0:02

Latitude Media, podcast at the

0:04

frontier of climate technology. So

0:08

last week we featured an interview with Brian Janis,

0:10

a former Microsoft VP who had a front row

0:12

seat to the AI energy boom and all the

0:14

grid constraints that are coming with it. It

0:17

was one of a few conversations we recorded from

0:19

Distribute Tech on the artificial intelligence theme. And

0:22

look, if you've been listening to this show

0:24

for a while, it's no secret that we

0:26

have AI on the brain at Latitude Media

0:29

because there's legitimately real commercial activity happening. In

0:31

fact, just this week we reported on NVIDIA

0:33

and Utilidata's partnership with Meter Maker Eclera to

0:35

roll out embedded AI to Smart Meters. Also

0:39

because the pathways to getting it embedded across

0:41

the power system are not simple. A

0:43

smart meter with 100 times the processing power

0:46

is a very cool technology, but utilities have

0:48

to make the case to pay for it.

0:51

And their track record for making good use

0:53

of the previous generation of smart meters is

0:55

spotty. There are a lot of

0:57

things that could hold back AI. The

0:59

biggie is such a common problem, it's almost

1:02

become cliche. It comes back to the way

1:04

data is managed or shared across a utility

1:06

or the lack of sharing. If

1:08

you talk to any vendor, this is one of

1:11

the biggest sources of frustration. And

1:13

so for a better understanding of how to change that,

1:15

I turned to a guy who spends a

1:17

lot of his time in the so-called data

1:19

cloud, Titian Palazzi, the head of power and

1:21

utilities at Snowflake. Any kind

1:24

of data naturally ends up in

1:26

different boxes, in different silos. And

1:28

when you then want to ask questions of the

1:31

data, it becomes really hard. You can't ask questions

1:33

across the enterprise. And Snowflake

1:35

is a data cloud platform. And

1:37

we sit on top of Amazon,

1:39

Microsoft, and Google, helping companies to

1:41

bring all their data together to

1:43

then create value out of it.

1:48

Snowflake is one of the hottest tech companies

1:50

that you may not have heard of. It

1:52

has a $53 billion market cap with thousands

1:54

of customers. Titian arrived there

1:56

after his company, Mist AI, was acquired by

1:59

Snowflake last year. year. In 2018, Titian

2:01

co-founded Myth with Peter Verhoeven, who built

2:03

some critical demand response applications for the

2:06

NEST thermostat. And Myth was focused on

2:08

time series forecasting for the grid. Over

2:10

the last 30 years, linear regression

2:13

or regression models were one of the

2:16

main ways in which forecasting was done.

2:18

So you would actually specify specific weights

2:20

for every factor that determined the forecast.

2:22

Let's say you want to anticipate the

2:25

output of a wind Under

2:27

linear regression, you apply weights to things

2:29

like wind speed, temperature, blade performance, and

2:31

then do some statistical analysis to make

2:34

a prediction. But time is

2:36

also an important factor. And

2:38

machine learning made it easier to

2:40

integrate temporal factors into forecasts. What

2:42

you saw was that a lot

2:44

of the progress which was made

2:46

in natural language processing, so the

2:48

ability for an AI algorithm to

2:50

understand words, applied to time series

2:53

data, because in the same way

2:55

that the words in a sentence,

2:57

their order matters, so

2:59

too, timestamps matter when you're doing a

3:01

forecast of something like energy demand. So

3:03

these were models in which you would not

3:06

specify any hard coded weights, but

3:08

you would basically tell the model, here are

3:10

all the things that will have an influence

3:12

on the thing we're trying to predict, such

3:14

as the output of that 200 megawatt wind

3:16

park come up with a prediction. What

3:18

they found was that these AI driven

3:20

time series models could improve accuracy by

3:23

30 to 50 percent. And that caught

3:25

the attention of Snowflake, where Titian and

3:27

his co-founder are now working on product

3:29

development and go-to-market strategies in energy. I

3:32

think that's a big shift that has taken

3:34

place in production by companies that are serving

3:37

real customers. AI is used much

3:39

more commonly, so a shift from hard

3:41

coded, pre-set up, fully visible

3:43

models into more machine learning

3:45

and AI. But

3:50

these models do have an Achilles heel.

