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SAP Business AI. Learn

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more at sap.com/AI. Hello,

0:41

and welcome to Decoder. I'm Neil Aptel, editor-in-chief of

0:43

The Verge, and Decoder is my show about big

0:45

ideas and other problems. We're

0:47

doing two decoders a week now. On Mondays,

0:49

we're going to have our regular interviews, but

0:51

our new Thursday episodes, like this one, are

0:54

all about deep dives into big topics in

0:56

the news. And for the next few

0:58

weeks, we're going to stay focused on one of the

1:00

biggest topics of all, generative AI.

1:03

There's a lot going on in the world

1:05

of generative AI, and maybe the biggest thing

1:07

going is the increasing number of copyright lawsuits

1:09

being filed against AI companies like

1:12

OpenAI and StabilityAI. So,

1:14

for this episode, we brought on Verge features

1:16

editor Sarah Jong, who is a former lawyer

1:18

just like me, and we're going to talk

1:20

about those cases. And the main defense the

1:22

AI companies are relying on, an idea called

1:24

fair use. Let's back

1:26

up a sec. All the big generative AI

1:28

models from every company are trained on huge

1:30

swaths of data that are scraped from the

1:32

entire internet. And big media

1:34

companies like the New York Times and

1:36

Getty Images have filed copyright lawsuits against

1:38

those AI companies, saying that basically, they've

1:40

stolen their work and are profiting from

1:43

it. A claim that amounts

1:45

to straightforward copyright infringement. I made something,

1:47

you made a copy without my permission,

1:49

that's copyright infringement. If there's

1:51

one thing to know about copyright law, it's

1:54

that it's still very much rooted in

1:56

the idea of making copies, and regulating

1:58

which copies are legal. in which

2:00

aren't. And since computers can't do

2:03

anything at all without making copies, copyright

2:05

law shows up again and again in

2:07

the history of computing, and especially the

2:10

history of the internet, which allows anyone

2:12

to make and distribute perfect copies faster

2:14

than ever before. But there's a

2:16

check on all of the control that

2:18

copyright law provides. Fair use. Fair

2:21

use is written right into the Copyright Act,

2:23

and it says that certain kinds of copies

2:25

are okay. You can quote things. You can

2:28

quote books in commentary about books. You can

2:30

run clips of movies in video criticism of

2:32

movies. And you can make copies of articles

2:34

to share in a classroom. There's a long

2:36

list of these things in the Copyright Act,

2:39

but since the law can't list or predict

2:41

everything that people might want to do, it

2:44

also has a four-factor test written into it

2:46

that courts can use to determine if a

2:48

copy is fair use or not. But

2:51

here's the thing about the legal system

2:53

in general, and fair use specifically, it

2:56

is not deterministic or predictable. I

2:59

know we have a lot of engineers and product

3:01

managers in the decoder audience, and it's tempting to

3:03

think about the legal system like a computer that

3:05

you put in some inputs, and you can predictably

3:07

get some outputs. But that's not

3:09

how it works at all. And it is

3:11

especially not how fair use works. Every

3:13

court gets to run that four-factor fair

3:16

use test any way they want. And

3:18

one court's fair use determination isn't actually

3:20

precedent for the next court. That

3:23

means fair use is a very vibes-based

3:25

situation. It's anyone's guess how a lot

3:27

of copyright lawsuits are going to go.

3:30

Many of them feel like a coin flip. And

3:32

when you add in the amount of hype,

3:34

uncertainty, and money that comes with AI, it

3:37

gets even more complicated. So I

3:39

wanted Sarah to come on and help me explain what's

3:41

going on to everyone. Sarah is one

3:43

of my very favorite people to talk to about copyright law.

3:45

I promise you, we didn't get totally off the rails and

3:48

running out about it. But we went a little

3:50

off the rails. But we had to

3:52

start at the start. The first thing we had

3:54

to figure out was how big a deal are

3:56

these AI-conforming lawsuits? I feel

3:58

like there's sort of a... potential extinction level

4:00

event on the horizon. It's pretty weird because

4:03

all the lawyers seem to think so and

4:05

for whatever reason, like the CEOs don't seem

4:07

to think so. My read when

4:09

I talk to the CEOs is that they think

4:11

this is a money problem. That something's going to

4:13

happen and their general counsels and their

4:16

policy people are going to walk through

4:18

some court cases and maybe get some

4:20

policy changes passed in Congress and

4:22

they'll have to pay some money, but it will be fine.

4:24

And in the end, the money is always fine. You

4:27

are an extinction level event. I'm feeling

4:30

like the noise I'm hearing indicates

4:33

extinction level event. Why

4:35

do you think it's that bad? I

4:38

mean, like we lived through Napster,

4:40

right? Like we like it's it which

4:42

is weird because the CEOs also lived

4:44

through Napster, but maybe

4:46

they didn't like maybe they're from another universe.

