All right, welcome to another deep dive.
Today, we're looking at something pretty fascinating.
The energy demands of this whole digital world,
especially with AI just exploding everywhere.
We've got some great insights actually
from energy expert Mark P. Mills.
He was on the decouple podcast.
And-
Yeah, really interesting conversation.
Oh, super interesting.
So get this, some of these data centers
are using as much power as a whole nuclear plant.
I know it's mind-boggling when you think about it.
It really is.
And we're gonna dig into that.
I mean, what's going on?
Why is that?
So, I mean, I think, you know,
when we think about the cloud,
we think of it as this kind of,
I don't know, this intangible thing.
Right, it's this sort of nebulous thing up there.
Exactly.
But you were talking about how it's actually,
it's a very real, very physical network.
Oh, absolutely.
I mean, we're talking massive data centers,
warehouse size buildings,
millions of processing units,
billions of miles of cables connecting everything.
It's truly staggering.
In fact, if you combined all that space,
it would be larger than all the skyscrapers
in the world combined.
Oh, wow.
Okay, so yeah, so it's not just all these ones and zeros
floating around out there.
There's this, I mean, this really power hungry
infrastructure behind it all.
Yeah, yeah.
So why is AI so power hungry?
Like, why does it use so much more energy
than just regular computing?
Well, the thing with AI,
especially with machine learning,
is that it needs these massive data sets
to train the algorithms.
Okay, so it's kind of like,
it's like teaching a kid to recognize a cat, right?
You're showing them thousands and thousands of pictures.
Exactly, it's the same concept,
but just on a ridiculously larger scale,
and all that processing power.
Well, it takes energy, lots of energy.
And Mills was saying that AI uses something
like 10 times the energy of traditional computing.
Yeah, yeah.
At least, and that's only gonna increase
as AI gets more sophisticated.
So, I mean, what are the implications of that then?
Especially as AI becomes more and more integrated
into our lives, I mean, we're already seeing it everywhere.
Right, I mean, we're looking at a pretty significant
increase in energy demand.
And this is on top of the growth we're already seeing,
from all the other digital services we use,
streaming, online gaming, all of that.
Right, right, so AI is almost like throwing
rocket fuel on that fire.
Pretty much, yeah.
And it's not just a tech issue, right?
This has geopolitical implications too.
Oh, absolutely, I mean, access to cheap and reliable energy,
that could become a major factor
in which countries kind of end up leading the way
in AI development.
And Mills specifically points to China.
Yeah.
Because they're investing really heavily
in both data centers and kind of surprisingly coal power.
Right, it's interesting.
Yeah, you think with everyone pushing for renewables,
it's a little counter-occuative.
It is, but it shows you the kind of tension
that exists between immediate energy needs
and these long-term sustainability goals.
Right, right.
China's prioritizing their position in the AI race,
even if it means maybe relying on energy sources
that aren't so environmentally friendly, at least for now.
So, I mean, how does the US kind of compare
in this whole energy AI landscape?
Well, the US and Canada,
they're still major hubs for AI innovation,
but they rely more on natural gas and renewables.
And those can be more expensive
and less consistently available than coal.
So, it raises some questions about whether,
maybe over-regulation in the West
could actually hinder its ability to compete in the long run.
Interesting, now Mills also talks about this concept
of in-silico research.
Can you maybe explain what that is
and why it's relevant to this whole discussion?
Sure, so in-silico, it basically means, you know,
performed on a computer or through computer simulation.
So, in the context of research,
it's about using computer models and simulations
to study biological processes.
Okay.
So, imagine creating a digital twin of your body
containing all your health data.
Wow.
And then scientists could use AI to run simulations
and predict how different drugs or treatments
might affect you.
So, that sounds like, I mean,
it sounds like something straight out of a sci-fi movie.
It does a bit, doesn't it?
It does, but what kind of energy demands
would this in-silico research have?
I mean, it sounds pretty intense.
Oh, it would be very significant.
You know, building and maintaining
those detailed digital models,
storing vast amounts of data,
running these complex AI simulations,
it all takes a lot of processing power.
And like we've been talking about, you know,
with any data heavy process,
energy consumption is a key factor.
So, we're not just talking about powering the cloud,
as we know it.
We're talking about a whole new level of energy demand
as these in-silico applications become more widespread.
Exactly.
And that brings up some even more, you know,
urgent questions about the future of energy production.
You know, where will all this power come from?
How can we, you know, ensure it's sustainable?
Yeah.
And those are the challenges that we're gonna be looking at
more in-depth as our deep dive continues.
All right, so we're back for part two
of our deep dive into AI's energy appetite.
And, you know, we've been talking about
the energy-intensive nature of AI in-silico research.