3:52

You need access to lots of clean

3:54

data, And we still have a long

3:56

way to go to unlock their full potential. The

4:00

Industry: There is a lot of

4:02

time series data coming from the

4:04

grid or power generation or electric

4:06

cars or even from electricity. Nothing's

4:09

at the same time. Using a

4:11

hard for forecasting is quite challenging

4:13

because every time you need to

4:15

create a new prediction unique to

4:17

have the latest data. and so

4:19

from an engineering perspective it was

4:22

quite complicated to do. This

4:26

is the carbon copy and Steven lazy. And.

4:30

This week, a conversation with snowflakes titian

4:32

policy on busting data silos some early

4:34

wins for a I in the power

4:37

sector, and what size of the transition

4:39

would. I

4:44

want to take a brief moment to talk

4:46

about the new season of the Big Switch

4:48

podcast. We've been working on this for the

4:51

last six months for so excited to bring

4:53

it to you Are production team at Latitude

4:55

Media has been working for years, the Doctor

4:57

Melissa Law in the team at Columbia University

5:00

Center on Global Energy Policy or To Make

5:02

The Big Switch it's a narrative, show them

5:04

how to rebuild our energy systems and we're

5:06

back with a five part series exploring the

5:09

supply chains behind lithium ion batteries and a

5:11

very complicated economic and political forces that com

5:13

as batteries. Take Over the World. So

5:15

in this season we break batteries apart,

5:17

go to mining operations, manufacturing facilities, recycling

5:19

plants, and talk to some of the

5:22

most prominent experts about the pitfalls and

5:24

promise of are expanding battery based energy

5:26

economy and you'll hear the trailer a

5:28

bit later in the shell. So if

5:30

this sounds like something you wanna listen

5:32

to, find a big switch Anywhere You

5:34

get your podcasts. So

5:41

where are we today in the

5:43

advancement in adoption of a I

5:46

like what's your read on the

5:48

technological moment we're in, right? I.

5:50

Would say at there are. A. Few real

5:52

changes that are happening. One

5:55

of them is. That

5:57

we're seeing. A new type

5:59

of model Generative A I was really

6:01

perform extremely well. And what that means

6:04

is that some of the previous ways

6:06

in which the I am now we're

6:08

done my at disappear become less relevant.

6:11

So for example, it's quite possible. but

6:13

in the not too distant future you

6:15

might actually do forecasting by asking another

6:18

lamps can you predict the next. Few

6:20

weeks of data for as to date

6:22

you would use very different algorithms for

6:24

net. I think the other big thing

6:26

that's happening is that previously whenever you

6:29

wanted to use an I mobile you

6:31

have to write code or maybe you

6:33

could use a user interface but now

6:35

we're able to use the I would

6:37

just natural language and I think because

6:39

of death and broadening of the aperture

6:41

that ability for people who. In

6:43

plain English or any other language of

6:46

their choice and ask questions. I think

6:48

you're seeing an opening up of the

6:50

user base and that actually leads to

6:52

the models becoming better since his kind

6:54

of cycle and cyclical effect and I

6:56

think that's quite powerful and I think

6:58

it will accelerate development. So.

7:00

Now you're a snowflake after Miss Day

7:02

I was acquire last year out what

7:04

is snowflakes approach to building out that

7:06

the data cloud And how does energy

7:08

actually fit into that? Yes. So. In.

7:11

Companies of all types data

7:13

get silos as the company

7:15

cross. It's a bit like

7:17

entropy. It happens naturally unless

7:19

you try to prevent it's

7:22

and snowflakes. Aim and vision

7:24

is to mobilize the data

7:26

so that business leaders can

7:28

ask questions that span across

7:30

the business. for example which.