4:49

But yeah, like it's the level of

4:51

copyright direness in these cases, the

4:54

effects on existing industries plus

4:57

how applicable law is lining up. It's

4:59

got Napster vibes to it. And

5:02

when Napster happened to the law, entire

5:04

companies went bust, entire industries went bust,

5:06

copyright changed forever in a way that

5:09

was not great. It was

5:11

an extinction level event and AI

5:13

has a similar thing going on there.

5:16

They got sued that went all the way to Supreme Court.

5:18

The Supreme Court made some changes

5:20

to copyright law in that case. When

5:23

most people think about Napster, I'm pretty sure they

5:25

think about Justin Timberlake playing Sean Parker in the

5:27

movie The Social Network. And they

5:29

might think about the idea that a

5:32

company that just quote unquote facilitates piracy

5:34

is a bad idea.

5:36

They do not think about the Supreme

5:38

Court eventually issuing changes to copyright

5:40

law wholesale that we now live inside. So

5:42

explain what you mean there. Quickly give people

5:44

the capsule summary of Napster and Groxner and

5:47

what happened to the law in those cases.

5:49

Yeah, we exit one era where we

5:51

had just sort of softened fair use

5:54

so that it was okay for people

5:56

to use their VCR setups to record

5:58

off the television. So that was the

6:00

arrow we were exiting, where it was like, okay, so

6:02

like, there's there are these new technologies, and people are

6:04

going to use them for themselves in

6:06

these, like, you know, pretty benign ways,

6:08

and that's okay. And copyright doesn't

6:10

have to restrict that. And

6:12

then we enter into sort of the Napster era where they go,

6:15

everyone can be a pirate now. And

6:17

that's not good. It could destroy this

6:20

industry. So now we have to change

6:23

copyright law in a way that we've never seen

6:25

before. That's

6:28

a really good example of something I want to

6:30

hammer on as we cover the AI companies. The

6:32

concept of fair use was enshrined into federal law

6:34

in the Copyright Act of 1976. It's

6:37

almost 20 years before the consumer

6:39

internet came along. So when

6:42

digital culture hit and companies like Napster arrived on the

6:44

scene, we had no idea what was going to happen.

6:47

No one in 1976 could predict Napster. And

6:49

the record labels and Napster had to go

6:51

to court to figure out if Napster was

6:53

legal at all. Turns out it wasn't. Napster

6:55

basically met its end in 2001 when a

6:57

federal appeals court upheld a ruling

7:00

that determined Napster by facilitating the

7:02

copyright infringement of its users was

7:04

also liable for copyright infringement. A

7:07

few years later, another peer-to-peer file sharing network

7:09

called Grokster went all the way to the

7:11

Supreme Court with a very similar lawsuit. Grokster

7:14

was a different company and the same

7:16

federal court that had shut down Napster

7:18

said Grokster was not infringing on copyright

7:21

law. But because Napster was the forerunner

7:23

of a whole bunch of file swapping

7:25

platforms ending in stir, Grokster ended up

7:27

being painted with the same brush. And

7:29

that left the Supreme Court to make

7:31

a big decision that had major ramifications

7:33

on everything that's happened online in the

7:35

last 20 years. Ultimately,

7:37

the court said if you market a

7:39

tool for people specifically to do copyright

7:42

infringement with, you are liable for the

7:44

copyright infringement that happens as a result.

7:47

That is a judicial construction. That idea had to be

7:49

invented. And there was a lot of disagreement about it

7:51

at the time. You can go into the history and

7:53

drama of that case, the cases that came before it

7:55

and the cases that came after it. But the point

7:58

I want to make is that no one knew

8:00

what was going to happen. The Supreme

8:02

Court had the power to effectively

8:04

create or destroy a company and

8:06

an entire industry based on its

8:08

understanding of copyright law at that

8:10

time. And at that moment,

8:12

they said, this is illegal and those

8:14

companies basically disappeared. And that is the

8:17

extinction level event that Sarah is describing.

