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Now on Instagram, WhatsApp, Facebook,

1:08

and Messenger. Hi,

1:24

everyone from New York Magazine and the Vox

1:26

Media Podcast Network. This is on with Kara

1:28

Swisher, and I'm Kara Swisher. My

1:30

guest today is Craig Peters, CEO

1:32

of Getty Images. You've

1:34

seen Getty's work, even if you don't know it.

1:36

Getty photographers are on the red carpets at the

1:38

Met Gala or at the White House or at

1:41

the Olympics for sure. Pictures that are then used

1:43

by media companies across the globe. Getty

1:45

also has a huge inventory of stock photos

1:47

and video. It has also one of the

1:50

largest archives of historical images in the world.

1:52

You'd think that would be enough, but

1:54

it's not these days. Craig Peters has been heading

1:56

Getty since 2019 and took the company back. public

2:00

in 2022. The same month as

2:02

Bad Luck would have it that

2:04

OpenAI launched Dali in beta, which

2:06

means questions about how to protect

2:08

copyrighted images got a whole lot

2:10

more urgent both for creators and

2:12

for the public. Questions that have

2:14

not gone away. Fittingly, our expert

2:16

question this week comes from former

2:18

Time Life and National Geographic photojournalist

2:20

Rick Smolin. Craig has been

2:22

an outspoken voice on improving standards and protecting

2:25

creators, but his stance on AI has also

2:27

shifted in the past few years. So

2:29

I'm looking forward to talking about how he

2:32

sees the business model for working collaboratively with

2:34

this new technology in the future. And

2:36

at the end of the day, this

2:38

is about transparency and trust and authenticity.

2:41

My biggest worry, of course, is if

2:43

this is a replay of what the

2:45

internet did to media the first go-round.

2:48

We'll talk about that and more, including

2:50

why they can't get fingers right. Those

2:52

fingers are creepy. Hi

3:02

Craig, great to see you again. Great

3:04

to see you as well. Thanks for having me.

3:07

Craig and I had breakfast in New York, I

3:09

don't know, a couple months ago, talking about these

3:11

issues. I'm trying to get my

3:13

arms around AI and all its aspects

3:15

and interviewed wide ranges of people

3:17

and obviously photos

3:19

are of course at the center of

3:21

this in many ways, even though the

3:24

focus is often on text, but photos

3:26

and video are critical. So you've been

3:28

at the forefront of conversations around generative

3:30

AI copyright issues, but Getty has also

3:32

been collaborating and utilizing AI like most

3:34

of media. I want to talk

3:36

about all that and where you see this heading for

3:38

creatives and for the public. But

3:40

first, I think most people know or have seen the

3:42

name Getty Images in a caption, but they don't have

3:44

a good understanding of the business as a whole. So

3:47

why don't you give us a very quick talk

3:50

about what it does. Alright, well first let

3:52

me start. We were founded by Mark Getty

3:55

of the Getty family and

3:57

Jonathan Klein back in 1991. So

4:00

the company's been around for almost 30 years. We

4:03

are a business to business content platform.

4:07

We provide editorial content, so

4:09

coverage of news, sports, entertainment,

4:12

as well as a really deep

4:14

archive, and creative content. So

4:17

this would be materials that would be used

4:19

to promote your brand, your company, your product

4:21

offerings, or your services. We

4:24

provide that in stills, so photography,

4:26

as well as video, as you

4:28

highlighted. We provide that in what

4:30

we call pre-shot, which means it's available

4:33

for download immediately. You can search our platform,

4:36

find content, and then download it and use

4:38

it. In

4:40

some cases, we provide custom creation,

4:43

right? So we might work on an assignment

4:45

to cover a certain event. Just

4:47

for full disclosure, you've done Fox Media events. Correct. And

4:51

you then own the pictures, but Fox gets

4:54

use of them, correct? Correct. We

4:56

represent over 600,000 individual photographers and over

4:59

300 partners. These are partners like

5:01

the BBC or NBC News or

5:03

Bloomberg or Disney or AFP. And

5:07

what we try to do is allow our customers

5:09

really, we try to deliver value

5:11

across four elements. So

5:14

we have editorial content, so when you

5:16

think about creating more efficiently, we'll

5:18

have a team of over 100 individuals in Paris

5:20

for the Olympics. And

5:23

we will allow the entities that are covering

5:25

the Olympics to have

5:27

access to high-quality visuals in real

5:29

time on- Big

5:31

events. Big events or stuff you're contracted for. Like

5:34

you did my last code conference, for example.

5:36

Correct. But then there's also,

5:39

again, what we call creative, which is also

5:41

referred to as stock imagery. It's

5:43

intended to be used for commercial

5:45

purposes, to promote a company's brand,

5:47

products, or services. Right. And

5:51

you've been a company since 2019. You took

5:53

it public through a SPAC in July

5:55

2022, just about two years ago. Correct. Coincidentally,

5:58

it was the same month OpenAI made its image available

6:00

in beta form. What

6:03

did you think at the time? Did you think

6:05

end times or did it not register with you

6:08

all? No, I think, I mean, first off, generative

6:10

AI is not something that is entirely

6:13

new. Sure. It's something that we

6:15

had been tracking for probably seven years prior

6:17

to the launch of DALI. We

6:19

had first had discussions with Nvidia who we now

6:22

have a partnership, which we could talk more about.

6:24

Yeah, I will. But

6:27

we knew those models

6:29

were in development. We

6:31

knew the capabilities were

6:33

progressing. So

6:35

it didn't surprise us in

6:38

any way, shape or form. And

6:41

fundamentally, our business is again, providing

6:43

a level of engagement, whether

6:45

that's, again, through a media outlet

6:48

or through your corporate

6:50

website or your sales and marketing collateral

6:52

in a way that relies on authenticity

6:54

that relies on creativity, it lies on

6:57

core concepts being conveyed. Those are difficult

6:59

to do. Yeah, so here's this thing

7:01

coming, you see it. Obviously it's been

7:03

around and they've been generating

7:05

fake images for a long, long time, absolutely.

7:08

But did you think, well, look,

7:10

there's six fingers, it's kind of weird looking.

7:13

They always have that sort of like a Will

7:15

Smith picture of him eating spaghetti and it doesn't

7:17

look great. Did you think this

7:19

could be a real problem?

7:23

No, I don't think we saw it necessarily as

7:25

a problem. I think we saw some of the

7:27

behaviors around it as a problem. So we

7:30

knew that hair lines and fingers and

7:32

eyes and

7:35

things like that were gonna resolve over time. What

7:38

we saw though was ultimately these platforms

7:43

that were launching these generative models

7:45

were scraping the internet.

