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Seek is distilling off of open

1:02

AI, open AI is rich enough

1:04

to complain to say, oh

1:06

well, you know, that's not

1:09

good even though they've trained

1:11

their models on everything that's

1:13

out there on the internet

1:16

without permission, but it's a

1:18

real moment. It's like a

1:20

shift or it's an indicator

1:23

of something. One source... actually

1:25

told me like a tech

1:27

person was like, look, this

1:30

is exciting in a way

1:32

because it lowers the cost

1:35

of the AI that's

1:37

on tap for like

1:39

founders and people who

1:41

want to develop

1:43

new products and

1:46

new services and

1:48

things like that. And

1:50

so it's an

1:52

opportunity. Senior Business

1:54

and Technology correspondent

1:56

for CBS News. Jolin Kent,

1:58

welcome back, Jolin. Hi Andy,

2:00

how are you? I'm doing great.

2:02

It's true. You did know

2:04

me when I was Andy

2:07

because we've known each other

2:09

for a long time. No,

2:11

no, it's all really good.

2:13

People will appreciate that. So

2:15

I wanted to have you

2:17

on because I've gotten a

2:19

whole slew of questions about

2:21

about Deep Seek, AI, markets,

2:24

tech, big tech, etc.

2:26

etc. And I thought who

2:28

could I bring on that is a

2:30

deep head in this stuff, has spent

2:32

time in Asia for years and years

2:34

and looks at this stuff professionally. And

2:36

I thought of you, so thank you

2:38

for coming on. I'm sure you got

2:40

some of the same stuff. Did anyone

2:42

ping you being like, hey, what the

2:44

heck is going on? Yeah, I mean,

2:46

people are reaching out and I'm hearing

2:48

from sources and stuff like that, but

2:50

I'm also. on parental leave right now.

2:53

I had a baby about three months

2:55

ago, as you know, and it's just,

2:57

it's been crazy. So I've been like

2:59

catching up. And it's interesting when you

3:01

are used to covering the news day

3:03

to day and then you kind of

3:06

consume it week to week. And people

3:08

have like a thousand different opinions in

3:10

the span of like seven days. And

3:12

so it's been fun to like track

3:14

people and be like, oh, you thought

3:16

this like five days ago and how

3:19

you think this? And how you think

3:21

this? kind of surprised. Well it's one

3:23

reason why I wanted to wait a

3:25

beat because there was like a flurry

3:27

of stuff that came out and then

3:29

there were some more stuff and then

3:31

Deep Seek then turned itself off

3:33

to anyone who didn't have a

3:35

Chinese cell phone number and then a

3:38

bunch of countries then said hey no

3:40

to Deep Seek and I think there

3:42

might be a bill in Congress saying no

3:44

to seek for... government workers

3:46

and maybe even more than that. So

3:48

can you help describe, well actually let's

3:50

give some, I'll do, I'll set the

3:52

table for people who have no idea

3:54

what I'm talking about and then you

3:56

don't go to the art. Thank you.

3:58

Oh no no problem. So Deep Seek

4:00

is a Chinese AI product. Well,

4:03

actually, it's a company. And then

4:05

they had they released a model

4:07

that is something of like a

4:09

Chinese version of chat GPT called

4:11

R1. And it works very, very

4:13

well. Some people said it works

4:15

better than open AI's latest models.

4:17

But the wildest revelations were that

4:19

it cost a very small fraction.

4:21

of what Open AI spent in

4:23

China. Allegedly. Allegedly, but then what's

4:25

wild, Jolie, and we can talk

4:27

about this, there aren't people, I

4:29

mean, everyone knows, like, take it

4:32

with a grain of salt, but

4:34

also people aren't disputing that that

4:36

heavily. They're not like, oh, you

4:38

know, this is nonsense or full

4:40

of shit. Like people are kind

4:42

of taking that on its face.

4:44

And then the other thing is

4:46

that it's open source. And so

4:48

those two things, and by the

4:50

way, the lower cost, it means

4:52

less energy, less computing power, presumably

4:54

less cost. So this came out

4:56

and then people started playing with

4:58

it and kicked the tires on

5:00

it. And then it had this

5:03

massive, gosh, maybe I think it

5:05

produced the biggest stock loss and

5:07

value loss for one company in

5:09

history, where Envedia plummeted. No, double

5:11

digit percentage was like 12, 15

5:13

percent, something like that, which was

5:15

very, very significant because going into

5:17

that, it was worth, you know,

5:19

several trillion, 3.6 trillion. It has

5:21

been the darling of the market

5:23

for a long time. Yeah, so,

5:25

so this new new development caused

5:27

everyone to double back and say,

5:29

wait a minute, and Vidia is

5:32

selling these chips for a lot

5:34

of money because they have the

5:36

stranglehold on the... processing chips that

5:38

are high-powered enough to do all

5:40

of the computing so every major

5:42

company that wanted to develop AI

5:44

was going to invidia and paying

5:46

really like, you know, through the

5:48

nose, like whatever invidia wanted, which

5:50

led invidious stock price to go

5:52

up and up and up, their

5:54

chips were selling for tens of

5:56

thousands of dollars, each even, and

5:58

the major tech companies were spending

6:01

a ton, and then Deep Sea

6:03

came out and said, hey, we

6:05

kind of produced something that's just

6:07

as good. for only $6 million

6:09

for a small fraction of the

6:11

price. And then the market had

6:13

this giant like, wait, what the

6:15

heck is happening? And there was

6:17

a bit of a freak out.

6:19

Right. And so basically it like

6:21

caused all of these stocks to

6:23

go down. And the question was

6:25

like, you know, for a lot

6:27

of people, you know, what is

6:29

the relationship with China? And like

6:32

what if you're using it from

6:34

like a personal standpoint, right? Like

6:36

how does it? work and like

6:38

what are you handing over at

6:40

that point you know to the

6:42

Chinese government and it's like there

6:44

was a lot of people out

6:46

there you know waiving this flag

6:48

saying like look this is like

6:50

surveillance and this is bad you're

6:52

basically like there was you know

6:54

discussion of whether or not you

6:56

know the Chinese government is collecting

6:58

data on people and if you

7:01

look at the terms I mean

7:03

it collects a lot of data

7:05

deep seek does collect a lot

7:07

of data like a lot of

7:09

data like a lot of the

7:11

other tech companies here in the

7:13

United States as well right like

7:15

they have access to other apps

7:17

and things like that but it's

7:19

like a the big question from

7:21

the consumer standpoint was like well

7:23

is there actually some fire there

7:25

and so It was interesting to

7:27

see people like see the market

7:30

freak out in this way, but

7:32

you know, one source actually told

7:34

me like a tech person was

7:36

like, look, this is exciting in

7:38

a way because it lowers the

7:40

cost of the AI that's on

7:42

tap for like founders and people

7:44

who want to develop new products

7:46

and new services and things like

7:48

that. And so it's an opportunity.