8:19

The justices might say, well, actually all

8:21

this is illegal. And then the entire

8:23

AI industry might disappear. So

8:30

now we come to the AI companies, which are also making a bunch

8:32

of copies. And the

8:34

argument that is getting made

8:36

everywhere is this is something called fair

8:38

use. Yep, we acknowledge that we've made

8:40

the copies, but we've done them

8:42

in a way that makes it OK because of something called

8:44

fair use. Can you quickly explain what fair use is? So

8:47

fair use is the escape valve for

8:49

copyright because it is wild for

8:52

the law to restrict other

8:54

people's speech based on whether or

8:56

not you've published it in a book or set

8:58

it on a tape or whatever. And

9:00

so you have these four factors in

9:03

the law. You can look them up, 17 USC 107. They're

9:06

like intertwined. There's not really like a clear

9:08

logic behind how the four are like lined

9:10

up. And in fact, if you go and

9:13

look at the cases where fair use is

9:15

implicated, you can see the factors being weighed

9:17

very differently per case. You don't even need

9:19

to meet all four depending on the

9:21

use. It's not super

9:23

clear. It is meant to

9:25

be very flexible because speech is important

9:28

and you want to have a really

9:30

flexible escape valve. But that

9:32

also means it's not super

9:35

predictive in cases of new

9:37

technologies. So AI companies are

9:39

getting sued. The New York Times, for example,

9:41

has sued OpenAI. And the New York Times has complaint is

9:44

very compelling because it has all of these

9:46

examples where OpenAI will just spit out word

9:49

for word recitations of New York Times

9:51

articles. They've obviously copied the information. OpenAI's

9:54

response is, yep, we've acknowledged that we've

9:56

made these copies. But that copying is

9:58

fair use. We're allowed to do it

10:01

and we're going to show it to you by

10:03

going through the four factors So the first factor

10:05

is purpose and character of the use. What

10:07

does that mean? And how do you think applies in this case? so

10:10

what's the difference between for instance a middle

10:12

school or opening up the New York Times

10:14

and like quoting the

10:16

New York Times in their book report about Soybeans

10:21

Like go right like it's just like you

10:23

you it's a source So why not

10:25

go to the New York Times to

10:27

like get the definitive information on some

10:29

news story? People are also going to

10:31

open AI against the advice of their

10:33

teachers to like copy paste

10:35

a paragraph about soybeans for their book

10:37

report Right. It's like the purpose and

10:39

character of the use including whether such

10:41

use is of commercial nature or

10:44

for nonprofit educational purposes Yes,

10:46

open AI is ultimately going to make money

10:48

off of this thing. They charge you

10:50

for GPT for right now, right? Right pay

10:53

them a subscription and you get access to

10:55

someone else's information That

10:57

seems tough, right? It is tough the

11:00

big missing word in this that's sort

11:02

of been added over time Through

11:05

the courts actually is that

11:07

they're looking for transformative use

11:10

That's just something that's evolved over

11:13

the years if a work

11:15

is transformed by the copying

11:19

There's like a stronger argument to be made

11:21

that it was a fair use So

11:24

you've got like, you know a parody you've

11:26

got mashups. That's like a classic one if

11:28

you're like doing a YouTube

11:30

like clap back and you like have

11:33

a little clip of the person you're

11:35

clapping back like that's a transformative

11:37

use but You

11:39

can kind of tell from all of those easy

11:41

examples I used you can definitely think of a

11:43

time Someone got in trouble with

11:46

copyright law for doing exactly that Which

11:48

goes again to like their use is kind

11:51

of a funky thing where like because

11:53

it's case-by-case Even if it seems

11:55

like it's easy, you can still get in

11:57

trouble might win in the end, but you'll still

12:00

get in trouble. If you get into like a

12:02

much more difficult scenario like open

12:04

AI, something that has never been to court

12:06

period, you're up leveling

12:09

the difficulty to another place.

12:11

And I feel like in the case of

12:13

open AI, whether or not

12:16

copying all the information on the internet so that

12:18

a robot can spit it back out at you

12:20

in slightly different formats, whether

12:22

or not that's transformative is wildly up in the

12:24

air. In some cases, it

12:26

clearly is transforming. But

12:29

the New York Times has all those examples where

12:31

the robot just spits it back verbatim.

12:33

It's not transforming. And so you can

12:35

sort of see like the New

12:37

York Times is trying to preempt that

12:39

transformativeness debate. They're like, yeah,

12:41

like you like if it's spitting it out

12:43

verbatim, how much transforming is actually going

12:45

on in here? You get kind of like

12:48

an almost circular argument there

12:50

where it's like if it's not doing

12:52

it verbatim in some cases, then

12:56

when it is doing it verbatim, it's still

12:58

transformative because that like whatever internal

13:00

guts are happening in there, like it's

13:02

like clearly changing things just because we

13:04

got like a one off

13:06

like verbatim quote, surely

13:08

that means it's like it's

13:11

still okay. It's a weird one. Yeah,

13:13

I will just point out to the audience.

13:16

We're already in the middle of like a

13:18

deeply existential debate on the very nature of

13:20

how AI systems work. And what

13:22

if they're transforming the source text? We're

13:24

at the first factor. We haven't

13:26

even made it out of the first one. There's three more to

13:28

go. And they're all they're all like this. And some of them

13:30

are even walkier. This is why when

13:33

I say it's a coin flip. This is what I

13:35

mean. Like I feel like Sarah and I could just

13:37

sit here debating whether or not AI is transformative for

13:39

the rest of the show and not

13:41

reach a conclusion. And it's not us who's

13:43

deciding it in the end. It's a bunch

13:45

of judges. And I don't know what they're gonna think. We

13:49

have to take a quick break. When we come back, Sarah

13:51

and I will start diving into the other three factors in

13:53

a fair use case. A

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Business A I learn more it as

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a P.com/a I. Was

15:12

back. We were just talking about factor one of

15:14

the ferries test which is purpose and character of

15:16

use and we talked about the idea that some

15:18

copies or transform it into the original work and

15:21

he turned him into something else. That

15:23

is major we up for debate in the

15:25

case of a generative Ai system. My church

15:27

between. The

15:29

second series factor is the nature of

15:32

the work is widely available. Is it

15:34

a secret is had stashed away the

15:36

copy, the new drugs? everyone else. How

15:38

does nature of the work place in

15:40

these cases? Some. Things are

15:42

considered like a little more. In.