7:47

Correct. And taking third party intellectual

7:49

property in order to create

7:51

these services. And fundamentally, we

7:54

believe that creates some real

7:56

problems. Yeah, that's top theft. They've

7:58

done it for many years. I would say

8:00

that there's kind of four things that go

8:02

into generative AI. So GPUs,

8:05

processing and power, and computer

8:07

scientists. And those three things,

8:09

these companies are spending billions and billions

8:12

of dollars around, right? But

8:14

the fourth item is something that they call

8:16

data, I happen to call content. And

8:19

it is something that they are taking, and they

8:22

are scraping and

8:24

taking from across the internet. And

8:26

that fundamentally, to me, raised some

8:28

questions about not

8:31

only should they be doing that,

8:33

is that allowed under the law? But

8:37

it also creates real IP risk for

8:39

end users of these platforms, if

8:42

they're using them commercially. Yes, but they're hoping

8:44

to get away with it just so you wear. But

8:46

you push back pretty hard against AI initially,

8:48

you ban the upload and sale of AI-generated

8:50

images in your database. But it seems you've

8:52

done more than you seem

8:54

to be embracing it more. Talk about

8:57

how it's evolved in your thinking.

9:00

Because at its base, they like to take things. They've been doing

9:02

it for a long time. I

9:05

had a lot of scenes in my book where

9:07

Google was just taking things and then reformulating and

9:09

spinning the book fight that they had. They

9:11

use the expression fair use quite a bit. Did your thoughts

9:14

change on it? Or do you think, well, this is going

9:16

to happen again, and I have to embrace it? I

9:18

think we thought we really

9:20

took a two-pronged strategy. And

9:22

I don't think it's changed or it's

9:24

evolved. I think it's been pretty constant.

9:27

But we are not luddites. We believe

9:29

in technology can be beneficial to society.

9:31

We believe it needs to have some

9:33

guardrails and be applied appropriately. But we

9:35

wanted to embrace this technology to enhance

9:37

what our customers could do. We

9:42

also wanted to make sure that we

9:44

protected the rights of our

9:47

600,000 photographers and 300

9:50

partners and of our own and try

9:53

to get this to be one

9:55

where it was done more

9:57

broadly beneficial, not just to the...

10:00

the model providers, but to the industries as

10:02

a whole. So

10:05

we pursued a strategy that's really twofold.

10:07

So one was we

10:10

did not allow content into our database that

10:12

was generative AI. We did that for two

10:15

reasons. One was that

10:17

our customers are really looking for

10:19

authentic imagery, high-quality imagery, that imagery

10:22

that can really relate to an

10:24

end audience. We think

10:26

that that is still best done

10:28

through professionals using real

10:30

models, etc. Accepting

10:33

AI imagery that's been created from

10:35

engines that are scraping the Internet

10:37

has potential risk. It has potential

10:39

risk from copyright claims. Correct. It

10:42

also has potential risk from people

10:44

that have had their image scraped

10:46

off of the Internet generally, and

10:49

it can replicate those types of things. So we didn't

10:51

want to bring that content onto our platform and then

10:54

ultimately give it to our customers and have them bear

10:56

that risk. Because one of the things that we try

10:58

to do for our customers is we try to remove

11:00

that risk. Right. Now, I'll get to your end-of-the- end,

11:25

that's creative content, not editorials content. So to

11:28

your point, we haven't trained it to know

11:30

who Taylor Swift is, or Scarlett Johansson is,

11:33

or President Biden. That

11:36

creative content is permissioned, it's released.

11:38

So we have model

11:40

releases from the people portrayed

11:42

within that imagery. We have property releases,

11:45

etc. So

11:47

it is trained off of a

11:49

universal content where we have

11:51

clear rights to it. There's a

11:53

share of revenues back to the content

11:55

provider. So as we sell that service,

11:58

we actually give a revenue share back. to

12:00

the content providers that its content was used

12:02

to train. As you said,

12:04

it can't produce deep fakes. It cannot

12:06

produce third party intellectual property. So if

12:08

you search sneakers or type a generative

12:10

prompt of sneakers, it

12:13

will produce sneakers, but it's not going to

12:15

produce Nikes. If you type in laptop, it's

12:17

not going to give you an Apple iMac.

12:21

It is high quality. So

12:23

you can produce models that

12:26

are of very high quality in terms of the

12:28

outputs that it produces. You don't

12:30

have to scrape the entirety of the internet.

12:33

This proved that. And it

12:35

is fully indemnified because, again, we know the

12:37

source of what it was trained on. We

12:40

know the outputs that it can produce,

12:42

and they're controlled. And it

12:44

ultimately gives a tool to corporations

12:48

that they can use within their business day

12:50

to day without taking on intellectual

12:52

property risk. So the idea is to

12:54

make a better version of this, but

12:57

there's so many AI-generating image products out

12:59

there already on the market. Dolly, Mid-Journey,

13:01

OpenAI is currently testing Sora. As

13:04

you said, these people have billions of dollars

13:06

put to task here.

13:09

So talk about your sort of unique

13:11

selling point. Obviously, cleaner database, less biased

13:14

than internet-scraped versions. And you also know

13:16

these images are backed by our uncapped

13:18

indemnification. So explain what you mean by

13:20

that. But let's start with you've got

13:23

a lot of competitors with tons of

13:25

money and have a little less care

13:27

than you do about the imagery. This

13:31

model is intended to be utilized

13:33

by businesses that

13:35

understand that intellectual property can carry real

13:37

risks as they use it in their

13:39

business. Those risks can come

13:42

with large penalties. And what we are

13:44

doing is providing a solution that allows

13:46

them to embrace generative AI, but avoid

13:48

those risks and still produce

13:50

a high-quality output. Other tools that are

13:52

out in the marketplace do

13:54

bear risk because they have scraped

13:57

the internet. You can produce.

14:00

third-party intellectual property with them, you can

14:02

produce a deep-fakes with them. That

14:07

ultimately we believe is a constraint

14:09

on the broad-based commercial adoption of

14:11

generative AI. This has been their

14:13

modus operandi for years and years

14:16

to do this to scrape and

14:18

then later clean themselves

14:20

up. I think it certainly

14:22

break things and move fast. Ultimately,

14:27

we think that this technology

14:29

should not just be thrust into

14:31

the world and

14:33

damn the consequences. We believe there should be

14:35

some thought, we believe there should be some

14:37

level of regulation. We should believe that there

14:39

should be clarity on whether it is in

14:41

fact fair use. One of the companies that

14:44

I'm not sure if you mentioned, which

14:48

Stability AI that launched Stable Diffusion. I

14:50

will. It's a- We knew that they

14:52

had scraped our content from across the

14:54

Internet. And used

14:56

it to train their model. We didn't believe that

14:58

that was covered by fair use in the US

15:00

or fair dealing in the UK. Right.

