video
update. It could generate these podcast -style
audio conversations directly from documents.
Oh, yeah, I remember that. You could feed it
a dense research paper. Right. And it would spit
out a two -person discussion, breaking down the
key concepts. made complex text way more accessible.
That was already pretty powerful. It really was.
But this latest update, the video generation,
takes it quite a bit further. Now, we should
be clear. This isn't about creating, you know,
Hollywood -level movies with AI actors and complex
scenes. No, no. Its beauty is really in its elegant
simplicity and how efficient it is. So what does
it actually create? It makes these clean, professional
-looking slide -style visuals. And crucially,
they're perfectly synchronized with the AI -generated
audio narration, which is also based on your
document. Think of it like a really dynamic,
intelligent presentation. OK, so like lecture
slides almost. Kinda, yeah. But smarter. As the
AI audio discusses a concept from your text,
a slide pops up with relevant text, maybe some
bullet points or a key quote, all pulled directly
again from your source material. So it's like
a podcast, but it comes with perfectly timed
visual A's that reinforce the points. Exactly.
And these aren't just generic templates either.
That's the cool part. Every slide, every bullet
point is dynamically generated based on the specific
content you fed it. So every video is unique
and perfectly tailored to your information. It
sounds like the absolute fastest way to turn
a block of text into a professional, shareable
video. It really is. Takes minutes. Whoa. Just
thinking about the scale of that. Imagine you
have recordings from an entire conference, all
the talks, the panels. You could turn that mountain
of content into hundreds of these short, engaging
videos, dynamically generated, all perfectly
aligned with what was actually said. The potential
for sharing knowledge there is immense. So it's
really about turning text into these engaging,
visually supported learning experiences faster.
Exactly. It makes complex information much easier
to understand and share visually and at serious
speed. Yeah. Okay, so now let's connect this
cool tech to actual practical income streams,
because these methods we're going to talk about,
they solve real problems that businesses are
already paying to fix. Right, moving from the
what's to the how you can use it. Let's dive
into method one, daily research automation. This
sounds like becoming sort of an indispensable
automated intelligence source for a specific
industry. Precisely. Think about we're all grounding
in information. Business leaders, investors,
policymakers. They spend hours every single day
just sifting through news, reports, articles.
Trying to find the signal and the noise. Exactly.
You can solve that pain point. Deliver concise,
maybe five -minute video briefing each day that
cuts right through it. You're not just selling
news, you're selling curated intelligence. You're
selling back their time. Okay, so how would you
actually do that? First, you pick a high -value
niche, something where timely information is
critical, maybe AI regulation updates or specific
crypto market movements or breakthroughs in biotech.
Something specific where people pay for insights.
Right. Then you automate the info gathering.
You could use tools like Manus AI to scrape specific
websites or set up really targeted Google Alerts.
That curated daily report becomes your source
document for Notebook LM. Got it. So you feed
that report in. Yep. with a prompt like, uh,
generate a four -minute video script summarizing
today's AI regulation news. Create visuals for
the key updates using two, three essential bullet
points per slide. And then you deliver that video
how? Through a paid newsletter, maybe, or a private
online community. Look at successful models like
morning brew or stratetry. They charge recurring
fees for valuable, concise intelligence. So you
could potentially charge, what, $29, maybe $99
a month for a daily video brief like this? Easily,
yeah, depending on the niche and how much value
you're providing. OK, interesting. What's next?
Method two, ghost thought leadership. This is
a really good one for busy executives, experts,
consultants, people with tons of knowledge and
unique insights, but absolutely zero time for
personal branding stuff on LinkedIn or X. Yeah,
you see those profiles that are just kind of.
dormant. Right. So this service basically mines
their intellectual property. You take their past
podcast interviews, maybe transcripts of talks
they gave, articles they wrote, even internal
meeting recordings if they have them. You upload
all that into Notebook LM. So Notebook LM becomes
like their personal knowledge base, their second
brain. Exactly. Filled with their ideas, their
unique perspectives, all organized and searchable.
Then, you use prompts to pull out sharp, insightful
video clips from that material. Give me an example
prompt. OK, maybe something like, analyze the
client's last three podcast transcripts. Identify
three non -obvious insights about managing remote
teams. Create a 90 -second video script for each
insight using simple slides that highlight the
key phrases. And these are short, shareable videos
for social media. Perfect for it. And here's
the kicker for really personalizing it. You can
use voice cloning services. Eleven Labs is a
popular one. Whoa, voice cloning. How does that
work? You just need a short audio sample of the
executive's voice. The AI learns their voice
profile, and then it can generate the audio for
the video script in their exact voice. So it
sounds like they actually recorded it themselves.
It's uncanny, as this incredible layer of authenticity.
