0:00

So there's a handful of people that I'll schedule like

0:02

a month monthly facetime call with and in most of

0:04

them, you know, almost all of them are in fact

0:06

all of them are not local. And

0:08

then there's a handful of people that I try to do

0:10

lunch with like once a month and my

0:12

good friend Sam, he and I had our monthly

0:14

lunch today and we went to a place. But

0:18

I had a problem. During

0:21

lunch, there was music

0:23

outside, which was good. But

0:28

the Jack Brown burger joint trolled me

0:30

because they were playing a fish

0:33

album during the entire lunch.

0:37

And all I could do was think about how

0:39

happy you would be if you were there or

0:41

if you at least knew this was happening as

0:43

it was happening in retrospect. I should have like

0:45

FaceTime or something just to be like, listen to

0:47

this joke. And the

0:50

worst part of all, the worst part of

0:52

all, you could tell

0:54

me what songs I heard and I would probably

0:56

be like, sure. But

0:58

there were a couple of songs that even

1:00

I recognized as fish songs. Like, you know,

1:02

not only did it have the vibe of

1:05

fish, but I like had heard the

1:07

songs before and recognize them. And

1:09

I forget which ones they were. The

1:11

only one I know by name is bouncing around the room and that

1:13

was not it. But there was one

1:15

and I'm sure this is describing half a

1:17

fish catalog, but where it was repeating the

1:19

same phrase over and over again. And it

1:21

was very catchy. That's fairly common. Yeah, exactly.

1:23

But anyways, David Bowie, maybe I'm

1:26

kind of proud of you that you recognize that

1:28

it was fish. I'm not sure I could do that.

1:30

I don't know any of their songs. Maybe

1:33

I could pick it up based on vibe, but I don't think I've

1:35

even heard that much. So like you must. When

1:38

are you listening to fish so much that you recognize

1:40

songs? I can give you a good heuristic, John. If

1:43

you hear a song that you don't recognize, you don't

1:46

think you've ever heard it on the radio before. Look

1:49

around the room. And if the

1:51

widest guy in the room is slightly

1:54

bopping his head to it. That's me,

1:56

though. And zero other

1:58

people are. There's a

2:00

decent chance. It's fish It

2:03

could be anything like I don't know I

2:05

think my chances of record Spontaneously recognize like

2:07

you're at a restaurant these music playing the

2:09

background spontaneously recognizing fish I think my odds

2:11

are very low. I guess I'd have

2:13

to look for somebody with the little red

2:16

blood cell You know pattern

2:18

on there on their clothing and if they were bopping to

2:20

it or something then I could figure it out Right now.

2:22

I know what that is That's the one thing I can

2:24

recognize Marco taught me what that is and now I see

2:27

it on people's like license plate surrounds I'm like, oh one

2:29

of them Anyway,

2:32

the worst part Marco the worst part of

2:34

this entire lunch and about the only bad

2:37

part of this lunch because I really do

2:39

Enjoy Sam so very much and I guess

2:41

yes, did you like some of it? It

2:44

wasn't bad So

2:49

we have a new member special we have

2:51

gone back to the well and

2:54

we have done another ATP tier list

2:56

John. Can you remind us all what is

2:58

a tier list? I can't remind you all

3:00

because everybody knows what a tier list except

3:02

for all people who Listen

3:05

to this podcast, but then they've also heard the specials

3:07

before so it's a tier list You rank things you

3:09

put them in tiers multiple things can be in a

3:11

single tier the top tier is s why nobody knows?

3:13

Except somebody knows but we don't really care the point

3:16

is is better than a it's a tier list and

3:18

it's grading It's like a through f and then s

3:20

on top of a mm-hmm And

3:23

we graded all the iPods No,

3:25

at least most of them anyhow and so

3:27

I am pretty confident that we did a

3:29

pretty good job on this There was a little bit of horse

3:31

trading involved, but I'm pretty happy with where we ended up We

3:34

made a handful of people that we know

3:37

very upset and I'm sorry that you're upset,

3:39

but we're right So if you are

3:41

curious to hear this tier list or any

3:43

of the others You can go to ATP

3:46

dot FM slash join and if you join

3:48

even for about a month But you should do

3:50

more then you can get to all of the

3:53

members specials We've been trying to do one a month

3:55

for what like a year or two now I forget exactly

3:57

how long it's been but that we've racked up a fair

3:59

number over the over the course of the last several months.

4:02

There's a handful of tier lists. We

4:04

do ATP Eats among

4:06

other things. There's a lot of, there's a lot of

4:08

good stuff in there and some silly stuff. So ATP

4:10

tier lists. And if you are a member and you

4:12

would like to watch the tier list

4:14

happen, which is not required, but is occasionally

4:17

helpful. There is a super

4:19

secret YouTube link in the show notes for members

4:21

where you can go and watch it on YouTube

4:23

as well. Please do not share that. It's the

4:25

honor system, but you can check it out

4:28

there as well. It's in the show notes

4:30

for the member special. Sorry, yes, thank you. When you

4:32

go to the iPod tier list member special, look in

4:34

the show notes, the first link will be the YouTube

4:36

video. I like this tier list because they always, we

4:38

always seem to, I think they reveal something about the

4:40

things that we are ranking. Something that

4:42

we, at least I usually didn't know going in. You

4:44

think, oh, you're just gonna rank them and people are gonna,

4:47

you know, have controversies over which is good and which is bad.

4:49

But I think in the end, when you look at the whole

4:52

tier list and you kind of look at the shape of it

4:54

and how it's worked out and how contentious the choices would be,

4:57

you learn something about it. Like I think our connectors tier

4:59

list was like that. And I think the iPod one turned

5:01

out like that too. And the reason

5:03

we made some people angry is because we

5:05

know a lot of really weird tech people

5:07

with very specific and often very strange opinions,

5:10

specifically than iPods. I think you could also

5:12

say incorrect opinions. Like

5:14

they have their reasons. At least most

5:16

of them have reasons that make some sense. I think

5:18

one of the things we learned, not to spoil too

5:20

much, is that a lot of people

5:22

have, you know, all the things that

5:24

we put in tier lists, people can have personal

5:28

sentimental reasons for. We all certainly do.

5:31

And, you know, listeners do as well. And I

5:33

think iPods, more than anything we've done before, like

5:36

the people who had opinions, they swayed

5:38

heavily into the sentimental, right? It

5:41

was, you know, it was like, this was my first

5:43

iPod. I really love this thing, right? Much

5:46

more so than the past tier

5:48

list we've done. So I think, you know, maybe

5:50

the iPod at that point was the most personal

5:52

product Apple had ever made. Yeah,

5:55

I mean, honestly, like, I had a lot of

5:57

fun with this one because, like. Even

6:00

though I hardly ever really

6:02

used iPods because by the time I

6:04

could really afford decent iPods, it

6:07

was only very shortly before the iPhone really

6:09

took over. So I only really had a

6:11

couple of years with iPods, but those couple

6:14

of years, I really liked the iPods. And

6:16

this was actually fun. So, and just for

6:18

coincidence sake, I happen to have bought a

6:21

couple of iPod Nano's off of eBay a couple

6:23

of years back, just to kind of play around

6:25

with. And I took

6:28

them out the other night after we recorded

6:30

this episode and charged them up. And well, the ones that

6:33

we'll accept to charge at least, charged them

6:35

up and got to play around

6:37

with the old iPod Nano. And I will

6:39

just say, I stand

6:41

by everything I said on that episode, everything.

6:44

So feel free to listen and tell

6:46

us how wrong we are. And you too listener can

6:48

pay us $8 a month to

6:51

yell at your podcast player just a little bit more.

6:53

So we encourage you to do that. That's

6:56

absolutely great marketing. Thank you, Marco.

6:58

And by the way, our membership

7:00

episodes are DRM free. And

7:02

so if you happen to use an

7:04

iPod to listen to your podcasts, we

7:07

are fully compatible. So you can pay us

7:09

$8 a month to listen to our member

7:11

content on an iPod if you actually have

7:13

one. And you can honestly buy one on

7:15

eBay for only a few months worth of

7:17

membership fee because they're pretty cheap these days.

7:21

Indeed. And hey, what would you listen to

7:23

on an iPod if not a podcast? Well,

7:25

you could listen to music and you could

7:27

listen to music on a U2 iPod. And

7:29

so Brian Hamilton wrote in with regard to

7:31

the red and black colored U2 iPod. We

7:33

were wondering, I thought we were wondering on

7:35

the episode, certainly there was some mumblings about

7:37

it on Mastodon afterwards, how did they get

7:39

to red and black for the color scheme

7:41

of the U2 iPod? And Brian

7:43

wrote into her mind, John and us, about

7:46

how to dismantle an atomic bomb, which was

7:48

released November 22 of 20, or

7:51

excuse me, of 2004. And the

7:53

color scheme on the cover art for that album

7:56

is red and black. We're worried on that one, John, Mr. U2.

7:59

Yeah, I remember. I heard it once I was

8:01

reminded of it. I mean, here's the thing. Like I

8:03

said on the episode, it's not as if red and

8:05

black became the iconic colors of the band. This was

8:08

one album that was released, obviously, at

8:10

the same time as the iPod as part of a

8:12

promotional thing, like the iPod, the U2 iPod, the first

8:14

U2 iPod was released in October, and the album came

8:16

out in November. So it's a tie-in, right? And then

8:18

there were future U2 iPods, and they were also red

8:20

and black. But at that point, U2 hadn't released a

8:23

new album. So they're all just tied to this one

8:25

album. But they have released a lot of

8:27

albums, and they were future albums, and they were past albums,

8:30

I can tell you that this one and this color

8:32

scheme did not become heavily associated with the band. But

8:34

that's the reason. That's why they went with red and

8:36

black, because of the cover of the album. Are you

8:38

saying that as an assumption? I'm genuinely asking. Are you

8:40

saying that as an assumption? No, once

8:43

I was reminded of it, I'm like, oh, yeah, that's why they did

8:45

it. I mean, it's not a great reason, but I'm pretty sure it's

8:47

the reason. Fair enough. Max

8:50

Velasco Knott writes in

8:52

that there's also another feature, and I'm using

8:54

air quotes here, on the U2

8:57

iPod. Max writes, the U2 iPods featured

8:59

signatures of the band members on the backside. I was

9:01

fine with the black-red color scheme, but couldn't stand seeing

9:03

Bono and company on the back whenever I turned them

9:05

over. Yeah, I'd forgotten about that

9:07

as well. I mean, obviously, it's a shiny back end

9:09

that doesn't show up that much. But if you really

9:12

just wanted a red and black iPod and didn't care

9:14

about the band, the signatures on the back messed

9:16

it up a little. Indeed. Nikolai

9:19

Bronvol Ernst writes to us with

9:21

regard to the DMA and Apple's

9:23

Cut. Nikolai writes, I really enjoyed

9:25

your last show, 593, not

9:27

a European lawyer. I'm also not a European lawyer, but I

9:29

am a citizen in the EU and wanted to provide a

9:31

single European's point of view. The DMA

9:34

has nothing to do with Apple's Cut in

9:36

the App Store or how much money Apple

9:38

earns from selling their hardware. It only has

9:40

to do with ensuring fair competition, citizens' rights

9:42

to freely choose services they want to use without

9:44

vendor lock-ins on interoperability, portability, and your own

9:46

data, which we here in the EU believe

9:48

belongs to the user. That was a pretty

9:51

good summary. A lot of people have written in

9:53

to say this, but I think people will get hung up with the

9:55

idea when they're like,

9:57

Apple's Cut and how The

10:00

EU is trying to control that and they're like, the

10:02

EU is not trying to tell Apple how much money

10:04

it can make, it's just trying to do this other

10:06

thing. But the

10:09

reason it gets mixed up and the reason

10:11

people send us these emails is

10:13

because what Apple did to, you

10:17

know, supposedly comply with the DMA

10:19

while also trying to prevent competition

10:22

is an application of fees. So it's that

10:24

we, you know, OK, well, the EU says

10:27

you have to allow for competition. Apple says, OK,

10:30

sure, we'll allow competition. But all of our

10:32

competitors have to pay us an amount that

10:34

makes it so they can't compete

10:36

with us, right? And the cut we're

10:38

talking about is not Apple's cut from its

10:40

own App Store. Like when you

10:42

sell through the App Store, you pay Apple

10:44

some cut. It's the cut Apple demands from

10:46

the App Stores and the people selling

10:48

through App Stores that are not Apples on App Store,

10:51

that are selling through third party App Stores. Apple is

10:53

using money, using fees to

10:56

make the competition less competitive.

10:58

And that's what we're talking about. I know it's

11:01

even confusing when we're talking about Apple collecting its

11:03

money or Apple having its fees and stuff like

11:05

that. So I think maybe that's the source of

11:07

the confusion. And the other thing is, by the way, that

11:10

plenty of countries, including the EU, do

11:12

actually tell companies that they can't

11:15

make a certain amount of money on a certain

11:17

thing that they do. Someone wrote in to give

11:19

us the example of like credit cards, like a

11:21

MasterCard and Visa, the two big credit card networks.

11:24

I think in the EU, the fees

11:26

they charge stores to

11:28

process the credit cards are essentially

11:30

capped. And the EU

11:32

has basically said, Visa and MasterCard

11:34

own the market. You can continue to do

11:36

that, but you can't charge merchants

11:39

any more than 0.1%. The

11:43

EU has not done that to Apple. They

11:45

haven't said to Apple, hey, Apple, you can't

11:47

charge more than 10% in your own App Store.

11:50

They haven't said that at all. They haven't said anything about what

11:52

Apple can charge in their App Store. What they just want is

11:54

more competition, and Apple is saying, OK, there

11:56

can be other App Stores, but they all have to give us an amount

11:59

of money that makes it up. unattractive. And

12:01

yeah, we'll see how that flies. Again, the

12:03

EU has not yet ruled on the

12:06

core technology fee and all the other things that

12:08

they're investigating. So far, they've only ruled on the

12:11

steering provisions about how Apple

12:13

restricts the way apps in

12:16

its own app store can link out to third party

12:18

payment methods. But we'll see how

12:20

those other decisions come out in the coming months and

12:22

years. I don't know how long this is going to

12:25

take. But right now it's not

12:27

looking good for the core technology fee. Let's say that. We

12:30

asked for mostly tongue in cheek,

12:32

but we asked for Brexit style

12:34

names for Apple leaving the EU.