15:03

So we've litigated on that

15:05

point and it's progressing through

15:07

the courts. I want to talk

15:09

about that in a second. I want to put

15:11

a pin in that. But you talk about what

15:13

uncapped indemnification means. It means that

15:16

Getty Images stands behind

15:19

the content that the generative model produces,

15:21

and that you're safe to use it.

15:23

And if there are any issues that

15:25

occur downstream, which there shouldn't

15:27

be, we will still stand behind it.

15:30

We will protect our clients. So AI gets

15:33

stuff wrong all the time though. Are you

15:35

worried about promising indemnification? I guess that's what

15:37

you're promising now. But are you worried about

15:39

the cost? If you do have copyright and

15:41

for instance, using this technology? No, because again,

15:43

we know what it was trained on. We

15:46

know how it was trained. And

15:49

we trained it in a way that it

15:51

couldn't produce those. I think, again, when you

15:53

just scrape the internet and you take in

15:56

imagery of children, when

15:59

you take in... imagery of famous

16:01

people or brands or third-party intellectual

16:04

property like Disney, Star Wars, or

16:06

the Marvel franchise, you can

16:08

produce infringing outputs. You can produce things

16:11

that are gonna run afoul of

16:13

current laws and potential future laws. This

16:16

was built purposefully from

16:19

the ground up to avoid the social

16:21

issues that are being

16:24

questioned as a result of this technology, but

16:26

also to avoid the commercial issues. Right, right,

16:28

which was these companies are gonna have the

16:31

same thing they did with YouTube and they

16:34

eventually settled it out. So I want to

16:36

get to the lawsuits in a second, but

16:38

the Pick Arts Partnership is different. I mean,

16:40

it's a licensing deal. They are using your

16:42

image database to train their AI, and these

16:45

are these licensing deals. You're not the only

16:47

one. AP is the license deals with OpenAI.

16:49

There's all kinds of people who are striking

16:51

deals like OpenAI

16:53

and Meta did one with your one of

16:55

your competitors Shutterstock. Let's talk

16:57

about those first. Well, I think, you

16:59

know, I think ultimately we believe that

17:01

there are solutions. These services

17:04

can be built that ultimately

17:06

do appropriately compensate for the

17:09

works that they're utilizing as

17:12

a service. And we

17:15

think, you know, the models that we've

17:17

entered into that you'd mentioned, so Nvidia

17:19

and Pixar, those are not payments to

17:22

us, a one-time payment, which is most

17:24

of these deals or a recurring payment.

17:27

They're actually, this is a rev-share deal,

17:30

so as those models produce revenues, they

17:32

will generate revenues that could flip back

17:34

to the content creators. And you

17:37

know, I think some of the deals that are getting

17:39

done today are trying to, in

17:41

essence, whitewash a little bit

17:43

the scraping that has historically been done

17:45

and show that they are willing to

17:48

work with providers. In some cases, it's

17:50

providing access to more current information that's

17:52

difficult to get in real time from

17:54

scraping. Sure. But ultimately it's a small

17:57

drop in the overall bucket. about

18:00

the lawsuit against stability AI, where

18:03

does it stand? And just again,

18:05

you're claiming the company unlawfully copied

18:07

and processed millions of Getty copyright

18:10

protected images, which are

18:12

very easy to track. I mean, more than text,

18:14

much easier. So tell us where that stands

18:17

now, because again, that's an expensive prospect for

18:19

a small company. Well, it's something

18:21

that we invest in. We think

18:23

it's important. We think

18:25

we need to get settled view

18:27

on whether this is in fact fair

18:29

use or not. We don't buy into

18:31

maybe what some of the providers are

18:33

stating is settled fact, that it is

18:35

in fact fair use. We believe that

18:37

that is up for debate. I

18:40

mean, we believe within the realm of imagery, we

18:42

think there's a strong case to be made that

18:44

it is not fair use. And we think there

18:46

are some precedents out there, most

18:49

notably in the US, Warhol versus

18:52

Goldsmith, which was settled at the court,

18:55

which highlights some case law

18:57

that would say that this is going to

18:59

be questionable whether it would qualify for fair

19:01

use. So we launched

19:04

that litigation. It is

19:06

moving forward a little

19:08

bit more at pace in the UK.

19:11

So it is proceeding to trial,

19:13

I expect will likely be a

19:15

trial end of this year, early

19:18

next. In the US, it's moving a little

19:20

more slowly as things take time

19:22

to move through the court system. And I don't have a magic wand

19:25

to speed that up. Talk about the

19:27

calculation of suing versus settling

19:29

essentially. And the costs, because here you are,

19:31

someone grabs something of yours, you have to

19:33

get hit on the head, you have to

19:35

take them to court for hitting you on

19:37

the head. But still, you've been hit on

19:39

the head, correct? Well, I think,

19:41

again, it's in a world where everyone's taking our imagery anyways.

19:43

Our imagery is all over

19:45

the internet. As you mentioned, it's on the

19:47

verge. It's on the media

19:52

platforms that you are visiting.

19:56

The one mistake I made in copyrighted imagery, I

19:58

paid a lot of money. It

20:00

was a lot of money and I never did

20:02

it again. I'll tell you that. So our imagery

20:04

is all over the internet and it's being scraped

20:06

and it's being taken and it's being used to

20:08

train these models. We

20:11

wanna get clarity if in fact

20:13

that is something that they have rights to do.

20:16

I believe it's good for the industry

20:18

overall. When I say the industry overall,

20:20

I'm not just talking technology, I'm not

20:22

just talking content owners, but to have

20:24

clear rules to the road. Is

20:27

this or is this not fair use? And then

20:29

you know what you need to do in

20:32

order to work within that settled law. And that's

20:34

what we're trying to get to. But let's get

20:36

back to the issue of content creation that you

20:38

also brought up. You aren't the only one that's

20:40

worried about losing money. Every week we get a

20:42

question from an outside expert. This week our question

20:44

comes from photojournalist, Rick Smolin, the creator of the

20:46

best selling day in the life book series. You've

20:49

seen the movie Tracks. He's a National Geographic

20:52

photographer portrayed by Adam Driver. Let's have a

20:54

listen to his question. Like many

20:56

of my peers, my photographs are sold

20:58

around the globe by Getty Images. And

21:01

we've always considered our relationship with

21:03

Getty as symbiotic. We

21:05

photographers risk life and limb to

21:08

provide Getty with our images and in

21:10

return Getty markets our photographs to buyers.

