0:00

The business of open AI gets weird. Tesla

0:02

now wants to be a robotics company as

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its stock price drops. Plus, when is it

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time to get off the hedonic treadmill? All

0:09

that and more coming up with the cast

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of Slate Money right after this. The

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pay off. Welcome

0:29

to Big Technology Podcast, a show for cool

0:31

headed, nuanced conversation of the tech world and

0:33

beyond. We have such a fun show today.

0:36

One I've been looking forward to, the cast

0:38

of the Slate Money Podcast, is here to

0:40

talk about a series of fun stories where

0:42

tech, economics, and finance meet.

0:45

We're going to do a home and home series. So they're here,

0:47

and then I'll come over to their show in a couple of

0:49

weeks, and I'm pumped for that. And so let's kick it off.

0:51

I just want to welcome the cast

0:54

here. Felix Sandman is here. He's the

0:56

chief financial correspondent at Axios. Felix, welcome.

0:58

Thank you very much. Emily Peck

1:00

is also here. She's the market's

1:02

correspondent at Axios. Emily, welcome. Hello,

1:05

hello. Excited to be described as

1:07

the member of a cast, because now I

1:09

feel like I play myself on Slate Money.

1:11

So that's spinning my head. Last

1:14

but not least, Elizabeth Spires here. She's a

1:16

contributing writer for the New York Times' opinion

1:18

section, and she writes Slate's Pay Dirt. Elizabeth,

1:21

welcome. Thanks for having us. Thanks

1:23

for coming in. I think that

1:25

their combination of economics and tech

1:28

is very fascinating right now, because we

1:30

have this very weird situation where companies

1:32

and investors keep plowing money into

1:34

these AI startups, and we're not really sure

1:36

what the return is going to be, what

1:38

they're actually using that money for, what the

1:40

business outcome. But yet, we start to hear

1:42

numbers like trillions of dollars of investment. Really,

1:45

that's what we're hearing now. That

1:47

was Sam Altman with one of the

1:49

craziest numbers. I think I might have

1:52

called it deranged on Axios that

1:55

I've ever heard. He came out and said,

2:00

Well, he didn't quite come out and say

2:02

he was reported to have said that he

2:04

was looking to raise $7 trillion

2:07

to build a whole new infrastructure

2:10

around AI, which is so

2:12

far beyond any amount

2:14

of investment that has ever been put

2:16

into anything ever. It

2:19

kind of makes you think that maybe he

2:22

just doesn't understand numbers. Well, it's

2:24

also double the most valuable company in

2:26

the world. That's

2:28

sort of what makes this conversation he had on

2:30

20 BC with Harry Stepping really interesting and sort

2:33

of we can riff on it because

2:35

we are trying to find out what the economics of

2:37

this AI business is. And so here's

2:39

what Stepping says. He goes, in terms of

2:41

marginal cost versus marginal revenue, how do we

2:43

think about when marginal

2:46

revenue exceeds marginal cost? Basically, like, are you

2:48

going to have a profitable business? And

2:51

Sam goes, I mean, truly, I think of all the

2:53

things we could talk about. That is

2:55

the most boring, no offense. That's the most boring question

2:57

I can imagine. Stepping

2:59

goes, why is that boring? And Sam goes,

3:03

well, you have to believe that the price of compute

3:05

will continue to fall on the value of AI as

3:07

the models get better and better

3:09

will go up and up. And like the

3:11

equation works out really easily. So that's all

3:13

I'm into. I want to know what his

3:15

equation is because he does seem to talk

3:17

about all of this as if the numbers don't

3:20

really matter. You're just putting in one bigger number

3:22

and there's a smaller one for the input. And

3:25

for you, where's the line

3:27

between techno-optimism and techno-naiveté? So

3:31

the argument he's making is

3:33

like he's making two different

3:35

arguments and both of them make sense, but I feel like

3:38

he can't make both at the same time. The

3:41

first argument he's making, and this is the

3:43

same argument that Jensen Huang has been making

3:45

from NVIDIA, Which is the price

3:47

of Compute has been coming down for decades and

3:50

has now reached the point at which AI is

3:52

possible and there is no indication that it's going

3:54

to stop coming down. And so long as it

3:56

keeps on going down at the same... Rated

4:00

it has been coming down which is more

4:02

or less Moore's Law. You

4:06

don't need to worry about. The.