7:33

Parts. Of our grid should we do

7:35

maintenance first to minimise a chance of

7:37

there's an outage, or how much capital

7:39

should we allocate to make sure that

7:41

we meet the risk requirements of a

7:43

market like are called. The

7:45

sofa data club sits on top

7:47

of the three. Cloud. Hyper

7:49

Skaters so we don't own

7:51

our own data centers and

7:53

as companies. Used as data

7:56

Cloud and Store or that the

7:58

clouds that and unlocks workloads. Like.

8:00

A I am machine learning

8:02

or applications that run on

8:05

their data or visualizations would

8:07

be really. A lock

8:09

is the ability to combine I

8:11

T date also data around at

8:13

unit sales or supply chain data

8:15

with Ot data which is data

8:17

from devices. so data from wind

8:19

turbines are data from the grid.

8:22

And by combining those two things

8:24

that really unlocks a lot of

8:26

these valuable insights for executives. the

8:28

take action on. City. Utility

8:30

industry is one of the greatest data generators

8:32

have any industry. If I'm sitting inside a

8:34

toilet, Sam and engineer a program manager and

8:37

I need access to certain data stream to

8:39

make sense of it. Like one of the

8:41

current limitations today. it's what do I see

8:43

where I have access to one of the

8:45

limits and dance? What is A Data clouds

8:48

functionally deliver this difference. Yes, I

8:50

totally agree. There's. So much

8:52

daytime the power utility sector to give

8:54

two examples. One is when it comes

8:56

to have to see market every five

8:58

minutes, every values time stamps or then

9:00

when you look at things like the

9:02

grid or at power generation. You

9:05

might have at data for every second

9:07

or millisecond that something is an operation

9:09

so completely agreed. Huge volumes of data

9:11

and debts or so I think were

9:13

really sometimes a challenge. Nice. So. Let's

9:16

say that you are Rico Energy provider and you

9:18

want to forecast electricity demand for your customers for

9:21

the next few weeks to mixture of a do.

9:23

At. By adequate supply of for

9:25

your customers would I've seen is at

9:28

often it can take three months, six

9:30

months or nine months to get access

9:32

to all the right data such as

9:34

smart meters the for your customer and

9:37

then only a couple of weeks or

9:39

couple of months to actually build and

9:41

deploy a predictive model. So would you

9:43

see that utilities and other companies in

9:46

the power sector of money to spend

9:48

a much larger amount of time on

9:50

data collection flaming then on the actual

9:52

a I'm at. Work. So.

9:55

i think one of the real challenges

9:57

is to bring all the data into

9:59

one place establish a strong foundation

10:01

on top of which you can build

10:04

predictive models and other AI. Why

10:06

is this a problem for AI?

10:08

This lack of clean data or lack

10:10

of access to data? I mean, it's

10:13

very obvious on its face. In

10:15

order to build powerful models, you need a lot

10:17

of information. But what are some examples of how

10:20

this holds AI applications back? Well,

10:22

I think the real problem is that as

10:25

part of the energy transition, there's so many challenges

10:27

that we need to solve really fast. For

10:30

example, I work with a

10:32

number of distribution system

10:34

operators and transmission system operators

10:37

that need to deal with enormous

10:40

amounts of new

10:42

interconnection requests for solar, wind, and

10:44

battery storage. And the reality

10:46

is that many of them don't really

10:48

have visibility into their network in a

10:50

way that allows them to dynamically manage

10:53

both new resources being added and

10:55

then to ensure that the grid

10:57

operates in a reliable manner. And

10:59

then as a result, what they have to do

11:01

right now is they have to essentially limit

11:04

with all kinds of levels of safety,

11:07

how much can be added. So

11:09

in some ways, the energy transition

11:12

is being blocked by the

11:14

ability for energy companies to have access

11:16

to that data. I work with a

11:18

number of grid operators around the world.

