artificial intelligence. It's really not just
science fiction anymore, is it? It's kind of
woven into our daily lives now. Oh, absolutely.
It's everywhere. Your digital assistant, how
you shop online, even those generative AI tools
like ChatGPT that so many people are using. Right,
exactly. It feels incredibly fast, almost seamless.
How integral it's become. Genuinely revolutionary.
It is revolutionary, definitely. But that speed,
that innovation. Well, it comes with a hidden
environmental cost, a pretty heavy one. OK. And
our sources today really dig into that. They
explore this massive growing footprint, not just
the software, but the real world demands on energy,
water. hardware. Yeah. And that's our mission
today, right, to try and quantify that demand
because the scale we're talking about here, it's
pretty staggering. We're not talking small changes.
Not at all. Yeah. Let's start with electricity.
Just think about this. In 2023, data centers
were already pulling 4 .4 percent of all electricity
in the U .S. 4 .4 percent. That's huge already.
It is. Enough to power whole states. But the
projections, they suggest that number could triple
by 2028. Triple. And AI is the main driver behind
that surge. Triple is, yeah. Hard to even picture.
And if you look globally, the strain on power
grids sounds almost, well, unimaginable. Exactly.
Some estimates are suggesting data centers could
account for maybe 20 % of the entire world's
electricity use by 2030, maybe 2035. 20%. That's
not just. Like needing a few more power plants.
That fundamentally changes global energy demand.
It really does. And you can see this growth directly
in the AI specific hardware. The energy used
just for those servers. It jumped from under
two terawatt hours back in 2017 to over 40 terawatt
hours in 2023. 40. That's a massive leap. It's
an explosion. And it's tied directly to how these
big AI models, these large language models, or
LLMs, are actually built and then how we use
them every day. OK, let's get into that, the
mechanics. Researchers often talk about two main
phases, training the model and then inference.
Why is the training part so incredibly power
hungry? Well, training involves this incredibly
complex process of adjusting billions, sometimes
trillions, of parameters in the model. It's like
trying to teach it everything all at once by
crunching massive data sets repeatedly. This
needs serious computing power. High -performance
computing, HPC, we're talking thousands of specialized
chips, GPUs and TPUs running flat out nonstop
for weeks or even months. That's constantly running.
Constantly. So that initial training phase, it's
one of the most resource -intensive computing
tasks humans have ever really undertaken. And
we have a number for that, right, for GPT -A4.
That really puts it in perspective. Yeah. The
estimates for training GPT -4 alone... are around
50 gigawatt hours of energy. 50 gigawatt hours.
And costing over $100 million just for the training
run. And to make that concrete for you listening,
that amount of energy could power, what, the
whole San Francisco metro area for about three
days? That's the estimate, yeah. Just for that
one initial training session. OK, so that's the
huge one -time upfront cost. But you said the
energy demand shifts after that. It shifts dramatically.
And this is where it really impacts daily use
and the overall footprint. After training, the
vast majority of the energy, like 80 to 90 percent,
according to research, goes into inference. Inference.
That's when we actually use it, right? When I
type a question into ChatGPT or ask an AI to
generate an image? Exactly. It's you, the user,
making a request. Yeah. Because millions, maybe
billions, of these requests are happening all
the time now, that's where the bulk of the continuous
energy drain comes from. So all those seemingly
small interactions summarize this, write that,
create this picture, they add up massively. They
really do. It's pretty striking that a single
query to something like ChatGPT can use about
five times more electricity than just doing as
a full Google search. Five times more, just for
one question. And what's really interesting and
maybe a bit concerning is how much that energy
use can swing depending on the model. How so?
Well, it depends heavily on the size of the model
you're querying and how complex the task is.
Our sources looked at open source models, like
Meta's Lama series, to try and get a handle on
this variance. OK, yeah. Let's look at that data.
That sounds like a key point. Right. So take
a smaller text model, like Lama 3 .1 8b. The
8b means 8 billion parameters. A single response
from that might use around 114 joules of energy.
Joules are tiny, right? Yeah. 114. How can we
picture that? Researchers use comparisons. So
114 joules is like riding an e -bike for maybe
six feet, or running your microwave for just
a fraction of a second, like a tenth of a second.