7:50

And thinking about like that market

7:52

tumble and what happened to the

7:54

stock market, I was. talking, I

7:56

was reading Dan Ives from Wedbush,

7:58

his, you know, analysis of these

8:01

tech companies and he's been on

8:03

all of these companies for so

8:05

long and he loves to use

8:07

those sport metaphors and sports metaphors

8:09

and all that. And he's like,

8:11

he still says like there's still

8:13

one chip company in the game,

8:15

right? Like, it's one company that's

8:17

actually launching things beyond like a

8:19

chat bot and like having one

8:21

LLLM, like large language model, you

8:23

know. you know, chatbot for consumer

8:25

use is like, that's one thing.

8:27

It's very important and very lucrative.

8:30

But it's another thing to think

8:32

about like all the other stuff

8:34

that comes with invidious. So I

8:36

don't know. It's, it's, it's, it's,

8:38

it really was like a wake-up

8:40

call moment, you know, in a

8:42

lot of ways, maybe people think

8:44

like, but it depends on where

8:46

you were coming from on it,

8:48

right, like it, as always. Well,

8:50

certainly if you're the scrappy entrepreneur

8:52

type, you might be like, ooh,

8:54

this is going to be good

8:56

because, you know, the barriers are

8:59

a little bit lower and maybe

9:01

we can use some tools. You

9:03

know, I talked also to some

9:05

investment professionals who were looking at

9:07

this and have covered the tech

9:09

companies for a while. And that

9:11

they think that these tech companies

9:13

have spent billions of dollars on.

9:15

Billions, yeah. Yeah, billions, I mean,

9:17

you know, like tens of billions.

9:19

On chips and processing power and

9:21

infrastructure to build these large language

9:23

models in large part, because they

9:25

wanted it to be this defensible

9:27

moat, and their concern was that

9:30

deep seek showed that the moat

9:32

actually is very, very crossable. Right,

9:34

you can pop right over. Yeah,

9:36

you can stop right over. And

9:38

one person explained it to me

9:40

as that, so what Open AI

9:42

did is Open AI took all

9:44

of these language inputs and then

9:46

did all, did like a, you

9:48

know, like gajilian. connections and calculations

9:50

so that if you ask it

9:52

a question or you know reference

9:54

certain data then like a certain

9:56

output would come out and deep-seek

9:59

essentially short-circuited all of the like

10:01

the guts in the middle and

10:03

just figured out okay what's the

10:05

input what's the output and did

10:07

that and distillation right yeah distillation

10:09

which so the argument that I

10:11

find compelling and I'm very very

10:13

eager to hear what you think

10:15

was okay. So you spend $100

10:17

billion or whatever it is on

10:19

this moat. And then some clever

10:21

Chinese company says, hey, I don't

10:23

know what's happening inside the moat,

10:25

but it turns out maybe it's

10:28

irrelevant because I can figure out

10:30

the inputs and outputs and distill

10:32

what's happening. And there is evidence,

10:34

by the way, that. Deep Seek

10:36

is very much based on chat

10:38

GDPT. There was a joke I

10:40

used to tell where people would

10:42

ask like how far behind is

10:44

China from the US in AI?

10:46

And the joke answer is 12

10:48

hours because they just wake up

10:50

and see what we did the

10:52

previous day. It's interesting because I

10:54

was at this fortune innovation conference

10:56

in Hong Kong last year, about

10:59

a year ago, and Kaifu Li,

11:01

he's the head of Google China,

11:03

and now it's like a big

11:05

AI, investor and leader in China,

11:07

had a really interesting perspective because

11:09

for a long time he was

11:11

like, well, it's probably going to

11:13

be like two coexisting ecosystems like

11:15

parallel to each other, developing through

11:17

different audiences. And now with Deep

11:19

Seek, that's the first thing I

11:21

thought of was like. Well, I

11:23

guess that doesn't hold, because like

11:25

now everything is like, you know,

11:28

if Deep Seek is distilling off

11:30

of Open AI, Open AI is

11:32

rich enough to complain to say,

11:34

oh, well, you know, that's not

11:36

good, even though they've trained their

11:38

models on everything that's out there

11:40

on the internet without permission. But

11:42

it's a real moment. It's like

11:44

a shift. or it's an indicator

11:46

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12:48

I want to pivot to something

12:50

very personal and small, but it

12:52

might be important. So you just

12:54

said, hey, look, Open AI trained

12:57

itself on publicly available data, the

12:59

internet. No one really got paid

13:01

for that. You know, the New

13:03

York Times is suing. Like there's

13:05

all this stuff happening. Yeah, yeah,

13:07

people are suing. So I got

13:09

a note the other day from

13:11

my publisher, which was fascinating, which

13:13

said an unnamed AI company wants

13:15

to train on your book. Oh,

13:17

and they are offering us not

13:19

a lot of money. Honestly, it

13:21

was like, you know, a few

13:23

thousand dollars for it to be

13:26

able to train on your book

13:28

and we'll split the money with

13:30

you 50-50. And so I looked

13:32

at this and was like, okay,

13:34

not a lot of money, but

13:36

it's, you know, not zero. And

13:38

on one level, I was kind

13:40

of impressed that the AI company

13:42

was. paying the publisher, you know,

13:44

that's actually a fairly important award.