11:21

Many of these companies are trying to

11:23

add more solar, wind, battery storage, and

11:25

other renewables to their grids,

11:27

as well as more flexible demand. And

11:30

one of the key challenges they face is that

11:32

they don't have good visibility into what's actually happening

11:34

on their grid. So that's just

11:37

one example of where access to data

11:39

is holding us back. And

11:41

these companies are typically engaged in

11:44

enormous programs with thousands of people just to make

11:46

sure that there are sensors on the grid and

11:48

that the data from those sensors is captured somewhere

11:50

in the cloud so that they can then run

11:53

analytics on top of that. I'm

12:01

Dr. Melissa Lott and I'm the host of

12:03

The Big Switch, a show about how to

12:06

rebuild our energy systems. Batteries

12:10

are finding their way into everything, from

12:12

cars and heavy equipment to the electric grid.

12:15

But scaling up production to meet

12:17

the demands of a net zero

12:19

economy is complicated and it's contentious.

12:22

If every country says we need to own the entire

12:24

supply chain because we want all of those economic benefits,

12:26

it's going to make the clean energy transition so much

12:28

harder. In a new five-part series,

12:31

we're digging into the global battery

12:33

supply chain, from mining to manufacturing.

12:35

And we're asking what gets mined,

12:37

traded and consumed on the road

12:39

to decarbonization. If we think

12:42

climate change is the existential threat facing our

12:44

planet, we have to be having a broad

12:46

conversation about where we want to get the

12:48

minerals that fill these products.

12:51

Listen to The Big Switch from Columbia

12:53

University's SIPA Center on Global Energy Policy,

12:55

available on February 28th, wherever you get

12:57

your podcasts. Well,

13:05

let's go to a couple of examples.

13:07

You've worked with a range of utilities

13:09

and retail energy providers and you said

13:11

grid operators on using AI for asset

13:13

management, for forecasting. Where

13:16

are the most compelling applications you're seeing today? When

13:19

I think of all these use

13:21

cases, I typically think of three areas. The

13:24

first is assets and operations. So

13:26

that has everything to do with

13:28

the physical infrastructure, grid, power generation,

13:30

et cetera. The next is finance

13:32

and markets. So that has everything

13:34

to do around power markets. And

13:37

the third is everything around customer, customer

13:39

360. So making sure that

13:41

every individual who is part of this energy

13:43

transition is treated in

13:46

the right way. So first, in assets and

13:48

operations, two examples come to mind. One

13:51

is we work with a retail energy

13:54

provider in the Midwest that serves more than a

13:56

million customers with electricity, gas, and energy. And the

13:58

second is the cost of the company. and

14:00

distributed energy resources. So they have an

14:02

offering, for example, for rooftop solar. And

14:05

I believe about 20,000 of their customers have

14:08

rooftop solar through the energy retailer.