But what about solutions?
I mean, where are we gonna get all this power?
Well, that's the big question, isn't it?
And Mills, he argues that we can't just rely on renewables,
you know, for this base load power
that these huge data centers need.
Base load power, what's that?
Right, so base load power, it's the minimum amount
of electricity that a grid has to supply at all times,
you know, to keep things running.
And renewables like solar and wind, they're great,
but they're intermittent, right?
They're not always available.
You can't always count on them.
Okay.
So data centers, they need this constant,
this reliable energy source.
And that's where, you know, all this talk
about nuclear power comes in.
Yeah.
And it's interesting because Mills,
he suggests that nuclear power,
which has, you know, obviously been somewhat controversial.
Just say the least.
Yeah, he's saying that it might be making a comeback,
not just for environmental reasons,
but just out of sheer necessity.
Yeah, I mean, he points out that some
of these data centers are using as much electricity
as a small nuclear power plant.
It's great.
It is.
And as the demand for AI and computing power keeps growing,
we have to consider all our options.
And nuclear, it does provide that steady,
that high output power that renewables,
you know, at least for now, they just can't match.
Yeah.
I mean, but isn't building, you know, nuclear plants,
isn't that a really lengthy and complex process?
Oh, absolutely.
Yeah.
I mean, it takes years and years.
And Mills, he acknowledges that.
He says, even if we started today,
it would be years before those plants came online.
So he suggests that in the meantime,
you know, we might have to think about
some less ideal solutions, like keeping existing
coal and gas plants running,
maybe even bringing back some that were recently retired.
I mean, that's bound to be, you know, unpopular,
especially with everyone pushing for cleaner energy.
Oh yeah, it's definitely gonna be controversial.
But his argument is that it's a pragmatic approach,
at least for the short term.
You know, we can't just wish away the energy needs
of the digital world.
And while the long-term goal is to shift
to more sustainable sources,
we need practical solutions right now
to kind of bridge that gap.
Okay, so where does that leave us
in terms of the long-term vision?
Well, Mills, he believes that ultimately,
it's gonna be a combination of nuclear power, renewables,
and advancements in energy efficiency.
That's what's gonna meet the demands of this digital future.
And he's especially enthusiastic about the potential
of small modular reactors, these SMRs.
Oh, right.
Which are, you know, they're smaller,
they're more flexible, you know,
they can be deployed more quickly
and with less upfront investment.
Okay, that sounds promising, but you know,
this whole energy issue, it goes way beyond
just data centers, right?
I mean, think about, you know, electric vehicles,
smart cities, all this tech that's changing our world.
Oh yeah, it's a systemic challenge, for sure.
And it has, you know, as we've been saying,
these geopolitical implications as well,
Mills points out that countries that have access to cheap
and abundant energy, they could have a real advantage
in this whole AI race.
And we were talking before about China's, you know,
investment in coal power.
Are there other countries that are kind of positioned
to benefit from this energy AI connection?
Yeah, definitely.
I mean, countries that have, you know,
significant hydropower resources, like Canada
or some South American nations,
they could be in a good spot.
The key is having a, you know, stable
and affordable energy source to power all this,
this massive computing infrastructure
that's needed for AI development.
And this is where, you know, the US,
which has more of a diverse energy mix,
this is where it might face a challenge
because, you know, regulations and infrastructure limitations,
they could slow things down,
especially if energy costs start getting, you know, really high.
So it's not just about, you know,
having the smartest people or the most advanced algorithms,
it's about having the energy resources
to actually fuel those innovations.
Yeah, exactly.
And it really highlights the need for strategic thinking
and long-term planning when it comes to energy policy.
Yeah, one of the things that really struck me
listening to the podcast was Mills' point about how
our demand for information is fundamentally different
from our demand for other resources.
You know, we have a limit to how much food
or material goods we can consume,
but our appetite for information, it just seems limitless.
Yeah, it's an interesting thought, isn't it?
He uses the evolution of the words we use
for large numbers as an example.
You know, we've gone from kilobytes to megabytes
to gigabytes, and now we're talking about
zettabytes and yottabytes.
I mean, these are numbers that are so large
that it's hard to even grasp them.
Yeah, he had that amazing analogy trying to visualize
like a zettabyte of data as a stack of dollar bills
reaching from the earth to the sun and back
like 700,000 times.
Yeah, which is just, I mean, it's mind-blowing.
But it really underscores the sheer scale
of this digital universe that we're creating.
And as we generate more and more data,
the energy needed to store it and process it,
it's just gonna keep growing exponentially.
Yeah, it sounds like we're really on the verge
of a computing revolution,
unlike anything we've ever seen,
but one that comes with this massive,
massive energy price tag.