13:46

I'm just ripping it off. Yeah,

13:48

yeah, because I mean, obviously, my

13:50

book's out there and like, they're

13:52

electronic copies, so you could just

13:54

feed it to the, it's called

13:57

a day. So the fact that

13:59

they actually went through the proper

14:01

channels. And by the way as

14:03

an author when I write a

14:05

book you know there are various

14:07

rights that you say it's like

14:09

hey audio book you know blah

14:11

blah blah blah. But yeah but

14:13

international but there was nowhere in

14:15

there that said hey and if

14:17

we feed it's AI like you

14:19

know maybe you get something. So

14:21

now they're. There. There I mean

14:23

I I said sure you know

14:26

because again on my hand I

14:28

was like kind of impressed that

14:30

they decided to push my publisher

14:32

that actual money. Yeah. I thought

14:34

that, you know, in my case,

14:36

I would take the money and

14:38

donate it to, you know, a

14:40

democracy org. I can, so I

14:42

was like, so I won't feel

14:44

bad like somehow, you know, I

14:46

mean, you know, though, though, you

14:48

know, I'm sure, you know, like

14:50

there are any number of authors

14:52

who are getting similar notes right

14:55

now. So, like, you know, you

14:57

could be a little bit happy

14:59

for those authors getting a little

15:01

bit of extra money that they

15:03

weren't expecting, though I'm sure they

15:05

all also had the sinking feeling

15:07

in their guts the way I

15:09

did, which is like, oh, I

15:11

guess, you know, that's what's happening

15:13

to my work. It's just going

15:15

to become one, you know, scintilla

15:17

of this anti-model. And, you know,

15:19

that also was a reason why

15:21

I was, I was like, sure,

15:23

because I kind of want my

15:26

book to be in my book

15:28

to be in the, My ideas

15:30

than like the model might answer,

15:32

you know, from with my stuff,

15:34

you know. actually makes me happy.

15:36

So you have open AI training

15:38

on all the publicly available information

15:40

and maybe even some copyrighted information

15:42

that some others may or may

15:44

not have access to. And then

15:46

you have deep seek in the

15:48

Chinese who in my view from

15:50

what I can tell from the

15:52

facts. They've built this sort of

15:55

efficiency layer on top of it

15:57

where it's like, okay, I don't

15:59

know what's going on in all

16:01

the guts of it, but I

16:03

can tell if I ask it

16:05

this, it says that, and then

16:07

I can make a version of

16:09

it that is very, very highly

16:11

efficient and usable and cheaper, and

16:13

I can make this available. at

16:15

very low cost, I mean, you

16:17

know, maybe next to nothing. And

16:19

one of the things that's been

16:21

unclear to me this whole time

16:24

has been, let's say that I'm

16:26

open AI and Microsoft and you

16:28

spend, you know, 80, 100 billion

16:30

dollars on large language models or

16:32

other things. Like right now, that's

16:34

a much, much higher sum than.

16:36

any revenue that's being realized from

16:38

from these models at present like

16:40

are there revenues yes some yeah

16:42

some subscription revenues yeah yeah but

16:44

like like how's have the revenues

16:46

caught up to the investment not

16:48

yet great And then the argument

16:50

is like, oh, but it will,

16:52

it will, it will, you know,

16:55

it will get there, like eventually,

16:57

you know, that this is going

16:59

to be a very, very big

17:01

line item, which on some level,

17:03

like on the enterprise side or

17:05

something, yes, yes, on some level,

17:07

I totally agree with. And by

17:09

the way, one of the things

17:11

I, and I'm even like a

17:13

small investor in a company that

17:15

does something that's related to this,

17:17

so when I was running for

17:19

president, I would say all the

17:21

president, I would say all the

17:24

president, I would say all the

17:26

president, I would say all the

17:28

When do you think AI is

17:30

going to do that job? And

17:32

then people would think about it.

17:34

And now if I ask that

17:36

question, people are like, immediately, yesterday,

17:38

I mean, I talked to the

17:40

head. You can't get through to

17:42

a human anymore. You know, or

17:44

eventually in the argument I was

17:46

making before is like you'll prefer

17:48

the AI or you might not

17:50

be able to tell the difference.

17:53

I talked to the founder of

17:55

a major. consumer facing company that

17:57

everyone would have heard of. And

17:59

he said that they used to

18:01

have 2,300 people in customer service,

18:03

and they're trying to get that

18:05

down to 100. That's wild. But

18:07

I think people are feeling that

18:09

nationwide. And I think that if

18:11

you look at some of the

18:13

survey data out there about like

18:15

how people feel about getting help

18:17

on like routine stuff, yes, AI

18:19

can solve a lot of things.

18:21

And it's, you know, nice to

18:24

have a chat bot. I guess

18:26

if your problem is simple. But

18:28

like so many people feel like

18:30

a frustration of like, this is

18:32

terrible. Like I, for example, I

18:34

called a major institution three days

18:36

ago, and it was like I

18:38

was getting the AI, like all

18:40

of the, you know, prompts and

18:42

everything. And it was like, endlessly

18:44

frustrated. Like I turned into like

18:46

an angry, angry monster. I was

18:48

like, I can't get what I

18:50

need because only a human can

18:53

handle it. But I do think

18:55

that's going to change and quickly,

18:57

quickly, quickly, quickly, quickly become. irrelevant

18:59

and like it won't be an

19:01

issue anymore. Yeah so on that

19:03

level there are definitely companies that

19:05

will pay you know real money

19:07

for AI that's going to be

19:09

able to replace 2,000 customer service

19:11

workers but the the revenue hasn't

19:13

caught up to the scale of

19:15

the investment yet. There's a lot

19:17

of hope that it will a

19:19

lot of projection that it will

19:22

and one of the incentives in

19:24

this structure, I think that's that's,

19:26

you know, going somewhat haywire in

19:28

my opinion, honestly, is that if

19:30

Microsoft announces that they invest $80

19:32

billion in open AI, Microsoft stock

19:34

value pops by some larger sum

19:36

than that. If meta says, hey,

19:38

we're going to increase our capital

19:40

expenditures by $15 billion in AI,

19:42

their stock pops by more than

19:44

that. It's like. like the stock

19:46

market will now reward anyone for

19:48

any money they're spending in the

19:50

space if you announce hey I'm

19:53

building it's like oh good you're

19:55

future-proofing yourself you end up I

19:57

mean that's definitely the historical trend

19:59

right like that is exactly what

20:01

has happened but like do you

20:03

think now with Deep Seek and

20:05

the development costs being like allegedly

20:07

like a lot lower obviously we

20:09

know what has happened here with

20:11

open AI but like do you

20:13

think that that like trend continues

20:15

for much longer And that's really

20:17

the thing I'm trying to lay

20:19

out and I want to talk

20:22

to you about is I don't

20:24

think it does continue. Like I

20:26

think that Deep Sea... It doesn't

20:28

make any sense anymore. Yeah, like

20:30

Deep Sea has kind of pulled

20:32

the, you know, the veil away

20:34

or just pointed out, look, like

20:36

this emperor's clothes are not on

20:38

or aren't great or don't, you

20:40

know, don't need to be there

20:42

because... To me, the business case

20:44

gets so much weaker on so

20:46

many levels if you're like, hey,

20:48

look guys, I'm spending $50 billion

20:51

on AI and this Moten, like

20:53

computing infrastructure and giant, you know,

20:55

giant data sets and everyone's like,

20:57

ooh, ooh, yeah, like you're saying

20:59

all the right word. Oh yeah,

21:01

you're going to control the world.