12:38

Jared counts was the first

12:40

we saw to suggest I

12:42

leave Frederick B Jormann suggested

12:44

accent and provided a truly

12:46

heinous but hilarious. I

12:49

presume AI generated image for this. My

12:52

personal favorite though, was suggested several times.

12:54

First we saw was from Oliver Thomas.

12:56

I quit. Yeah, that's pretty good. I

12:58

leave and I quit. We had many more suggestions. These are

13:00

thought these were the top three. The I leave and I quit are

13:03

cute, but I kind of like accent

13:05

because it's as close to Brexit and

13:07

the axe thing like the picture has

13:09

like a EU themed

13:12

Superman holding an axe and an apple. And

13:14

yes, it does look AI generated. It's interesting

13:16

how due to the way the various models

13:19

that we're familiar with have been trained, most

13:21

people can now look at an image and

13:23

identify it immediately as AI generated based

13:26

on like the shading and the

13:28

weirdness of hands and all sorts of other stuff.

13:30

It is kind of strange how quickly that happened.

13:32

But anyway, I kind of like

13:34

accent, but I don't

13:36

think we get to pick this name. So I mean,

13:38

I don't, MacBook one didn't really catch on. Neither did

13:41

a MacBook. Oh please, it sure did. Well, within our

13:43

little circle of podcasts. Yes. But I don't see the,

13:45

uh, the New York

13:47

Times running with accent or I quit. Yeah,

13:49

we don't really seem to have naming power

13:51

in the, in the greater ecosystem. If

13:55

we try hard enough, we can make fetch happen. All

13:57

right. Uh, someone anonymously wrote in

13:59

with. So

18:00

maybe there's some other factors there. How does it, how

18:02

would it possibly be measuring the sound on the inside

18:04

of your ear? Is there a microphone that's facing the

18:06

inside of your ear? I think there might be. Isn't

18:09

that how they do some of the calibration stuff? So

18:11

anyway, the point is my experience actually

18:14

using them, it

18:16

really does not feel like I'm

18:19

hearing a 95 decibel concert for three hours.

18:22

It feels like what it says of 85. Well,

18:25

how loud was the concert outside of the

18:27

year? From your seat, did you

18:29

look at the decibel meter? If I had nothing

18:31

on, what would the level be? Yes. So I did

18:33

a couple times where I would take the AirPods

18:35

out and put them

18:38

away so they turn off and just listen and watch

18:40

and see how the watch measures the concert fully. And

18:44

it was, I don't remember exactly, but

18:46

I remember it was somewhere in the high 90s, I think.

18:48

So not quite as loud as there. So maybe

18:51

the difference is that they were

18:53

coming from 105 decibels. And

18:55

they came out to 95. And I was coming, I think, from somewhere in

18:57

the 90s down to 85. So maybe that's

18:59

the cause. Or it could just be differences

19:01

in fit. I don't know exactly

19:04

how good is the seal with their artificial ear

19:06

setup compared to my actual ear. I don't know.

19:08

There's no good way to know that. So

19:11

I think the conclusion to draw here is, first

19:13

of all, what we kind of already knew,

19:15

which is they provide

19:17

some protection, suitable

19:20

for occasional concert goers, not suitable if you're

19:22

going to be working in a factory every

19:24

single day. There's different degrees of

19:26

protection that you might need. This

19:28

is not everyday protection. But also, it probably

19:31

varies a little bit between both

19:33

fit and between what exactly you're

19:36

actually listening to, like how loud

19:38

is your environment. Maybe

19:41

it can't bring down 105 decibels, but

19:44

maybe it can bring down 95 decibels. So

19:47

obviously, there are other variables here. So

19:49

I think the advice that

19:51

I would give remains the same, which is,

19:54

if you have really serious hearing protection needs

19:56

or very frequent hearing production needs, get real

19:58

hearing protection. an

20:00

occasional concert goer like me and you want

20:02

basic hearing protection for occasional concerts, this

20:05

is probably fine unless you are standing

20:07

like directly next to the giant PA

20:09

speaker. Maybe you might need a little

20:11

bit more protection. But this

20:13

seems fine to me. And every time I've

20:15

used them, I feel great afterwards and my ears

20:18

don't ring at all. And it doesn't, there's no

20:20

fatigue. So like, it seems to be

20:22

working. So maybe it just has a limit to

20:24

how much it can work. Apple

20:26

is apparently using Google Cloud infrastructure

20:29

to train and serve AI. This

20:31

is from HPC wire. Apple is

20:33

two new homegrown AI models, including

20:35

a 3 billion parameter model for

20:37

on device AI in a larger

20:39

LLM for servers with resources to

20:41

allow, to answer more queries.

20:44

The ML models developed with TensorFlow were

20:46

trained on Google's TPU. John, remind me

20:48

what TPU stands for. Tensor processing unit,

20:50

some of that we talked about, the

20:53

actual hardware on a past show and how many

20:56

billions of computations or whatever they do

20:58

and how many different operands are in each

21:00

operation. But I think it's like a Tensor

21:02

processing using unit or something. It's basically, so

21:05

Google doesn't buy its GPUs from Nvidia and

21:07

put them in, it makes its own silicon

21:09

to do machine learning. It has for many,

21:11

many years, it's not a new thing. They're

21:13

called TPUs. And that's what they're currently using

21:16

to train Gemini and stuff. And if

21:18

you pay them, just like you pay AWS or whatever,

21:20

you pay Google Cloud, I believe

21:22

they will rent you their TPUs and you can train your models

21:24

on it. And that's what Apple did. Indeed.

21:28

Apple's AXLearn AI framework used to

21:30

train the homegrown LLMs creates Docker

21:32

containers that are authenticated to run

21:34

on the GCP or Google Cloud

21:36

something. What is that? Google Cloud

21:38

computing? I don't know. Computers. GCP

21:42

is like AWS. It's Amazon Web

21:44

Services, but Google. Anyway,

21:46

to run on the GCP infrastructure,

21:48

AXLearn supports the Bastion orchestrator, which

21:50

is supported only by Google Cloud.

21:53

This is a quote from their

21:55

GitHub documentation. While

21:58

the Bastion currently only supports Google. Cloud Platform.

22:00

There you go. I should have kept reading. My bad. Google

22:02

Cloud Platform Jobs, its design is cloud-agnostic. And

22:05

in theory, it can be extended to run

22:07

on other cloud providers, Apple stated on its

22:09

AX Learn Infrastructure page on GitHub. Yeah,

22:12

so this is, I mean, we didn't

22:14

put this in the notes, but the rumors

22:16

are that the deal between Apple and Google

22:18

to use Gemini as part of iOS 18

22:20

as an option alongside a chat GPT, that

22:22

deal is reportedly getting closer. But this is

22:24

from the past of like, hey, Apple's got

22:26

these models, the one that's going to be

22:28

running on people's phones or the various ones

22:30

that are running on their phones, which are

22:32

smaller. And the big ones, they're going to

22:34

be running on their private cloud compute. And

22:36

these are Apple's own models, and they train

22:38

them themselves. And how do they train them?

22:40

They paid Google to use TPUs to train

22:42

their models. And so I feel

22:44

like this is interesting in that Google's

22:50

unfriendly relationship, let's say, with Nvidia

22:52

continues. And

22:54

their friendly relationship with Google continues. It's kind of a

22:56

surprise that Google didn't do the deal. Maybe the rumors

22:59

are, I think we talked about this on a past

23:01

show, that nobody's paying anybody for

23:03

the open AI thing, whereas maybe Google wanted

23:05

to be paid. So we'll see how this

23:07

works out. But yeah, there seems to be

23:10

a cozy relationship between Apple and Google, because

23:12

apparently Apple either doesn't have yet or doesn't

23:14

plan to have fleets of

23:16

massively parallel machine learning silicon that

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Photon Camera for sponsoring our

24:51

show. John,

24:56

I hear that you have

24:58

asked Apple for help and they have said,

25:00

you know what you need? You need a

25:03

Mac Studio. Because why would anyone need a

25:05

Mac Pro? This went around,

25:07

I think, a week or two ago. Apple's got

25:09

a page, apple.com/Mac slash best hyphen

25:11

Mac. And the title of the page is

25:13

Help Me Choose. Answer a few questions to

25:16

find the best Mac for you. And

25:19

when this was going around, the first thing

25:21

I did was launch this page and

25:23

I wanted to go through the little wizard and answer

25:25

a bunch of questions to see if

25:27

I could reach the win condition, which is

25:30

having this tool recommend the Mac Pro. Is

25:32

that the win condition? It is the win condition. Are

25:35

you sure? And the answer

25:37

was very clear. And I was mostly telling the truth, but

25:39

occasionally I would exaggerate to

25:41

make sure I go on the Mac Pro path. And

25:44

I did not end up at a Mac Pro. It

25:46

recommended Mac Studio to me and that a bunch of

25:48

other people pride. So a bunch of people tried to

25:50

use this tool to get Mac Pro. Nobody could do

25:52

it. And Julia Montier tried it and found out how

25:54

to cheat to win the game. If

25:57

you look at the source code, you can

25:59

see that a JSON file that

26:01

defines the options for

26:03

the endpoints. And that JSON, it's

26:05

not a JSON file, but it's not a JSON. That JSON does

26:08

not contain the Mac Pro. It

26:11

contains pretty much every other Mac that

26:13

Apple sells, but there

26:15

is no way to get to the Mac Pro because

26:17

the Mac Pro is not one of the options. That's

26:20

weird. Is it? No, this

26:22

is Apple telling you that literally nobody wants this

26:24

computer and nobody should have it. We all agree

26:26

on this show that the current Mac Pro is

26:28

not a great computer. But it is

26:31

a computer that exists. And on

26:33

top of that, there is at

26:35

least one very specific

26:37

reason why someone might want to use it. If

26:40

one of the questions had asked, hey, do

26:42

you have a bunch of PCI Express cards that you

26:44

need to use? If

26:46

the answer to that is yes, it's literally the only

26:48

computer Apple sells that you can do that on. And

26:51

that is really the only thing to recommend. Do

26:53

you think the people who made this quiz know

26:55

what a PCI Express card is? I

26:58

mean, it's Apple. They

27:01

have questions and answers for every other computer. It

27:03

just seems weird to me. Now, again,

27:05

I can understand saying, well, this is not a

27:08

great computer. And really, honestly, no one should really

27:10

buy it. I agree with all of that. But

27:12

when you make a help me choose tool on

27:14

your website, you should have

27:16

all of the things as endpoints. And yeah,

27:18

make the Mac Pro pretty much impossible to

27:20

get to unless you need it. But there

27:23

is a reason someone might need it. If

27:26

someone is going through this tool and saying, I

27:28

don't know what I'm going to do. I've got all these audio cards

27:30

that I need to use for, you

27:32

know, my old Mac is dying. Is there some

27:35

other computer that I can use? How would you

27:37

determine that Apple still

27:39

sells computers with card slots

27:41

in them? Everyone on Mass.

27:43

I'm saying, OK, well, the people who need the Mac Pro

27:45

know it, and so they don't need to use this tool.

27:48

That's not how these tools work. You could say the

27:50

same thing about, well, the people who need an iMac know they

27:52

want an all-in-one things that they don't need to use this tool.

27:56

And if you know which computer you need, yes, you don't need

27:58

this tool. But the tool exists to lead you. to

28:00

whichever product that Apple sells is best suited

28:02

for you. And it's weird to

28:04

leave just one out. And

28:08

I would just love to know if the thinking behind that process is

28:10

like... Look, if Apple doesn't want to sell them,

28:13

don't sell them, right? But they're selling them. You

28:15

can buy them for a huge amount of money.

28:18

And the tool can make

28:20

it difficult or almost impossible to get there because when

28:22

it says, how many PCI Express cards do you need

28:24

to use? The default choice should

28:26

be zero or I don't know what a PCI Express

28:29

card is. Like have a million options that regular people

28:31

will click and they will lead them off that path

28:33

and say you shouldn't buy this. But if the person

28:35

says three or any

28:37

number other than zero, you have to leave them to

28:39

the Mac Pro because it's literally the only computer they

28:41

sell with card slots. I mean, you're

28:43

gonna hate this, but so I did the whole quiz

28:45

trying to get to the Mac Pro before

28:48

you said it wasn't an option and just putting

28:50

in all like the highest requirements. Like, you know,

28:52

I need all that. I do 3D editing and

28:54

content creation and video editing and audio editing. I

28:56

need all these tools. I need to connect a

28:58

bunch of stuff to my Mac and

29:01

it recommended exactly what I'm

29:03

using right now. The Macbook Pro

29:05

16H. I

29:08

thought for sure I'd at least get a Mac Studio, but

29:10

nope. Well, no, because the question it asks is do you

29:13

do all your work in a single location or do you

29:15

need to be portable? Did you say all I do all

29:17

my work in a single location? I said like the on

29:20

the one desk option, the very top option where it's

29:22

like I do everything at the same place on a

29:24

desk. Like I even I thought for sure I'd at

29:26

least get a Mac Studio. I think a lot of

29:28

the endpoints recommend two computers. Like I didn't

29:30

just get the Mac Studio. I got recommended the Mac Studio and

29:32

the Macbook Pro. Oh, I also got two computers,

29:35

the Macbook Pro $4,000 configuration and

29:38

the Macbook Pro $3,500 configuration. I

29:42

don't know how you didn't end up with desktop because

29:44

there must have been some question that's differentiating portability.

29:46

Obviously, if you mention you ever need to take it

29:49

somewhere, they're not going to recommend it at all. Yeah,

29:51

I don't know. I don't know how great this tool

29:53

is. Wizards in general are not great.

29:55

I like their comparison ones like for the phones where it does

29:57

like columns and you can list all the features and scroll and

29:59

see they are different from each other. This

30:02

doesn't do that. But I do

30:04

think it's very strange to not

30:06

have a single one of your

30:08

computers. Remember when they were selling the trash can for

30:10

years and years, and really nobody should be buying that,

30:12

right? But if you needed

30:15

whatever GPUs it came with, for a while

30:17

it still did have the most powerful GPUs

30:19

you could buy in an Apple computer. And

30:22

if you needed those GPUs and they had a tool that was

30:24

asking you a bunch of questions, they should have had a question

30:26

that said, do you use Maya at

30:28

Pixar and need this much GPU power and then it

30:30

will lead you to the trash can? But I

30:33

don't know. It's weird. Anyway, if someone at Apple knows why

30:35

the Mac Pro is emitted from this tool, please

30:38

tell us. I'm sure it's the obvious reason, which is like,

30:40

no one should buy that. And we kind of agree, but you're selling

30:42

it, so put it in the tool. Pretty

30:44

sure it's very clear why it's emitted.