15:45

The purview of copyright than

15:47

others. So like you know,

15:49

arts plays, that anything you

15:51

know, creative works. that's. Much

15:54

more like. valuable than

15:56

say the the sequence

15:58

structure an organization of

16:00

Java, the language, right?

16:03

Which is, by the way, copyrightable. But

16:05

it's like kind of

16:07

a little bit less copyrightable, like on the

16:09

down low, than the other stuff. Because it's

16:11

weird. It's just a weird thing that you

16:13

don't really want to be like, oh yeah,

16:15

this is clearly what the founding fathers

16:18

wanted us to protect with copyright

16:20

to the law. I

16:22

always think of nature of the work as how

16:25

the judge feels about it. Like

16:28

it's just up for whatever it is. It's like,

16:30

how do I feel about the song Pretty Woman?

16:32

Is it important that I'm going to turn up

16:34

the dial on this one and say you've got

16:36

to overcome it a lot? Is

16:38

it a list of APIs in a spreadsheet in

16:40

order? I'll turn the dial down a little bit

16:42

because that seems pretty silly. And that, as

16:45

far as I can tell, is how this

16:47

one gets assessed. It feels

16:49

like the entire contents of the New

16:51

York Times archive might get the, I

16:53

feel like this is pretty important, waiting.

16:55

It's hard to know. I think the dial

16:57

is kind of in the middle on this one because on the one

16:59

hand, it's the kind of

17:02

creativity that you want to incentivize. But on the other

17:04

hand, it's full of facts. And

17:06

when it comes to facts and

17:08

history, facts themselves aren't copyrightable.

17:11

And you don't want to put a big fence

17:13

around the first draft of history, essentially. So

17:15

I think it does end up sort of

17:17

in the middle there. The part

17:19

of the nature of the work factor that I think is

17:21

also a bit of a coin flip here is

17:24

how widely available something is. So the

17:26

New York Times thinks of

17:28

itself as the news. It has

17:30

a very high self regard. And it thinks of itself

17:33

as pervasive. And it is the first draft of history

17:35

in all the ways the Times thinks about itself. It

17:37

is also famously behind a paywall. It's a thing you

17:39

have to pay for. You have to pay a lot

17:41

of money for it. They can mail you pieces of

17:43

paper, which is a very interesting way of receiving the

17:46

news. There's a tension there. The

17:48

tension is in the complaint. They're saying,

17:50

look, Chat CPT lets people get past the paywall. It's

17:53

the thing you have to pay for. And you can pay someone else to

17:55

get it for free. The argument is you're

17:57

reducing the market for the New York Times. How do you think those two

17:59

things? square. Yeah, like chat GPT,

18:02

letting you get around the

18:04

New York Times paywall, and the

18:06

New York Times being paywalled in the

18:08

first place. That does

18:11

seem to run in the

18:13

favor of the New York Times and against

18:15

open AI. Yeah. And again,

18:18

I will tell the audience, you can see

18:20

how complicated this is from the jump. This

18:22

is not, there's nothing deterministic about

18:24

this analysis. At every point, you can have

18:27

a pretty existential argument. But to

18:29

me, it's the nature of the work, it is

18:31

the least deterministic. Because if the judge decides that

18:33

they don't like the New York Times that day,

18:36

they can just turn the knob down and

18:38

say something like the news is the news. It's

18:41

not a poem. Everyone

18:44

has the news. It's open, I didn't steal it from

18:46

the New York Times, they would take it from the AP. And it's the same

18:48

news. And like, that is a

18:50

thing that they could logically say here to

18:52

devalue this factor. Yes, they

18:55

could say that. We're

18:58

about to get into the third and fourth theories factors.

19:00

And one thing we're going to talk a lot about

19:02

is Google, specifically Google books. 20 years

19:04

ago, in 2004, Google said it wanted to

19:06

scan and make searchable all of the books

19:09

in some research libraries. The

19:11

copyright holders, authors and publishers said, No,

19:13

you can't do that. And they filed

19:15

copyright lawsuits. It took nearly

19:17

a decade. The case was

19:20

finally resolved in Google favor in 2015.