21:03

And look, I'm someone who very

21:05

much believes that, you know, incredibly

21:07

revolutionary tools, data is the new

21:09

oil, like I obviously believe all

21:11

that stuff. But there is like

21:13

a time curve, and then there

21:15

is how much of that money

21:17

you're going to be able to

21:19

charge in revenue, you know, for

21:22

like different actors. And then you

21:24

have, if the Chinese come and

21:26

say, hey guys, look, large language

21:28

model, really whizbang, great, awesome, like

21:30

you can use ours for next

21:32

to nothing. computing costs are lower

21:34

the energy consumption is lower and

21:36

and then one of the things

21:38

I got asked you know on

21:40

CNN when I was talking about

21:42

this is like oh that won't

21:44

there be some censorship if it's

21:46

a Chinese product like if you

21:48

decide to ask the model about

21:51

Tiananmen Square or whatever and I

21:53

and I was like look for

21:55

the average consumer in Africa or

21:57

Latin America or Europe, that's not

21:59

their concern. Like their concern is,

22:01

can this thing do what I

22:03

needed to do at the lowest

22:05

cost possible? And one of the

22:07

things that is happening to, which

22:09

you and I can also talk

22:11

about, is, you know, it's like,

22:13

this was Deep Seek until they,

22:15

you know, took it, they made

22:17

it unavailable. It was the number

22:20

one app in the app store,

22:22

you know, like, Americans were downloading

22:24

it like wild. And the security,

22:26

the data concerns are very much

22:28

secondary or tertiary to most consumers.

22:30

And you can see that with

22:32

Tiktok. I mean, there was like

22:34

this huge rage in Congress saying,

22:36

oh, we got to get rid

22:38

of Tiktok, you know, it's exactly

22:40

what they're doing now with deep

22:42

sea, right? Like, that's, there's that

22:44

LaHood Gotheimer, Gotheimer, right? It's the

22:46

same, it's the same person who

22:48

did the same person who did

22:51

the What I want to understand

22:53

though is when you think about

22:55

all the things you just said

22:57

and you think about like the

22:59

amount of money that has been

23:01

made so far on AI, I

23:03

was just curious like, do you

23:05

believe what like the Dan Ives

23:07

like investment professionals like these analyst

23:09

guys are saying, which is like,

23:11

look, is, you know, we kind

23:13

of touched on that earlier, but

23:15

basically like, bigger AI infrastructure is

23:17

still like American led and like,

23:20

Deep Sea doesn't really like change

23:22

the game in that. Do you

23:24

buy that? Do you disagree with

23:26

it? Like, I'm confused by the

23:28

case that someone like that's making

23:30

because if you have computer infrastructure

23:32

that enables you to do things

23:34

that, you know, that can only

23:36

be done through having, you know,

23:38

gajilian servers and all that, but

23:40

then if someone can. reverse engineer

23:42

the product of your work and

23:44

not have to redo all the

23:46

calculations and figure it out just

23:49

based on inputs and outputs, then

23:51

it doesn't matter. what you spend

23:53

in the middle, if someone can

23:55

just take input output and say

23:57

like, you know, to die, figure

23:59

it out, you know, at least

24:01

what the, because one of the

24:03

elements of this is that if

24:05

you ask even the creator of

24:07

the AI who has all the

24:09

servers, hey, when I asked the

24:11

AI this and it gave me

24:13

this, like, why or how did

24:15

it do that? Yeah. No one

24:17

knows. Like whether it's like, you

24:20

know, even if it was my

24:22

AI, like I can't necessarily train

24:24

it to figure out the problem

24:26

for the for itself. Yeah, yeah,

24:28

I can't necessarily tell you. And

24:30

so if if the Chinese can

24:32

do functionally the same thing because

24:34

of all the work you've done

24:36

and you spent, you know, 10

24:38

or 50 or 100 billion dollars

24:40

on it, then like the bigger

24:42

your spend is the more you're

24:44

benefiting. both you and them, but

24:46

they don't have to spend the

24:49

50 or 100 billion dollars. Like

24:51

the business case gets a lot

24:53

weaker, especially because now you're having

24:55

this, you know, you're already having

24:57

what Kaifu was proposing, which we

24:59

all kind of projected and feared

25:01

and imagined, which is that the

25:03

West is going to particularly the

25:05

US is going to try and

25:07

set up its own AI universe.

25:09

And then there's going to be

25:11

a giant turf war for... other

25:13

countries, so you would figure the,

25:15

you know, EU would be like,

25:18

sure, we'll work with the states

25:20

on this because we don't want

25:22

to work with China on it.

25:24

And then my guess would be

25:26

that Africa is like, screw it,

25:28

we'll use Chinese AI. Yeah, I

25:30

mean, they're taking all that other

25:32

investment anyways, yeah. It

25:43

will be this then. What happens?

25:45

Okay, so we have the Stargate

25:47

factor, right? Like you have President

25:49

Trump very much, you know, trying

25:51

to shore up AI investment and

25:53

you've got what, $500 billion? Sam

25:55

Altman standing there next to the

25:57

president at the White House. Where

25:59

does... that does that help? Do

26:01

you think like does that actually

26:03

like further or is it just

26:05

like digging more into the current

26:07

positioning of the US AI you

26:09

know companies and just you know

26:11

here's my here's my instinct on

26:13

it is that if deep seek

26:16

is real if you take it

26:18

is real and by the way

26:20

there are a lot of you

26:22

know very serious techies who looked

26:24

at it and are not disputing

26:26

the elements that we've laid out

26:28

here. It isn't like, hey, this

26:30

is bullshit. It's like, okay, I'm

26:32

going to take this as if

26:34

it's real and true, and then

26:36

we're going to respond. The problem

26:38

is that everyone's incentives, and using

26:40

Stargate, it's a perfect example. So

26:42

Trump comes up and says, hey,

26:44

we're going to spend $500 billion

26:46

on AI, compute infrastructure, and win

26:48

the AI arms race with China.