14:11

Now, sometimes issues crop up

14:13

with rooftop solar. There might

14:15

be soiling on the panel, so the

14:17

panel might be dirty, or maybe the

14:19

wires weren't connected properly and the panel isn't

14:21

actually producing, or maybe a tree grew

14:23

and now there's more shading. And historically,

14:26

this company basically had to wait until a

14:28

customer called and said, I'm looking

14:30

at my bill and it doesn't look like the solar is working

14:33

as it used to be. Can you

14:35

come and investigate? And they built, based

14:37

on the smart meter data coming from

14:40

the solar panels, a predictive model that

14:42

basically says, here's how much

14:44

solar we would expect based

14:47

on the location

14:49

of the solar system, the tilt of the

14:51

roof, these kinds of things. Here's

14:53

what we're actually seeing. And if there is

14:55

a big discrepancy, then the customer service team

14:58

will get an alert and they will either

15:00

call the customer or send a crew to

15:02

go and check. So now often,

15:04

before the customer has even noticed that something

15:06

is going on with their bill, the

15:09

retail energy provider has already contacted them. Another

15:12

example in the same group is, we work

15:14

with one of the biggest renewable asset owners

15:16

in the world, LightSource, which is a part

15:18

of BP. They

15:21

operate more than five gigawatts of

15:23

solar globally. And a big

15:25

problem for utility-skilled solar

15:28

is hillstorms. So some of the

15:31

bulk of insurance claims for solar asset

15:33

owners actually hill damage to panels, just

15:36

breaking the glass. And

15:38

so they developed a predictive

15:40

system where they're incorporating hill

15:42

warnings from a variety of weather sources.

15:45

And when hill is expected to

15:48

come near a solar system, a

15:51

utility-skilled solar system, they will turn

15:54

the panels, because many panels can be turned

15:57

On a single-axis tracker, so that they

15:59

are... Position frantically so that the

16:01

hill doesn't damage to panels and thereby the

16:03

stay. Avoid Huge. Out the

16:05

just as well as financial cost

16:07

of replacing the panels. Another example

16:10

is more around the wholesale market

16:12

side so. A utility we

16:14

work with closely that has about

16:16

a million an electric and gas

16:18

customers. They collect the smart meter

16:21

data or am I data for

16:23

all their customers in snowflakes and

16:25

with that allows him to do

16:27

is that day Now. Create.

16:30

Electricity demand forecasts for the next

16:32

few weeks based on all the

16:34

data would is amazing is that

16:36

this data science team the typically

16:38

has very little interaction with the

16:40

business suddenly got a call from

16:42

the chief financial officer of the

16:45

company because apparently by improving their

16:47

predictions they save more than five

16:49

million dollars in a single month

16:51

by avoiding exposure to a bit

16:53

real time prospect. It's a

16:55

good less cause it illustrates just

16:57

how why the applications are and if

16:59

you think about where adoption is

17:01

in the power sector broadly enough we

17:04

think on on one end of the

17:06

spectrum where any enhancements sais and

17:08

then maybe at the other end of

17:10

the spectrum it's full automation, human out

17:13

of the loop. like where are

17:15

we in that Sais and and what

17:17

is the eventual say? Steve, These

17:19

things by the robots are not yet

17:21

taking over and I air. Utilities

17:25

are not known to be the

17:27

fastest adopters, so. Although. There

17:29

are studies that will say that a very

17:31

high percentage of utilities are. Already

17:34

or see the the importance of a

17:36

I I think the A Monkey Timothy

17:39

said have fully production I use Cases

17:41

for a I Live is relatively small,

17:43

and I think that that is the

17:46

reflection of a few things. First is,

17:48

as we said earlier, it's sometimes hard

17:50

to get all the data. Second is

17:53

that utilities don't always have the most

17:55

sophisticated difficulties. asserting might

17:57

also be culture what i see in

18:00

other industries is that what's really

18:02

powerful for an organization is to

18:05

have the culture and the technical systems

18:08

that allow staff to experiment and to

18:10

make small prototypes for things. Because

18:13

it's now so easy to put things together with

18:16

LLMs and existing tools, that

18:19

it is quite powerful if your teams can

18:21

explore and try new things and see what

18:23

sticks. I think that there's a long way

18:25

to go. And I think that what

18:28

we will see is that AI

18:31

will drive efficiency throughout the

18:33

process. It will be used in all kinds

18:35

of business divisions. It will be used

18:37

in the form of standalone

18:39

applications sold by software companies,

18:42

as well as homegrown solutions

18:44

built on their own cloud

18:46

platforms. And I'm excited to see what's

18:48

coming. So let's go deeper about what

18:50

needs to happen inside a utility

18:52

to grapple with that transition

18:54

and actually make appropriate investments.

18:58

Many utilities are partnering with outside

19:00

teams. Some are hiring their own

19:03

in-house data science teams, AI experts,

19:05

some of whom don't have experience

19:07

in the power sector. They may

19:11

be AI specific experts who are just now

19:13

figuring out how to apply it to the

19:15

power sector. And so you have a number

19:17

of different approaches inside of utilities.