19:22

The federal appeals court held that turning

19:24

books into search snippets was fundamentally transformative

19:26

of the original work. There's a factor

19:28

one, and said that even though Google

19:30

has scraped the entirety of the books

19:33

and made a profit from offering its

19:35

services, it is not hurting the

19:37

market for the original books. That's

19:39

where factors three and four come in. Factor

19:44

three in the analysis is how much of the

19:46

work was used. This one

19:49

might be the most obvious one on its face, which

19:51

is that's all of it. It's bad. If it's as

19:53

little as possible, it's good. Is there any nuance to

19:55

this one? I actually do think there's a little bit

19:57

of nuance to this one. This is where the Google

20:00

Books cases, I think, sort of cut

20:02

in favor of OpenAI where like, yes,

20:04

they're taking 100% of the

20:07

New York Times, like as much of the New York Times as

20:09

they can get. Yes. But like, what's

20:12

spitting out is small

20:15

in comparison to what they're taking. And

20:17

so like, the fact that they've taken everything

20:20

is sort of minimized in the Google

20:22

Books cases, for instance, where they're like,

20:25

yeah, they had to read

20:27

everything. But because a

20:29

robot was doing all of the reading, that

20:32

means a lot less. That

20:35

makes sense. Like the human literate

20:38

part of the copying

20:40

is what's important here is sort

20:42

of, I think, the upshot

20:44

of like those cases. And

20:46

so here, I think it's

20:48

a little bit more mitigated for OpenAI.

20:52

So even though OpenAI has taken everything the fact

20:54

that it's literally really everything, the

20:56

fact that it's not sort of immediately available,

21:00

kind of diminishes this factor. Yes.

21:02

That's fascinating. I honestly wonder

21:05

if that argument will be

21:07

made well, and if the judges will accept it

21:09

well, given that the main thing

21:11

that AI companies have to do using the state is

21:13

train on it, right? Google Books is like we made

21:15

an index of all the books, and

21:18

we can show you parts of the index and then kick

21:20

you out to buying a book. OpenAI

21:22

is like, we copied everything, we trained

21:24

a model on it. And

21:26

we might have even thrown away the database of

21:28

copies that we made. And now the model can

21:30

just go converse. And

21:32

it's like you had to take all

21:35

of the stuff to train the model.

21:37

And that just seems like

21:39

very complicated to me, because there's

21:41

a little bit of technical nuance

21:44

to how AI models work, that

21:46

requires all the stuff, even if all the stuff isn't

21:48

out in the world, or

21:50

exposed to the user. I mean, that's also

21:52

how search engines work, right? Like, it's like you have to

21:54

have all the stuff in order to be able to search

21:56

it. I mean, the technology is subtly different,

21:59

how the technology is used. is being used is subtly different. The

22:02

other part of it that no one wants

22:04

to like really even think about or hear

22:06

about is that the Google Books cases, we

22:09

don't have a Supreme Court decision out of

22:11

them. Like the important bits come out of

22:13

the appellate courts and it's been

22:15

10 years. So it's like almost 10 years.

22:17

So it's like you're, we've seen a shift

22:20

in how copyright, especially fair use is

22:23

being addressed by the Supreme Court. It's a completely different

22:25

court. Like we could get something

22:28

very strange out of this that

22:30

is unexpected. Let's talk

22:32

about the fourth factor here, which feels like

22:34

the most important one in this

22:37

case and often feels like the most important

22:39

one in any fair use case, the effect

22:41

of your copy on the market for the

22:43

original work. I make a song, it samples

22:46

your song. The sample

22:48

is de minimis in some way, it's just

22:50

like background noise. My song is really popular.

22:53

That doesn't mean people are going to listen to my song

22:55

instead of listening to the original. And in fact, it

22:58

might mean that people love the sample so much that

23:01

sales of the original song go up. So

23:03

you've had some impacts on the market. It could

23:05

be positive or negative or nothing. And we're going

23:07

to try to figure that out. And if it's

23:09

positive or nothing, maybe that use

23:11

is fair. If I have taken so much

23:13

of your stuff and replaced your work

23:15

with it and your market goes down and

23:17

your sales go down, that's negative and that's

23:20

going to cut against fair use. I

23:23

don't know how to evaluate that in the case of AI

23:25

at all. It feels like

23:27

they're racing the market for all human

23:29

generated content in the world. But

23:32

then I use the tools and I'm like, I

23:34

think I think I still have something to say here. I

23:38

mean, I actually think that the fourth

23:40

factor is really, really

23:42

against open AI. And

23:44

I think that it's because of the

23:46

Warhol case. So we have this case

23:48

where you get like an Andy Warhol

23:51

style portrait of Prince, or

23:54

you've got the famous Marilyn Monroe thing

23:57

where it's like the cutouts and

23:59

the choppy. print thing. So

24:01

a magazine thinks about

24:03

putting a picture like a photo of

24:05

Prince on their cover, and they're like, no,

24:07

everyone's going to put a photo of Prince

24:09

on their cover after he died. Let's

24:12

get an Andy Warhol style thing instead

24:15

from the Andy Warhol Foundation. So

24:17

they like make a portrait of

24:20

Prince in the

24:22

style of Andy Warhol. And

24:24

the base that they use is a

24:26

photograph that someone else took. They

24:29

do not license the photograph. And

24:32

the person who took the photograph

24:34

is the kind of person whose

24:36

photographs get licensed to

24:38

be put on the cover of magazines. And

24:40

the court basically just goes, look, you like snapped

24:43

up an opportunity that this person theoretically had.

24:45

And the Warhol Foundation is like, no, this

24:47

is like, it's not the thing. They didn't

24:49

want a photograph. They wanted Andy Warhol. They're

24:51

like, yeah, but it's like the same market.