4:08

Long term prices from keep exists gonna go to

4:10

to zero very quickly and then or you need

4:12

to weigh about his do I get any revenues

4:14

and if the revenues are. Going.

4:16

Up and send as some point no plans call

4:19

for me. Become a very profitable company. That's

4:22

a perfectly reasonable position

4:25

to hold. And.

4:27

Then however at the same time some

4:29

open has this other position which is

4:32

basically in order for the price to

4:34

come of computer come down to. A

4:37

level where I as profitable

4:39

we need seven trillion dollars

4:41

of investment and. That

4:43

is objectively something that is never going

4:45

to happen. There just isn't seven trillion

4:47

dollars of freely available cash in the

4:49

world to invest in anything. And if

4:51

there was, it would. Not. Be

4:53

going into a I mostly so

4:55

I'm. So. That kind

4:57

of his own. Rhetoric.

5:00

Is under cussing his own rhetoric that.

5:03

Guy. Inject some nuance here which is

5:05

that does Seven trillion Dollars is a

5:07

number that Sam hasn't fully confirmed yet

5:09

can scan it also something that will

5:11

will be I for compute and potentially

5:13

other things may be training but. But.

5:16

We but I think the corby argument is

5:18

totally right which is that the stuff is

5:20

going to cost a ton of money to

5:22

train and it does to of contradict this

5:24

idea of that. the the idea of cunt

5:26

than the cost of computing is gonna come

5:29

out in has already cost a ton of

5:31

money and every if you look at right

5:33

now you know how much it costs to

5:35

to form a single chancy be tit for

5:37

tat Cbt to give a single answer to

5:39

a single question the how much guff image

5:41

any to produce a single image? it's. A

5:44

huge amount of money and all of these

5:46

companies are losing money on every. You.

5:48

Know response basically and

5:50

this is. A.

5:54

Reprise of the famous split scaling

5:56

model in our until arising have

5:58

a great column. I'm the

6:01

information about this, he basically what

6:03

is going on right now if

6:05

you have a handful of companies

6:07

led by Open A I Who.

6:10

Trying. To invest as much money

6:12

as they can as early as

6:14

possible in order to gain market

6:16

share and I P and get

6:19

them get their. A I

6:21

basically own the eyes two and half and

6:23

reach a point where no one else can

6:25

afford to build one as and or they

6:27

own it in a way that you know.

6:30

They. Have happens on the or something as

6:33

it was very unclear but. They.

6:35

Want to monopolize a i

6:37

going forwards and all of

6:39

these incredibly high value with

6:41

these multi billion dollar valuations

6:43

that with seeing only make

6:45

sense In a world where

6:47

the companies have some kind

6:49

of comparative advantage have some

6:51

kind of monopoly on something.

6:54

And this is definitely the way

6:56

the Us tech industry has evolved

6:58

over. The. Course of this

7:01

century Rights Debt is is the you

7:03

have a small handful of multi trillion

7:05

dollar tech companies that.that way by investing

7:07

huge amount of money and getting a

7:09

bunch of market share before anyone else

7:12

and then creating that kind of mode

7:14

and becoming you know basically impossible to

7:16

compete with. And the

7:18

bet that the investors are making is the

7:21

same thing is going for happen and there's

7:23

just gonna be a handful of Ai companies

7:25

rather than a I being a foot of.

