Kate, Rich, welcome.
Great to see you both again.
Kate, to you first.
Talk to me, if you would, about the role that AI is playing, and
that it can play, in the climate fight.
Yes.
Rich and I, and our teams, worked together on a piece of
research that we actually published just before the COP
kicked off, looking at the opportunity for AI to accelerate
existing technology and solutions that we have in this
decisive decade.
The headline from the study was: by 2030, AI could enable 5-10%
emissions reductions globally, again, by accelerating those
existing technologies.
To put that into context for you, that's roughly the
equivalent of the emissions of the European Union, so a big
opportunity space.
And we also identified three areas where we see promise.
The first is around information, using AI to better organize data
and provide insights that are actionable.
The second, optimization. This is the real superpower of
machine learning.
How do we use optimization to reduce energy use, to reduce the
use of natural resources, which also has a big double bottom
line incentive to it.
And then the last is around prediction, whether that's
predictions around how wind might come onto the grid 36
hours in advance, but also in the space of adaptation and
resilience, giving us better insights into flooding, into
wildfire.
So, a real basket of solutions that, again, are here today,
they're tangible, and they can accelerate our progress.
So, a lot of opportunities, Rich. The solutions, optimism
around this, but how can businesses identify
and then use AI to get the most to actually achieve their
climate ambitions? It's not just a question of grabbing AI,
chucking loads of money at it, surely.
Well, it's a couple of elements.
First, it's really critical to pick the critical opportunity
areas.
The phrase we use internally is 'outcomes at scale'.
What are the places that, if you put it in, and you can make it
work, you can really drive broad outcomes.
The second is a huge agenda on people and how do you bring
people along so they understand, there's reskill, organizations
are built.
But the two points I'd like to quickly highlight are data and
partnerships that I think have been so integral to this. Rather
than talking about them in theory, I'll give just two quick
examples. On data,
we built CO2 AI, which was a platform, that is now a SAAS
platform.
It is its own little company now that's out there with the best
data in the world about emissions, so that you can track
emissions not just in your own organization, where you often
have good data, but across your entire supply chain.
We worked with a healthcare company.
They worked with a healthcare company that could use the 50
times more insight and data to get to 20% more precise
predictions and exceed very ambitious targets by 120%.
The other example is one from Google. It's a personal favorite
because I fly so much. They teamed with Breakthrough Energy
and American to take on contrails.
People don't realize, but the little lines we see in the sky
coming out the back of planes are responsible for more than
30% of the emissions, the GHG impact of an airplane.
When I first learned this a few years ago I was shocked.
And that's actually one we can really tackle quickly.
It doesn't require a whole new technology supply and using AI,
they worked with Breakthrough and American to design new tools
for pilots to be able to use that seem to be able to reduce
those emissions, the contrail part, by over 50%.
It's incredible and it's fast.
Maybe just to pick up on this, I think back to this theme of AI
as an accelerant, and being able to use it on top of our existing
technology stack and solutions.
So I love the contrails example.
The other one I would highlight is work that we've been doing
that we call Green Light.
This is an initiative where our Google research team has been
working with city traffic engineers to give them insights
into how to better sequence traffic lights in a city.
Because what we know is, not only is it really annoying to
sit at red lights, there's 29 times more emissions happening
when cars are idling at red lights.
So if you can create that Green Wave effect that we all love,
where you feel like you're just hitting green light after green
light, that is not only a better experience as a driver, but it
also reduces air pollution and emissions.
What we've found in tests with an initial 12 cities is we can
reduce starting and stopping events by 30% and emissions by
10%.
So we're doing that in Bangalore, in Rio, in Haifa, and
coming to many more cities soon.
The joy we feel when we run the green lights, we can also feel
climate joy, as well as joy for just getting somewhere faster.
Obviously there's so much excitement and
there are lots of opportunities. I don't want to sort of bring a
downer on the conversation, but we do have to identify the risks
here.
What are the potential risks of AI?
How can you overcome them as leaders, or at least keep them
in check?
Indeed. With this paper, we certainly focused on the
opportunity space and these real, tangible, examples that
Rich and I have been giving.
But also, we thought it was really important to look at the
environmental impact side of AI.
We want to ensure that the opportunities we're unlocking
far outweigh the impact, but we wanted to look at the literature
and really share what we know today.
What we found is that in 2022, data centers globally, which
enable these AI solutions, made up about .1 to .2% of global
emissions.
And then from a water consumption basis in the US,
data centers are about .06% of water use.
So it's still a relatively small footprint.
But also we need to be thoughtful because AI is at an
inflection point and what we found is that there isn't really
good information yet on what we think that future footprint
might be.
So that's an area that requires more research, but also there
are tested best practices for designing data centers
efficiently, for how we train and optimize the machine
learning models, the hardware we use.
And then of course, crucially, carbon-free energy.
We have a goal at Google, that by 2030, we want to run all of
our offices and data centers on 24/7 carbon-free energy on all
the grids where we operate.
We're 64% of the way there.
So we need to be thoughtful about the impact as well.
I also want to highlight this because when people talk risk,
they tend to think about the downsides of AI that we need to
manage,
as Kate said. AI can make an enormous difference on
addressing risks.
I mean, we think about what's coming, not just for mitigation,
but adaptation.
There's a wonderful example from Google on identifying floods
before they happen to give people time to protect
themselves.
We've been working with cities around the world, like Lagos, on
how they use AI to optimize their investments to reduce the
risk of climate impact
down the road.
There'll be tons of opportunity to help farmers reduce risk in
their agriculture operations.
There are negative risks to manage, but there are enormous
risks from climate that AI will be critical to help address.
Thank you so much.
Thank you, Georgie.