30:48

Even the very first day this Mac Pro came out, nobody

30:50

should be buying it, let alone now. It's

30:54

not nobody. It is the only computer with slots.

30:57

That's not a great reason for it to exist, and it's not

30:59

a reason for you to pay twice as much as Mac Studio,

31:02

but especially since they don't support, I believe

31:04

they don't support at all anymore, the PCI

31:07

Express breakout boxes like they used to on

31:09

the Intel things, it's literally

31:11

your only choice if you have cards. And

31:14

that's one of the reasons they should continue to make it. And do continue

31:16

to make it, and they just never ask about that. Oh,

31:19

yeah. It made me laugh quite

31:21

a bit that nobody was coming up with the Mac

31:23

Pro. I don't know. Maybe that's

31:25

a feature, not a bug. All

31:28

right. For the main, main

31:30

topic this week for your main course, we

31:33

have a plethora of different AI

31:35

related topics. And I'm going

31:38

to try to take us on a journey.

31:40

We'll probably fail, and that's OK. But basically,

31:42

this next section is AI. Huh.

31:45

That's a thing, isn't it? And

31:48

so we start on the 17th

31:50

of June, for what it's worth, with our

31:52

friend John Voorhees at Mac Stories, which

31:56

is them saying, hey, the article is entitled

31:59

How We're trying to protect Mac stories from AI

32:01

bots and web crawlers and how you can too. And

32:04

it seems like both John and

32:06

Federico are getting very wrapped around

32:08

the axle with regard

32:10

to AI stuff. And I'm not saying I don't

32:12

mean to imply that they're wrong or that's bad,

32:14

but they are getting ever

32:16

more perturbed on what's

32:19

going on with AI crawlers. And

32:21

I mean, to a degree, I get it. So,

32:23

uh, that was on the 17th of June. John

32:25

says, here's how you can protect yourself from crawling.

32:27

And then on the 21st of June, business insider

32:29

writes, it says, Oh, open

32:32

AI and anthropic seem to be ignoring

32:35

robots.txt. And if you're not familiar, if

32:38

you have a webpage or website, I

32:40

guess I should say, where, um, where

32:42

you control the entire domain, you can

32:44

put a file called robots.txt at the

32:46

root of the domain. So,

32:48

you know, it would be

32:50

marco.org/robots.txt and any self-respecting

32:52

and ethically clear crawler will start

32:55

crawling marco.org or whatever the case

32:57

may be by attempting

33:01

to load robots.txt and seeing if

33:03

there's anything there. And if so, it,

33:05

there's a, a mechanism, a schema, if you will,

33:07

by which, um, the

33:10

robots.txt will dictate who or really what

33:12

crawlers should or should not be allowed

33:15

to crawl that site. And it's by, it's by path.

33:17

They can say everything in this directory, you shouldn't crawl

33:19

everything here. You can crawl it. So you can sort

33:21

of subdivide your site to say which parts are accessible.

33:24

Yeah. And I have thoughts on that, but we'll come back to that. Yeah.

33:27

I mean, whenever you're ready to interrupt, to be honest,

33:30

feel free. Okay. Let's talk about robots.txt. Uh,

33:32

so, so, well, let me just actually very quickly, I apologize.

33:34

I gave you the, we gave you the green light and

33:36

I'm giving you the yellow light. Just very quickly, it's already

33:38

in the intersection. It's

33:40

important to note that robots.txt has never been

33:42

enforced in any meaningful

33:45

way. It's been kind of a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, kind

33:51

of a friendly agreement amongst pretty

33:53

much the entire world wide web. But there's

33:56

never been any real, um, wood behind the

33:58

arrow or, or, or, or, or, whatever the

34:00

turn of phrase we call advisory. Yeah.

34:02

Advisory locking. It is a

34:04

scheme that people who agreed to

34:06

that scheme can use that scheme to collaborate

34:08

and work together, but there is no actual

34:10

mechanism stopping anyone from doing anything. It is

34:12

literally just a text file that you can

34:15

choose to read or not. Right.

34:17

So with that said, Marco carry on. Yeah. And,

34:19

and so robots.txt is

34:22

basically a courtesy. It is

34:24

a website saying, please

34:26

maybe follow these rules if you, if

34:29

you would, you know, but it

34:32

is not a legal contract. It is not a

34:34

legal restriction. Um, it

34:36

is not technically enforced or

34:39

enforceable really. It is

34:41

also not universally used and respected.

34:43

And so, and, and I can

34:45

tell you, I operate crawlers of

34:47

a sort and I don't use robots.txt. So

34:50

when, when overcast crawls podcast feeds, I

34:53

don't even check for robots.txt. I just

34:55

crawl the URL as the users have entered them

34:57

or as they have submitted them to iTunes slash

35:00

Apple podcasts. What robots.txt

35:02

advisories are were originally

35:04

for was not

35:06

like, Hey, search engines, don't

35:09

crawl my entire site. That's not what they were for.

35:12

What they were for was mostly

35:14

to prevent like runaway crawls on

35:17

parts of a site that were potentially infinitely

35:19

generatable. So things like if you had like

35:21

a web calendar and you can just click

35:24

that next month, next month, next month button

35:26

forever if you want to. And so a

35:28

web crawler that like, you know, indexes a

35:30

page and then follows every link on that

35:33

page. If it's hitting like a web calendar,

35:35

it can generate basically infinite links, uh,

35:37

as it goes forward or backwards in time.

35:40

So the main purpose

35:42

of robots.txt was to kind

35:44

of advise search engines. And

35:47

it was specifically for search

35:49

engines. It was to advise

35:51

them areas of the site that

35:53

crawlers should not crawl mostly for

35:55

technical reasons, occasionally for some kind of privacy

35:57

or restriction reasons, but usually it was just.

38:00

Was some kind of like legal contract that

38:02

said you must obey my rules That really

38:04

has never been tested until fairly recently like

38:06

that that was never really something that really

38:08

ever came up I mean there were been

38:10

a couple of things here and there was

38:12

like Google News and news publishers in certain

38:14

countries and stuff But like for the most

38:16

part that the basic idea

38:18

of robots.txt was really just

38:21

please Like that's it. It was like, please

38:24

do this or don't do this And

38:26

even then like it was often

38:28

used in ways that harmed The

38:32

actual customers using using things or or

38:34

did things that were unexpected this is

38:36

why I don't use it for overcast

38:38

feed crawlers because if you

38:40

publish an RSS feed and Submit

38:42

it to Apple podcasts. I'm

38:44

pretty sure you intend for that to be a public

38:46

feed And so I

38:48

feel like it is not really my place

38:51

to then you know Put up an alert

38:53

to my user to say hey this person's

38:55

robots.txt file actually says you know It's disallow

38:57

star on this one path that

38:59

this feed is in and so I actually

39:01

can't do this for you Like that that

39:03

would feel like I would have

39:05

I would have first of all no incentive to

39:07

do that and second of all because of It's it

39:11

because of its intention and context as a standard

39:13

for search engines, which I'm not This

39:15

doesn't really apply to me and my use and

39:18

and there were all sorts of things over the

39:20

years too Like you know that you could specify

39:22

certain user agents like alright Google bot do this

39:24

Yahoo bot Do this like and even and that

39:26

was also problematic over the years too because it

39:29

Disadvantaged certain companies if you just had

39:31

like bad behavior once or if a

39:33

site owner Just had like

39:36

one bad thought about one of these companies

39:38

once and then like never revisited it or

39:40

whatever Like then that company was allegedly like

39:43

disallowed from crawling this site. Why well, I

39:45

mean, it's not even that It's like, you

39:47

know for people in all of the technology

39:49

behind it You know

39:51

don't allow Google bot the way you identify Google

39:54

bot is by the user agent string Which is

39:56

part of the HTTP request and anybody can write

39:58

anything there and more

52:00

and more applications over time of

52:03

technologies like AI summarization and

52:05

action models and things

52:07

like that, where some

52:09

fancy bot basically is going to

52:12

be browsing and operating

52:14

a web page on behalf

52:16

of a user. That is kind

52:18

of like a browser, but it's

52:20

a very different form that I

52:22

think breaks all those assumptions with publishers.

52:25

This is one thing that I faced when I was

52:27

making Instapaper a thousand years ago. Instapaper would save the

52:29

text of a web page to read later and only

52:31

the text, not like all the ads and the images

52:33

and everything like that. I was very careful,

52:35

though, to not make features

52:38

that would enable somebody to

52:40

get the text of a page without having

52:42

first viewed the page in

52:45

a browser or a browser-like context. It would

52:47

load the whole page. They would see the

52:49

page. If there were ads, those

52:51

ads would load on the page. They would see

52:53

those ads. Then they could save what they were

52:55

seeing. Then part of that would be saved

52:57

in Instapaper and shown to them later. That

52:59

was always a very tense

53:03

balance to try to maintain

53:06

because what I didn't

53:08

want was widespread scraping of people's text

53:10

without loading their ads, but

53:13

I figured that seemed like an okay trade-off because that

53:15

was literally just saving what was already

53:17

sent to the browser and what the user was already

53:19

looking at. But a lot

53:21

of these new technologies, first

53:23

of all, I probably wouldn't attempt that today, but a lot

53:25

of these new technologies

53:29

break a lot of those little details.

53:31

If you have some kind of bot

53:33

that's doing something on

53:36

a website, suppose

53:38

it's one of these action models where you're saying, all right, book

53:41

me a flight. This stupid

53:43

book me a trip thing that all of

53:45

these AI demos from these big companies keep

53:47

trying to do even though nobody ever wants

53:49

that. Suppose you have a

53:51

book me a trip kind of thing with an AI model and

53:53

the idea is that model will go behind the scenes

53:56

and will go

53:59

operate experience. media or orbits behind the

54:01

scenes for you and manipulate things back

54:03

there to find the best flights and

54:05

hotels and whatever else. Well,

54:07

those sites make some of their

54:09

money via ads and affiliate things

54:11

and sponsor placements on those pages.

54:14

If you have some bot operating the site for

54:16

you, kind of clicking links for you behind the

54:18

scenes in some kind of AI context, that

54:21

bot is not going to see those ads. It's not

54:23

going to click those affiliate links. It's not going to

54:25

pick the sponsored listing. It's going to just

54:27

kind of get the raw data and that's it. And

54:29

that will be violating those sites business models that that

54:31

happened. That really has

54:33

not happened at massive scale until fairly

54:35

recently. So this really has not been

54:37

challenged. This really has not been legally

54:40

tested that much. This really has not

54:42

been worked out. Like what are the

54:44

standards? What are the laws? What are

54:46

the legal precedents? How much of this

54:48

is fair use versus not? You

54:50

know, for the most part until very recently,

54:52

we could pretty much just say, all

54:54

right, if you serve

54:56

something publicly via

54:58

public URLs and anybody can

55:00

just download it, then nothing

55:03

bad would really happen to you and your business

55:05

model for the most part. If

55:07

some bot came by sometimes and parse that page for

55:10

some other purpose, it wasn't a big deal. But

55:13

now there's a pretty

55:15

significant difference in scale

55:17

and type of replacement. Now

55:20

with a lot of these AI products

55:22

and with Google search itself, you know,

55:24

increasing over time and then more recently,

55:26

rapidly increasing, what we're seeing now is

55:28

full out replacement of the need for

55:30

the user to ever look at that

55:32

page. That's a

55:34

pretty big difference. And it's really

55:36

bad for web publishers and kind of,

55:38

you know, then consequently really bad for

55:41

the web in general. We

55:43

have a pretty serious set of challenges on

55:45

the web already, even before this new wave

55:47

of LLMs came by to

55:50

further destroy the web. We

55:53

already had a pretty bad situation for

55:55

web publishers for lots of other reasons

55:57

over the years to have

55:59

something that reminds us of the web.

56:01

moves the need for many people to

56:03

visit a page at all, that is

56:05

going to crush publishers. And so

56:07

it does make sense why everyone's freaking out about this.

56:09

It makes a lot of sense. I

56:12

do caution people though,

56:14

I don't think it's a

56:16

very good business move or a very good technology

56:18

move to say, I'm going to just block AI

56:21

from being able to do any, to see any

56:23

of my stuff. Because

56:25

that's a pretty big hammer and that's

56:27

a pretty big blanket statement. And

56:30

you can't actually block them anyway. Like that's when

56:32

it comes down to technically speaking, you can't, you

56:34

literally can't stop them, right? Unless

56:36

you stop everyone from viewing your website, in which case you

56:38

don't have a website. Right.

56:40

So I think it is wise to

56:43

focus on trying

56:45

to prevent uses of your content that

56:47

remove the need to visit your page.

56:50

Because that is a direct attack on your business model. That

56:52

makes a lot of sense. I don't

56:54

think it's wise to say, I don't

56:56

want any AI training or any AI

56:59

visibility of my page. That

57:01

I think is probably

57:03

short sighted and probably a bit too

57:06

much of a blanket statement. And that I

57:09

don't think it's good for any

57:11

party involved to

57:13

have that kind of blanket ban on it. I know a

57:15

lot of people want though that what people, the publishers

57:18

in particular want is

57:21

they want an ecosystem

57:24

of members who do agree to

57:26

some rules of politeness and

57:28

say, look, we should agree on a system that

57:30

lets me tell you that you shouldn't do

57:33

X, Y and Z on my site and you should agree to it

57:35

and we'll feel better about you if you do that. And part

57:38

of the reason I think Instapaper, your

57:40

example was not a particularly big

57:42

problem is like you said, scale. And anything

57:44

with AI in the name these days, people

57:46

flip out about it and think this is

57:48

going to be as big as Google. Instapaper was

57:50

not as big as Google. No,

57:53

it did not have billions and billions and billions

57:55

of users. If it did, if Instapaper

57:57

had Google scale, I bet there would have been a.