Sounds tiny. OK, almost nothing. Seems efficient.
Seems efficient. Now, compare that to a much
bigger model, Lama 3 .1405B. That's over 50 times
larger in terms of parameters. OK. A single response
from that model jumps to around 6 ,706 joules.
Whoa, OK. From 114 to over 6 ,700, that's a huge
leap. What's the microwave comparison for that?
That's like running your standard microwave for
about eight full seconds. Eight seconds versus
a tenth of a second, just for a similar text
output. Potentially, yeah. The researchers use
microwaves because they're a familiar high -power
appliance. It gives you a more visceral feel
for the difference in energy consumption. So
why? Why use the big, energy -hungry model if
a smaller one could do the job for many simple
queries? Why the massive jump in energy use?
Well, the thinking, which has some backing, is
that bigger models are generally smarter, more
versatile, less likely to make errors or hallucinate.
They can handle a wider range of tasks. So capability
trumps efficiency? Often, yes. A platform might
default to the bigger, more capable model, even
if your specific query didn't really need it.
So you end up paying the higher energy cost for
every single request, basically. Scale is kind
of the enemy of efficiency here. And we've only
talked about text so far. What about generating
images or even video? Oh, yeah. That's where
the numbers just go into another league entirely.
Generating, say, a five second video with an
open source model like Cog Video X. That single
task takes about 3 .4 million joules. Million?
Wow. That's over 700 times the energy needed
to generate a pretty high quality static image,
or sticking with a microwave comparison. Let
me guess, it's longer than eight seconds. It's
like running your microwave continuously for
over an hour, just for one five second video
clip. OK, that's... That's genuinely shocking.
An hour of microwave power for five seconds of
video. Yeah. It really highlights how the type
of AI task drastically changes the impact. Absolutely.
It's not just if you use AI, but what you're
asking it to do. And this enormous energy use
leads directly to another major, often overlooked
issue. Which is? Water. All those chips working
so hard generate a phenomenal amount of heat.
Right, they need cooling, massive cooling systems.
Exactly, and modern data centers rely heavily
on water for that cooling. We're talking vast
amounts of fresh water. The estimate is around
two liters of water for every single kilowatt
hour of energy the data center consumes. Two
liters per kilowatt hour, and is that water recycled?
Often, no. It's what's called consumptive use.
A lot of it evaporates in cooling towers or becomes
contaminated with chemicals and can't just be
put back into the local water system. So it's
competing directly with local water needs. Drinking
water, agriculture. Directly. And we're seeing
the impact in corporate water usage figures.
Google's data center water use jumped 20 percent
between 2021 and 2022. They use five billion
gallons in 2022 alone. 5 billion gallons. And
Microsoft's use went up 34 % in the same period.
These are huge numbers, but they become really
concrete when you look at local impacts. Like
in Newton County, Georgia? That was a case study
in the source material, wasn't it? It was. A
really stark example. Metta built a huge data
center there. And almost immediately, nearby
residents who relied on private wells started
having serious problems. Yeah, the local reporting
described people's taps running dry or the water
coming out full of sediment. Wells they relied
on for years suddenly became unusable. And replacing
a well is incredibly expensive for a homeowner.
Meanwhile, the Meta facility is estimated to
be using around half a million gallons of water
per day. Half a million gallons a day. But it's
not just that one facility. The systemic issue
is that Newton County is already projected to
face a water deficit by 2030. Residents are looking
at potential rationing and water rates are expected
to jump by about 33%. That's terrifying for the
people living there. And yet, new data centers
are still being proposed. Oh yeah. The reporting
mentioned new applicants requesting permits for
facilities needing up to six million or even
nine million gallons of water a day. Nine million?
That's more than the entire county uses currently,
isn't it? That's what the sources suggest, yeah.
It points to a fundamental disconnect in planning.
How do the tech companies justify this? Prioritizing
cheap electricity over local water security seems...
Hard to defend. Is it just about the jobs and
tax revenue they bring? That's the argument,
jobs and taxes. But critics argue those benefits
often don't cover the immense strain on local
infrastructure, like water and the power grid.
The core problem seems to be site selection criteria.
