All right, diving right in today,
we're talking all about the future of AI,
and we've got some seriously insightful interviews to unpack.
Really fascinating stuff,
especially our chat with Jensen Huan, the CEO of NVIDIA.
Yeah, he's leading a company that's right at the heart
of this whole AI explosion.
It's incredible to see the role they're playing.
Definitely, and what's interesting is a lot of people
think of NVIDIA as just a GPU company.
Right, like they just make the graphics cards
for gaming and stuff.
Exactly, but it goes so much deeper than that.
What we uncover in this deep dive
is this fascinating moat around their business,
this kind of hidden layer of competitive advantage.
Okay, I'm intrigued.
Break it down for us.
What makes NVIDIA's position so unique
in this whole AI landscape?
So imagine you're building a house, right?
You need all the right materials,
you need the lumber, the nails, the concrete,
all of it, right?
Yeah, makes sense.
Gotta have the right tools for the job.
And individually, those things are important,
but they're not that impressive on their own, right?
NVIDIA's got all those pieces for AI,
they have the hardware, the GPUs, the CPUs, you name it.
But their real advantage comes from
how they fit it all together,
how they've mastered not just the ingredients,
but the whole recipe.
Jensen actually calls it their combinatorial advantage.
Interesting, so it's not just about selling the bricks,
it's about selling the entire construction company,
having the whole package deal.
You got it.
And a big part of that is the layers they've built.
They have the hardware, yeah,
but then they have CUD, their programming platform,
that makes it easier for developers
to actually use that hardware.
And on top of that,
they've got these crazy optimized software libraries
and even their own AI algorithms,
all fine-tuned to work together seamlessly.
So they've streamlined the entire process
from the ground up.
Precisely, it's like they've created this whole ecosystem
that's way more powerful than just the sum of its parts.
It's not just raw processing power,
it's the way everything works together so smoothly.
And that's where this flywheel concept
comes into play, right?
Yeah, I've heard Jensen talk about that,
this idea that they're constantly building momentum.
Exactly.
The flywheel analogy is all about how they accelerate
the entire cycle of AI development.
Think about it.
They make data curation and preparation faster,
they speed up the training process of those AI models,
and get this, they even accelerate inference,
which is how those trained models are then used
in the real world.
So they're involved in every step of the process,
making each one faster and more efficient.
And because of that constant improvement across the board,
they just keep gaining this incredible advantage.
It snowballs on itself,
making their platform even more attractive,
bringing in more users, generating more data,
which then fuels even more development.
It's a really smart cycle they've got going.
It's like this self-perpetuating loop of innovation.
They make it faster and cheaper for everyone else
to build and use AI,
which in turn makes them even stronger.
And here's the other thing.
They don't just abandon their older technology.
They keep updating the software, the algorithms,
which means even their older hardware
stays relevant and valuable.
Like, remember that story about OpenAI
decommissioning their Volta system?
Oh yeah, and then they realized
they could still use it for other tasks
because Nvidia had kept the software up to date.
They basically got a second life out of that investment.
Exactly.
That ongoing compatibility and support
across their product range is huge,
and it's a big part of why Nvidia
holds such a powerful position in the AI world.
Okay, so for our listeners
who are maybe hearing all this for the first time,
what's the key takeaway here?
Why does this moat around Nvidia really matter
in the grand scheme of things?
Because they're not just providing the tools.
They're essentially shaping the future of AI.
That level of influence, that reach they have,
it's going to be hard to replicate.
So whether you're building AI, using it,
or just trying to wrap your head around it all,
Nvidia's influence is going to be everywhere.
It's kind of wild when you think about it, right?
Nvidia went from powering our favorite video games
to building the foundation for artificial intelligence.
It's a huge jump.
Really speaks to their ability
to kind of see where things are headed
and even create those markets from scratch.
Totally.
It's like they invented the whole 3D gaming PC market,
and now they're doing it again, but this time it's AI.
And the scale of it is, well, Jensen actually predicts
that every single workload is going to be touched
by this acceleration.
Not most, not some, but every single one.
Every single one, yeah.
It's a bold statement for sure.
It really is.
But what does that even look like in the real world?
Give us the lay of the land.
So picture this, everything from scientists
crunching massive data sets
to businesses trying to streamline their operations.
Even the apps on our phones are becoming powered by AI.
And all of those things require serious processing power.
Which is where Nvidia comes in.
They're building the infrastructure
for this AI powered future we've been talking about.
Spot on.
But here's the really mind blowing part.
Jensen throws out these numbers,
talks about trillions of dollars being spent
to replace outdated data centers
with this new AI optimized infrastructure.
Trillions. Trillions of dollars.
Okay, now my head's spinning.
That's hard to even imagine.
It's a massive number, no doubt.
But when you really think about it, our current systems,
they just weren't built for the demands of AI.
Right, they weren't designed for these giant data sets
or the really complex algorithms that AI needs to run.
Exactly, it's like trying to run the latest,
super immersive video game on a computer from the 90s.
It just ain't happening.
Total system meltdown.
Pretty much.
And that's exactly why companies are racing to upgrade.
And it's creating this huge opportunity for,
well you guessed it, companies like Nvidia.
And this isn't just some theoretical thing, right?
Look at Elon Musk.