26:50

Now. It turns out that of

26:52

that 500 billion, most all of

26:54

it had already been committed by

26:56

various tech companies. So Microsoft was

26:59

80 billion of the 50 billion.

27:01

I think they might have announced

27:03

the 80 billion even before. So

27:05

what they did is they they

27:07

said Microsoft is good for 80

27:09

and Met is good for some

27:11

number. And then they glued all

27:13

the tech companies together and said

27:15

together. It's 500 billion. Yeah, it's

27:17

500 billion. And what's happened is

27:19

the market has rewarded everyone who's

27:21

in that group and, and Vidia

27:23

for sure, and some of the

27:25

companies that produce the server farms

27:27

and everything else. Like, you know,

27:29

I mean, 500 billion is a

27:31

lot of money. I mean, like,

27:33

it's literally changing the landscape. And

27:35

what no one is in position

27:37

to say. But I'm beginning to

27:39

have this sinking feeling It's like

27:42

hey guys, it could be that

27:44

we're not going about this the

27:46

right way that like this money

27:48

might not be well spent That

27:50

we were not sure And but

27:52

the thing is everyone's incentives now

27:54

are around the spend because if

27:56

I the tech company announced that

27:58

I'm spending 20 billion on AI

28:00

my market cap goes up by

28:02

100 billion and then I and

28:04

then I go to. You know

28:06

and bid and give them like

28:08

you know like billions of dollars

28:10

worth of orders like that there's

28:12

like this giant. arrow heading in

28:14

a particular direction and it reminds

28:16

me of some other times in

28:18

my life I'm a little bit

28:20

older than you. Thank you. Does

28:22

this remind you of like what

28:25

was it like the tech bus

28:27

like is it the same sort

28:29

of like money for money's sake

28:31

without evaluating like what why are

28:33

we committing this much money to

28:35

something? This feels like it's being

28:37

driven by money to your point

28:39

like that like this or you

28:41

know like it to me it's

28:43

like funny money yes yes what

28:45

you you kind of disregarded the

28:47

laws of business physics that sometimes

28:49

apply I think Tim O'Reilly call

28:51

this super money where you can't

28:53

compete with super money when it

28:55

starts going it comes out of

28:57

the White House right like when

28:59

it's you know with such in

29:01

addition to like the incentive in

29:03

the markets to spend more to

29:05

make more it's also like You

29:08

know, you have a president who

29:10

has this very unique and very

29:12

powerful hold on these billionaire founders,

29:14

you know, inauguration case in point,

29:16

and I'm sure much more behind

29:18

closed doors. And so it's like

29:20

all of these factors are coming

29:22

in plus like, what is the

29:24

Trump influence like with the US

29:26

trying to relationship on deep seek

29:28

and this kind of stuff? It's

29:30

true. Because the numbers have gotten

29:32

to this scale, like it's definitely

29:34

now starting very directly with government.

29:36

You know, you had a bunch

29:38

of the tech CEOs at Trump's

29:40

inauguration and some people like, oh,

29:42

that's terrible. My opinion. is that

29:44

these tech CEOs are just pragmatic

29:46

and being like, you know, like

29:48

if it had been common, they'd

29:51

be standing there too. Like if

29:53

they were asked to, I mean,

29:55

I don't know if common would

29:57

have asked them to. I think

29:59

Trump asked them to because Trump

30:01

just likes the sense of it's

30:03

like, see, I've got these guys

30:05

and it's like all the money

30:07

in the world or like, you

30:09

know, I mean, and then if

30:11

you're in their situation, you're like,

30:13

all right, all right, I guess

30:15

I'm going to. I mean, some

30:17

of them are definitely more into

30:19

it. Some of them are just

30:21

in my opinion, it's like, well,

30:23

you know, like, whatever. You can

30:25

read those facial expressions in the

30:27

room and then pretend. You could

30:29

see who was there for the,

30:31

I was called to be here

30:34

and stand here versus like, I

30:36

am here to play ball for

30:38

sure. Yeah, yeah, you're right. There's

30:40

a mixed bag. But at this

30:42

point, the scale is so large

30:44

that it's like, look, I mean,

30:46

we're talking about I'm leading a

30:48

trillion dollar company. We're going to,

30:50

you know, invest hundreds of millions

30:52

in this stuff. And then government's

30:54

going to be there to help

30:56

me and, you know, there'll be

30:58

other resources flowing. It's all heading

31:00

in a particular direction. And to

31:02

me, like the reckoning that Deep

31:04

Seek represents is like the fact

31:06

that invidious stock went down and

31:08

then I think is mostly recovered

31:10

since then. Yeah, it's bounced back.

31:12

I'm not sure the exact numbers

31:14

here, but it's largely, I think,

31:17

recovered. And people aren't questioning the

31:19

fundamental story. But so when I

31:21

was referring before it's like, hey,

31:23

I'm a little bit older or

31:25

whatever, I do remember the technical

31:27

bust of the first bubble, you

31:29

know, 2000. I started my first

31:31

company in 2000 and NASDAQ was

31:33

5,000, went down to 2000, but

31:35

there was that period when anything

31:37

with the.com in it and said,

31:39

hey, internet, internet, internet, you know,

31:41

pets.com, e toys, like you name

31:43

it, would just go and then

31:45

they'd get driven to the moon.

31:47

And there were elements of the

31:49

story that were very real and

31:51

true. It's just that the revenues

31:53

didn't match up for a particular

31:55

time. Like, you know, there's like

31:58

a time horizon. this reminds me

32:00

of too, it's like if you

32:02

ask me, hey, is AI real?

32:04

Oh yeah, AI is very real.