Meaning? Companies often prioritize places with
the cheapest, most abundant electricity. Water
availability often seems to be a secondary concern
even if the location is already water -stressed
like say Phoenix, Arizona or parts of Chile or
Uruguay. So they build in deserts or drought
-prone areas because the power is cheap. It appears
that way sometimes, yeah. Water is treated almost
like an externality, a cost someone else often
the local community has to bear. And this lack
of foresight, this lack of maybe community consultation
or impact assessment links back to another big
issue, doesn't it? The accountability gap. Exactly.
The water issue is partly visible because, well,
you can see the impact on wells. But the energy
consumption and the resulting carbon emissions,
that's often much harder to track. It's kind
of a black box. Because the big AI companies,
OpenAI, Google, Microsoft, they run... closed
models, right? They don't share all the details.
Precisely. Key operational data, like the exact
size of the models, how many queries they handle,
their precise energy use per query, the energy
mix they use. A lot of that is guarded as proprietary,
as a trade secret. So researchers trying to figure
out the true environmental costs have to essentially
guess or reverse engineer the numbers? Pretty
much. They have to make estimates based on the
open source models, like we discussed with Llama,
or based on published papers, which might be
out of date. It makes getting accurate real -time
figures incredibly difficult. And this secrecy
matters hugely because of the type of power these
data centers need. Yes. They need power 247,
365 days a year, non -stop. You can't run these
massive AI operations solely on intermittent
renewables like solar and wind, not with current
grid technology and storage. So they need that
constant, reliable baseload power. Which often
means? Often means fossil fuels. Natural gas,
sometimes coal, depending on the region's grid.
One study found the electricity used by data
centers had a carbon intensity that was, on average,
48 % higher than the U .S. grid average. 48 %
higher. So they're not just using a lot of power,
they're often using dirtier power. That's the
implication. And data centers tend to cluster
geographically, like in Virginia, which has the
highest concentration in the U .S. And Virginia's
grid relies heavily on natural gas, doesn't it?
It does. Plus, you have the issue of backup power.
When there are grid fluctuations or failures,
data centers often rely on massive diesel generators
to keep things running. That adds another layer
of emissions. It seems like a cycle of high demand
met by often carbon -intensive energy. Are governments
or regulators starting to push back on this lack
of transparency? There are signs of movement,
yes. In the U .S. there's a proposed bill that
would require federal agencies to assess the
environmental impact of their own AI use. And
the EU's AI Act includes requirements for developers
of high -risk AI systems to report on energy
and resource consumption. So some initial steps
towards accountability. Small steps, but important
ones. Opening up that black box is crucial. But
in the meantime, who's paying for all the new
infrastructure needed to meet this demand? The
grid upgrades, the new power lines? Well, that's
another really contentious point. Research looking
at utility agreements suggests that, in some
cases, the costs associated with building up
the grid specifically for these massive data
centers might actually be passed on to regular
consumers. Wait, so ordinary households could
end up paying higher electricity bills to subsidize
the power needs of these giant tech companies?
That's the concern. One analysis for Virginia
suggested residents could potentially face an
extra $37 .50 on their monthly bills just to
cover these data center -related grid costs.
$37 extra per month to power facilities for some
of the richest corporations on the planet. How
is that justified? That's the question utility
regulators are grappling with right now. It really
challenges the narrative that these centers are
purely economic windfalls for a region if the
public has to subsidize the essential infrastructure
they require. Okay it feels like we've painted
a pretty concerning picture so far. Massive energy
use, water scarcity, lack of transparency, costs
potentially pushed on to consumers. It is concerning
no doubt. But the source material also pointed
out that AI isn't just the problem, right? It
can also be part of the solution. Yes, absolutely.
That's the paradox. AI is incredibly powerful
at optimization, finding efficiencies. And sometimes
those efficiencies can lead to huge environmental
benefits that might even outweigh AI's own footprint.
Like the example with airplane contrails. Exactly.