57:59

hell of a lot more scrutiny on even the very

58:01

conservative things that you did. But because it was small,

58:03

it's not a big deal. Like that's, that's part of

58:05

the sort of the ecosystem of the web is there's

58:08

all sorts of small things that don't have

58:10

particular big scale to do and all sorts

58:12

of weird stuff. Nobody cares about them. We

58:14

allow them to exist. It's fine. But now

58:16

these big names and AI, AI is the

58:18

next big thing. You're an AI company, you

58:20

have a lot of funding. Everyone looks at

58:22

them and think that could be the next

58:24

Google. That could be the next thing with

58:26

billions and billions of users. So we better

58:28

take whatever weird stuff they're doing way more

58:30

seriously than we would take overcast. And with

58:32

even with Google, the, you know, the current

58:35

giant in the world of search and they're, you

58:37

know, trying to replace sites and giving answers on

58:39

the side or whatever. Neil I Patel coined a term,

58:41

I think it was says about this called Google zero,

58:43

which is the point at which publisher

58:46

websites get zero traffic from Google search,

58:49

right? Because it's been going down and down over the

58:51

years because, hey, you'd write type Google search and look,

58:53

the answer to my question that I typed it's right

58:55

on the Google results page. It's unattributed. And I don't

58:57

have to, if it was attributed, I don't have to

58:59

click on any link to get to it because the

59:01

answer is right there. And so Google has been sending

59:03

less and less traffic to websites and Google zero is

59:05

when you notice, hey, you know what, you know how

59:07

much traffic we're getting from Google searches? Zero.

59:09

I don't know if it's absolutely zero for everybody, but

59:11

it's sure going down. And it's a scary

59:14

world to have

59:16

what was once the massively largest

59:18

source of your traffic to your website disappear.

59:23

But yeah, like whether or not it is wise

59:25

to exclude, to try to, to ask

59:28

to be excluded from pick, you

59:30

know, whatever, whatever AI crawler thing

59:32

from whatever open AI perplexity or whatever.

59:35

I think most publishers just simply want that

59:38

choice. And to have that choice, the,

59:40

the crawlers need to agree. Because again, there is no

59:42

technical way to stop this short of doing like putting

59:44

your entire site behind a paywall. And even that's not

59:46

going to stop them. Cause they'll just pay another crawler,

59:48

go through it. Like it's, that's, that's the thing about

59:50

publishing on the web. You do, it's

59:52

like DRM. You want people to

59:54

see your movie. You can't make it impossible to

59:56

see your movie. You have to give the viewer

59:58

an ability to see. your movie. But once you

1:00:00

give the viewer the ability to see your movie,

1:00:02

they can see your movie. Like, what

1:00:05

if they see it, but also record

1:00:07

it? I want them to see it,

1:00:09

but not be able to see it. Can I do that? And

1:00:11

the answer is no. Right? So if you're

1:00:13

publishing on the web, you have

1:00:15

like, it's like anything else. That's why Marco was right to

1:00:17

call this illegal thing. Like things are published

1:00:19

all the time. They were published in paper, you know, like

1:00:21

the books or whatever. It's like, but I can take the

1:00:24

book and look at it. I can see all the letters

1:00:26

in it. Haha. The book is mine. Well, no, actually we

1:00:28

have laws about the stuff

1:00:30

that's in that law, that book. We

1:00:32

have this thing called copyright. And even

1:00:34

though you can technically read it and

1:00:36

you can technically copy it increasingly more

1:00:39

easily over time with technology, we have laws surrounding it

1:00:41

to control what you can do it. And

1:00:43

ROASD text, people who think of ROASD text as some

1:00:45

kind of like technological bank vault. It's no more of

1:00:47

a bank vault than you could put on a book.

1:00:49

Like you do want people to read it and you

1:00:51

can't stop them from being able to copy it. And

1:00:54

these dates is really, really easy to copy a book, especially

1:00:56

if it's an ebook, right? Setting aside

1:00:58

the whole DRM thing. What

1:01:00

you want is some either in

1:01:03

a sort of polite society, an agreement

1:01:05

among the large parties that actually are

1:01:07

significant to get along and then

1:01:10

failing that you want laws to provide whatever protections

1:01:12

you think are due to you. And yeah,

1:01:14

the Google search stuff has, I feel like been

1:01:16

hashed out probably in the Altavius today's, but who

1:01:19

knows? And the AI stuff has not

1:01:21

yet been hashed out. And so to move on to this next

1:01:23

one, because we have a lot of these items, Microsoft,

1:01:25

at least someone in Microsoft has

1:01:27

a very interesting notion of

1:01:30

what the deal is on the

1:01:32

web and potentially what the law should be surrounding

1:01:34

it. So this is

1:01:36

a post on the verge by

1:01:38

Sean Hollister, who writes, Microsoft AI

1:01:41

boss Mustafa Suleiman incorrectly

1:01:43

believes that the moment you publish anything on

1:01:45

the open web, it becomes quote unquote freeware

1:01:47

that anyone can freely copy and use. When

1:01:49

CNBC's Andrew Ross Sorkin asked him whether AI

1:01:52

companies have effectively stolen the world's

1:01:54

IP, Mustafa said, I

1:01:57

think that with respect to content that's already

1:01:59

on the open web, the social contract of

1:02:01

that content since the nineties has been that

1:02:03

it is fair use. Anyone can copy it,

1:02:05

recreate it with recreate it, reproduce with, sorry,

1:02:07

recreate with it, reproduce with it. That

1:02:10

has been freeware if you like, and

1:02:12

that's been the understanding Microsoft

1:02:14

is currently the target of multiple lawsuits alleging

1:02:16

that it and open AI are stealing copyrighted

1:02:18

online stories to train generative AI models. So

1:02:20

it may not surprise you to hear Microsoft

1:02:23

exec defend it as perfectly legal. I

1:02:25

just didn't expect them to be so very publicly and

1:02:27

obviously wrong. And I'm not a lawyer writes Sean, and

1:02:29

that's also true for me, but I

1:02:31

can tell you that the moment you create a work,

1:02:34

it is automatically protected by copyright in the U S

1:02:36

you don't even need to apply for it. And you

1:02:38

certainly don't void your rights just by publishing it on

1:02:40

the web. In fact, it's so difficult to rate to

1:02:42

waive your rights that lawyers had to come up with

1:02:44

special web licenses to help. This

1:02:46

is so gross. Like I'm

1:02:49

not as riled up as a lot of

1:02:51

people about people about, you know, these AI

1:02:54

bots crawling my website, like sitting

1:02:56

here now, I don't find it that off

1:02:58

putting. I don't love it, but whatever this

1:03:01

though, this is disgusting. So this is such

1:03:03

a weird statement because everybody knows how copyright

1:03:05

works. I'm sure this person knows as well.

1:03:07

But to say that like, Oh, it's once

1:03:09

you put it on the web, it's freeware,

1:03:11

which is a term that mostly applies to

1:03:13

software. But like, the idea is you can

1:03:16

recreate it, reproduce it, uh, you know, copy

1:03:18

it. Like, no, no, no.

1:03:20

Like there, those are specifically

1:03:22

the things we actually do have laws around. Well, we

1:03:24

don't have laws around or the more complicated things like,

1:03:26

well, can I train AI on it or whatever? And

1:03:28

we'll get to that in a little bit. But like,

1:03:30

it's such a weird thing to say that like, Oh,

1:03:32

as everyone knows since the nineties, once you put it

1:03:35

on the web, you forfeit all ownership. That's

1:03:37

not true at all. And I think that's, that's, like,

1:03:39

it's one of the things that's great about the web

1:03:41

is, Oh, it's just like books. It's printed word, right?

1:03:43

And especially in the beginning, it was just a bunch

1:03:45

of words and we already have laws surrounding that, right?

1:03:47

And that's why there were cases about search engines. Like

1:03:50

our search engines, copying it because, you know,

1:03:52

we got this whole, you know, giant library

1:03:54

of laws about copying texts. My website has

1:03:56

text on it and Google's copying it and

1:03:59

they've had to do good. out and say,

1:04:01

actually, what Google's doing is fine within these

1:04:03

parameters, blah, blah, blah. But

1:04:06

that fight was fought because it was an

1:04:08

example of copying. But yeah, this... I

1:04:12

mean, obviously, the Microsoft AI

1:04:14

leadership, this guy is not a

1:04:16

lawyer either. But

1:04:19

that's not how you should defend this. You shouldn't

1:04:21

defend it by saying, you know, every single web

1:04:23

is a free for all. Because that's never

1:04:25

the way it's been and it's not the way it is now. There's

1:04:29

another foot in the mouth problem from Microsoft. I'm not

1:04:31

sure what's going on over there, but they really need

1:04:33

to take a lesson from

1:04:35

Apple and maybe try to speak with one

1:04:37

voice instead of having individual lieutenants make really

1:04:39

terrible statements of the press. Yeah.

1:04:42

So, Louis Mantia writes with regard to permissions on

1:04:44

AI training data from the 22nd of June. Louis

1:04:48

writes from John Gruber today on the 22nd of June.

1:04:52

It's fair for public data to be excluded

1:04:54

on an opt-out basis rather than included on

1:04:56

an opt-in one. And then Louis

1:04:58

continues, no, no, it's not. This

1:05:00

is a critical thing about ownership and copyright in

1:05:03

the world. We own what we

1:05:05

make the moment we make it. Publishing text or

1:05:07

images on the web does not make it fair

1:05:09

game to train AI on. The public in public

1:05:11

web means free to access. It does not mean

1:05:13

free to use. Also, whether reposting

1:05:16

my content elsewhere is in good faith or not,

1:05:18

it is now up to someone other than me

1:05:20

to declare whether or not to disallow AI training

1:05:22

web crawlers in the robots.txt file

1:05:25

to allow, excuse me, to add insult to injury,

1:05:27

that person may not have the knowledge or even

1:05:29

the power to do so if they're posting content

1:05:32

they don't own on a site that they also

1:05:34

don't own like social media. So this

1:05:36

is, he's so close to getting to the crux

1:05:38

of this. In the first

1:05:40

little paragraph here, he's basically declaring

1:05:43

that training AI on your data

1:05:45

is exactly the same

1:05:47

as copying and reproducing it. And that is

1:05:49

not something that the world agrees on. His

1:05:52

opinion is that it is. The courts have

1:05:55

not yet weighed in. I think

1:05:57

to the average person they would say, are those

1:05:59

the same things? seem like they might be a

1:06:01

little bit different. Kind of in the same way

1:06:03

that indexing your content in Google is a little

1:06:06

bit different than just literally copying it and reposting

1:06:08

it on the website, right? But

1:06:10

anyway, if you agree that it's the same as copying,

1:06:12

then yeah, sure. But then the second bit is getting

1:06:14

to even more of the heart of it here, which

1:06:16

is like, okay, so let's say we do agree that

1:06:19

it's the same much, you know, not

1:06:21

proven yet, but anyway. What

1:06:24

about when somebody like posts a link to

1:06:26

your site on the social media network, and

1:06:28

on that website, they do a little embedding,

1:06:31

inlining of like the first paragraph or whatever,

1:06:33

like what if someone copies and pastes a

1:06:35

paragraph of your thing on another

1:06:37

website, right? Even if you had absolute,

1:06:39

somehow magical technical control to stop

1:06:42

AI crawlers crawling your website, if

1:06:45

people can read your website and quote

1:06:47

from it or embed little portions of it or

1:06:50

screenshot or do whatever on other websites, of course,

1:06:52

you don't control those other websites. And so if

1:06:54

they allow crawling, your stuff's going to end up

1:06:57

in the Google search index in the

1:06:59

AI training model or whatever, even

1:07:01

though you disallowed it from your

1:07:03

website. And I would say

1:07:05

that for the most part that we also

1:07:08

have laws covering can someone take a portion

1:07:10

of the thing that you made and

1:07:12

quote it elsewhere, there's all legal framework

1:07:14

deciding whether that is fair use or

1:07:16

not, and it's complicated. And the

1:07:19

law is not a deterministic machine, as the other

1:07:21

Patel, who I mentioned before, is always fond of

1:07:23

saying, but we do have a legal framework to

1:07:25

determine, can I copy and paste this

1:07:27

paragraph from this thing on this person's site and quote it

1:07:29

on my site so I can comment on it? Yeah,

1:07:32

in general, you can. Can I make a

1:07:34

parody of this article on my

1:07:36

website? Yeah, you can. There's a whole bunch

1:07:38

of things around that that have been fought

1:07:40

out in court that we have a system

1:07:42

for dealing with. But all of

1:07:45

those things, say the court determines, you sue them

1:07:47

and they say, actually, this person was allowed to

1:07:49

quote that snippet, right? You lost your fair use

1:07:51

case because it's pretty open and shut. That's fine.

1:07:54

That just got indexed by an AI

1:07:56

training pot because that person's website allows

1:07:58

them, you know, polite AI bots or

1:08:01

whatever, nevermind again, nevermind that you can't stop them.

1:08:03

Anyway, right? That's

1:08:05

just the nature of publishing.

1:08:08

No matter what, you do not

1:08:11

have absolute control over every single character

1:08:13

that you made. You

1:08:15

do have control over the entire work and the

1:08:17

reproduction of the entire work, but you don't have

1:08:19

control over other examples of fair use. And

1:08:22

Louie's saying, oh, it shouldn't be like, I shouldn't have

1:08:24

to opt out. The default should be that nobody can

1:08:26

crawl me. I mean, that's just like,

1:08:30

not only is it technically impossible, but

1:08:32

like, that's not the

1:08:34

way the web has ever worked. It has

1:08:36

always been, we're going to crawl you unless

1:08:38

you tell us don't. And

1:08:41

even the polite ones, you know, they'll

1:08:43

read the thing that you said not to do it, but

1:08:45

by default, they're going to crawl you. And I think asking

1:08:47

for a world where everything you

1:08:49

publish on your website is not only

1:08:51

not crawlable by the things

1:08:53

you don't want it to crawl up, but also not able

1:08:55

to be quoted by other people is clawing

1:08:59

back rights that we've

1:09:01

already decided belong to other people through fair

1:09:03

use. So then the music

1:09:05

industry decided to get involved. Yeah. Multibillion

1:09:08

dollar companies are entered the chat, as they would say.

1:09:11

We talked about this before of like, Hey, Louie Mantian doesn't

1:09:13

want people to crawl on his website. What

1:09:15

can he do about it? He's just one person. Uh, the

1:09:18

music industry, they have a lot of money. They

1:09:20

have a lot of IP. This is

1:09:22

where the stuff really starts

1:09:25

going down. Yeah. So reading

1:09:27

from our Stendica on the 24th of June, universal

1:09:29

music groups, sunny music and Warner records

1:09:31

have sued AI music synthesis companies, Oudio

1:09:34

and Suno for allegedly committing mass copyright

1:09:36

infringement by using recordings owned by the

1:09:38

labels to train music generating AI models.