32:06

Is it gonna change a lot

32:08

of things? Yeah, like are we

32:10

ahead of it? Yeah, we're ahead

32:12

of it by a lot. Like,

32:14

you know, like do the numbers

32:16

make sense? You know, maybe eventually,

32:18

but like, you know, you'd have

32:20

to project a lot out into

32:22

the future. So I talked to

32:24

a professional investor friend who was

32:26

sizing this stuff up. and video

32:28

yes we are okay now you

32:30

know that's where he went which

32:32

means that's clear which means that

32:34

he thinks it's overvalued he thinks

32:36

other things overvalued too and I

32:38

was like oh because I respect

32:41

this person's opinion I was like

32:43

you know I I have that

32:45

sense that that like the fundamentals

32:47

have kind of become disconnected or

32:49

unrelated to a lot of what's

32:51

happening in this space. But when

32:53

this heads in a particular direction,

32:55

you know, no one wants to

32:57

stand in its way because like

32:59

who the heck, you know, for

33:01

people who weren't around for the

33:03

first.com boom and bust, like the

33:05

echo of it in a different

33:07

sector. was the mortgage busts circuit

33:09

2008 where there were people looking

33:11

up being like hey some of

33:13

this stuff doesn't make much sense

33:15

but it's like what are you

33:17

gonna do? Lose your home no

33:19

big deal I mean it's it's

33:21

yeah it's what's interesting about the

33:24

comparison to you is if you

33:26

look at the companies that did

33:28

survive the tech bust right it's

33:30

such a small percentage and so

33:32

if you do that just rough

33:34

math thing right to how many

33:36

AI companies how much they're making

33:38

you know, the incredible climb they've

33:40

had. There will be the several

33:42

AI companies that come out like

33:44

winners and infrastructure and they'll provide

33:46

that foundation right of like AI

33:48

going forward. But it is like

33:50

the big question is like every

33:52

single company is trying to turn

33:54

itself into an AI company to

33:56

take advantage of this premium and

33:58

this sort of. like, ride to

34:00

the moon. And it's like, it's

34:02

not, the laws of, yeah, financial

34:04

physics don't really work like that.

34:07

Yeah, I mean, I have friends

34:09

who raise money for AI companies

34:11

and raise a lot of it

34:13

at our very high value. Yeah,

34:15

exactly. Especially like a year ago,

34:17

it was like, if you were

34:19

an AI company doing like, AI

34:21

pets, right, you could probably make,

34:23

you know, easily raise around. This

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36:10

So that's like the sense of the

36:12

era that we're in. And the fact

36:14

that Deep Seek now isn't available in

36:16

the US. I feel like it kind

36:19

of, you know, was this comet and

36:21

shows up and is like, wow, like

36:23

we're here, we're free. And then everyone's

36:25

like, and it's gone. But it's not

36:27

gone. I mean, it's being used by

36:30

millions and millions of people in China.

36:32

And even what Kaifu Li had originally

36:34

said then last year, like a year

36:36

ago, like last March, holds that and

36:38

like are we going week or can

36:41

we never go back to sort of

36:43

like two systems in one universe? I

36:45

think that we're going to end up

36:47

in two parallel AI universes. There's going

36:49

to be the China sphere and the

36:52

US sphere. The problem for the US

36:54

companies though is that as far as

36:56

I can tell, China's going to be

36:58

able to under price by a lot.

37:01

You know, because if I've got a

37:03

company that spent $100 billion and I

37:05

need you to pay for it, I'm

37:07

going to price it very differently than

37:09

if I spent, you know, $10 million.

37:12

You mentioned if you add on to

37:14

the fact that like a lot of

37:16

these companies are heavily government subsidized, right?

37:18

Like in the same way that the

37:20

e-commerce companies were, you know. subsidizing supported

37:23

by the government too. But by the

37:25

way, I mean, the Chinese were forced

37:27

into this position because the US tried

37:29

to cut off their supply of high-powered

37:31

chips, though they did get their hands

37:34

on some. But one of the things

37:36

that popped into my mind during this

37:38

process, Jolling, was just the phrase, necessity

37:40

is the mother of invention. That the

37:42

US tried to box out Chinese firms

37:45

from being able to make advance an

37:47

AI being like, ha ha, like, you

37:49

know, we're going to try and cut

37:51

the chip, the, you know, the chip

37:54

access off to you. China does have

37:56

access to its own data. So, you

37:58

know, that like that, you know, I

38:00

mean, they have more of it, you

38:02

know, and also it's centralized. But. But

38:05

they pushed the Chinese into a situation

38:07

where the Chinese had to figure out

38:09

some kind of hack and, you know,

38:11

in reverse engineering. Yeah, build their own

38:13

on top of, you know, GPT's model

38:16

it looks like. And so one of

38:18

the jokes I had, aside from, you

38:20

know, that they're 12 hours behind us

38:22

and AI, was, does it really shock

38:24

anyone that the Chinese figured out how

38:27

to build something cheaper? It's been kind

38:29

of their jam for, you know, like,

38:31

quite some time. And they're also very

38:33

unmindful of... you know intellectual property rights

38:36

and like and that stuff I mean

38:38

they'll just lift it if they can

38:40

and in this case this is the

38:42

ultimate choice of words but but this

38:44

is the ultimate irony is that the

38:47

AI companies also just frankly appropriated most

38:49

of this data you know from us

38:51

you know hundreds of billions of dollars

38:53

worth and then they managed to build

38:55

these like mega enterprise mega moats and

38:58

then the Chinese have essentially done the

39:00

same thing to their handywork and then

39:02

they're in this really weird position where

39:04

it's like oh man well that stinks

39:06

and you're open source oh man like

39:09

I mean because some of the techies

39:11

originally were like oh we should do

39:13

as open source but then you can't

39:15

can't charge billions of dollars if you're

39:18

open sourcing it. And so it's like

39:20

maybe we won't open source it. Maybe

39:22

we're going to use it. This is

39:24

why Elon's so mad at Sam Altman

39:26

is because opening I started out of

39:29

something that was supposed to be kind

39:31

of, you know, for the commons or

39:33

whatever. It was a non-profit. Non-profit. And

39:35

then they converted for profit. But then

39:37

it turns out that like the money

39:40

for money's sake, it's a very American

39:42

story, I feel like. It's just someone

39:44

saw this and was like, oh, like,

39:46

you know, money to the moon. And,

39:48

and, and, but, and then so now

39:51

they're in this weird position where it's

39:53

like, okay, we spent gajillions of dollars.