Contrails those white lines. Planes leave in
the sky. They actually trap heat and contribute
significantly to warming. Surprisingly so. I
didn't realize they were that significant. They
are. And AI algorithms have been developed that
can guide pilots to make very slight adjustments
to their flight paths to avoid the atmospheric
conditions where contrails form. OK. One estimate
suggested that if the entire aviation industry
adopted this AI guidance, the reduction in warming
effect the CO2 equivalent saved could be greater
than all the emissions generated by global AI
use back in 2020. Wow. So one specific optimized
AI application could potentially offset the entire
sector's footprint at that time. Potentially,
yes. It shows the immense power of AI for positive
environmental impact when it's applied strategically.
So the path forward involves harnessing that
optimization power while simultaneously tackling
the hardware and energy issues we've discussed.
What are the main solutions being proposed? Researchers
point to a few key areas. First, making the models
themselves more efficient by focusing on developing
smaller, more specialized models designed for
specific tasks rather than constantly pushing
for ever larger general purpose models that are
often overkill for what users actually need.
tailoring the tool to the job essentially. Makes
sense. Less wasted computation. What else? Second
is hardware innovation. Moving beyond the current
generation of GPUs. Things like neuromorphic
chips. Neuromorphic? What are those? They're
designed to mimic the way the human brain processes
information very parallel. And crucially, they
use energy mainly when they're actively processing
data, not just sitting idle. That could offer
huge energy savings. Interesting. And also things
like optical processors, which use light instead
of electricity to move data around. Less resistance,
less heat, potentially much lower energy use.
These are longer term hardware shifts. OK. So
smarter models, better hardware. What about the
grid problem, the reliance on dirty energy in
places like Virginia? That needs systemic change.
Yeah. Obviously, transitioning data centers fully
to renewable energy sources is key. But they're
more ambitious ideas, too. Like distributing
the computing tasks globally based on time zones.
Shifting demanding AI workloads to regions where
renewable energy production, solar or wind, is
peaking at that particular moment. Sort of like
following the sun and wind around the planet.
That sounds complex, but clever. using renewable
energy where it's most abundant at any given
time. It's a big logistical challenge, but it
tackles the intermittency problem head on. Okay,
so there are paths toward a more sustainable
AI, but we can't wrap this up without touching
on that classic economic concept, the Jevons
Paradox. Ah yes, the rebound effect. Explain
that. If we make AI way more energy efficient,
doesn't that just solve the problem? Not necessarily.
The Jevons Paradox basically states that when
you make the use of a resource cheaper or more
efficient, demand for that resource tends to
go up, often increasing overall consumption in
the long run. So making AI cheaper and easier
to run might just lead to us using it way more,
potentially erasing the affilency gains. That's
the risk. AI is often described as an accelerant
for everything. Making it more efficient accelerates
its adoption across the board for good applications
like the contrail example, but potentially also
for more energy intensive, maybe less critical
uses, the total footprint could still grow. That's
a sobering thought. So efficiency alone isn't
a silver bullet. It's necessary, but probably
not sufficient on its own. This deep dive has
really hammered home one thing for me. This AI
revolution is driving energy and water demand
at a pace and scale we maybe haven't seen since,
I don't know, the Industrial Revolution or mass
electrification. It's a valid comparison, I think.
The trajectory is incredibly steep. And tackling
it needs more than just individual users thinking
twice about a query. It needs real transparency
from the companies building these systems, and
it needs smart systemic regulation. Exactly.
The focus really needs to be on the system level,
demanding accountability on energy sources. on
water use, on model efficiency. Pinning it on
individual consumption kind of misses the forest
for the trees, given the scale and the secrecy
involved. Right. Which brings us to our final
provocative thought for you, our listener, to
chew on. We're moving pretty fast towards a future
where AI isn't just answering questions, but
acting as agents. Autonomous agents performing
complex tasks. Exactly. Agents that might plan...
research, generate multiple options, run simulations,
tasks that are inherently sequential and much
more computationally intensive than a single
query is today. they'll require significantly
more energy per job. Yeah, the energy profile
could shift again, dramatically. So the question
is, in that near future, with these powerful,
energy -hungry AI agents becoming potentially
ubiquitous, how do we policymakers, citizens,
consumers, ensure that the environmental toll
of all this amazing innovation doesn't just completely
overwhelm our planet's finite resources, especially
energy grids and fresh water supplies? How do
we keep that acceleration in check?