21:12

But the advent of AI, it's sort of starting

21:15

to feel a little bit like a marriage where

21:17

one partner in the relationship is

21:19

caught having an affair with a much

21:21

younger, more attractive person while assuring

21:24

their spouse, this is nothing more than

21:26

a dalliance. We

21:28

assume that our images are being sampled

21:30

to generate the synthetic images that Getty's

21:33

now selling. And many of us

21:35

are very concerned that our work has become a commodity

21:38

like coal being shoveled into

21:40

Getty's LLM engine. And

21:43

also sort of like the cheated upon older

21:45

spouse, many of us believe it's

21:47

only a matter of time before we're cast

21:49

aside for this much younger and much sexier

21:51

young thing called AI. So

21:53

my question to Craig Peters is whether

21:55

this marriage is over but you

21:57

just haven't admitted to us yet. told

22:00

us, or if it's not

22:02

over, how does Getty Images

22:04

envision its relationship with actual

22:06

photographers versus prompt jockeys? And

22:09

how is Getty going to compensate us,

22:11

your long-term partners, fairly for our images

22:13

in the future? Thanks. Okay,

22:16

that's a really good question. I hear it a

22:19

lot from photographers. They don't know who to trust.

22:21

So how are you compensating the creators whose work

22:23

is being fed into this tool, talked about how

22:25

it works, and can people opt out if they

22:28

don't want AI trained on their

22:30

work? Right. Well, first off, let me start

22:33

with, I think we don't train off

22:35

of any of our editorial content. And

22:37

the coverage that he is talking about

22:39

that he does, and that we are

22:41

fortunate enough to represent, would

22:44

be considered editorial coverage. We

22:47

believe that the job that he does is

22:49

incredibly important to the world. The

22:53

images that they produce, the

22:55

events that they cover, the topics that

22:57

they cover are incredibly important. Right behind my desk,

22:59

Kara, there is an image that

23:02

was taken in Afghanistan and it was taken

23:04

by one of our photographers, staff photographers, a

23:06

guy named Chris Hundress, who happened to lose

23:08

his life in

23:10

the pursuit of covering conflict around

23:12

the world. And

23:15

that's incredibly important. And I have it there because

23:17

it reminds me that

23:19

we have a very

23:21

important mission and that the individuals that

23:23

are producing this image are incredibly important

23:25

and they are taking very real risks.

23:27

And we value that and we never

23:29

want to see anything

23:32

that we do undermine that or misrepresent

23:34

it. And we think it's important to

23:36

the world going forward and we think

23:38

it's very important that persists.

23:41

So we do not train off

23:43

of editorial content. This model was

23:45

not trained off of editorial content.

23:47

And we believe that that type

23:49

of work has a level

23:52

of importance today and we'll have a level

23:54

of importance going forward in the future. And

23:56

we think that importance only increases as we

23:58

see more and more deep-fake

24:00

content produced by these generative

24:02

models that are trying

24:04

to undermine true knowledge and true truth.

24:06

So the compensation system stays the same?

24:09

So the compensation, so again, we have

24:11

creative imagery, this would be, you could

24:13

also refer to it as stock imagery,

24:16

where we have permissions from the

24:18

individuals that are contained within those

24:20

images. We have the right to

24:22

represent that copy of my, broadly,

24:24

that is training these models. These

24:27

models, when they produce a dollar's worth of

24:30

revenue, we give a share of

24:32

that revenue back to the creators whose content

24:34

was used to train that model. That

24:37

is the same share that they would get if

24:40

they licensed an image, so

24:42

one of their images off of our platform. So

24:45

on average, Kara, we give

24:48

royalties out around 28% of our revenue. We

24:52

invest a lot in our platform and sales and

24:54

marketing and content creation,

24:56

etc. But on average, we're

24:58

sending 28 cents back for every image that

25:01

we license. And you can think the exact

25:03

same thing will happen when

25:06

we license a

25:08

package of, you know, someone subscribes to

25:10

our generative service. So who owns the

25:12

images created by these AI tools? And

25:14

it sounds like in the weeds. That's

25:17

a very good question. It's, again, an

25:19

unsettled dog because we're in new territory.

25:21

So in the US today, you cannot

25:24

copyright a generative output. I

25:27

think over time, that might evolve to where

25:29

it depends on the level of human input

25:31

and involvement in that process. But

25:33

right now is not one that you can

25:35

copyright this image. But what we take a

25:37

stance is for our customers that are using

25:40

our generative tool is since

25:42

they gave us the generative prompt, they

25:44

are part of the creation. And therefore,

25:46

we don't put that imagery back into

25:49

our libraries to resell. It

25:52

is essentially a piece of content that

25:54

is, quote unquote, owned by the customer

25:56

that produced it using the generative model.

26:00

copyright is one that we can't convey,

26:02

that has to be done by the

26:04

US Copyright Office. But we essentially take

26:06

the position that the individuals

26:09

or the businesses that are using that service

26:11

own the output to that service. They own

26:14

the output, and it would be a similar

26:16

thing. But right now, whether they can be

26:18

copyrighted is unclear, even if so, is the

26:20

prompt owner the copywriter? Correct. And

26:22

I think, again, I think that will evolve over time to

26:25

one where I think the level

26:27

of human input will have

26:29

a bearing on whether that

26:32

is, in fact, copyrightable. It will all be. It's

26:34

a question of how much you get paid along the line. So

26:37

you've pushed back against the idea that these licensing

26:39

agreements are Faustian, though, meaning basically you're making a

26:41

deal with the devil. Shutterstock's deal

26:43

with OpenAI is for six years, while

26:45

Red Teamers at OpenAI are testing Sora.