24:53

It's about the same. Right. I

24:55

don't think that there's been a Supreme

24:57

Court case that emphasize Factor 4 that

25:00

heavily before. And it's,

25:03

I think, like sort of a

25:05

warning shot, actually, for these new

25:07

technologies. I don't know if they had the new

25:10

technologies in mind. But like, definitely, this is

25:12

not something we've seen out of courts

25:14

before. Is that heavy of an emphasis

25:16

on Factor 4? One thing that's

25:18

interesting about that note on Factor 4, which is

25:20

the economic factor, you could call it, is that

25:22

in a time since you and I graduated from

25:24

law school, and now there is

25:26

a movement called law and economics in

25:29

the law that really emphasizes these ideas.

25:31

Like the law should be measurable. We

25:33

can apply economic thinking to it. That

25:36

was not so much in vogue when

25:38

the Napster cases were getting decided, the Groschke cases

25:40

were getting decided. And so now you have this

25:42

other fair use thing where it's like, is a

25:45

painting replacing the market for a photo? And

25:47

the judges are like, we can do some economic

25:49

thinking here. And we're going to prove it by

25:51

saying, yes, there is a market for

25:54

depictions of prints. And

25:56

this painting can serve that market as well as

25:58

the photograph, which sounds... But

26:02

I think in a case of open AI,

26:04

the economics of it actually become pretty

26:07

direct. There is a

26:09

market for information or writing or what

26:12

books in this robot for

26:14

20 bucks a month can

26:16

just substitute for

26:18

users all of the other kinds of products that they

26:20

might otherwise buy. Again,

26:23

I think there's complexity

26:25

here because I actually don't think the

26:28

GPTs right now, the GPT-4,

26:31

can't actually do the work.

26:33

It's not quite good enough. So

26:35

you have to pull your mind ahead to where it

26:37

obviously will be good enough in the future. But

26:41

right now, I wonder if the difference between

26:43

what it can do right now and what

26:45

it might do will weigh into this analysis.

26:48

You were putting open AI in the position of going like, our shit's

26:50

not that good. So you

26:52

can't sue us because our... I mean, any big

26:54

company will argue that it is a piece of

26:56

crap if that is legally advantageous. I

26:59

mean, yeah, but that's

27:02

where they're going to have to go in order

27:04

to make Factor 4 work with them, right? It's just

27:06

like, oh, yeah, we're not that great. We actually suck

27:09

and we're always going to suck. Like,

27:11

therefore, we will never impact the

27:14

commercial value of this work. I actually don't

27:16

know if they're going to be willing to go that far because

27:18

it's a bit much. I straight up

27:20

think that they are going to have to really

27:22

minimize Factor 4 as much as they can and

27:24

just talk around it and try to really push

27:27

their case on the other factors. I think Factor

27:29

4 is like, that's a

27:31

rough one for them. I think it's especially

27:33

rough given that the

27:35

most recent various cases we have out of SCOTUS

27:37

is a Factor 4 case. I

27:40

think that might be the biggest sign to me that

27:42

we're headed towards an extinction level event. We

27:46

have to take another quick break. We'll be right back. Thank

27:58

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at sap.com/AI. We're

29:09

back. I'm talking about fair use with Verge Features

29:11

Editor, Sarah John. We've talked about

29:13

what the four factors in a fair use case are,

29:15

but that leaves us with a really big question. How

29:18

is this all gonna play out for the AI companies?

29:22

I often use the blurred lines case. Oh

29:24

no. An example of how much of a

29:26

coin flip all of this is. The

29:29

state of Marvin Gaye and Pharrell

29:32

Williams get into a dispute. The estate of

29:34

Marvin Gaye wins, even though there's no copying

29:36

in that song at all, which is still

29:38

crazy to me. Later

29:40

on, the estate of Marvin Gaye goes

29:42

after Ed Sheeran. There is actual musical

29:47

similarity in the two songs there, but Ed

29:49

Sheeran takes the stand. He's very sympathetic. He

29:51

says, this is the death of all music. And the jury agrees with him,

29:53

and he wins. Total

29:55

coin flip. The facts actually,

29:58

in both cases, were the what they

30:00

needed to be for the outcomes in my opinion. But

30:02

they are straightforward fair use analyses. Why do

30:04

you think that's applicable or not applicable in

30:06

the case of opening eye? I don't think

30:08

it's applicable. The thing about the

30:10

blurred line skater, it's a case about vibes, right?

30:13

Does this sound vibe with Marvin Gaye? And

30:15

is that infringement? It's just

30:17

such a terrible case. Yeah, it's really bad. Yeah,

30:20

it's a really terrible, I mean, it's a good

30:22

example of the fact of like, this

30:24

is why you never want to go to trial for

30:26

literally anything. The court is random, right? The jury

30:29

is random and the decision is random. And then

30:31

eventually you end up in an appellate court with

30:33

a bunch of unelected weirdos and they're extraordinarily random

30:35

lately. This is why the CEOs think

30:37

it's just gonna be some money, right?