7:28

Lord Public Utility likes a

7:30

Tcp Ip that everyone can

7:33

use. That. It also just creates

7:35

an incentive for any taxi. Or that

7:37

sign that model that kind of stick a finger

7:39

in the air in terms of determining how much

7:41

capital a seat and taking the biggest number possible

7:43

which seems to be part of it up. and

7:46

say, That. The Defense: The difference

7:48

here says the last time around that

7:50

the building of the Big Tech monopolies

7:52

which seems to be how we ended

7:54

up is that the cost of entry

7:56

into the As Space First started is

7:59

so high that. You. Already have

8:01

monopoly. He is open as ready. Pretty much

8:03

a monopoly on a I'm in Its and

8:05

it's mostly funded right by. A big

8:07

tech company? There's not a that doesn't seem like there's

8:09

a lot of. Innovation around

8:11

the start up space because of the

8:13

costa entry is so high. What we

8:15

have we have one of genuine

8:17

monopoly and I would choose in

8:20

video right Everyone everyone in the

8:22

I space is using the same

8:24

h one hundred chip and. One

8:27

of the fit one of the reasons why

8:29

family and once a much bigger border ecosystem

8:31

is that he feels that it is unhealthy

8:34

for and video to be the only company

8:36

making a I have since I I want

8:38

to build fabs Thanks. For. The

8:40

one that's not go seven trillion dollars

8:42

a complete fifty billion dollars pay to

8:45

like Even raising fifty billion dollars have

8:47

to build a fab is very hard

8:49

given that. You. Know a

8:51

large number of companies have tried

8:53

to build. He left a to

8:55

be a fabs and have failed.

8:57

Really Only Cfm see has shown

9:00

itself capable of building those those

9:02

fat size as far as is

9:04

that like in video Tsmc duopoly

9:06

that is still really owns most

9:08

of the sun is moat around

9:10

here. Is some almonds right

9:12

and the cost of the computers come down.

9:15

There's also the other side of the scale.

9:17

Riot and whoop the revenue. Peace. And are

9:19

we at the point yet? Alex, you saw

9:21

this more closely. that. There's. Like

9:23

a lot of money to be made in ah

9:26

for real like I know and videos making lot

9:28

of money selling tips the companies who hoped that

9:30

they make a lot of money from add that

9:32

has anyone done anything worth likes? That's the I

9:34

phone of their whatever. They certainly don't know

9:37

consumer facing a I product that his is

9:39

making any revenue but like you're right in

9:41

that kind of middle. There's.

9:43

Like on who was a common that

9:45

was some big consultancy company. You

9:48

that accenture or something said that they

9:50

just made six billion dollars on a

9:52

I consulting guess everyone in a like

9:54

derivatives states for derivatives like it is

9:56

very very hard. That. One of

9:58

the things that we have seen him. Whatever. As like

10:00

a year and a half his cecchetti be

10:02

t came out and course you know all

10:04

of the you know. Crazy. Is

10:07

that there is very little

10:09

real consumer demand from normal

10:11

human beings. are willing to.

10:14

Who Who who want to pay cash for this.

10:17

The one last thing I think could be

10:19

a money maker down the line is the

10:21

labor cost savings. Like Jeffrey Katzenberg had some

10:23

quo and access to their thank them for

10:25

next Had it where he said like with

10:27

a I the timeline for making a movie

10:29

is basically cut in half. The amount of

10:31

labor you need is cut in half on.

10:34

That. Seems like amazing amounts of money, but

10:36

it's not sexy like. So.

10:40

I do want to push back on the

10:42

idea that Open A I as as a

10:44

monopoly in this because you do have other

10:46

companies and this is going to lead and

10:48

dear dear other question but you doing you

10:50

do have other companies building these frontier models

10:52

with it are in foundational miles with the

10:54

that matter with Lambda Three and Anthropic with

10:56

cause like Cod recently surpassed Open A I

10:59

for a moment and then that This is

11:01

where the interesting question about the economics happens

11:03

for me which is that. You. Know

11:05

if all these models become come out

11:07

of I'd like you're going to have

11:09

met Islam. Are not lama three available

11:11

for free open source and where's the

11:13

actual value created? And. Does it

11:16

actually as a crude? To the malik

11:18

raiders or to the people that built

11:20

on top and I strongly believe that

11:22

is gonna be accruing to the companies

11:24

that build on top of these models

11:26

or the that is a consumer product

11:28

or business. This is like labor saving

11:30

and these business efficiencies. The. Companies

11:32

that use them innovative we are the ones that

11:34

like will actually make the money here and that

11:36

sort of goes to Met. As bet that psych

11:38

meds just give this way for free. It's not

11:40

going to be worth. Really? Anything. And

11:42

maybe that's going to Sam's point that

11:44

the cost of intelligence is gonna be

11:46

low. but then you have a real

11:48

are? Oh I question. Yeah, no, this

11:50

is exactly correct vetting. Emily's point is

11:52

very well taken on Jeffrey Katzenberg splinters.