1:09:40

The lawsuits filed in federal courts in New

1:09:43

York and Massachusetts claim that the AI companies

1:09:45

use of copyrighted material to train their systems

1:09:47

could lead to AI generated music that directly

1:09:49

competes with and potentially devalues the work of

1:09:51

human artists. So from

1:09:54

the verge article, there's a quote from

1:09:56

RA a chief legal officer, Ken Doroshow.

1:09:58

And that quote is these. are straightforward cases

1:10:01

of copyright infringement involving unlicensed copying of

1:10:03

sound recordings on a massive scale. Suno

1:10:05

and Udeo are attempting to hide the

1:10:08

full scope of their infringement rather than

1:10:10

putting their services on a sound and

1:10:12

awful, lawful, excuse me, footing. And again,

1:10:15

that was the RIAA chief legal officer.

1:10:18

Mikey Shulman, the CEO of Suno, says

1:10:21

the company's technology is transformative

1:10:23

and designed to generate completely

1:10:25

new outputs, not to memorize

1:10:27

and regurgitate preexisting content. Shulman

1:10:30

says Suno doesn't allow user prompts

1:10:32

based on specific artists. Reading

1:10:35

from the lawsuit, the use here is far

1:10:37

from transformative as there is no functional purpose

1:10:39

for Suno's AI model to ingest copyrighted recordings

1:10:42

other than to spit out new, competing music

1:10:44

files. That Suno is copying

1:10:46

the copyrighted recordings for commercial purpose

1:10:48

and is deriving revenue directly proportional

1:10:50

to the number of music files

1:10:52

it generates further tilts the fair

1:10:55

use factor against it. Andy

1:10:58

Baio writes, 404 Media pulled together a video

1:11:00

montage of some of the AI-generated examples provided

1:11:02

in the two lawsuits that sound similar to

1:11:04

famous songs and their recording artists. Then

1:11:08

finally, we'll put a link in the show

1:11:10

notes to a Verge article that discusses what

1:11:12

the RIAA lawsuits mean for AI and copyright.

1:11:14

You know, I saw somebody say this a

1:11:16

few days ago, I don't remember who exactly

1:11:18

it was, but what's

1:11:20

going on if the RIAA are suddenly the good guys?

1:11:22

This is a weird place to be. Well, are they

1:11:25

though though? So here's the thing. Like this is the

1:11:27

tricky bit with this and we talked about this with

1:11:29

the image generators or whatever. So this is significant because

1:11:31

they're big rich companies and you have to take them

1:11:33

seriously when they bring a lawsuit because this is the

1:11:35

kind of like who can stop open AI and Google

1:11:38

and whatever. Well, you know, it's clash

1:11:40

of titans. You need other titans in here to be

1:11:42

duking it out, right? I

1:11:47

think this needs to

1:11:49

be fought out in a court in some way. I

1:11:51

say that before we see what the result will be

1:11:53

because maybe the result does not probably want

1:11:55

to happen. But like as

1:11:58

with the image things, These companies that you

1:12:00

type in a string and they produce a song for

1:12:03

you, right? These models

1:12:05

are trained on stuff. And

1:12:07

these record labels say, yeah, you trained them on

1:12:09

all our music, right? Gets back to

1:12:12

the question, is training something? Is

1:12:14

AI training? How does that relate to copying? Is

1:12:16

it just like copying? Is it not like copying

1:12:18

at all? Is it somewhere in the middle? Do

1:12:20

any of our existing laws apply to it? And

1:12:22

we've discussed this on past episodes as

1:12:24

well, especially when

1:12:27

the company doing the training, then

1:12:29

has a product that they make money on.

1:12:32

And as I said with the image training, these

1:12:35

models that make songs are worthless without

1:12:37

data to train them on. The

1:12:39

model is nothing without the training

1:12:41

data. This company that wants to make money,

1:12:43

you pay us x dollars, you can make

1:12:45

y songs, right? That's their business model. They

1:12:47

can make zero songs if they have not

1:12:49

trained their model on songs. So

1:12:51

the question is, where do those songs

1:12:54

come from? If they've licensed them

1:12:56

from somebody, if they made the songs themselves,

1:12:59

no problem, right? Again, Adobe training

1:13:01

their image generation models entirely on

1:13:03

content they either own or licensed.

1:13:06

Nobody's angry about that. That's the thing

1:13:09

you're doing. You own a bunch of images, you license

1:13:11

them from a stock photo company or whatever, you

1:13:14

train your models on them, you put the feature

1:13:16

into Photoshop, you charge people money for Photoshop, they

1:13:18

click a button, it generates an image. Whether people

1:13:20

like that feature or whatever, legality seems

1:13:22

fine. These other situations

1:13:25

where it's like, hey, we crawled your site because

1:13:27

we don't care about your robust ed text. We

1:13:29

trained our models on your data, on

1:13:31

your songs, on your whatever, right? And by the way,

1:13:33

we have no idea if these companies

1:13:35

actually paid for all the songs. Let's just

1:13:37

assume they did. They bottle the songs from,

1:13:39

you know, Sony Music

1:13:41

Warner Records or whatever, or they paid for a training

1:13:43

service. They got all the songs, they trained their model

1:13:45

on them, they're charging people to use their model, right?

1:13:49

Just like the image processing, I've always thought that if

1:13:53

you have a business that

1:13:55

would not be able to exist without content

1:13:57

from somebody that you did not. pay

1:14:00

anything for, that is very

1:14:02

different than are we trained in

1:14:05

AI model for research purposes, are we trained

1:14:07

it for, you know, for some purchase that

1:14:09

is not like literally making money off of

1:14:11

you. And this particular case is like, okay,

1:14:13

not just that they're making money, but the

1:14:15

thing they're providing is, quote, not transformative. They

1:14:17

keep using that word because that's one of

1:14:19

the tests for like fair use. Is the

1:14:21

work transformative? Have they taken the

1:14:23

thing that existed but made something new out

1:14:25

of it? And that's the argument in court,

1:14:27

whether it is not transformative.

1:14:29

And also, is it a substitute? There's

1:14:32

another one of the fair use tests. Is it

1:14:34

a substitute for the product? Is someone not going

1:14:36

to buy a Drake

1:14:38

album because fake Drake sounds

1:14:40

just as good and they just listened

1:14:42

to fake Drake, right? Is it a substitute

1:14:44

for it? It doesn't mean does it sound exactly like it? That's

1:14:46

a whole other sad area of

1:14:49

law of like, does song A sound too much

1:14:51

like song B and they have to pay them

1:14:53

whatever when they're all made by humans, right? This

1:14:55

is like, would someone pay for

1:14:57

this instead of paying for this?

1:14:59

Is one a substitute for the

1:15:02

other? And that's what they'll be

1:15:04

duking it out about. But I think

1:15:06

at its root, it is sort of like,

1:15:08

where does the value of this company come from?

1:15:12

Every company has to take input

1:15:14

from somewhere. They manufacture something and they sell it to

1:15:17

you or they have a service, they wrote the software

1:15:19

for it, they pay someone to run the servers and

1:15:21

they sell it. There's a there's sort of a value

1:15:23

chain there. And a lot of these companies are like,

1:15:27

we would make more money if we

1:15:29

don't have to pay for the things that make

1:15:31

our product valuable. So we don't want

1:15:33

to have to license all the music in the world, but

1:15:35

we do want to train an AI model on all the

1:15:37

music on the world so that we can make songs that

1:15:39

sound as good as all the music in the world. But

1:15:42

we don't want to have to pay for any of that. And

1:15:44

that seems to be not

1:15:47

a good idea for from my perspective. And this is one

1:15:49

of the different ways you can look at this. All

1:15:52

ethical, legal, I think one

1:15:54

of the frameworks that I've

1:15:56

fallen back on a lot is practical.

1:16:00

If, you know, for any given thing, say, if

1:16:02

we allowed this to happen, would

1:16:04

it produce a viable,

1:16:07

sustainable ecosystem? Like,

1:16:11

would it produce a market for

1:16:13

products and services? Would it

1:16:15

be a rising tide that lifts all boats? Or

1:16:17

would it like, burn the forest to the

1:16:19

ground and leave one tree left in the middle? Right? You

1:16:21

know what I mean? Like, that

1:16:23

practical approach, people like to jump on, like we talked

1:16:25

about before with the Vittici and Max stories and everything,

1:16:27

like they want to go to the moral and ethical

1:16:29

thing. They're stealing from us. It's our stuff. They have

1:16:31

no right. And even when I was saying before, like,

1:16:33

oh, they're, they don't want to pay for this stuff,

1:16:35

but they want to make money off of it or

1:16:37

whatever. But practically, and this is not the way the

1:16:39

law works, but this is the way I think about

1:16:41

it. Practically speaking, I'm always asking myself, if

1:16:44

this is allowed to fly, what

1:16:46

does this look like? Fast forward this. What,

1:16:48

you know, is this viable? Right? What

1:16:51

if, if everyone's listening to fake Drake,

1:16:54

does Drake, the next Drake, are not able to

1:16:56

make any money? Does human

1:16:59

beings making music become an unviable business? And

1:17:01

all this is just an increasingly gray soup

1:17:03

of AI generated stuff that loops in on

1:17:05

itself over and over again. Right? Like

1:17:07

where are the, you know, and we have the same

1:17:10

thing with publishing on the web. Like, does Google destroy

1:17:12

the entire web because no one needs to go to

1:17:14

websites anymore? They just go to Google. Right. Unfortunately,

1:17:18

when these cases go, you know,

1:17:20

to court, no one is thinking that that's not how

1:17:22

the law works. The law is going to be, is

1:17:24

this fair use, whatever? Just does Congress pass new laws

1:17:26

related to this or whatever. But what I really hope

1:17:28

is that the outcome of all these things and the

1:17:30

thing I'm always rooting for is can we get to

1:17:33

a point where we have an

1:17:35

ecosystem that is sustainable, which

1:17:37

means it's probably, you know, whatever they're suing for is like, I think they

1:17:40

want like $150,000 for every song or something. That is not a sustainable solution.

1:17:45

You can't train an AI model when you pay $150,000 for each song that you

1:17:47

trained it on because you need basically

1:17:49

all the songs in the world. That's a

1:17:51

big number. That's stupid. We do

1:17:53

want AIs that can make little songs, right?

1:17:55

I think that is a useful thing to have, right?

1:17:58

So we need to find a way where we can... have that, but

1:18:01

also still have music

1:18:04

artists who can make money making actual music, setting aside

1:18:06

the fact that the labels take all the money and

1:18:08

the artists get rarely anything anyway, which is separate

1:18:10

issue. Right. And there was a good article

1:18:12

about that recently about how the labels, the

1:18:15

label Spotify and the artists and the terrible relationship

1:18:17

there that screws over artists. Anyway,

1:18:22

I think I really hope that the outcome

1:18:24

of this is some kind of situation where,

1:18:26

where there's

1:18:30

something sustainable. There's like, I keep using ecosystem, but

1:18:32

it's like, you know, you have to have enough

1:18:34

water, the whole water cycle. This animal

1:18:36

eats that animal. It dies. It

1:18:39

fertilizes the plant. Like the whole, you know, a sustainable

1:18:41

ecosystem where everything works and it goes all around in

1:18:43

a circle and everything is healthy. And there's growth,

1:18:45

but not too much and not too cancerous. And it's

1:18:48

not like everything is replaced by a model culture and

1:18:50

only one company is left standing and all that good

1:18:52

stuff. Right. But right now

1:18:54

the technology is advancing in a way that if

1:18:57

it's not, if we don't do

1:18:59

something about it, uh, the,

1:19:01

the individual parties involved are not

1:19:04

motivated to make a sustainable ecosystem.

1:19:06

Let's say, I mean, that's kind of what the DMA is about

1:19:09

in the EU and these AI companies definitely

1:19:11

are not motivated to try to make sure they have

1:19:13

a sustainable ecosystem. They just want to make money. And

1:19:16

if they can do it by taking the world's music and

1:19:18

selling the ability for you to make songs that sound like it

1:19:20

without paying anything to the music that they ingested, they're going to

1:19:22

try to do that. Yeah.

1:19:27

It's, I don't know. It's all just so weird

1:19:29

and gross. And it's, it's hard because I don't

1:19:32

want to be old man who shakes fist at clouds.

1:19:35

Right. And it

1:19:37

seems like AI for

1:19:39

all the good and bad associated with it

1:19:41

is a thing. It's certainly a flash in

1:19:43

the pan for right now, but I

1:19:46

get the feeling that where blockchain

1:19:50

and Bitcoin and all that sort of

1:19:52

stuff was very

1:19:55

trendy, but anyone with a couple of brain

1:19:57

cells to rub together would say, eh, that's

1:19:59

all good. a fade or it's certainly not going to

1:20:01

work the way it is today. I

1:20:03

think there's a little of that here, but I

1:20:05

get the feeling that this is going to stick

1:20:08

around for a lot longer. And I think

1:20:11

that there needs to be some wrangling

1:20:13

done, some legal wrangling. And I

1:20:16

get the move fast and break things mentality of

1:20:18

these startups that are doing all this, but I

1:20:21

don't know. It just feels kind

1:20:23

of wrong. Like again, I'm not nearly

1:20:26

as bothered by it as some of our peers

1:20:28

are, but it just doesn't feel right. And

1:20:31

it definitely doesn't feel sustainable. Like practically, like if,

1:20:33

if, like regardless of how you feel about right

1:20:35

or wrong, if you say, if we just let

1:20:38

them do this, like, and

1:20:41

these, you know, these models get better and better and

1:20:43

produce more and more acceptable content, you can

1:20:45

see that it's taking, again, regardless of

1:20:47

how this lawsuit ends up with the whole

1:20:49

record labels, you can see that it is

1:20:51

taking value away from human beings, making

1:20:54

music and pushing that value to models,

1:20:56

making music. But those models are absolutely worthless

1:20:58

without that human generated music, at least initially,

1:21:00

right? Again, maybe in the future, there will

1:21:02

be models trained entirely on model generated music,

1:21:04

but then you have to trace it back

1:21:06

to where that model get trained. Like in

1:21:08

the end, these models are trained

1:21:11

on human created stuff and

1:21:13

there's, there may not be

1:21:15

enough officially licensed human created stuff to train them

1:21:17

on at this point. I

1:21:19

think that's, you know, I think we, we

1:21:22

want these tools. They are useful for

1:21:24

doing things. Even if you think, oh, they make

1:21:26

terrible music. Sometimes people need terrible

1:21:28

music, right? Sometimes people just need a little jingle. They

1:21:30

can describe it. They want it to be spit out.