30:40

Because they're gonna say, look, I don't want to go to trial.

30:42

I doubt the New York Times wants to go to trial. Like

30:44

we'll just pay you a bunch of money and you'll go away

30:46

and we'll build our businesses under a

30:48

legal regime that exists now. Yeah, Getty,

30:50

The Times, the media, authors,

30:52

basically everyone who works in a

30:54

creative industry is very

30:57

mad and very concerned. And

31:00

they've seen sort of their bottom line eaten away

31:02

by big tech. And

31:05

this is like no longer an arrow where you

31:07

go, oh, well, we can form these partnerships with these companies.

31:09

And it'll work out for us in the long run. Like

31:12

people are tired of having, getting

31:14

their backstabs essentially. Like that's sort of

31:16

the perception, I think. I

31:18

think that like there is an appetite to take this

31:20

all the way to the end rather

31:22

than give in and

31:25

let things blow up the future.

31:28

And like whether or not that's like

31:30

a warranted feeling, maybe,

31:34

but it's like the feeling

31:36

is just different. So you don't think there are

31:38

settlements here? If you have nothing to

31:40

lose, then of course go to trial, right? If

31:43

you have literally nothing to lose. And I think like we've sort of hit

31:45

the wall where you go, oh, either

31:47

we go to court and we destroy copyright law

31:49

forever, maybe. Yeah. Or

31:52

we lose everything, right? Like I

31:54

think that we're like running up against the wall where

31:56

people are sort of like, oh, it's not just that

31:58

the robots are gonna take our jobs. the robots

32:00

are going to take like the

32:02

future literacy of humanity. Like it's

32:04

like people are starting to spin up these visions

32:06

of the future that are increasingly

32:09

apocalyptic. And I

32:11

think that there's like an appetite to take this to

32:13

the end. So I want to end there

32:15

on kind of a big thing. This

32:17

is an idea that I have ruthlessly stolen from you over

32:19

the years. It's the idea that

32:21

copyright law is the only real limiting regulation on the

32:24

internet. Because it's the only thing

32:26

that can consistently get things taken down. Like

32:28

everything else aside from child

32:31

pornography and sex trafficking to

32:33

some degree even. It's those

32:36

two things in copyright law. And

32:38

those are the regulating factors on the internet. Those

32:40

are things that you can send a letter

32:42

and something you can hand down if you claim it's copyright infringement.

32:45

And then you have this chaotic fair

32:47

use argument happening in the background. Like

32:49

the exit ramp is supposed to be

32:51

really flexible and you know

32:53

lend itself to these kinds of existential arguments

32:56

that might go either way in front of

32:58

a court. Entire industries might

33:00

live or die depending on how people are

33:02

feeling that day about the nature of the

33:04

New York Times. Is

33:06

this working? Like I, is

33:08

this the right way to go about it? Because it's

33:10

what we've been doing for a long time. And

33:13

I still don't know if

33:15

we've made any of the correct policy

33:17

choices using copyright laws are only real

33:19

tool. I think it's a

33:21

terrible tool to regulate

33:24

speech. It's clearly

33:26

not working out I think in the

33:29

context of like you know individual

33:31

creators. Like we've set up just

33:33

a very bizarre kangaroo court system

33:35

essentially through platforms. Everyone is

33:37

familiar with the idea of copyright

33:40

strikes and DMCA and everyone

33:42

sort of knows that it like it's a lot

33:44

of BS and doesn't work super well. Doesn't make

33:46

a ton of sense and it's weaponized.

33:50

That said it's like when you're looking

33:52

at sort of the changes that are

33:54

coming to the culture through generative AI

33:57

and what that poses for.