11:54

Very well taken. This enough. One.

11:57

Way to make lots of money out of

11:59

a technology is. To. Take. A

12:01

technology in charge for it. Another

12:03

way to make lots of money out

12:05

of the technology is to take a

12:08

technology and use it to cut your

12:10

costs. And that does seem to be

12:12

something that people are already doing with

12:14

some genuine. Profitable.

12:17

Effect and businesses are doing and is

12:19

gonna is gonna become much more common

12:21

over the next few years and that

12:23

is going to be good for the

12:25

economy and that is gonna be good

12:27

for all of the companies that do

12:29

it and on some level you know

12:31

if the cost savings a high enough

12:33

then the companies will be willing to

12:35

pay some non civil amount of money

12:37

for the I that that using on

12:39

the other hand. If.

12:42

The cost savings: A. Curve.

12:45

The same. No. Matter which hey

12:47

I use and you know, Some.

12:49

Of the eyes are open source and or

12:51

even just build your own with open source

12:53

tools. And

12:56

then they'll probably go that way and there

12:58

won't be a lot of the right revenues

13:00

to the I companies and for a I

13:02

will be distance forth for productivity and profitability

13:05

in the economy and. The. Ai companies

13:07

themselves in Open A I am for

13:09

up Aiken and the rest of them

13:12

will turn out to be not particularly

13:14

valuable and this by the way as

13:16

an outcome that Open Ai has always

13:19

envisaged, right? Like in the early days

13:21

when they were asking for funding, they

13:23

said we would like you to consider

13:25

your. You. Know funding

13:28

to be in the spirit of

13:30

a donations and says still a

13:32

non profit and. If

13:35

that is the outcome the open A I

13:37

winds that's of making everyone else profitable without

13:39

being profitable itself. That is a good

13:41

outcome for the economy and that as a

13:44

good outcome for the were another factor here

13:46

that as I am am not sure

13:48

if op minute spoken directly to this is

13:50

that yeah you don't have. Incident

13:52

data and they cost the data

13:55

acquisition. Is not as it certainly isn't falling

13:57

the way that cost of computing is falling.

14:00

You know, right now you have AI companies

14:02

looking at buying traditional book publishers just so

14:04

that they can add to the corpus of

14:06

things that they're training the models on. So

14:08

the inherent value of the business isn't

14:10

just about the

14:12

algorithmic model, it's about what you can

14:14

do with it within the limitations of the

14:17

data you have to train it on. Are

14:19

we thinking too small here? That's like the other

14:21

question that's coming up because there's another thing that

14:23

Sam Altman said last week that went even more

14:25

viral than the thing that I mentioned. Whether

14:29

we burn 500 million a year or 5 billion

14:32

or 50 billion a year, I don't

14:34

care. I genuinely don't. As long as

14:36

we can, I think, stay on a

14:38

trajectory where eventually we create way more

14:40

value for society than that, and

14:42

as long as we can figure out a way to pay the bills, like

14:44

we're making AGI, it's going to be expensive. It's totally

14:46

worth it. So, yeah, I mean,

14:49

it really is... I

14:51

hate to say this, but it kind

14:53

of smells a little bit like Sam

14:55

Bankman Freed, you know? Not

14:58

saying that he's a... But that kind

15:00

of like, it doesn't matter how much it

15:02

costs just as long as you have a

15:04

positive EV somewhere down the road. That

15:08

idea of you

15:11

can lose any amount of money just so

15:13

long as the value

15:15

of your company is rising faster than

15:17

the losses are piling

15:19

up, is a very

15:24

dangerous game to play if you

15:26

don't really have a sort

15:28

of, let's call it, three

15:31

to four year plan for turning

15:33

it into profits. Like, Sam's idea here seems

15:35

to be like, well, maybe at some point,

15:37

10 years down the line, we will have

15:39

AGI and then we will make lots of money.

15:41

And there are two problems

15:44

with that, which is one, that 10 years down the line

15:46

is a very long time to be burning $50 billion a

15:48

year. But two

15:50

is that he seems

15:52

to just assume that once there's

15:55

AGI, then OpenAI will be a trillion

15:57

dollar company and worth lots of money.