1:21:32

Right. By most people's definitions, all of my music is

1:21:34

terrible music. They

1:21:37

do useful things like, unlike, you know, cryptocurrency,

1:21:39

which does a very, very small number of

1:21:42

useful things that is not in general purpose.

1:21:44

The AI models do tons of useful things.

1:21:46

Apple's building a bunch into their operating systems.

1:21:48

You know, people use them all the time.

1:21:50

They do tons of useful things, right? We

1:21:53

should find a way for them to

1:21:55

do those things without

1:21:58

destroying the ecosystem. I think

1:22:00

we can find a way for that

1:22:02

to happen. If you look at the

1:22:04

awful situation with Spotify and record labels

1:22:06

and music artists, that's a pretty bad

1:22:08

version of this. And yet still,

1:22:10

it is better than Spotify saying, we're going to stream

1:22:13

all these songs for free and not

1:22:15

pay anybody. I wish I could find that article for

1:22:17

the notes. I'll try to look it up. But

1:22:19

even that is better than the current situation

1:22:22

with AI, which is like, we're

1:22:24

just going to take it all for free. Come sue us. And

1:22:27

they say, okay, we are suing you. And they'll

1:22:29

battle it out in court. And like, either way

1:22:31

this decision goes with the music, they could go

1:22:33

bad in both directions. Because if they say, oh,

1:22:35

you're totally copying this music, all AI training is

1:22:38

illegal. That's terrible. That's

1:22:40

bad. We don't want that, right? And

1:22:42

if they say, no, it's fine. It's transformative. You can

1:22:44

take anything you want for free. That's also bad. So

1:22:47

both extremes of the potential decision that a

1:22:49

court could make based on this lawsuit are

1:22:51

really bad for all of us for the

1:22:53

future. So that's why I hope we find

1:22:55

some kind of middle ground. Like again, with

1:22:57

Spotify, they came up with a

1:22:59

licensing scheme where they can say, we want to

1:23:01

stream your entire catalog of music. Can

1:23:04

we figure out a way to exchange money

1:23:06

where you will allow that to happen legally?

1:23:09

And they came up with something. It's not a great

1:23:11

system. They came up with, again, if I can

1:23:14

find that article, you can read and see how bad it is. But they

1:23:16

didn't just take it all for free. Right. And

1:23:18

they also didn't, the music labels didn't say, okay, but every time

1:23:20

someone streams one of these songs, it's 150 grand. Right.

1:23:23

That's also not sustainable. So obviously they're

1:23:26

staking out positions in these lawsuits and they're trying to

1:23:28

put these companies out of business with big fees or

1:23:30

whatever. But yeah, it's

1:23:32

like, it's scary. It's scary when Titans clash.

1:23:35

And I do worry about how the results of these cases

1:23:37

are going to be. But I think, I think

1:23:40

we have to have these cases or, and I

1:23:42

know this is ridiculous in our country, or we

1:23:44

have to make new laws to address this specific

1:23:47

case, which is different enough from

1:23:49

all the things that have come before it, that we

1:23:51

should have new laws to address it. It

1:23:53

would be better if those laws weren't created by

1:23:55

court decisions, but our ability

1:23:57

and track record for creating technology.

1:24:00

technology related laws for new technology

1:24:02

is not great in this country.

1:24:04

So there's that. Yeah.

1:24:06

And then it continues because

1:24:08

Figma, a popular, um, I

1:24:11

don't know how to describe this, like a user interface,

1:24:14

uh, generation tool, um, yeah,

1:24:17

design tool. Thank you. Uh, they pulled

1:24:19

their AI tool after criticism that it blatantly

1:24:21

ripped off Apple, uh, Apple's weather app. So

1:24:23

this is the verge by Jay Peters. Figma's

1:24:26

new tool make designs. Let's use our users

1:24:28

quickly mock up apps using generative AI. Now

1:24:30

it's been pulled after the tool drafted designs

1:24:32

that looked strikingly similar to Apple's iOS weather

1:24:35

app. In a Tuesday interview with

1:24:37

Figma, CTO, Chris Rasmussen, I asked him point

1:24:39

blank if make designs was trained on Apple's

1:24:41

app designs. His response, he couldn't say for

1:24:43

sure. It

1:24:46

was not responsible for the training AI models that

1:24:48

used it all. Who knows who trained it? It's

1:24:50

just our model. We don't know. Do you know

1:24:52

who trained? I don't know. Does anyone know who

1:24:54

trained it? It's just, we just found it on

1:24:56

our doorstep and this is a model with the

1:24:58

real trainer. Please stand up. Uh,

1:25:01

quote, we did no training as part of the

1:25:03

generative AI features. Rasmussen said the features are quotes

1:25:05

powered by off the shelf models and a bespoke

1:25:07

design system that we commissioned, which appears to be

1:25:09

the underlying issue. So if you commissioned it, then

1:25:11

you should know we had someone else do it

1:25:13

and they gave it to us and we just

1:25:15

took it and we're like, we didn't ask him

1:25:17

any questions. It's fine. Whatever, whatever

1:25:19

you got, just give it as it's probably

1:25:22

fine. Uh, the key AI models that power

1:25:24

make designs are open AI's GPT four O

1:25:26

and Amazon's Titan image image generator G1. According

1:25:28

to Rasmussen, if it's true that Figma didn't

1:25:30

train its AI tools, but they're spitting out

1:25:32

Apple app lookalikes anyway, that could suggest that

1:25:34

open AI or Amazon's models were trained on

1:25:37

Apple's designs. Open AI and Amazon

1:25:39

didn't immediately reply to a request request for

1:25:41

comment. This is seriously the like the

1:25:44

Spiderman pointing at other Spiderman's

1:25:47

image. It's just, it's not my fault. It's their fault.

1:25:49

Well, it's not my fault. It's their fault. Oh, no,

1:25:52

no, no, no, no. It's not my fault. It's their

1:25:54

fault. It was that company. I think it was open

1:25:56

AI or whatever the Sora model that does it makes,

1:25:58

makes movies essentially. Someone who was

1:26:00

responsible for that, was asked in an interview,

1:26:02

was your model trained on YouTube? They

1:26:05

didn't give an answer. Like, maybe, I don't

1:26:08

know. Listen, if you run an AI company,

1:26:11

figure out how and where your

1:26:13

models were trained. I don't know, maybe

1:26:15

you trained them on good things, bad things, whatever,

1:26:18

but have an answer. Don't say, we don't know.

1:26:21

Someone else did it. We like, this

1:26:23

seems like table stakes. You

1:26:25

should know where and on what your

1:26:27

model was trained on. Not like granular, like every single individual

1:26:30

thing, although ideally that would be great, but there's too much,

1:26:32

I get it, right? But when someone says, hey, did you

1:26:34

train on YouTube? You should be able to answer that with

1:26:36

a yes or no. Right, not

1:26:38

weasel about it. And this one, was this trained on Apple's

1:26:40

apps? I mean, anyone looking at it

1:26:43

is gonna be like, well, if it wasn't, this is the

1:26:45

world's biggest coincidence because it looks just like Apple's app. As

1:26:48

Gruber pointed out, right down to the really weird

1:26:50

like line chart that I never really understood until

1:26:52

I saw it explained in Apple's weather app, right?

1:26:54

Was obviously trained on Apple stuff, but

1:26:57

you have to have an answer, right? If you don't

1:26:59

have an answer, say, I don't know, but I'll find

1:27:01

out for you and then come back. But

1:27:03

like the bar is real

1:27:05

low here. Anyway, same

1:27:08

situation, different thing. Images, songs,

1:27:10

text, UIs, a

1:27:15

mock-up tool that makes UIs, it's

1:27:17

based on a model. That model

1:27:19

is worthless without being trained on

1:27:21

a bunch of UIs. Were you gonna get enough UIs to

1:27:23

train it from the world of

1:27:26

UIs that we take essentially without permission? Is

1:27:28

that okay? If we sell that as part

1:27:30

of our application, is that okay? I

1:27:33

mean, I wrote a

1:27:35

big post about this, what, in January? Excuse

1:27:37

me, I made this. Yeah, and

1:27:39

we talked about it on the podcast before

1:27:41

and I took a while to write

1:27:43

this because actually speaking of Neil Patel, I was listening

1:27:46

to the Decoder podcast and there was an episode where

1:27:48

I was debating with somebody about the New York Times

1:27:50

lawsuit at the time, like New York Times was suing

1:27:52

some company that had trained its AI on the New

1:27:54

York Times and they said, you

1:27:56

can't do that. And going back and forth about like, well, the

1:27:58

model is just doing what a person would do. it's learning and

1:28:00

blah, blah, blah. Is the person the same

1:28:03

as the model? Does the model have the same

1:28:05

rights as a person? And I was trying to

1:28:07

write up something related to that. And as usual,

1:28:09

writing helps me clarify my thinking, but it is

1:28:11

a fairly complicated circuitous route to sort of really

1:28:13

dig down into that thought to

1:28:16

get to what's at the heart of it. And

1:28:19

I wrote this thing and I think I did get to the heart

1:28:21

of it as far as I was concerned, but it's

1:28:24

complicated. So every time I try to like summarize it

1:28:26

on the podcast, I find myself like tongue tied and

1:28:28

rather, you know, you just quote from the paragraphs. Like

1:28:30

I think if you read the post, my thoughts are

1:28:32

in there, but a lot of people have read it

1:28:34

and like no one has commented on it. So maybe

1:28:37

I'm doing a poor job communicating it, but I

1:28:40

was coming at it from the other angle. We talked all about

1:28:42

training data in this section of the show here. I was coming

1:28:44

at it from the angle of like what

1:28:47

was then one of the hot

1:28:49

topics, which is say I use one of these

1:28:51

tools. Say I use the Figma tool to generate

1:28:53

a UI. I use the song tool to generate

1:28:55

a song or whatever. That

1:29:00

thing that I made, what

1:29:02

is the legal, ethical,

1:29:04

moral, practical ownership

1:29:07

deal with that? If

1:29:09

I use Figma to make that

1:29:11

auto create UI thing and it makes me

1:29:14

a UI and I put

1:29:16

that in my app, do I

1:29:18

own that UI? If I make a song with

1:29:20

the song making tools, do I have the copyright

1:29:23

on that song? There's been legal cases about this. And I think

1:29:25

the only ruling we have now is something like if you make

1:29:27

it with any AI generator tool, you don't have the copyright on

1:29:29

it or whatever. But

1:29:31

the reason I got to that, because I was getting with the whole

1:29:33

like, oh, training is just like what

1:29:35

a human would do. They read all these articles in

1:29:38

the New York Times, then you ask the human the

1:29:40

answer, and they read all those articles, and they have

1:29:42

the knowledge from reading those articles, and they give you

1:29:44

an answer. Well, that's just what our AIs are doing.

1:29:46

I'm like, yeah, but a human is a human, and

1:29:48

AI is an AI, and is that really what the

1:29:50

root of the thing it is? And I kept chasing

1:29:52

that down, chasing that thought down, and got to sort

1:29:54

of the thing that confers ownership. When

1:29:58

you make something, it's yours. You write something. something on

1:30:00

your blog, you have the copyright to it because you

1:30:02

created it. It's so clear, right? What

1:30:05

if you draw a picture on a piece of paper? Okay, you

1:30:07

got the copyright on the picture, right? What

1:30:09

if you use Photoshop to make a

1:30:11

picture? Well, now you use this software tool written

1:30:13

by a bunch of other people, just plain old

1:30:15

Photoshop, not like AI generally, like Photoshop 3.0, right?

1:30:18

With layers now. You

1:30:20

use Photoshop, but you didn't write Photoshop. A bunch

1:30:22

of people wrote software to make Photoshop, and you

1:30:24

then paid Adobe for, then they gave you that

1:30:26

software product. You use Photoshop to make a picture,

1:30:29

but still we say, well, you made that picture.

1:30:31

You have the copyright on it. You are the

1:30:33

creator. You own it, right? Then

1:30:36

we say, all right, but what if you can't draw?

1:30:40

What if you tell somebody, like, I can't draw. Here's

1:30:42

what I want. I want this picture, or whatever example I gave a

1:30:45

thing, a polar bear riding a

1:30:47

skateboard, but I can't draw. So I

1:30:49

asked somebody else, say, can you draw me a picture of

1:30:51

a polar bear riding a skateboard? So someone goes and they

1:30:53

draw a picture of a polar bear riding a skateboard. At

1:30:55

that point, the person who drew it owns it. Maybe they

1:30:57

use Photoshop, maybe they don't. They own it because they created

1:30:59

it. They drew it, right? But then you say, okay, this

1:31:01

was a work for hire. I'll give you 10 bucks. And

1:31:03

our contract says, I give you 10 bucks. You give me

1:31:06

the polar bear drawing. Now I own the polar bear drawing

1:31:08

because I paid you for it. That is

1:31:10

a market for creative works. Someone

1:31:12

was an artist. I can't draw. They

1:31:15

could. They drew it. They asked

1:31:17

for money. I gave them money. The

1:31:20

copyright is now mine, right? And

1:31:22

the act of creation is clear. The person who drew

1:31:24

it, they created it. I paid money for it. They

1:31:27

sold me their creation. Now I own it. All, you

1:31:29

know, normal, right? Now

1:31:31

I say, make me a picture of a polar bear

1:31:33

on a skateboard. But I don't say it to

1:31:35

an artist. I say it to an

1:31:37

image generator. It's the exact

1:31:39

same thing as I did before. Before

1:31:42

when I did it, it was clear that I don't own

1:31:44

anything until I pay for that, right? Now

1:31:46

when I do that exact thing, but instead of typing

1:31:48

it into an email to an artist, I type it

1:31:50

into an image generator and

1:31:52

I get an image back. Who

1:31:55

created that image? I didn't

1:31:57

create it. But if you're going to say I didn't create the

1:31:59

one that the artist drew for me. Because you just told the

1:32:01

artist what to draw, but you didn't create it. Well, if I

1:32:03

didn't create that one, I certainly didn't create this one because I

1:32:05

literally did the same thing. I just typed the text in a

1:32:07

different text field. It could literally be the same text. It could

1:32:09

be an AI prompt, emailed to an

1:32:12

artist or sent to an AI. So I'm not

1:32:14

gonna say that I am the creator of that.