34:00

society and for the way we

34:02

live and you know all kinds

34:04

of things like how do we

34:06

learn in schools right the

34:08

nature of creativity itself the value

34:10

of literature and art I

34:13

like don't even know what how to quantify

34:15

the changes that are coming down the pipeline or

34:17

what to do to address them and historically

34:21

when you're looking at a technology

34:23

that's about to blow up culture itself bring

34:27

in the copyright actually like right like it's like

34:29

the printing press shows up you bring in sort

34:32

of proto-copyright like the stationers monopoly

34:34

right that you you bring in something

34:36

like copyright does it work super well

34:38

is it a good thing I don't

34:40

know kind of not

34:43

not totally I'm not 100% on

34:45

board but like yeah like traditionally what

34:47

we do when technology is about to

34:50

blow up culture is we bring on

34:52

something like copyright and so like I

34:55

don't know if that's the right tool for this because I

34:57

don't even know if we really understand what

35:00

generative AI is about to do to us but

35:02

I think it does make sense to me that

35:04

it's shown up at this time as

35:07

sort of the front line does it make sense

35:09

that it's shown up to you as sort of an extinction level

35:11

event for these companies it makes

35:13

sense to me in like oh

35:16

yes this is checkouts gun right moment

35:18

right like it makes sense and like

35:20

oh yes this was this was the

35:22

destiny of copyright law and the destiny

35:24

of generative AI but will

35:26

it be a good tool about

35:29

as good as anything else I think it's

35:31

like not great it's not super good but

35:33

like if I run my head

35:35

through anything else that we've got on the

35:37

books I don't think that there's like

35:40

something where I'm like oh yeah this isn't a copyright

35:42

thing this is a something else thing there's one I

35:44

can there's one I can think of actually yeah this

35:46

is a hint towards our next episode deepfakes

35:50

there's no copies there's some there's

35:52

a copy somewhere in the model but

35:55

then you you're looking at the deepfake of Taylor Swift

35:57

or Joe Biden or Donald Trump or whoever and it's

35:59

not a copy of anything. So

36:01

if those characters want to show up and say

36:04

take this down, they have to

36:06

use some other tool because they

36:08

can't just go to copyright law and say that you're not

36:10

authorized to use that photo of me. The

36:12

way that I don't know, even celebrity revenge

36:14

porn gets taken down because they own the

36:16

copyrights, the underlying images that gets stolen. Like

36:19

there's something else that needs to happen

36:21

for in particular deep fakes that

36:24

I don't think that we have an answer to yet either. It's like,

36:26

I don't know, we've had

36:28

senators on the show proposing new causes of

36:30

action around likenesses, which

36:32

just gets to it's just the other weird places.

36:35

But it's like everyone will have the same rights as any

36:37

celebrity to endorse or not endorse

36:39

Twitter. And it's like that is really weird. But

36:42

it feels like we're going to need that for the deep fake problem.

36:45

Yeah, that problem is just another

36:47

rat's nest because it makes copyright

36:49

look easy. Because

36:51

like once you get into sort of the deep

36:53

fake problem and likenesses. Oh,

36:56

man, like you didn't copyright socks. Wait until

36:58

you get to the right of publicity slash

37:00

the right of privacy, which is the same

37:02

thing depending on which state you're in. Amazing.

37:05

Well, that's a big hint towards the next episode. I got to

37:07

ask you though, just to wrap this one up. How

37:10

do you think New York Times versus open eyes gonna play out? Oh,

37:13

why would you ask me? Like it's

37:15

you're basically just setting me up for

37:17

like, whatever I answer is the wrong

37:19

thing. I think that this is one

37:21

of those things where I think the

37:23

most you can really hope for

37:25

is that whatever comes out doesn't

37:28

damage copyright law in a way

37:30

that makes it unworkable. Like

37:32

that is like the worst case scenario. The worst

37:34

case scenario isn't actually that generative

37:36

AI gets banned forever, or

37:39

that it gets a green

37:41

light forever. The worst case is that

37:43

copyright law changes in a way

37:45

that's unworkable. I really do

37:47

feel like the tension you've identified with

37:49

the CEOs of these companies continue to

37:51

make huge investments is that

37:53

they feel they can solve the problem with money. And I

37:56

think the tension is you actually have to solve

37:58

the problem. My prediction is

38:02

that if the time's not just

38:04

a big early victory, OpenEye will just throw money

38:06

at the times and make it

38:08

go away, right? They will just throw money until the

38:10

time says, fine, we'll do a 10

38:12

year deal. But that doesn't stop the author's guild

38:15

and that doesn't stop Getty. And

38:17

then suddenly it becomes too costly to run. I

38:20

think that first case, the trial case

38:22

actually determines a lot of what happens next because

38:26

the authors win there, the creators win in

38:28

any of those early cases, the

38:30

entire AI industry is going to light up with

38:32

settlement offers. And then the prices

38:34

are just going to rise and maybe that will determine what

38:36

happens next. But if the AI

38:38

companies win first, I do

38:40

think it's existential for all these creative companies and they

38:42

are going to fight tooth and nail until they do

38:44

get in front of a Supreme Court and

38:47

then all bets are off. And there's the also

38:49

the sort of thing where some

38:51

of these companies have a worst case than the others. So

38:54

whichever ends up in front of a court first is also

38:56

going to be interesting, I think some

38:59

of these companies have played fast and loose with

39:01

copyright a little more than others. And

39:05

you're right that the first test bullet, like

39:07

the first trial balloon is going to determine

39:09

a lot of what happens next. Thanks

39:14

again to Verge features editor Sarah Zhuang for joining us on

39:16

the show. I hope you can tell Sarah and I love

39:18

talking about this stuff. That was a lot of fun. From

39:21

now on, we're going to keep bringing

39:23

you second episodes of Decoder every Thursday

39:25

to deliver more analysis and storytelling like

39:28

this. In addition to our classic regular

39:30

weekly interviews with CEOs, lawmakers and automakers,

39:32

stay tuned for parts two and three of our

39:35

series over the coming week. Your

39:37

thoughts about this episode or what you'd like to hear

39:39

us talk about more. You can email us at decoder

39:41

at the verge.com. We really do read every email. You

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time to consider SAP Business AI.

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That's SAP Business AI. Learn

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more at sap.com/AI.