16:00

Again, that's not

16:02

obvious either. To

16:05

Elizabeth's point, I think

16:08

Sam is already trying to move on

16:10

from the LLM model. Right

16:16

now, most of the AI

16:18

that is getting most of the buzz are

16:20

these large language models that need to

16:23

be trained on a bunch of existing

16:25

language. I think everyone is in

16:29

agreement that if you're going

16:31

to get AGI, artificial general

16:34

intelligence, it's not going to

16:36

be a chatbot

16:38

that basically gives

16:41

language answers to language questions because

16:43

it's trained on language models. He's

16:46

going to need to invest a huge amount of money in something

16:48

much bigger than that. Yeah, we don't even know if

16:50

AGI is possible. So it's sort of putting

16:53

any timeline underneath it is

16:55

speculative and I realize that

16:57

that's part of your

16:59

job if you're working

17:01

in an innovative frontier tech

17:04

company. But in the case

17:06

of AGI specifically, even experts

17:08

who have been studying this for decades aren't

17:10

sure that we will ever get to

17:12

AGI. So even making

17:14

estimates about what sort

17:16

of resources it would take and how long

17:19

it is. It's very strange

17:21

to look at a company, it's not

17:23

a public company, I guess, again, Altman

17:26

can make speculative statements about it,

17:28

but he does so with such

17:31

confidence when the underlying goal

17:33

is not even something we know

17:35

can be achieved. Right,

17:37

but this is one of the

17:40

things that Silicon Valley VCs love

17:42

is people who have great confidence

17:44

about things that are highly improbable.