1:32:17

The AI model can't be

1:32:19

the creator because computer

1:32:22

programs can't own things. They

1:32:24

don't have rights. Computer

1:32:28

programs are made by people who have rights, just like

1:32:30

people who wrote Photoshop. They have the rights to Photoshop

1:32:32

and so on and so forth. But the people who

1:32:34

wrote Photoshop have no rights to the things that people

1:32:36

made with Photoshop, despite Adobe's little snafu

1:32:38

with their license agreements recently, which they

1:32:40

clarified. But anyway. So

1:32:43

I didn't make that picture of the polar bear.

1:32:45

The large language model didn't make it. Who

1:32:50

owns that picture of the polar bear based on

1:32:52

the act of creation? Where is the act of

1:32:54

creation there? How did that model

1:32:56

create the polar bear? Well, it created the polar

1:32:59

bear picture because it had been trained on tons

1:33:01

of other images that maybe were

1:33:03

or weren't licensed. But still I'm looking around of

1:33:06

like if ownership is conferred by the act of

1:33:08

creation and there's no act of creation here, what

1:33:10

the hell are we, what's going on here? Who

1:33:12

owns the picture of the polar bear? Every

1:33:16

time I dig down into some kind of like,

1:33:18

oh AI's allowed you to do this and you're

1:33:20

allowed to train, it's just what people do or

1:33:22

whatever and computers aren't people. I always go through

1:33:24

to looking for how we confer ownership of stuff

1:33:26

like this, how we confer ownership of intellectual

1:33:29

property, how we exchange money for intellectual

1:33:31

property, how the market for intellectual property

1:33:33

works. And none of the

1:33:35

existence systems make any sense in a world where

1:33:38

I can say the same thing to a human and

1:33:41

a generator that is

1:33:43

clearly not me creating anything and yet I do

1:33:45

get a picture out of it that came from

1:33:47

somewhere and there's no like, there's no human act

1:33:49

of creation. It's an indirection, right? And

1:33:51

so I think we need new ways to think

1:33:53

about a new laws for that type of indirection

1:33:56

to say, what is the chain

1:33:58

of ownership here? It's kind of like, not

1:34:00

quite the same thing, but remember the whole

1:34:02

thing where the monkey took

1:34:04

a picture of itself with the camera? Do you remember? Oh, yeah.

1:34:06

It was like a camera set up in the jungle or whatever

1:34:08

and a monkey comes up to it and snaps a picture of

1:34:10

himself and the photographer is like, well, it's

1:34:12

my camera, so I own the copyright to the picture.

1:34:14

I'm like, well, doesn't the monkey own the copyright? Because

1:34:17

it took the picture, right? And it's like, but the

1:34:19

monkey can't own the copyright. It's not a person, right?

1:34:21

And believe me, a monkey is way closer to a

1:34:23

sentient being than an LLM, right? It's

1:34:26

a real living thing. No one's going to argue

1:34:28

in court that a monkey is not alive. And

1:34:30

they're going to say, well, does it have legal

1:34:32

rights? Well, I would say a monkey has more

1:34:34

legal rights than a large language model, which is

1:34:36

just a bunch of numbers in memory. And

1:34:39

so this is the kind of conversation we're having. And

1:34:42

honestly, this would be so much easier to

1:34:44

have if we had actual artificial intelligence, as

1:34:46

in sentient artificial beings. But we don't. That's

1:34:49

just science fiction. Large language models are not anywhere close

1:34:51

to that. That would be so much easier because you'd

1:34:53

be like, well, conscious beings have rights and we need

1:34:55

the, you know, whatever they always have names of this

1:34:57

in sci-fi movies, the AI consciousness act

1:35:00

of 2732 that gives rights to the AIs to avert

1:35:02

a global war and plunge

1:35:07

us into the matrix apocalypse. You know what I mean? Like,

1:35:10

it's so much easier when you say, well, people

1:35:12

have rights and computer programs that are basically people

1:35:14

have rights and it's straightforward, but

1:35:16

we're nowhere near there. So now we're arguing about monkeys,

1:35:19

if they have the copyright pictures,

1:35:21

and we're arguing about huge matrices

1:35:23

of numbers, whether they can

1:35:25

create anything or you're saying like, oh, you're saying

1:35:28

basically like the people who wrote Photoshop own every

1:35:30

picture that's made from because like, well, no, the

1:35:32

LM doesn't own it. And the person who wrote

1:35:34

the prompt doesn't own it. But you know who

1:35:36

does own it? Open AI because they wrote the

1:35:38

program that crawled all the pictures in the world

1:35:40

that train the model that you paid to use.

1:35:44

None of those answers are satisfactory

1:35:46

anyway. Like it doesn't feel right.

1:35:49

It doesn't seem right. It doesn't seem sustainable. And

1:35:51

yet we do need some kind of answer here,

1:35:54

even if the answer here is that anything again,

1:35:56

like that one wall precedent we had is like,

1:35:58

if you make something out of AI, you don't

1:36:00

own the copyright. It is not copyrightable. Nobody owns

1:36:03

it. It's garbage. It's slop. It's a thing that

1:36:05

exists, but nobody can claim that they own it

1:36:07

So it is free for anybody to take and

1:36:09

do whatever they want with but you

1:36:11

certainly can't like sell it to someone because you didn't

1:36:13

Own it. It's very confusing.

1:36:16

I know that I haven't made this any clearer You can try

1:36:18

reading my post to see if it becomes any more clear but

1:36:20

really this is this is a dizzying

1:36:23

topic if you think about it for any amount of

1:36:25

time and I think a lot of

1:36:27

people are doing a

1:36:29

lot of feeling about it, which makes perfect sense

1:36:31

and Honestly, it is more

1:36:33

straightforward to feel things about it than it is to

1:36:35

think about it because thinking about it gets you Into

1:36:37

some weird corners real fast It's

1:36:40

just it's a mess in It's

1:36:44

a mess and I don't know what the right

1:36:46

answer is right like it's it's so gray from

1:36:48

top to bottom And I just I don't know

1:36:51

I just don't know well And I think we're

1:36:53

gonna have to be fighting this and working this

1:36:55

out for a while I mean

1:36:58

look at how much disruption

1:37:00

to existing businesses existing

1:37:02

copyright law and existing norms was

1:37:04

caused by the web and then the

1:37:06

rise of other things on the

1:37:08

internet like this is Just this is

1:37:10

how technology Goes

1:37:13

there are massive disruptions to

1:37:16

what has been established what we what many

1:37:18

people have held dearly there

1:37:20

there's massive disruptions to that when new tech comes

1:37:22

around sometimes and Sometimes

1:37:24

it takes a decade or two to really

1:37:27

settle out and work out. What are the

1:37:29

norms? What should the laws be what is

1:37:31

copyright mean in this new world things like

1:37:33

that? like that takes a long time to

1:37:35

work out sometimes the rise

1:37:38

of these AI techniques and models is

1:37:41

Potentially as disruptive to

1:37:44

existing business models

1:37:46

and norms and perspectives as

1:37:48

the web was when it first came out a

1:37:50

thousand years ago, so I really think we're in

1:37:52

for a while of just

1:37:55

not knowing there's gonna be a

1:37:57

lot of damage and destruction

1:37:59

along the path to get from

1:38:02

where we are now to where kind of where

1:38:04

things settle out. It will destroy

1:38:06

a lot of businesses and it

1:38:08

will, you know, make it hard for a lot

1:38:11

of people to do what they've been doing. It

1:38:13

will also create a bunch of new businesses and

1:38:15

create a bunch of new value and new opportunities,

1:38:17

just like any other massive disruption. I think this

1:38:19

is, this is a very large disruption and it

1:38:22

is, it's mostly only going

1:38:24

to start to

1:38:26

become visible of like, you know, what the other side

1:38:28

looks like just after a bunch of time has passed

1:38:30

and we've gone through a bunch of messiness and we're

1:38:32

just, we're in such early days, it's really hard to

1:38:34

know where we're going to end up right now. I

1:38:37

feel like this is going to be in some

1:38:39

respects, not all, but in some respects, even more

1:38:41

disruptive than the initial web, because the initial web

1:38:43

was kind of like text.

1:38:45

We have laws governing that. It was

1:38:48

a massive shift of wealth. Obviously newspapers

1:38:50

go out of business. Craigslist gets

1:38:52

rich. You know what I mean? Like, like, but

1:38:54

we saw that giant shift in paper magazines, like

1:38:56

the shift of publishing, right, and web search and

1:38:58

doing all that or whatever. But during that entire

1:39:00

thing, people were upset and it was

1:39:02

a big turmoil because it was like, these things used

1:39:04

to be huge. Every city had 25 newspapers. A newspaper

1:39:07

reporter was a big job. And, you know, it was

1:39:09

like, and all of a sudden all that money's going

1:39:11

elsewhere to these.com things or whatever. But during that whole

1:39:13

process, there was mostly agreement

1:39:15

that like, newspapers own what

1:39:17

they publish, websites own what they publish. Like

1:39:19

we have existing copyright laws for this. There's the

1:39:21

whole Google search index thing that we can figure

1:39:24

out and, you know, fair use on the

1:39:26

internet and stuff. But in general, it was just

1:39:28

a massive shift of power and money from

1:39:30

older industries to newer ones, mostly

1:39:32

following along the shape of laws

1:39:35

and ideas and morals and

1:39:37

ethics and societal understanding about

1:39:40

the written word, mostly in the early days of the web,

1:39:43

especially before social media really came and mixed that up

1:39:45

a little bit, right. With the whole aggregation of humans

1:39:47

all talking to each other and quoting things and linking

1:39:49

out or whatever. Right. That in

1:39:52

hindsight, that seems much less disruptive, disruptive than

1:39:54

AI stuff, which is like, it's

1:39:56

a free for all. No one knows anything. No one knows

1:39:58

what's legal. What's not. What's sustainable? What's

1:40:00

not? What should we do? What can we

1:40:03

do? What are people doing? How valuable is

1:40:05

this? How useful is it? Like just

1:40:07

so many questions and we like all the laws that

1:40:09

we have that seems like they could apply this and

1:40:12

some of them do apply. It's like, yeah, but there's

1:40:14

these huge areas where it's like here be dragons on

1:40:16

the map and they draw the big dragon and the

1:40:18

thing is like, nobody knows what's there. And

1:40:20

there's a lot of money behind it. And a lot of people

1:40:22

running in that direction. And it's not even clear where

1:40:24

or how this will shift the power. Like

1:40:26

on the internet in the early days, it

1:40:29

was pretty clear paper newspapers, powers going away

1:40:31

from them and towards websites. Like that trend

1:40:33

was visible to anybody with a clue and

1:40:35

it was just a question of how fast,

1:40:37

how hard, you know, whatever. Here, is this

1:40:40

going to shift power massively to the record labels because

1:40:42

they own all the music, for example, or is it

1:40:44

going to destroy them because everything they have is now

1:40:47

worthless because AM models can be trained on it and

1:40:49

it's a perfect substitute for what they previously made and

1:40:51

no one wants anything like you can't even tell which

1:40:53

direction it's going to go at this point. It's so

1:40:55

early and I just don't think that was true of

1:40:57

the web. So this is an exciting

1:41:00

time to be alive in many

1:41:02

ways, especially if you're in any

1:41:04

industry, any creative industry that involves

1:41:06

intellectual property that AI touches at

1:41:08

all. And at this point, that's

1:41:11

nearly all of them. Right. And

1:41:14

right now, what it does

1:41:16

is not, you know, not

1:41:18

particularly amazing, but it is good enough

1:41:20

for so many use cases. And this

1:41:22

stuff generally doesn't get worse over time.

1:41:25

Thank you to our sponsors this

1:41:27

week, One Password and

1:41:30

Photon Camera. And thank you to our

1:41:32

members who support us directly. You can

1:41:34

join us at at.fm slash join members,

1:41:37

get a bunch of perks, including ATP

1:41:39

overtime. This is our weekly

1:41:41

bonus topic. That's an extra segment

1:41:44

that only members get to hear

1:41:46

ATP over time. This week is

1:41:48

going to be about a rumor

1:41:50

reported by the information and Mark

1:41:52

Gurman about some

1:41:54

changes and plans to what Apple is going

1:41:56

to be working on for the next Vision

1:41:58

Pro and kind of what they

1:42:00

can maybe do to make the next vision

1:42:03

pro cheaper and how they're going to possibly

1:42:05

do this and everything. That's what we're talking

1:42:07

about in ATP over time this week. Join

1:42:09

out a listen ATP.FM slash join. Thanks everybody.

1:42:11

And we'll talk to you next week. Now

1:42:17

the show is over. They

1:42:19

didn't even mean to begin

1:42:21

because it was accidental. Oh,

1:42:25

it was accidental. John

1:42:28

didn't do any research. Marco

1:42:30

and Casey wouldn't let him

1:42:32

because it was accidental. It

1:42:35

was accidental. And

1:42:38

you can find the show notes at

1:42:40

ATP.FM. And

1:42:43

if you're into mastodon, you

1:42:46

can follow them at C A

1:42:49

S E Y L I

1:42:51

S S. So that's Casey Liss M A

1:42:54

R C O A R M.

1:42:57

And T Marco R men. S

1:42:59

I R A C U S A

1:43:02

C. Recuse it's accidental.

1:43:07

They didn't mean to. Tech.

1:43:18

Not so real time follow up on my

1:43:20

earlier statement about Apple

1:43:22

Silicon Max not being able to use PCI

1:43:24

breakout boxes. That is not true. You can

1:43:26

use Thunderbolt PCI breakout boxes. Obviously you can't

1:43:28

use it. It's just not GPUs. Yeah. But

1:43:30

you can't use GPUs internally either. That's the

1:43:33

thing. Yeah. So still Apple should

1:43:35

have put the Mac Pro in the configurator. Or

1:43:37

I suppose they could have said, Hey, use PCI

1:43:39

cards buying it except you. You can use PCR cards.