26:50

Are you worried that you're all doing what we

26:52

in the media did with, say, the Facebooks of

26:54

the world, the Googles of the world, who now

26:57

sucked up all the advertising money and everything

26:59

else? What is your biggest worry when

27:01

you're both doing these deals

27:04

and suing, just waiting for clarity,

27:06

I assume, from courts and copyright

27:08

officials? I believe that many

27:11

of the deals that are happening today are

27:13

very Faustian and go back to

27:16

ultimately some of the mistakes that were made early

27:19

on in digital media and or

27:22

in social media, making trades

27:24

for things that weren't in the long term health

27:26

of the industry. What we are trying to do

27:29

very clearly in the partnerships that we are striking and

27:31

in the deals that we are striking and in our

27:33

approach is we are trying

27:35

to build a structure where the

27:41

creators are rewarded for

27:43

the work that was included, and fairly

27:45

so. I gave you

27:48

my four components of generative

27:50

AI, GPUs, processing, computer scientists,

27:52

and content. That fourth

27:54

right now is getting an extremely

27:57

small stake. Some companies are doing

28:00

no deals, so like the mid

28:02

journeys and such, and they

28:04

are providing no benefit back to it.

28:06

We wanna see a world where content

28:09

owners, content creators

28:12

share in ultimately economics

28:15

of these. And I

28:17

think if you equate it again back to a

28:19

Spotify, I mean, the labels

28:21

and Spotify can argue over whether

28:23

the stake of that is fair

28:25

and that's a commercial negotiation, but

28:27

ultimately it has created

28:29

a meaningful revenue stream back

28:32

to artists that support artists in the

28:34

creation. And that's what we'd

28:36

like to see flow out of generative

28:39

AI. That's the world that we believe

28:41

is right. Ultimately the creative industries represent

28:44

10% of GDP, more

28:48

than 10% of GDP in the US

28:50

and the UK and many developed economies

28:52

around the globe. And we think the

28:56

economic impacts of undermining that contribution

28:59

to GDP can't be net beneficial

29:01

to society from

29:03

an economic standpoint, if you ultimately just

29:05

allow them to be sucked dry. I

29:08

use the example, everybody's watched

29:10

the movies, The Matrix and

29:12

ultimately what were humans in The Matrix? The humans

29:15

in The Matrix were the batteries. They

29:17

were used to create the power to fuel

29:19

the AI. Well, right now, I feel like

29:21

the creatives are already being consumed as batteries.

29:24

And the media is already being a battery. We are already

29:27

not being valued. We're

29:29

basically just being used in

29:32

order to create one of

29:34

the core pillars that is necessary to create

29:36

generative AI. It's

29:38

just being stripped and taken. And

29:41

I don't wanna see a world where there

29:43

aren't more creatives. I don't wanna see a world where

29:46

creatives aren't compensated for the work that they do. And

29:49

I don't wanna see a world that doesn't have creativity in

29:51

it. We'll

29:54

be back in a minute. Support

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34:03

So I want to zoom in a bit

34:05

to talk more broadly about the issue of

34:07

copyright intellectual property and standards. You've called for

34:09

industry standards around AI. What

34:11

do you think those standards should include?

34:13

Watermarking, copyright protections. Obviously the more confusing

34:15

it is, the better it is for

34:17

these tech companies who, this is me

34:19

saying this, could give a fuck about

34:21

creatives. They do not care.

34:23

They do not care. They never have. Well,

34:26

let me start. I mean, there are four pillars that we

34:28

think are important with respect to

34:30

visual generative AI. One of

34:33

those is transparency of training data so that

34:35

you know what's being trained and

34:37

where they're training. And that's not only important

34:39

to companies like Getty Images and creatives that

34:41

are creating works around the globe, but that's

34:43

important as a parent or someone

34:46

with children. If

34:48

I post this image to this social

34:50

media platform, is it being used to

34:52

train outputs in these models? So

34:55

that's one, permission of copyrighted work. So if

34:57

you are gonna train on copyrighted data, you

34:59

need to have permission rights in

35:02

order to do so. We want

35:04

these models to identify

35:06

their outputs in a way that

35:08

is persistent. Now that's likely to

35:10

involve watermarking. It might involve some

35:13

other standards like hashing to the

35:15

cloud, but the

35:17

technology is still in development, but we want

35:19

that to be at the model level.

35:22

So the model actually does the output and

35:24

it's persistent. And then

35:26

we want to make sure that model

35:28

providers are accountable for

35:31

the models they put out into the world. In essence,

35:33

there's no section 230, which kind

35:35

of protected platforms back in

35:37

the original Digital

35:39

Millennium Copyright Act, which

35:41

basically gave them indemnity

35:44

for many claims for content that was posted

35:46

on their platform. We wanna see a world

35:49

where model providers have some level of accountability

35:51

aren't given a government exemption in

35:54

indemnity. We wanna see people

35:56

be accountable. Who would should set up

35:58

the standards in your opinion, Paul, too? industry leaders,

36:00

there's business advocacy groups like C2PA, which

36:03

is the Coalition for Content, Provenance, and

36:05

Authenticity, which includes Google and OpenAI, and

36:07

your competitors, Adobe and Shutterstock. And who

36:10

should be responsible for enforcing them? Is

36:12

it a combination of courts and regulations?

36:14

I'll come back to C2PA, but I

36:17

think it's going to be a combination

36:19

of regulators, of legislators, and

36:22

industry, which is typically how all standards

36:24

evolve over time. I think like happened

36:26

in the EU AI Act, there's a

36:28

general requirement, but the definition of how

36:31

that requirement actually manifests itself is still

36:33

yet to be put

36:36

forward. And that's going to take collaboration

36:38

from the technology industries and

36:40

from creative industries in order

36:42

to get to something that works.

36:45

I think the C2PA, to be very

36:47

specific, it

36:50

is a foundation, but there's still some

36:52

flaws. It is relying upon the end

36:54

user of these platforms to identify the

36:56

generative output versus the model itself. That's

36:58

what happened with YouTube initially, as you

37:01

recall. Exactly. So if you say, if

37:03

I'm going to use a generative model and I'm a bad actor,

37:06

I can say it's authentic and

37:08

label it as such. Well, that's a

37:10

point of failure in the C2PA model

37:12

as it exists today. It's already been

37:14

exploited and we want to see that

37:16

close again. So that's why we want to see these models provide

37:19

the output at the model

37:21

level versus at the user level. It

37:24

also is one that they're trying to

37:26

shift the cost of

37:28

implementing C2PA to the individual versus

37:31

the model provider. There are

37:33

going to be costs if you're producing outputs and

37:36

then you have to store the output

37:38

in the cloud, the original and kind of hash it

37:40

and so that can be referenced and referenced back to,

37:43

I don't think that's a reality where individuals

37:46

are going to be making that investment. So you're

37:48

bearing all the costs. I believe the model providers

37:50

should bear that cost. So do I. We

37:53

are a model provider in partnership with

37:55

Nvidia and Pixar and others and

37:58

I believe that we should create standards.