17:47

They have a bunch of, you know,

17:50

they've learned by looking

17:52

that if you fund someone

17:55

who is very confident about something that seems

17:57

impossible, then there's a good chance you'll lose

17:59

all your money. But there's also like

18:01

those are the ones that have the biggest

18:03

attempts as well It's

18:06

good more value for society

18:11

What what are the problems that AGI Like

18:14

I can like rattle off many problems with

18:16

society and none of them in my head

18:18

can be solved by Sam Altman and his

18:20

company I think the big answer

18:22

on that front is scientific discoveries like I think

18:24

it's no accident that one of the things that

18:27

deep mind will tell you about is Alpha

18:30

fold like in the first breath where they've

18:32

been able to decode proteins because they think

18:34

that You know will help for drug discovery

18:36

and maybe there's an idea that you train

18:38

these bots on You know all

18:40

the scientific literature and you give it some problem

18:42

sets and the things that they're able to

18:44

do now Or there everybody's working

18:46

on his reasoning so they can break it

18:48

down to the component parts and then you

18:50

know Try different solutions on each step and

18:53

eventually get you to a solution and

18:55

I do it So I do wonder let's say we

18:57

don't get to AGI But let's say we get some

18:59

things that might you know, maybe short

19:02

but close right? So these agents that

19:04

take action for us disability to reasoning

19:06

to reason scientific discovery Making

19:09

our everyday business operations more efficient. Maybe

19:12

that is something that's that's quite valuable

19:14

I don't I wouldn't you know happily

19:16

burn 50 billion dollars a year on

19:18

it But you know to earnestly take

19:21

up Sam's case like maybe

19:23

there is something there I guess it's like

19:25

cure for cancer. That's like the answer, but

19:27

it's not just a cure for cancer It's

19:30

like a highly individualized cure for

19:32

cancer, right? it's it's

19:34

it's the ability for an Individual

19:37

person with an individual genome to go

19:40

in and with an individual, you know

19:43

cancerous growth and get

19:46

a A treatment that

19:48

is tailored for them at a

19:50

very low cost right now that kind of

19:52

exists But it costs like over a million

19:54

dollars and if we can bring that down

19:56

from a million dollars to you

19:58

know, a hundred dollars It does, that's

20:01

because of all the money being spent

20:03

on the healthcare system

20:05

is so inefficient and expensive and the

20:07

problems are so basic. Not

20:10

to be all like there are starving people

20:13

in other countries kind of an argument, but

20:15

there are more immediate and solvable

20:17

healthcare problems that these billions and

20:19

billions and billions of dollars could

20:21

go to solve to better society

20:24

right now versus spending $150

20:26

billion or $5 billion a year on

20:28

something we may not ever

20:30

come to fruition and maybe no one

20:32

can afford in the final answer. If

20:38

you look at the people who

20:40

are funding this, some of them

20:42

do have what you

20:45

might call quasi-philanthropic goals. They

20:48

do think of this as a form

20:51

of for-profit

20:53

philanthropy, Matthew Bishop would call it

20:55

philanthropic capitalism and okay,

20:58

fine, we can have a whole other segment on that if

21:00

we want, but I

21:03

don't think anyone is ... There's a small pocket of

21:05

true believers saying that this

21:09

is the first best place to invest money for

21:11

the sake of the well-being of the planet and

21:13

if you want to help the poor, then this

21:16

is the best way to do it. That

21:19

small pocket kind of lost

21:21

a lot of credibility when FDX imploded

21:23

because a lot of them were effective

21:26

altruists of some flavor and

21:28

I think we've kind of moved

21:30

on from that. I think that to say that it

21:32

is not the first best philanthropic

21:34

place to invest your money to help the

21:37

poor is not to say that it's a

21:39

bad investment. I agree with that, but I

21:41

think there are two other things that we

21:43

have to look at. One is that a

21:46

lot of the strategic money that's going into AI

21:49

right now is still just about AI

21:51

hype and whenever you sort of scratch

21:53

the surface of what a lot of

21:55

people like Altman are saying, they're clearly

21:57

relying on the fact that most people when

21:59

they think about AI can't distinguish between, say,

22:01

a large language model

22:03

or machine learning

22:05

or image-based visual

22:08

or image-based generative AI. It's

22:11

all just one category. And these are very

22:13

different technologies. And I know

22:15

we were going to talk about Tesla a little

22:17

bit. Elon Musk is now claiming that Tesla

22:19

is an AI company. And when

22:22

I see that, I just see an

22:24

attempt to get money that's already

22:26

flowing into a very specific sector to

22:29

start flowing in his direction. Well,

22:32

he's calling it a robotics company, which is different than AI.

22:34

And we can talk about that. Yeah. He's

22:36

also got a separate company called XAI, which is

22:38

an AI company that he's raising like $5 billion

22:41

for. He also says that his robotics model will

22:43

be cineered by 2025. And

22:46

if that's, you know... Elon

22:48

says lots of things. But to your

22:51

point, Elizabeth, insofar as the people making

22:53

these investments, and to be clear, these

22:55

investments are large, but they're not enormous.

22:57

They're like, you know, some

22:59

fraction of the VC money out there. And

23:01

the VC money out there is some small

23:04

fraction of the total, you know, investor

23:06

base. Insofar as

23:08

the people making these investments are

23:11

being silly and making category errors and doing

23:13

all of the things that you say that

23:16

they're doing, like these

23:18

are VCs. Being

23:21

silly and making category errors is

23:24

what VCs do. And the whole

23:26

point about VC money is it's risk

23:28

capital that literally everyone who is invested

23:30

in a VC fund can't afford to

23:32

lose. Like, this is the correct money

23:34

to make dumb bets that are going

23:37

to lose. It is

23:39

not dangerous for VCs to light

23:41

a billion dollars on fire. It is perfectly fine.

23:43

They always have and they always will. Can

23:46

I... Let me give you a

23:48

counterpoint on that one. A lot of the money

23:50

funding these companies have come from the big tech companies.