39:55

You know, you have an open source

39:57

competitor over there. So let's try and

39:59

shut the door, ban it. China and

40:02

was like, okay, like, let's pretend that

40:04

didn't happen, just keep on investing and

40:06

digging and building and spending. And keep

40:08

doing what we were doing and maybe

40:10

not acknowledging the lessons that

40:12

have been learned here. That's my strong

40:15

feeling. That's my take. That's my

40:17

take. And so when you've been

40:19

talking to people and they've run

40:21

the gamut, like what what have

40:23

the conversations been like? Because you

40:26

have contacts here, China like. Yeah. You know,

40:28

I think most of my conversations of late

40:30

have been like US sources people who

40:32

are in tech companies or around tech

40:34

companies and you know investors and stuff

40:36

like that and I think that there

40:38

is like a couple schools of thought.

40:41

I mean, we've talked about some

40:43

of it already, but there's a

40:45

sense of like, okay, like, let's

40:47

make it cheaper. So it makes

40:50

people, it makes it easier for

40:52

people to develop stuff. So there's

40:54

another option. It's good competition. You

40:56

know, all ships rise in the

40:59

end. But there's the surveillance

41:01

aspect of course, like the

41:03

net set folks are like, okay, you

41:05

know, in the same way that people

41:07

who have told me. I would never use a

41:09

little red book on my actual iPhone. I

41:11

have like a burner phone and these are

41:13

tech people, right? So I have burner phones

41:15

like do that because they really do believe

41:17

that there is a national security threat of

41:19

some kind regarding our personal data. So it's,

41:21

you know, when you think about Deep Seek,

41:23

it's like, if you think about it from

41:26

an investor perspective and like your skepticism about

41:28

where the, how much money AI can make, you

41:30

know, that's, that's one big pot of questions. But

41:32

the other one is really like, is really like,

41:34

like, like, like, like, like, like, like, like, like,

41:37

like, like, like, like, like, How much do you

41:39

give up when you're dealing with a

41:41

TikTok or a Little Red Book or

41:43

a Deep Seek? And like, what are

41:46

some of the things that the US

41:48

government ought to be doing in this

41:50

new administration to actually protect people who

41:52

care about those kinds of things? Because

41:55

I think increasingly so many people just

41:57

like, you just download the app, you

41:59

agree to the... terms and like most

42:01

you know, you know, 100% like you hear, like

42:03

it's just like I just want to

42:05

use the thing that I'm addicted to.

42:07

And so there doesn't seem to

42:09

be there's obviously an outcry

42:11

in Washington on this front

42:14

and there's, you know, the

42:16

legislative proposals and things like

42:18

that. But there what I'm waiting to

42:20

happen and I don't know if it

42:22

will in 2025 is will there be like

42:24

a consumer driven shift in like

42:27

how we use things and like especially

42:29

like social media and AI, because once

42:31

you know what is being collected, do

42:33

people care? I would argue most

42:35

people don't care. Yeah, I agree. Because

42:37

they still use it. But if there is an

42:40

increasing national security threat

42:42

in the geopolitical environment,

42:45

you know, hardens and there's more like

42:47

borders and lines drawn, so to

42:49

speak, then do we care more? Do lawmakers

42:51

care more? Does that enforcement

42:53

mechanism then affect how people

42:55

consume stuff on their devices?

42:57

I just wonder if 2025 is going to be the

42:59

year of like a little bit of a tipping point

43:01

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here's the stuff I'm hearing is that

44:52

there are some consumers that I know

44:54

like regular people Who are now trying

44:56

to turn off Amazon Prime because

44:58

they're pissed at Basos or that

45:00

they're trying to get rid of

45:03

Facebook or Instagram because they're pissed

45:05

off at Zuck like that now.

45:07

There's like a There's like a

45:09

personal, an anthropomorphization of these tech

45:11

companies where now like, you know,

45:13

Zok equals meta and it's like,

45:16

oh, I'm mad at Zokka with

45:18

something. So I don't know. Yeah, they

45:20

saw him in inauguration, like all these

45:22

simple little pieces. Yes, yes. It's

45:24

something as simple as that.

45:26

So like I think that there

45:29

are some people making consumer-based decisions

45:31

about some of those tech companies.

45:33

But the single biggest emblem

45:35

of this has been Tik. And.

45:37

I mean, this was bipartisan and

45:39

a bunch of legislators came together

45:41

and said, hey, this is a problem. It's

45:44

our kids' data, their brains. We need

45:46

to find a U.S. buyer or this thing is going

45:48

to be banned. And then I'll tell story

45:50

too, Jolin, because I'm on TikTok. You

45:52

know, I mean, I have a TikTok

45:54

account with 400, I'm not like, I

45:56

don't consume, I'm happy to report. I'm

45:58

not like sitting there watching. videos all

46:00

day. That's what you say. That's what

46:03

you say. But what happened was I

46:05

have a staffer who runs my account

46:07

and said, hey, do you want to

46:09

put out a farewell to TikTok message

46:11

because a lot of people are putting

46:13

that out. when it's being banned and

46:15

I was like, no, because I do

46:18

not think it's going to be banned.

46:20

And then the person asked why. And

46:22

I said, number one, it's because all

46:24

these people who are on ticked dock

46:26

are super pissed in our political class.

46:28

Really, they have no backbone. It's like

46:31

if people are pissed at them, they're

46:33

like, oh, okay, okay. Like, you know,

46:35

like even if I did vote for

46:37

that legislation, like, let's make you happy

46:39

because I don't like people mad at

46:42

me. Like if he doesn't ban it and

46:44

it's out there and he's like, hey, I'm

46:46

going to find a buyer, it's like

46:48

TikTok, probably called Instagram. Yeah,

46:50

then Trump doesn't get any, you

46:52

know, bargaining power or money from

46:55

that. But if he doesn't ban

46:57

it and it's out there and he's

46:59

like, hey, I'm going to find a

47:01

buyer, maybe like, you know, create

47:03

the sovereign wealth fund, then there's

47:05

money in it for him. or these

47:08

power. And so he'd

47:10

prefer that path because,

47:12

you know, it's just

47:14

purely transactional and like it's

47:16

not about like privacy or

47:18

kids data or, you know,

47:20

their mental health. So I

47:22

was like, they're not banning

47:24

it. Like, you know, and

47:26

it turns out that, you know,

47:28

my, I think I was largely

47:30

correct. President Trump into the

47:33

zeitgeist of a pajillion Tiktak users, right?