38:00

standards that are immutable that store with

38:02

it. And I think C2PA is a

38:04

foundation, but I think it's

38:07

one that we need to kind of continue

38:09

to evolve it in partnership with the technology

38:12

industry to get it to something that truly

38:14

meets the challenges versus is a nice kind

38:16

of cover when you get

38:18

called to Congress or Parliament

38:21

or into the EU. Sure.

38:24

That you can just kind of throw it out is that we've got

38:26

a solution. We really want to prevent deep

38:28

fakes. We really want people to

38:31

have fluency in terms of the imagery

38:33

that they're looking at. And

38:35

we think C2PA can evolve to that and

38:37

we're going to work hard in

38:39

partnership with the members of C2PA to try to get

38:42

it there. And ultimately what I

38:44

don't want to see though, you go

38:46

back to some of the original mistakes of digital media

38:48

and such, is I don't want to see it be

38:50

a big cost shift from

38:52

technology industries into the media

38:54

industry where what I

38:56

mean by that is I'm hearing some,

38:58

well, we're never going to be able

39:01

to identify generative content. No, it's very

39:03

hard. Yeah, it's very hard. It's a

39:05

hard problem. That's their excuse. We can

39:07

invent these tools, but we can't invent

39:09

a way to identify. So what we

39:11

want the media industry to do is

39:13

we want them to identify all of

39:15

their content is authentic. So they have

39:17

to buy all new cameras. They have

39:19

to create entirely new workflows. They have

39:21

to fundamentally change how

39:23

they bring content onto their websites.

39:27

That investment, we want the

39:29

media to make. And I don't think that's the

39:31

right solution. I'm not saying

39:33

that Getty Images won't invest in

39:36

technologies in order to better

39:38

identify track and identify provenance

39:41

of content. But I don't think kind

39:43

of saying, well, the solution for the tools that

39:46

were created that create all this generative content and

39:48

are producing the problem that D fakes, the

39:51

creators of those tools don't have any accountability

39:53

for creating solutions to identify. Absolutely. I think

39:55

we have to have a balanced solution. Absolutely.

39:58

It does play into the hands. that actor

40:00

is for sure. That's right. There's a really

40:02

interesting book that I read a

40:04

couple of years back, Power in Progress. And

40:07

it's an interesting study where I think we've

40:09

always been adapted. I'm a pro technologist. Me

40:11

too. I started my career out in the Bay Area. But

40:15

we always assume that just technology for technology's

40:17

sake will be beneficial to society. That's what

40:20

we've been fed and that's

40:22

what we have been taught. And

40:24

their study really looks at

40:26

technology over a thousand years.

40:28

And whether it is innately

40:30

beneficial to society or whether

40:32

society needs to put some

40:35

level of rules around

40:38

it in order to make it

40:40

net beneficial. And you go

40:42

back to industrial revolution and basically

40:45

the first 100 years of the industrial

40:47

revolution were not beneficial to society

40:50

as a whole. They benefited a

40:52

small number of individuals but largely

40:54

gave society nothing more than disease.

41:00

And it took things like regulations

41:02

on limiting work week, regulations

41:06

on child labor. It

41:08

took certain organizations like labor

41:10

unions, et cetera, to

41:13

ultimately get that technology

41:15

leap forward to be something that was

41:17

broadly beneficial to society. And I think

41:19

that is something that we need to

41:21

think a little bit more about is

41:23

not how do we stop AI. Not

41:26

how to, I'm not trying to put this technology

41:28

in a box and kill it. I

41:31

think it could be net beneficial to society. I

41:33

think we need to be thoughtful about

41:36

how we bring it into society. And

41:38

I don't think racing it out of the

41:40

box, absent

41:43

that thought is necessarily gonna lead

41:45

us to something that is net

41:47

beneficial. Yeah, you're gonna lose your

41:49

libertarian card from the boys of Silicon Valley.

41:51

Just so you know, you just lost it.

41:56

We'll be back in a minute. Have

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period at shopify.com/tech, all