1:43:41

Well, you can buy a Mac studio and also this

1:43:44

third party product that we don't even sell or

1:43:46

you could buy a Mac Pro, which is a product

1:43:48

in their lineup. I think two

1:43:51

things are simultaneously true. Number one,

1:43:53

they should keep making the Mac Pro because

1:43:56

it does have uses. And number two, absolutely.

1:43:59

Nobody should. by the Mac Pro effectively.

1:44:01

Like, anybody who's going to a

1:44:03

page on apple.com saying, what Mac should I buy?

1:44:06

None of those people should buy it. No,

1:44:08

the app, they should. That should be, look, the

1:44:10

whole point of this is it's a path that

1:44:12

leads to all of our products. And maybe there's

1:44:14

only one very lonely overgrown path that leads to

1:44:16

the Mac Pro, but it's got to be there.

1:44:19

I would say number three, your product chooser should let

1:44:21

you choose from any of the products, depending on which

1:44:23

things you answer. Put as many scary questions in there

1:44:25

as you want. There's just got to be a path

1:44:28

that lands in the Mac Pro. Because otherwise, what they're

1:44:30

saying with this is, no

1:44:32

one should buy this product. And I don't think Apple believes that.

1:44:34

If you ask them, they said, well, some people should. Like, OK,

1:44:36

great, but you have a tool that lets people choose, and it

1:44:38

has every single Mac you sell except for that one. That just

1:44:40

seems like a bug to me. Someone should report it, and they

1:44:42

should fix it. You should report it. I

1:44:45

just wanted the Mac Pro that's worth buying. I

1:44:48

mean, that's a bug. Maybe

1:44:50

they're working on that. We'll see. So

1:44:52

I feel like we covered this in

1:44:54

the past, but what are you waiting for? What

1:44:57

would make it worth buying? Am I waiting

1:44:59

for anything in particular? I don't know. Because

1:45:01

again, with the gaming situation on Apple Silicon

1:45:03

Macs is entirely unclear. If I did buy

1:45:06

a Mac with a big beefy

1:45:08

GPU, bigger than a Mac Studio GPU, that

1:45:10

would be a speculative purchase. It would not

1:45:12

be like my current Mac Pro, which I

1:45:14

literally knew I could run Windows games on

1:45:16

and do, and they work fine. And I

1:45:18

run literally put into Windows. That's not speculative.

1:45:20

That's a thing, right? If

1:45:22

I decide, hey, I want a bigger than

1:45:25

Mac Studio GPU in an ARM Mac, I

1:45:27

am crossing my fingers that some magical point

1:45:29

in the future, I will be able to

1:45:31

do interesting gaming things on it. I

1:45:34

don't know if I'm going to make that speculative purchase. I don't

1:45:36

know if Apple's going to make a Mac with a better than

1:45:38

Mac Studio GPU in it. And

1:45:40

maybe they make it, and it's just too

1:45:42

rich for my blood, and I can't spend

1:45:44

that much money on something speculative. Like I

1:45:46

said, my default is an M4 Mumble Mac

1:45:49

Studio is potentially the computer

1:45:51

I will replace this with whenever they release that like next

1:45:53

year or towards the end of this year or whatever. But

1:45:57

I would like to see, show me something.

1:45:59

Show me the Mac Pro. Pro show me something that's not

1:46:01

a Mac studio in giant cavernous case, right?

1:46:03

That's what I would like to see from them and then I can

1:46:05

decide is it worth it for me to get

1:46:07

that? Because it's not a slam dunk like the toy well,

1:46:09

it's not as big a slam dunk as 2019 was because

1:46:11

again, that's just not speculative but It's

1:46:14

just kind of wishful thinking at this point to think

1:46:16

you're gonna be running Windows arm games natively on you

1:46:18

know You're gonna be booting Windows for arm on your

1:46:20

you know Apple silicon Mac

1:46:23

Pro or you're gonna be running Windows

1:46:25

caliber games in Mac OS

1:46:28

Because Apple will have gotten all the triple-a game developers

1:46:30

on board. That is all just Twinkle

1:46:33

in someone's eye right now. It is not a real thing.

1:46:35

I just I feel like

1:46:37

and I'm

1:46:39

gonna say this and I know and I

1:46:41

understand why it's not appealing to you But

1:46:43

I feel like so many of your

1:46:45

problems which well, maybe not even not even problems But so

1:46:47

much of your life would be so much better if

1:46:50

you would just get a damn

1:46:52

Mac studio and a damn Windows PC

1:46:54

And I get it. I don't want to

1:46:56

run Windows anything I don't and I know

1:46:58

you are even worse than me in this

1:47:00

capacity But like that would make so many

1:47:03

things so much better in your life I would probably have a

1:47:05

gaming PC if I had a place in the house for it,

1:47:07

but I don't so I mean I

1:47:09

hate to break it to you But I really don't think that

1:47:11

there is ever going to be a Mac Pro that does the

1:47:13

things that your current Mac Pro does And I mean that may

1:47:15

be true like I'm rooting for it. But like right now the

1:47:18

outlook doesn't look so great Yeah, I

1:47:20

would definitely not hold your breath on that I

1:47:23

mean like the thing is it's actually kind of if

1:47:25

I thought like it two years ago like Predicted

1:47:28

how this would go actually I'm kind of

1:47:30

surprised at how much motion there is here

1:47:33

the copout plus PC how hard? Microsoft is

1:47:35

pushing into arm PCs after doing such a

1:47:37

bad job with Windows RT, right? Apple

1:47:40

with its whole game porting toolkit like

1:47:42

both those parties both Microsoft and Apple

1:47:45

are actually surprising me with how

1:47:47

Hard they're trying to make my

1:47:49

dream happen. They're just not

1:47:51

succeeding right, but they They're

1:47:54

trying more than I thought they would right? I

1:47:56

did not I didn't think they'd be like both

1:47:58

on both sides I have been pleasantly

1:48:00

surprised by the additional effort that they

1:48:02

are putting in. I think everyone kind

1:48:04

of is. It's just like I just

1:48:06

they're just not really doing

1:48:09

it. Alright, but I give

1:48:11

them kudos for the effort. If

1:48:13

I had to pick one thing like I

1:48:15

would I would wish that Microsoft would commit

1:48:18

to a transition to ARM but that's not what

1:48:20

they want to do. They seem to

1:48:23

think that they're going to have a they're

1:48:25

going to support x86 and

1:48:27

ARM forever off into the future

1:48:29

which I think is a dumb strategy but that

1:48:31

seems to be what they're doing and that doesn't

1:48:33

help me and that doesn't help Windows games get

1:48:35

ported to ARM. All that does is bifurcate their

1:48:37

market and say well all the all the AAA

1:48:39

games will still be on x86 with Nvidia cards

1:48:42

and ARM will just free for people's laptops and Microsoft may

1:48:44

be perfectly happy with that but it doesn't help me over

1:48:46

here with Apple Silicon. I mean

1:48:48

what what PC games are you playing with regularity

1:48:50

right now? Destiny. I don't know if you know

1:48:52

this but Destiny runs on PC. But

1:48:55

that's the thing like is there no

1:48:57

other appliance that you can buy to run Destiny

1:48:59

can't you do it on PlayStation? Destiny runs at

1:49:01

higher resolution and higher frame rates on gaming PCs.

1:49:03

I don't really play it on my Mac Pro

1:49:05

I play it on my PlayStation 5 for a

1:49:07

variety of reasons but it does run

1:49:09

better as defined

1:49:11

by resolution and frame rate even on my

1:49:13

Mac Pro. The PlayStation maxes out of 60

1:49:15

frames per second right and I can

1:49:17

get higher than that depending

1:49:20

on settings and you can go way higher you can

1:49:22

go I've played it I actually have played Destiny on

1:49:24

my PS5 at 120 frames per second on my TV

1:49:26

but it has to lower the quality substantially and I

1:49:28

generally don't play Destiny on my TV because it'll burn

1:49:30

it in right but I did try that just to

1:49:32

see what it was like. 120

1:49:35

frames per second is good all the like the

1:49:37

Destiny streamers who are out there playing Destiny they

1:49:40

occasionally have their frame rate displayed in the corner they're

1:49:43

triple digits always hundreds of frames per

1:49:45

second sometimes pushing them in 200 it

1:49:47

makes a difference it it looks and feels

1:49:50

smoother especially in PvP they're like that's you

1:49:52

know and even if I'm playing

1:49:54

on a controller because at this point sadly I'm better with

1:49:56

a controller in Destiny than I am with mouse and keyboard

1:49:59

and also controllers. way better for my RSI so

1:50:01

I'd be doing it anyway. But yeah, Destiny

1:50:03

is one choice. And games come out all the

1:50:05

time and they come out for PC. They

1:50:08

don't come out for the Mac until three years later when Apple

1:50:10

puts in the key note, right? So there's

1:50:13

past games, there's future games, there's my gigantic

1:50:15

Steam library that I still haven't played through.

1:50:19

I mean, I'll get a PlayStation 5 Pro, I'll

1:50:22

get a PlayStation 6, I do like

1:50:24

consoles, they're great. Maybe someday the gap

1:50:26

between PC and console will be

1:50:28

diminished. But even now I would say it's more diminished because

1:50:30

60 frames per second on PS5 is such a change from

1:50:32

30 on the PS4 that I

1:50:34

feel like the gap has narrowed. Because Destiny players

1:50:36

were playing at 100, 200

1:50:39

frames per second back when I was playing 30. Now

1:50:42

they're playing at 100, 200 frames per second and I'm playing at 60, right?

1:50:45

I'm gaining on them. So maybe at some point I'll be like,

1:50:47

you know what, I don't need a big GPU and I'll just

1:50:49

get a Mac Studio and be happy with it. And

1:50:52

that's looking like the most likely situation

1:50:55

right now. But we'll see. I

1:50:57

mean, to be clear, as much as I'm giving you a hard time,

1:51:00

I want you to have what you want.

1:51:02

I can make an argument, even I can

1:51:05

make an argument for the Mac Pro, for

1:51:07

a really beefy Mac Pro that's useful for

1:51:09

people that work outside of a music studio.

1:51:12

I'm not saying that your desires are wants as much

1:51:14

as I'm giving you grief about it. I'm not saying

1:51:16

your desires are wants or unreasonable. I

1:51:18

don't think Apple will be

1:51:21

achieving them, but I don't think they're

1:51:23

unreasonable. It's exciting that they did with 2019, because

1:51:26

again, I've said before, I don't, despite

1:51:28

my gaming things, this is not a purchase. This is not

1:51:30

a rational purchase. It's the same way that you don't need

1:51:32

a Ferrari to get to work faster. People just like fast

1:51:34

cars, they like fast cars. I just like

1:51:36

powerful computers because I like powerful computers. It's exactly

1:51:38

the same thing. Trying to justify a Mac,

1:51:41

me trying to justify a Mac Pro is like someone trying to

1:51:43

justify a Ferrari. It's like, well, I need a car this fast

1:51:46

to get to my work. No, you don't. Nobody

1:51:48

does. But people want them because they're cool.

1:51:51

Right. And that's fair and

1:51:54

that's totally fair. But I feel

1:51:56

like from to my eyes, we're

1:51:58

starting to cross. from, oh, it's

1:52:00

kind of adorable that John is still rocking

1:52:02

his Mac pro to like, man,

1:52:05

I kind of want you to move on to a Mac

1:52:07

studio. Cause I think you might enjoy it a lot more,

1:52:09

you know? Well, I mean, I'm not buying an M two

1:52:11

one at this point. Well, that's fair. No, that's fair. This

1:52:13

is not the time to buy a Mac studio. So it

1:52:15

isn't, it isn't hanging in there for the M four one.

1:52:17

Yeah. I think when the next one comes out, I think

1:52:19

that's your move. I can't, I just cannot

1:52:21

see a future in which they

1:52:23

make the Mac pro that you want. And

1:52:26

so you might as well get the Mac

1:52:28

studio, which is the Mac pro without slots.

1:52:30

Like that is the new Mac pro. I

1:52:32

can't say it enough. And with a wimpy

1:52:34

or GPU, but they, they just like the

1:52:37

Mac studio is the Mac pro. They should

1:52:39

have called it Mac pro. That is the

1:52:41

Apple Silicon Mac pro. They should not have.

1:52:44

No. Can you imagine the aneurysm he would

1:52:46

have had? It doesn't make sense. They

1:52:48

sell, I think all the Mac pro it's way bigger, but

1:52:51

it's the same computer. It's just a built-in

1:52:53

PCI breakout box. I know. It's still got

1:52:55

the slots. It's still anyway, but we'll see

1:52:57

how good, and by the way, by the

1:52:59

time I do get this, like my computer

1:53:01

is essentially five years old now already.

1:53:04

This is a pretty good run for a computer that

1:53:06

I bought at the, you know,

1:53:08

just before the, the, you know, processor

1:53:11

transition, right? Which is, you know, unfortunate for the, we

1:53:13

already said when it happened, like, Oh, my poor Mac

1:53:15

pro or whatever, but I love this machine and

1:53:17

I've already gotten five years out of it, which granted is half of

1:53:19

what I got out of my last Mac pro, but you know, processor

1:53:22

transition, right? So if

1:53:24

I ditch this machine at six years old, that's

1:53:26

longer than any of your laptops lasted, right? It's

1:53:29

a pretty good run. Hey, we're just excited

1:53:31

if Marco makes it six months, much less six years.

1:53:33

He's been pretty good with 16 inch. I think it's

1:53:36

almost two years old now, right? No, it's the M3

1:53:38

max. It's a, it's the black one. Sorry.

1:53:41

I mean, to be honest, lately I haven't

1:53:43

been much better, so I shouldn't be casting stones

1:53:45

in this glass house, but generally

1:53:48

speaking, Marco is much more frequent on, on

1:53:50

his purchases. So I mean, like no matter

1:53:52

what, like. I feel like I've gotten

1:53:55

a good run out of this Mac pro and I'm enjoying

1:53:57

it for, you know, as long as like I'm excited that

1:53:59

it's a. runs on it. That's

1:54:01

cool. Next year, probably not, right? So it's really

1:54:03

putting a deadline on this. Like I said, I'm

1:54:05

willing to run this with last year's version of

1:54:07

the operating system for some period of time if

1:54:10

I have to wait, right? But, you

1:54:12

know, well, we'll see what happens. Like I'm, you know,

1:54:14

I keep my cars for a long time and keep my Macs for a

1:54:16

long time.