19:19

What are the most common approaches now? And

19:22

are there any that you think are particularly

19:24

effective when it comes to team

19:26

building and then eventually like testing out

19:29

products and collaborating and building this sandbox

19:31

approach so that they can figure out

19:33

how to actually scale an application? Yes,

19:36

I think that there are a few key components.

19:39

One is, as you mentioned, unlocking

19:41

a data foundation in which all the data is there

19:43

so that you can actually run

19:46

models. The second

19:48

one is around experimentation.

19:51

So something specific

19:53

I've seen work very well is to

19:55

embed teams of data

19:58

scientists and software engineers with the... lines

20:00

of business. So to make sure that somebody

20:02

who is a data scientist is actually sitting with

20:04

a trader for a week, or sitting with the

20:07

grid management team for a week, or

20:09

sitting with line crew for a

20:11

week, and maybe even going with them, to get

20:13

a clearer sense of the issues

20:15

that they grapple with. And then part two of

20:17

that is setting

20:19

up those technical teams to experiment

20:22

quickly. So for example, creating sandbox

20:24

environments that do not have the

20:27

same requirements as the fully skilled

20:29

production models that power the utilities

20:31

and customers, in which

20:34

they can, within a matter of days,

20:36

deploy new applications that are initially

20:38

used by just a small subset of users

20:40

to see what works and what doesn't. The

20:43

final thing I'll say on accelerating AI adoption

20:45

is that I think

20:47

sharing data in the utility industry

20:51

is not yet delivering on its full

20:53

promise. So one of the amazing things

20:55

about some parts of utilities is that

20:57

they're actually not competitive. And

21:00

as an example, in California, the

21:02

smart meter data that the three

21:04

big investor-owned utilities have actually

21:07

should be shared with a variety

21:09

of different players. So for example,

21:11

the distributed energy companies like Sunrun

21:13

or Sunpower, they

21:16

can really benefit from having smart meter

21:18

data for their customers so that

21:20

they can make them better offerings. And

21:22

there are actually technical ways also through

21:24

Snowflake to make it very easy to

21:26

share that data from one organization to

21:29

the other while maintaining all

21:31

the right levels of security so that you

21:33

don't need to duplicate data

21:35

and send it around. What today

21:37

still sometimes happens through email or

21:40

FTP so that you

21:42

can actually solve these use cases that are

21:44

not within the bounds of a single company,

21:46

but really within the bounds of the industry.