43:34

lowercase. That's shopify.com/tech. That's shopify.com

43:36

slash tech. Speaking of deepfakes before we

43:38

go, I do want to talk about

43:40

the election and political images. Getty has

43:42

put editorial notes on doctored images like

43:44

the ones of Princess of Wales, Kate

43:46

Milton that were sent from the Royal

43:49

Palace. You said earlier you don't use

43:51

editorial content for your AI database, but

43:53

your competitors like Adobe and Shutterstock have

43:55

been called out for having AI-generated editorial

43:57

images of news events in their databases

43:59

like the... were in Gaza, which were then

44:01

used by media companies and others. And as

44:03

we said before, their databases are also being

44:05

licensed to train some of the biggest AI

44:08

models. So, you know, I think it's

44:10

the expression in software is garbage

44:12

in garbage out. How

44:14

worried should we be about synthetic

44:16

content, basically fake or altered images

44:18

impacting the outcome of elections, including

44:20

ours in November, and

44:23

those images then circulating back into the system

44:25

like a virus? I think we

44:27

should be very worried. I think it

44:29

ultimately, it goes back to some of the

44:32

things that why we're really focused

44:34

in on some of the, you know,

44:36

the premises of the regulatory elements I

44:38

talked about. We want to see a

44:40

situation where we can

44:43

identify this content. We can

44:45

give society as a whole fluency

44:47

in fact, right? If you don't have fluency

44:50

in fact, you don't fact have one of

44:52

the pillars of democracy. So

44:54

I think it is something

44:56

that we should be very worried about. I

44:59

think the pace of AI

45:01

is moving so fast. The ability

45:03

for it to permeate society across

45:05

social networks and those algorithms,

45:07

I think is something that we need to be

45:09

highly concerned about. And I think we

45:12

need industries and governments

45:14

and regulatory bodies to come together

45:16

in order to mitigate

45:19

that risk overall. I'm

45:22

hopeful that we can get there. It's

45:24

a tough problem and it's going to take a lot

45:26

of people putting energy against it

45:28

to solve for it. What can news

45:30

or should news or issues do about

45:32

this specific kind of visual misinformation and

45:34

what responsibly do the agencies have? Obviously

45:36

you are not doing that, but your

45:38

competitors are. Well, I think ourselves,

45:40

I think the AP, I think Reuters,

45:42

I think other news agencies around the

45:44

world, I think we have

45:47

to continue to make sure that our services

45:50

and our content is incredibly

45:52

credible and ultimately

45:54

lives up

45:58

to a standard that is beyond the And

46:00

I think then we need to work

46:02

with the technology industry and again, regulatory

46:04

bodies and legislative bodies on solutions that

46:07

address the generative side of things. Because there

46:09

are bad actors in this world and there

46:12

are models that allow people

46:14

to create not only misinformation

46:16

with respect to elections, but

46:19

some very other societally harmful

46:22

outcomes like deep

46:24

fake porn that

46:26

we need to address. And

46:28

these tools are readily available. What's your

46:30

nightmare and how quickly can you react

46:32

when the Kate Middleton things took a

46:34

few days? It's not the biggest deal

46:36

in the world, but it was still

46:39

problematic. Like that took a while. So

46:42

how do you, it's

46:45

expensive to do this to really constantly

46:47

be picking up trash that everybody's throwing

46:49

down. It's very important that we spend

46:52

a ton of time, first

46:54

off, vetting all of our sources of content. It's

46:56

not something we just take any piece of content

46:58

and put it on our website and then provide

47:00

it out. There is a

47:02

tremendous amount of vetting that goes into the

47:04

sources of content that we have. So

47:07

like the NBC News is or the Bloomberg's

47:09

of the world or the BBC's

47:11

of the world, but all the way down to

47:14

the individuals that we have covering conflicts in Gaza

47:17

or in Ukraine and making

47:20

sure that we know that

47:22

the provenance of their imagery is authentic.

47:24

We know the standard of their journalism is high.

47:28

That takes a tremendous amount of

47:30

effort, time, and investment in order

47:32

to do. And we

47:35

need to make sure that we don't in

47:37

any way, shape, or form reduce that investment.

47:39

We need to increase that investment in the

47:41

face of generative AI. We need to tell

47:43

that story. We need

47:45

to make sure that our customers have,

47:49

who are the media outlets like yourself, know

47:51

that that is something that we're doing.

47:55

Sure. So what's your

47:58

nightmare scenario? Give me a nightmare.

48:00

The primary scenario is that we

48:02

ultimately continue to undermine the public

48:04

trust in fact. And

48:08

we've seen that over

48:11

recent history. It's

48:14

been enabled by some technology

48:16

and platforms. We

48:18

need to make sure that we

48:21

don't restrict the

48:23

public square in terms of debate

48:25

and ideas and different

48:29

opinions, but at the same time that

48:31

we don't feed into an undermining of

48:33

what is real and what is authentic

48:36

and what is fact. And

48:38

those two things to me sometimes get conflated, and

48:41

I think that's a false conflation. I

48:44

think they can both stand as true. We

48:46

can have debate. We can have

48:48

different points of view, but

48:51

we can't have different facts and we can't have

48:53

different truths. And

48:55

ultimately, I think that's the long haul. It's

48:58

not a particular event. It's

49:02

a continual undermining

49:04

of the foundation of that truth. Well, it is

49:06

certainly easier to be a bomb thrower than a

49:08

builder. One of the issues is the fall off

49:10

in economy for all media,

49:12

not just your company, but everybody.

49:15

The costs go up, the revenues go down.

49:17

In the case of media, they suck up

49:20

all the advertising. You see declines in all

49:22

the major media who are fact-based, I would

49:24

say. Getty's value dropped

49:26

by two-thirds since you went public. Now, I

49:28

know SPACs have had all kinds of problems.

49:31

That's a trend in the SPAC area, which is how you

49:34

went public. But even with

49:36

all your AI moves, your shares are down.

49:38

Do you think it's because you're taking a more

49:41

cautious approach than your competitors? And

49:43

what turns it around? I want you to

49:45

speak for all of media, but you're running

49:47

a company here, and you're trying

49:49

to do it a little safer and

49:51

more factual. And that's not as profitable

49:53

as other ways. How

49:55

does that change from an economic point of

49:57

view? And I think all

50:00

of media. has this challenge going forward?

50:02

Well, I can't speak to specific drivers

50:04

of stock price or not. What

50:07

I can tell you is that we aren't taking

50:09

a more conservative view or

50:11

cautious view. We are taking a long-term

50:13

view. We are taking a

50:16

view that we are going to spend money and

50:18

invest to bring services to the market that we

50:20

think are helpful to our customers. We're

50:22

going to invest in lawsuits that we

50:25

think underpin the value of our content

50:27

and information that we have. We

50:31

are going to continue to invest

50:33

in bringing amazing content and services to

50:35

our customers. We think over time that

50:38

is something that will be to

50:40

the benefit of all stakeholders and

50:43

Getty Images, our photographers and videographers

50:45

around the world, our partners around

50:48

the world, our employees and our

50:50

shareholders. That's what we are

50:52

focused in on doing. I

50:55

think there is a backdrop within

50:57

recent trading of the entertainment strikes

50:59

and the impacts of those against

51:01

our business, some of the massive

51:03

impacts against large advertising and some

51:05

of the end-use cases where

51:07

our content gets utilized. Your

51:10

customers are also suffering too, yes. It

51:13

hasn't been a great environment for media companies

51:16

in the recent 18 months. I

51:18

think the end of the average... I would say 10

51:20

years, but okay. But more

51:22

recently, it's been more acute. You're seeing

51:24

streaming services cut

51:26

back. You're seeing ad revenues

51:29

down double digits. So

51:31

there are some challenges out there. But you aren't

51:33

seeing that for the tech companies. No, you're not.

51:35

You're not. Again, that goes where

51:38

I think we need to have some rules to

51:40

the road as we approach generative AI that we

51:42

already learned some of the things that maybe we

51:44

didn't do 20 years ago or 25 years ago. But

51:49

our business is about a long term.

51:51

The images has been around almost 30

51:53

years. And we've focused

51:55

in on making sure that the foundations of

51:59

copyright and... intellectual property remains strong throughout. Yeah.

52:01

And so that's what we'll continue to do

52:03

going forward. The hits keep on coming and

52:05

I don't mean good hits. Then

52:08

I have a final question for you. Are they

52:10

ever going to get fingers right? What

52:13

is the fucking problem? Well, look, I think if

52:15

you use our model, and I know that you're

52:17

pretty sure I had some time with it and

52:19

hopefully you'll get some time with it as well.

52:21

I think you'll see that we largely do. We've

52:24

trained on some things to try to make sure

52:26

that those outputs are of high quality. The

52:28

reality is it does matter. You

52:31

said kind of crap in, crap out.

52:33

Well, I would argue the exact opposite.