47:35

Because that error message that came up

47:37

was like, oh, we are, I screen

47:39

grabbed it. I can't remember exactly what

47:42

it said, but it was like, President

47:44

Trump is like, working on this, right? And

47:46

then like, a few hours later, Tiktak is

47:48

back. So it's like, it all seems like it's just

47:51

like a show, in a sense of like power, or

47:53

of, you know, what's interesting though, jolings,

47:55

is that the legislators, is that

47:57

the legislators were sincere, I think,

47:59

We're going to ban it. And then...

48:02

And remember who started all of this?

48:04

Who started all of this? This was,

48:06

remember the first, no one, the funny

48:08

thing to me, and even as a

48:11

journalist, like our brains are so full

48:13

of information, things are coming so fast

48:15

and furious and it's crazy right

48:17

now. But like, the Trump, the first

48:19

Trump administration, it was

48:21

all about the TikTok, at the, towards

48:23

the end of the first four years.

48:26

And so it is just wild

48:28

to me that you fast forward,

48:30

you've got President Biden before, years

48:32

now, President Trump is back, that

48:35

this is being done again, it

48:37

comes as no surprise, of course,

48:39

but like, it's just fascinating how

48:41

short our memories are when it comes

48:43

to, you know, something that is so

48:45

essential to so many people's lives,

48:47

like social media, right? Like, this

48:49

all began during the first

48:52

Trump administration. And then, I'll

48:54

never forget this. Trump was like,

48:56

yeah, we got to like get rid

48:58

of TikTok. And so I'm reporting on

49:00

all this. And at some point, like,

49:03

some other major world event happens

49:05

and it's like the Trump DOJ

49:07

or was the FTC or something,

49:09

they kind of go quiet on

49:11

me. And I'm like, I was

49:13

talking to sources inside, you know,

49:15

TikTok and bite dance. And I was

49:17

like, forgive me if this is a

49:20

silly question, but did they

49:22

forget that they just. initiated

49:24

this entire situation in all of

49:26

this legal battle, and the other

49:28

side, tick-tock and by dance, they

49:30

were just like, it appeared, we are

49:33

not getting responses from the

49:35

US government anymore. So it's

49:37

like at the convenience of President

49:39

Trump, it appears that this

49:42

entire controversy ebbs and flows. Well

49:44

you were referred to a little red

49:46

book and that there was this moment

49:48

when people thought Tik Tak was going

49:50

away when people were switching the little

49:53

red book even though that's very natively

49:55

Chinese I mean even like the name

49:57

is based on you know Mao's little

49:59

red book. And there were a bunch

50:01

of Americans who were jokingly, like, I

50:03

pledge allegiance to, you know, the little

50:06

red book. It just goes to show

50:08

how little people care about who's getting

50:10

their data, like the national entities. You

50:13

know, it's like, like, as long as

50:15

I get my, my, whether or not

50:17

they can even be understood by millions

50:19

of people who use that current app.

50:22

Like I had so many friends that

50:24

were getting, they were posting, like, comments

50:26

in Mandarin, they'd screenshot them, because I'm

50:28

not. I didn't put a little red

50:31

book on my iPhone. They'd screenshot it,

50:33

text me, and be like, Joe, what's

50:35

to say, translate it for me? And

50:38

I was like, oh, this really is,

50:40

like, people don't care. They just want

50:42

to be seen? Yeah. Is that it?

50:44

Like, yeah, it's just plugging into their

50:47

brainstem where, you know, you get dopamine

50:49

hits based upon responses. I'm really glad

50:51

I was a functional adult for all

50:54

this stuff came along. I'm sure you'll,

50:56

you know, keep your girls away from

50:58

it for a while, anxious generation and

51:00

all that. It's all very, very intimidating

51:03

to me though, because I feel like

51:05

the more you know about it, the

51:07

worse it weighs, like the heavier it

51:09

weighs. But I agree. I think most

51:12

Americans do not fundamentally care who has

51:14

their data or where it goes because

51:16

we have been conditioned as a society

51:19

to just... release it. Like think about

51:21

all of the credit card breaches and

51:23

all of the payment breaches over the

51:25

years. Like, there's some crazy stat out

51:28

there that is like, you know, the

51:30

vast majority of Americans have had their

51:32

private information hacked, right, and stolen in

51:35

some way, not just Equifax or the

51:37

target hack or whatever. It's like, it's

51:39

out there. Yeah, it's a factor in

51:41

life and you hope it doesn't rise

51:44

to the point where you have to

51:46

change your credit card. Or you show

51:48

up to your website and they're like,

51:50

hey, you might want to change your

51:53

password and you're like, oh God, like

51:55

I said something has happened here if

51:57

you're asking. to do this. Thank you

52:00

so much for helping us dig into

52:02

issues that I get asked about all

52:04

the time. And it sounds like you

52:06

do too. I know that you're hanging

52:09

out with a family right now, but

52:11

if someone wants to keep up with

52:13

you and your work, how can that

52:16

yourself? Yes, I will be back on

52:18

the air at CBS News at the

52:20

second half of March and I'm on

52:22

social at Joe Lincoln across all platforms,

52:25

but always good to be with you

52:27

Andrew Yang, Andy Yang. I don't know

52:29

what I should, whatever you want, Joe

52:31

Lang, whatever you want, but congratulations to

52:34

you on your work, on the family,

52:36

on everything. It's kind of awesome to

52:38

see how far you come, how far

52:41

we all come. I mean, wow, things

52:43

have really. We've all been through a

52:45

lot and it's crazy to think that

52:47

I probably first met you in 2013

52:50

or 14. Yeah, is that right? Yeah,

52:52

I think that's right. I was a

52:54

normal human. Happy 10 year friend aversary

52:57

or something. Drinks on me next time

52:59

I see you. Well, thanks for having

53:01

me on. It's great to chat with

53:03

you. I always love hearing your perspective

53:06

and what all your listeners are saying

53:08

about what the heck is going on

53:10

right now. Thank you, Joolin. Come back

53:12

any time. Oh, thank you. I will.

53:15

Thanks.