23:53

Right, so you think about Microsoft as being a

23:55

huge funder of OpenAI and Google and Amazon have

23:58

been huge funders of Amazon. and

24:01

Metas has used its own money to build Vama

24:03

3. So like try to find someone who's like

24:05

really made, taken VC monies

24:07

and put it into the development of large language

24:10

models and it's a little bit tough to find

24:12

it without big tech money. So actually I think

24:14

what you have is instead of VCs taking this

24:16

money and sort of squandering it, you

24:18

have these tech companies taking

24:21

the investment capital of retail

24:24

investors and institutions. It is not the

24:26

investment capital of retail investors, it is

24:28

their own profits. It is all highly

24:30

profitable companies. Microsoft is famously

24:32

just giving Azure compute more

24:34

than is actual cash

24:36

dollars. Google has

24:38

definitely invested a lot of money into

24:40

DeepMind over the years and

24:44

Facebook has famously bought billions

24:47

of dollars worth of H100 chips and

24:49

yeah, fine. But this is money

24:51

they can afford to spend and

24:54

again, I'm not, these

24:56

are already multi-trillies. So

24:59

if you look at the other multi-trillion

25:02

dollar companies, if they

25:04

burn a few billion dollars, they will

25:06

still be multi-trillion dollar companies. It's

25:09

kind of no harm, no foul. Okay,

25:11

so we've talked a little bit about Elon Musk in

25:14

trying to pivot to robotics within Tesla. Why

25:17

don't we take a break and come back and unpack that. So

25:19

we'll be back right after this. The LinkedIn

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backslash promises pay off. Hi,

25:52

I'm Thorber Korn, LinkedIn's chief product officer.

25:55

On my podcast, Building One, we dive deep

25:57

into what it takes to build great products.

26:01

Recently we had Zach Perret, the CEO of

26:03

Platt, and he shared about his struggles building

26:05

a financial app for consumers and how he

26:07

was able to turn it all around with

26:09

a critical pivot. Take a listen. I

26:12

personally couldn't resonate as much with the consumer set

26:14

that we were trying to reach. I just didn't

26:16

have that level of empathy. When we

26:19

made the shift to building a B2B product though,

26:21

I was building the product that I wanted. My

26:23

co-friend and I were creating the product that we wanted

26:26

ourselves and we had so much empathy for what that

26:28

product was. Such a great insight. You know, in that

26:30

sense we got lucky because we were creating a thing for

26:32

ourselves. And then the people that we were talking to also

26:34

had the same problems we did. They were FinTech developers. We'd

26:36

been the FinTech developer. We've been trying

26:38

to build a FinTech product for a year. And

26:40

so we had such deep empathy. We

26:42

had such a clear ability to... If you want

26:45

to hear more of Zach Perret's story and the

26:47

lessons that follow, listen and subscribe

26:49

to my podcast, Building One. And

26:53

we're back here on Big Technology Podcast with

26:55

the cast of Slate Money. Great to have

26:57

you all here. We're cast members. The

27:00

cast members. I remember when I

27:02

joined Disney, they were like, congratulations on becoming a

27:04

cast member. And I was like, okay, this is

27:06

a really weird company to work for. Yes.

27:09

Well, okay. So maybe we'll use a different word.

27:11

The hosts of Slate Money. How's that? Anyway,

27:14

so we talked a little bit before the

27:16

break about the Tesla robotics play. It's happening

27:18

in this moment where Tesla seems to be

27:20

in rough shape. And I know you've talked about

27:22

it on the show, but just

27:24

for context, it's down 25% year to date.

27:28

It's up 8% over the past one

27:30

year, which is interesting, sort of kind of

27:32

lost in this narrative. But BBC just

27:34

had a story asking if the wheels have come

27:36

off for Tesla, saying there was a time where

27:39

it seemed like it could do no wrong,

27:41

but now the company is struggling and it really

27:43

captures it with falling car sales, intense competition from

27:45

Chinese brands, problems with the Cybertruck,

27:47

low sales have hit revenues and hurt profits,

27:49

and the share price has gone more than

27:52

a quarter since the start of the year.

27:54

It's now in the process of cutting 14,000 employees

27:57

and it's also cut the entire team responsible

27:59

for it. It's a much

28:01

admired supercharger network. So

28:04

what is going on with Tesla? And then we

28:06

can get in a little bit to this robotics

28:08

pivot. But what's the thing? I know you've talked a lot about it. My

28:11

big picture theory of Tesla is

28:14

that it had first move

28:16

for advantage and for a

28:18

long time its EVs

28:20

were three years ahead of everyone else.

28:24

And they're not anymore. And

28:27

now they're basically zero years ahead of

28:29

everyone else. Or

28:31

maybe like a

28:33

tiny bit, depending on what you're looking for.

28:37

And if you look at the stock

28:40

market valuation, it is trading at

28:42

50 times forward earnings compared to

28:44

standard car companies that trade at

28:47

like four or five times forward

28:50

earnings. And good ones like Toyota.