52:35

So quality in means quality out. And

52:38

so we've addressed those issues with the

52:40

model. Those aren't going to

52:42

be the hard things. Fingers in the

52:44

eyes. Fingers, eyes, hair, hair lines, smiles

52:47

and teeth. I think

52:49

we've largely solved those items. But

52:51

what we're always going to struggle

52:53

with is solving for the blank

52:55

page issue that customers have. And how

52:57

do you really get to authenticity? I think one of

52:59

the most interesting things to me, and I know we're

53:02

short on time, is how generative

53:04

AI comes directly in

53:06

conflict with another massive trend of the last

53:08

10 years plus, which is

53:10

authenticity. And how

53:13

does authenticity, how

53:15

am I portrayed? How am I

53:17

viewed? Am I positively represented? How

53:20

am I brought to bear? And what do I

53:22

see in the content that's presented to me? Body

53:26

representation, gender representation. How

53:29

did those things come? And I think that

53:32

is still a world that that trend

53:34

is not going away. And I think

53:36

it's a world that Getty Images helps

53:38

our customers address. Although I'm

53:40

still going round after round with Amazon about

53:42

my fake books and the fake Kara Swishers

53:44

who look corny. They just look corny. Strange

53:47

Kara Swishers are all over Amazon. Go

53:50

check it out. Anyway, I really

53:52

appreciate it. This is a really important discussion. People

53:54

understand what's happening to companies like yours. Well, I

53:56

appreciate you making time and thank you again for

53:58

having me. Thanks so much. Craig. On

54:05

with Kara Swisher is produced by

54:07

Christian Castro Roussel, Kateri Yocum, Joly

54:09

Myers and Megan Bernie. Special thanks

54:11

to Kate Gallagher, Andrea Lopez-Druzado and

54:13

Kate Furby. Our engineers are

54:15

Rick Kwon and Fernando Arudo. And our

54:17

music is by Trackademics. If you're already

54:19

following the show, you're authentic.

54:22

If not, be careful that you're not

54:24

being used as a battery. Go wherever

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you listen to podcasts, search for On

54:28

with Kara Swisher and hit follow. Thanks

54:30

for listening to On with Kara Swisher

54:32

from New York Magazine, the Vox Media

54:34

Podcast Network and us. We'll be back

54:36

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55:18

If you've been enjoying this, here's just

55:20

one more thing before you go from

55:22

New York Magazine. I'm Corey Sica. Recently,

55:25

our writer Rebecca Tracer noticed something. Republican

55:27

and right wing women have been flourishing

55:29

and prospering in the last year. From

55:31

Marjorie Taylor Greene to Kristi Noem. They're

55:33

tough, they're chaotic, and they tend to

55:36

have really great teeth. They're also swirling

55:38

in the orbit of Donald Trump as

55:40

he seeks to seize the country with

55:42

an iron fist this fall. Rebecca wondered,

55:45

is this empowerment or are

55:47

they just Trump's handmaidens? I

55:49

brought her in to explain to all of us what's going on here.

55:52

Hello, Corey. I'm gonna start

55:54

asking really boring questions. Here's one.

55:56

Which of the many exciting recent

55:59

dustups incited to want to talk

56:01

to and write about right-wing women

56:03

politicians. The first time I floated

56:05

a version of this was after Katie

56:07

Britz post-State of the Union. But I

56:09

can't say that at that point I

56:12

thought like I want to do a

56:14

whole scope of Republican women. I was

56:16

just really into Katie Britz because it

56:18

was very old school in certain ways

56:20

like the kitchen, like it

56:22

was very white suburban, middle

56:25

class, mommy presentation. But it

56:27

was also like, gothic

56:30

horror. You know, there's blood

56:32

of the Patriots right here in

56:34

my kitchen with an apple. But it wasn't

56:36

enough. I wasn't going to write a whole

56:39

piece about Katie Britz. It might have been

56:41

the infomercial that South Dakota Governor Kristi Noem

56:43

cut for the dental work she'd had done

56:45

where I was like, what is happening with

56:48

the Republican women? Right.

56:51

Like, so Kristi Noem did sort

56:53

of a physical self-redivation to make

56:55

herself either more palatable or more

56:57

powerful. I'm not sure. When

56:59

she began, she had a very no-nonsense,

57:02

boxy, Pelosi-esque, Hillary sometimes haircut, that like

57:04

sort of choppy haircut. And then in

57:06

recent years, since she's become a little

57:08

bit of a right-wing star, one of

57:11

the things she's done is really changed

57:13

her look. Now, Donald Trump is very

57:15

open about how he feels about women,

57:17

how he evaluates women. And Noem has

57:20

clearly remade herself into somebody

57:22

who looks like somebody Donald Trump has expressed

57:24

physical appreciation for. So part of my question

57:26

in this piece is, what does political power

57:28

mean if you conform to those kinds of

57:30

aesthetic standards, but then in some way that

57:32

winds up diminishing the respect that the people

57:34

who set those standards have for you? Well,

57:36

your point is a great one that Trump

57:38

hangs over a lot of this. Both soliciting

57:42

him for a big job at the same

57:44

time as they know he has standards. But

57:47

also at the same time, Trump's big innovation

57:49

was like performance is power and they're enacting

57:51

their own narratives. They've all become Trumpy in

57:53

their own weird way. Yeah. And I have to

57:56

tell you that it's very frustrating for me because I read about politics

57:58

and I hate the thing where everything is about I

58:00

always want to make it not about Trump. But writing

58:02

about these women really challenged that conviction

58:05

in me because it is clear that

58:07

at least for some of them, so

58:09

many of the new behaviors they're enacting

58:11

are in response to

58:13

Trump, are about the single demand

58:16

in the Republican Party right now,

58:18

which is showing him loyalty, fealty to

58:20

this guy. Like, all these

58:22

people, Valentina Gomez, Laura Loomer, they're enjoying

58:24

the fruits of choice in career

58:26

and motherhood. Like, does this

58:28

mean feminism won? Ah. Well,

58:32

this is what's so dystopian and scary

58:34

about their project, is that they're all

58:36

doing these things which are really fascinating,

58:38

right? Marjorie Taylor Greene's lifting weights

58:40

in a video and not behaving

58:43

classically demure and all of this

58:45

sense of empowerment is absolutely

58:48

what feminism gave to women. OK, so great.

58:51

Here is its success. But also, the

58:55

party and the ideology that

58:57

these women are using these

58:59

feminist gains to promote is

59:02

openly dedicated to the rolling back

59:04

of those feminist gains. That's

59:07

Rebecca Tracer. You can read her

59:09

work on Republican Women and more

59:12

in your home in our glorious

59:14

print magazine and at nymag.com/lineup.