21:49

We're here at Distribute Tech where

21:51

there are like 17,000 people

21:54

here and hundreds of companies

21:56

and if you walk around the floor,

21:58

you suddenly see... AI slaps

22:00

on everything and like I'm sure

22:03

there's you know, some companies it's more

22:05

of a cynical play But like in reality a lot of

22:07

these companies are Starting to build

22:09

out AI offerings as an extension of what

22:11

they were already doing I mean when you

22:13

look at the tech platforms of

22:15

some of these third-party Service

22:17

providers, how much are you

22:19

seeing AI getting integrated into

22:21

their products and teams? I

22:24

think broadly AI is being

22:26

infused in all kinds of solutions for all

22:28

business units and I think that they will

22:30

only accelerate You see it

22:32

show up in surprising ways. Maybe so for

22:36

example utilities that might

22:38

have handwritten maintenance reports

22:40

for things like substations or lines

22:42

can now actually digitize all the

22:45

data using the power of llms

22:48

and now they have a queryable

22:50

so questionable database that they

22:52

might ask Which

22:54

what are the most common fault mechanisms

22:56

that we see in our substations and

22:58

which substations should we do preventative maintenance

23:00

on next? so Even

23:03

areas that historically seemed oh hi is

23:05

never going to get there are now

23:07

increasingly accessible to AI if

23:10

you think about Let's say the next

23:12

decade of advancement in AI,

23:14

which is a very long time I mean

23:16

we see such radical improvements in the technology in a

23:19

12 month time frame So a decade is quite long

23:21

But if you think about how it will play out

23:23

in the power sector where you see more conservative

23:26

adoption What will

23:28

hold it back and what will accelerate it? Yeah,

23:31

well one thing that could hold it

23:33

back is certain regulatory changes if those occur at

23:36

the at the you know National super national

23:38

level on what we can do with AI

23:41

One thing specific to energy that will hold it

23:43

back is what we've seen is that in the

23:45

last? 15 20 years while the amount

23:50

of compute and data storage has increased

23:52

a lot in some cases more than

23:54

10x depending on the geography and time

23:56

frame The energy consumption has actually

23:58

barely budged. It has barely And

24:01

that's mostly thanks to the increasing efficiency, the energy

24:03

efficiency of data centers. And

24:05

it looks like that trend may end. So

24:08

it looks like if we look at the coming

24:10

years, actually,

24:13

data center energy efficiency might not increase that much, while

24:16

our use of data centers is rapidly

24:18

growing, in part because of the

24:21

generative AI hype. And so I think one

24:23

thing that could hold it back is, can

24:25

we build enough data centers with access to

24:27

data centers? With access to

24:30

power, ideally zero carbon power, that

24:32

would be one thing that could hold it back. In

24:35

terms of acceleration, I think a

24:37

lot of that is already happening. And I

24:40

certainly am frequently

24:42

amazed by how quickly new

24:45

models, new

24:47

types of models are coming out and what

24:49

it can be, whether it's now most recently,

24:52

OpenAI's Sora model, in which

24:54

you can generate one-minute videos

24:57

with just a few lines of text to

25:00

all the things that we will see next. So

25:02

if we look at what could hold back AI, it's

25:05

chip availability and power infrastructure. And

25:07

there are a lot of questions

25:09

about what the power demands

25:12

of data centers look like as AI use

25:15

expands. And so that

25:17

has a lot of people hand-wringing about the

25:19

energy intensity of the data

25:21

center industry and whether it will cause us to run

25:23

in place and whether we'll need a lot more clean

25:25

resources just to make up

25:28

for AI computational infrastructure. But

25:30

then there are all these other great benefits that AI

25:33

can unlock for the grid

25:35

and even unlock for the benefit of data centers.

25:38

What do you think about the net impact of

25:40

AI in the energy space, both

25:43

as a energy consumer,

25:46

potentially an exponential energy consumer, and

25:48

as an unlock for clean resources? So

25:51

I think we might see an

25:53

increase in energy consumption from AI. I've

25:55

seen some utilities in their integrated resource

25:58

plans Mention... Wrote

26:00

have to x five x, even ten

26:02

x in the next decade. Driven primarily

26:04

by it's more data center bill that

26:06

first, I'm not sure if that's what

26:09

will happen. History tells us

26:11

that we are not always very good

26:13

at forecasting what's happening in the long

26:15

term, so I think maybe will not

26:18

be that much increase energy consumption. But

26:20

then the other thing is, I think

26:22

Ai has so many applications to drive

26:25

energy efficiency and reduce emissions. Whether it's

26:27

about how we manage global supply chains

26:29

a hobby routes planes and ships, or

26:32

whether it is on all the opportunities

26:34

within the entered system to run things

26:36

more efficiently. So. I generally

26:39

think that. We should

26:41

proceed with their technology innovations. That

26:43

proceedings. And in fact remain of

26:45

really have the full control for not doing

26:47

so and that. Their advances

26:50

that we will get in

26:52

emission reduction in energy consumption

26:54

reduction will far outweigh the

26:56

increased energy consumption of data

26:58

centers running. At. To

27:01

some policy. Thank you So much strength! Even a

27:03

pleasure to be heard. That

27:11

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27:13

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