He built that massive supercomputer for X.AI
in like, what, less than three weeks?
19 days, it's mind blowing.
Seriously, it shows you the urgency, right?
Yeah.
These big players are betting big on AI
and they need the infrastructure to support it.
No doubt.
And as AI keeps getting more integrated into,
well everything, that demand is only gonna go up.
We're not talking about some far off future.
This is happening right now.
So our listeners are probably starting to realize
the world's about to get a whole lot smarter.
But we're also at this crucial point
where the companies that supply those building blocks,
they have a huge influence on how this all plays out.
Couldn't agree more.
Understanding those forces at play,
especially the role of a company like Nvidia,
it's key for anyone who wants to be ready
for what's coming.
Absolutely.
Okay, so we've been talking a lot
about training these AI models,
which is obviously important,
but there's this other side of the coin
that often gets overlooked.
And that's inference.
Right, inference, yeah.
It doesn't get as much hype, but it's essential.
Training is all about building the model,
teaching it how to think,
but inference is how we actually put those trained models
to work in the real world.
So if training is like writing the software,
inference is like actually running it.
Perfect analogy.
Every time you ask Siri a question,
every time you use facial recognition to unlock your phone,
or Netflix recommends a show
that's all inference happening behind the scenes.
And Jensen had this pretty amazing prediction about inference.
He said it's gonna be a billion times larger than training.
A billion.
That's not just a little bit bigger,
that's a whole different universe.
It is.
And it speaks to this fundamental shift
in how we'll be interacting with AI in the future.
As we move toward more complex,
more interactive applications, the demand for inference,
it's gonna be off the charts.
You know, it's easy to get lost
in all the technical stuff with AI,
but we gotta talk about the human side of all this too.
Like, what happens to our jobs when AI can do so much?
It's a question a lot of folks are asking.
Yeah, absolutely.
And it's a big one.
It's something that Jensen Wong addresses head on, actually.
And his vision isn't about AI replacing us, you know.
It's about AI boosting our capabilities,
making us even better at what we do.
So instead of taking our jobs,
AI becomes a tool to actually improve them.
Right.
Think about it.
What if AI could help doctors diagnose diseases
way more accurately,
or help architects design eco-friendly cities,
or even help teachers personalize lessons for each student?
We're talking about a whole new level
of efficiency and possibility.
Yeah, it's a much more, I don't know,
optimistic way to look at it
than that whole robots are gonna steal our jobs
thing you always hear.
For sure.
And it goes hand in hand with this idea Jensen talks about,
this idea of people becoming like CEOs
of their own AI agents.
Imagine having super smart tools
that can help manage your schedule,
take care of all those little tasks,
even help generate creative ideas.
It's not-
It's not interesting.
It's interesting.
It's ours.
So working smarter, not harder,
using AI to multiply our own skills and talents.
Exactly.
And the cool thing is,
this isn't some far off sci-fi scenario.
Jensen himself, he uses AI tools
every single day in his own work,
whether he's doing research, brainstorming,
making big decisions, you name it.
He's like a walking example
of how we can integrate this tech into our lives
and do incredible things.
I like that.
Feels like we're at this point
where we can choose to be scared of AI,
or we can choose to embrace what it could help us create.
Totally.
It's about shifting our perspective, right?
Moving from this fear of the unknown
to like a collaborative spirit.
The real magic of AI,
it lies in its ability to work with us,
to make us smarter and stronger.
Okay, now before we wrap up,
we gotta touch on this whole debate
that's been swirling around the AI world.
Open source versus closed source models.
What's the deal with that?
It's a big one, for sure.
Lots of different viewpoints.
So on the one hand, you have closed models, right?
Like what OpenAI does, keeping their code under wraps.
This allows them to protect their inventions,
maybe even develop even more advanced systems.
But then there's that whole other side that says,
hey, if we make these models open source,
if everyone can access the code,
innovation will skyrocket and more people will benefit.
Exactly, and that's where it gets really interesting
because Jensen Hong,
he doesn't think it has to be one or the other.
He actually argues that we need both.
Closed models, they drive that cutting edge research,
that investment.
But open models, they make AI more accessible,
more democratic.
So it's all about finding that balance,
that sweet spot where everyone wins.
Exactly.
We need a healthy AI ecosystem
and that means both open and closed models
playing their part, even in video
and they're leading the pack,
even they see the value in open source.
They released their own open source model a while back
that's actually designed to help make other models better.
It's pretty cool.
It really is.
So for everyone listening, it's not about taking sides.
It's about understanding both perspectives,
working together and making sure this technology
is developed in a way that benefits everyone.
And hey, maybe that brings us to the big question,
the one we all need to be thinking about.
The billion dollar question, right.
So we've talked about how AI is growing at an insane pace,
all this investment,
billions of these interactions happening every second,
but where do we fit in?
Exactly.
Are we just gonna sit back and watch
as AI changes the world around us?
Or are we gonna jump in, use these tools
and actually shape what this future looks like,
not just for ourselves, but for everyone?
Because let's be real, the future of AI,
it's not set in stone.
It's being written right now.
And every single one of us has the power to grab the pen
and add our own lines to the story.
And that's what this deep dive is all about,
equipping everyone with the insights and the knowledge
to navigate this incredible moment in history.
Thanks for joining us.