And the executive didn't have to spend any time
recording. That sounds like a seriously premium
service. It is. Standard executive ghost writing,
just for text posts, can easily command retainers
of $2 ,000 to $6 ,000 a month, sometimes more.
Your video offering is arguably more valuable
because video is so engaging. Wow. Okay, so these
first two methods, they're really about leveraging
time -saving and deep personalization for potentially
high -value clients. Yeah. Yes, exactly. They
save clients huge amounts of time and create
highly personalized professional content with
minimal client effort. Sponsor. All right, let's
get these methods coming. What's number three?
Method three, evergreen brand education. This
sounds like tackling repetition within companies.
That's exactly it. It's about transforming a
company's repetitive internal and external explanations
into a permanent library of on -demand videos.
The broken record content, you mean? Yeah. Every
growing business has it. Sales teams explaining
the same product features over and over. HR managers
going through the company values with every single
new hire. Customer support answering the same
frequently asked questions day in day out. That
sounds like a huge, often invisible drain on
productivity. And consistency, too, probably.
Totally. So your service identifies these frequently
repeated explanations. Think sales pitch components,
employee onboarding modules, common FAQs, product
how -to guides. You gather the source material
for those maybe handbooks, internal wikis, call
scripts. And you build a knowledge base for the
company in Notebook LM using that stuff. Right.
Then you generate a library of short professional
videos, each tackling one specific topic. So
imagine, from the employee handbook, you create
a crisp five -minute video explaining the company's
code of conduct. Clear slides for each main policy
point narrated clearly. Or a quick video demo
of a complex software feature that usually takes
a support agent an hour to explain on a call.
Exactly. Now think about the cost savings. Traditional
corporate training videos can cost thousands,
like $2 ,000 to $5 ,000, sometimes more. for
a single video. Wow. And studies show that automating
even just one hour of typical employee onboarding
can save a company over $1 ,300 per employee
in lost productivity time. So you could package
these, like the new higher onboarding video package,
five key videos for, say, $3 ,000. Absolutely.
You're offering a clear ROI based on time saved
and improved consistency. It's a strong value
proposition. OK, makes sense. Method number four.
Method four, nonprofit story mining. This one's
about helping nonprofits unlock the power hidden
in their own archives, transforming dense reports
and old documents into powerful, emotionally
resonant stories. Because donors connect with
stories, not just statistics. Precisely. Nonprofits
often have a goldmine of incredible human stories
buried in old annual reports, maybe board meeting
minutes, grant applications, beneficiary testimonials.
But... They rarely have the time or, frankly,
the tools to dig them out and package them effectively.
So you become their sort of digital archivist.
Kind of, yeah. You feed all that historical material,
reports, testimonials, old press clippings into
Notebook LM. You essentially create an AI historian
for them, a searchable, intelligent database
of their impact. And then you use it to find
the stories. Right. You prompt Notebook LM, maybe
something like, scan all uploaded documents for
personal anecdotes related to our youth mentorship
program. Structure a two -minute video script
focusing on a story of transformation using powerful
quotes from beneficiaries or staff. Create simple
slides for the key quotes. And these short, focused
videos would be perfect for... Fundraising emails,
social media campaigns, grant applications, website
impact sections, anywhere they need to connect
emotionally. What's the value comparison there?
Well, a dedicated nonprofit storytelling agency
might charge upwards of $11 ,000 a year just
to produce maybe four polished stories. With
this AI -driven approach, you could potentially
deliver a library of, say, five to seven compelling
video stories for maybe $3 ,000 to $5 ,000. And
good storytelling can directly boost donor conversion
rates, sometimes by as much as 300%. Okay, so
these two methods, three and four, are really
about creating scalable, consistent communication
assets for organizations, businesses, and causes.
Yes, spot on. They turn time -consuming, repetitive
tasks into valuable, consistent, and easily scalable
video content. Alright, one more method to cover.
Method five. Event knowledge mining. This sounds
like making conference content live longer. Exactly.
Companies spend huge amounts of money on conferences,
summits, big corporate events. They bring in
amazing speakers, share cutting -edge insights.
And then a few weeks later, most of it's forgotten.
Pretty much. It's a huge pain point for event
organizers. How do you extend the value, the
ROI, beyond the few days of the event itself?
You solve that by turning all that valuable event
content into an on -demand knowledge library.
So how do you get the content? You'd gather the
transcripts, keynotes, panel discussions, breakout
sessions. Often the event organizers have these
from the AV recordings. You feed those potentially
hundreds of pages into Notebook LM. And let it
process all that information. Right. Then you
prompt it to extract the key insights and themes.
For example, analyze all 15 speaker transcripts
from the Future of Finance conference. Identify
the top 10 recurring themes and actionable insights
discussed. Create a three -minute summary video
for each theme. Start each video with the most
relevant speaker's name and a key quote, then
elaborate with supporting points from the transcripts.
create a whole menu of video assets from the
event content. Exactly. Individual session summaries,
videos focused on specific recurring themes,
maybe even a greatest hits compilation video.