Absolutely, and that brings us back
to the core question here.
How do we balance this insatiable demand
for information, for computing power,
with the need for sustainable energy solutions?
It's a tough question, with no easy answers,
but maybe we can at least start by acknowledging
the connection between energy technology and our future.
Yeah, I agree.
And that awareness needs to be reflected
in our policies, our investments,
our individual choices.
We all have a role to play in shaping a future
where AI and this digital world can thrive
without damaging the planet.
Welcome back to the final part now
of our deep dive into AI's energy appetite.
It's been quite a journey.
It has, lots to think about.
We've talked about the energy demands of AI,
the potential of this in-silico research,
the role of nuclear power.
But before we wrap things up,
there were a couple more key takeaways
that I thought were worth digging into a bit.
Yeah, I agree.
We've definitely covered a lot of ground.
But I think it is important to kind of step back
and consider the bigger picture here.
We talk about AI and the digital world
as if it's its own separate thing.
But the reality is it's very much tied to the physical world,
especially when it comes to energy.
Yeah, and Mills makes this really interesting point
about how the infrastructure of the cloud,
the data centers, the networks, the servers,
it's almost like this hidden world
that most people don't even think about.
Right, out of sight, out of mind.
Yeah, exactly.
I mean, we interact with the digital world every day,
constantly.
But we rarely stop and think about this massive,
this physical infrastructure that's making it all possible.
Yeah, it's kind of like an iceberg, right?
We see the tip, our smartphones, our laptops,
the apps we use.
But underneath, underneath the surface
is this huge network of energy-intensive machinery
that's keeping it all running.
Right, right.
And as we move towards even more complex
and sophisticated AI applications,
like the in-silico research we talked about,
the size of that iceberg, it's only gonna grow.
And that raises some really big questions
about sustainability.
I mean, can we keep expanding the digital world
at this pace without putting an unsustainable strain
on our energy resources?
That's the, I mean, the million dollar question, right?
Yeah.
And there's no easy answer.
Mills, he argues that we need this kind of,
this multi-pronged approach.
First, we gotta keep investing in renewables,
solar, wind power.
But as we've discussed, renewables alone,
they can't meet the baseload power demands
of these data centers.
Right, because they need that constant,
reliable source of energy.
Right, exactly.
Which is why Mills really advocates for
a serious look at nuclear power.
He sees it as a crucial part of the solution,
especially as we try to move away from fossil fuels.
He points out that nuclear plants,
they have a very small carbon footprint
and they can provide that steady,
that high output power that data centers really need.
I mean, but nuclear power, it comes with its own set
of challenges.
Right, yeah.
I mean, the risk of accidents,
the issue of nuclear waste.
Absolutely, and those are serious concerns.
And Mills, he doesn't shy away from that.
He acknowledges that.
But he argues that with advances in technology,
in safety protocols, the risks associated
with nuclear power, they're manageable.
And he also believes that we need to invest more
in research and development to find safe,
effective solutions for dealing with nuclear waste.
So it sounds like the future of AI and the digital world,
it's gonna require this really kind of complex
and nuanced approach to energy production.
One that balances the need for clean energy
with the need for this reliable, this base load power.
It's a balancing act for sure.
And it's not just a challenge for governments
and corporations to figure out.
It's something that's gonna require
a collective effort from all of us.
So what can individuals do?
What can people like me, our listeners do
to make a difference?
I mean, it can feel kind of overwhelming.
It can, yeah.
It's a big issue.
But even small changes in our own behavior can add up.
We can be more mindful about our own energy use.
We can support policies that encourage renewable energy.
We can choose products and services from companies
that are committed to sustainability.
So it's not just about the technology itself.
It's about the choices we make,
both individually and as a society
about how we use and produce energy.
Exactly.
And as our deep dive into AI's energy appetite,
as it wraps up here, I hope it's clear
that this isn't just a technical issue.
It's something that touches on economics, on geopolitics,
even on our own personal values.
Yeah, it's a good reminder that the digital world,
it's not this abstract, this intangible thing.
It's built on a very physical infrastructure
that needs real resources, especially energy.
And as AI continues to evolve
and become even more a part of our lives,
it's really important that we have these conversations,
these thoughtful and informed conversations
about how to power this digital revolution
in a way that's sustainable.
Couldn't have said it better myself.
The future of AI, the future of our planet, it depends on it.
Well, thank you so much for joining us
and for sharing your expertise with us today.
This has been a really insightful discussion.
And to our listeners, thank you for joining us
on this deep dive as well.
We hope you learned something new
and that you'll keep exploring this really fascinating
and ever-changing world of AI and how it's shaping our lives.