This provides massive long -term value for attendees
who want to revisit the content, for sponsors
looking to showcase their involvement, and for
the organizers themselves as marketing material
for next year's event. And compared to traditional
event videography. Well, just getting basic highlight
reels can cost $1 ,500 to $3 ,000. packages capturing
and editing multiple sessions run into the tens
of thousands easily. Your AI -driven process
means you could potentially deliver a comprehensive
library of these focused summary videos within
days of the event ending while the buzz is still
high. That's a huge advantage. OK, five really
distinct valuable methods there. But how does
someone listening actually go from hearing these
ideas to earning income? Is there a practical
plan? There is. The source lays out a pretty
clear five -step action plan. Step one, choose
your niche. And this is critical. Pick just one
of these five methods to start. Whichever one
genuinely interests you the most, or maybe aligns
best with people you already know. Don't try
to do everything at once. Focus. Focus is key.
Okay. Step two. Step two, master your toolkit.
Get hands on. Notebook LM itself is free, so
sign up and start playing with it immediately.
Feed it some articles, generate some videos,
see what kind of prompts work best. If you pick,
say, the research automation method, Maybe explore
tools like Manus AI. If you're interested in
the Go's leadership angle, check out the free
tier of 11 labs for voice cloning. Just experiment.
Get comfortable with the tools. You know, I still
wrestle with Promtript myself sometimes. even
after doing this for a while. That's when the
AI's responses kind of subtly veer off track
if your problems aren't super clear over time.
So really practicing, seeing the limitations,
understanding what it does well, that's absolutely
crucial. Understand the tech deeply helps you
sell the solution with real confidence. Makes
sense. That vulnerability helps, knowing even
experienced folks are still learning. Okay, step
three. Step three, find your first pilot client.
Don't wait until you feel like a total expert
because that day might never come. Leverage your
existing network. Who do you know? Small business
owner. Someone at a nonprofit. Offer a small,
very low -risk pilot project. Maybe even offer
to create one free sample video from one of their
existing blog posts or reports. Lower the barrier
to entry for them. Exactly. It removes their
risk, lets them see the magic firsthand, and
gives you a real -world case study and valuable
feedback. Okay. Pilot client secured. Step four.
Step four. Price with confidence. This is important.
Remember what you're selling. You're not selling
a cheap AI trick that takes you five minutes.
You're selling a solution to significant, often
costly, problem -saving time, ensuring consistency,
boosting professionalism, providing peace of
mind, delivering measurable ROI. So anchor your
price to the value you create for the client,
not just the time it takes you. Value -based
pricing. Got it. And the final step. Step five.
Systematize and scale. Once you land that first
client and hopefully knock it out of the park,
immediately document your entire process. What
steps did you take? What prompts worked best?
This becomes your standard operating procedure
or SOP. It allows you to deliver consistent quality
much faster next time and always ask for a testimonial
and ask for a referral. Happy clients are your
absolute best marketing engine. Okay, so recapping
that path. Pick a focus area, really learn the
tools, get that first pilot client on board,
price based on the value you deliver, and then
build a system to scale it. That's the roadmap
right there. Focus, learn, pilot, value price,
then systematize. All right, let's just quickly
recap the big picture here. This deep dive, we've
really explored how Google's Notebook LM, especially
with its grounded AI approach and its new pretty
elegant video capabilities, how it's unlocking
a really significant and still very early stage
opportunity. Yeah, it's about becoming almost
like a digital artisan. You're using this reliable
AI not just to automate, but to craft highly
valuable customized content. Content that solves
real, often expensive problems for clients. You're
creating tangible assets incredibly quickly.
And we looked at five powerful ways to do that.
Daily intelligence briefings, ghost thought leadership.
Evergreen brand education, nonprofit story mining,
and extending the life and value of event content.
Each one taps into a proven market need and offers
clear value. And it feels like the timing is
critical here. It really is. Think about that
technology adoption curve model. We are squarely
in the innovator and early adopter phase right
now for this kind of application. That means
there's still minimal direct competition. And
clients are often genuinely amazed by the results
because it's new to them. But that window, it's
definitely closing as awareness grows. So looking
ahead, maybe a year from now, you listening will
probably be in one of two positions. You'll either
be glad you decided to jump in and start experimenting
today, or you'll likely wish you had. Yeah, the
choice ultimately is yours. The technology is
here. It works, and the market need is clearly
waiting. So if any of these five methods or just
the potential of this tool resonated with you
today, our advice is pretty straightforward.
Pick one method that excites you. Find one potential
pilot client, even just for a small test. Start
today. Thank you for joining us on this deep
dive. Until next time, keep digging, keep learning.
UT Row Music.