28:56

So something doesn't compute. Something doesn't

28:58

add up there. The idea behind

29:01

that massive multiple

29:03

that it trades on is

29:06

that it has some kind of

29:08

unique competitive advantage over the

29:10

rest of the car industry. And

29:13

if you look around at who's making the

29:15

best EVs and the best value EVs out

29:17

there, it's BYD. It's

29:19

not Tesla. And we're talking about global

29:22

companies here. Tesla has

29:25

a nice little advantage in the United States because

29:27

the United States government is doing everything it can

29:29

to avoid Chinese EVs being sold here. So it

29:32

gets to avoid that competition in the US. But

29:34

that's not the case in the rest of the

29:36

world. And the rest of the world is all

29:38

that. Also, on our show, we've talked about Tesla

29:40

not infrequently as a meme stock. And while it's

29:42

not game stock, there is a lot of the,

29:44

I think, stock, the value of

29:47

the stock is heavily wrapped up in

29:49

Elon as a personality and a

29:51

brand. And so some of this, I think,

29:53

is at least Ross Gerber, who's

29:55

a big Tesla shareholder, argues that some of

29:57

the fall in the stock price is really...

30:00

about Elon sort of being a

30:02

chaos monkey within his own company. Right.

30:05

And Elon, he can't stop founding new

30:08

companies, right? He's

30:10

got XAI now, he's got Neuralink,

30:12

he's got the boring company, he's

30:14

got Twitter. I'm sure

30:16

there's a few I'm forgetting. He's

30:19

very... SpaceX. SpaceX, of

30:21

course. Yeah. And like, you

30:24

know, he's trying to do all of these

30:26

things at once while tweeting maniacally through the

30:28

whole thing. And

30:30

so at some point you have to

30:32

ask when does Elon stop being the

30:35

reason why Tesla's multiple

30:38

is 10X everyone else and starts

30:41

being actually a weight

30:43

on the stock that is under if he

30:45

left, you know, the stock price would go

30:47

up rather than down. I

30:49

wonder if he's just... So on

30:51

our show, I guess last week,

30:54

we talked about the supercharger situation,

30:56

you know, layoffs and

30:59

cutting out this part of Tesla's

31:01

business that is widely admired and

31:03

believed it can someday be profitable.

31:05

And why does this make

31:08

sense? And I tried to argue that I think one of

31:10

our readers calls it the 40 chess,

31:12

you know, argument that like, it

31:14

seems so irrational. There has to

31:16

be some reason that Elon Musk

31:18

did this, that like, he

31:21

can't be this like unhinged and

31:23

wild. And so I kind

31:25

of thought that even though I'm not like exactly

31:28

like an Elon Stan or anything. And

31:30

someone wrote in and was like, no, this

31:32

was just really unhinged and wild. And his

31:35

no one wants him to do this, his own company

31:37

doesn't didn't want this to happen. He's a long history.

31:39

It's just possible the man is out of control. He

31:42

has a long history of erratic and impulsive behavior too.

31:44

And sometimes people I think part of

31:46

his lore is that you

31:48

can be a certain kind of charismatic entrepreneur and

31:50

there's a class of people who admires you

31:52

for that kind of chaos or

31:55

the sort of very

31:57

confident, You

31:59

know, impulsive. That you're making were toys framed,

32:01

as you know, I lived with my gut

32:03

and you I'm sort of in bodies that

32:06

and some people admire. By

32:08

I personally think it's a sign

32:10

of a Ceo is not terribly

32:13

stable. they wouldn't like a different

32:15

investor by I understand the appeal

32:17

to certain people. That.

32:20

At a certain point it's like the wheels have

32:22

come off and and the stuff. He's to do

32:25

isn't working anymore like. You know, used to never

32:27

do your homework and get great math grades and then

32:29

at some point. He malware eighty five

32:31

realizing that don't understand how out in the word

32:33

as frog or. Exactly. But

32:35

isn't that showing him a tiny bit short

32:37

Ny? I agree with lot of this, but

32:40

also like he did, he has been able

32:42

to build Tesla and Spacex is doing well.

32:44

I mean Axis I think a disaster, but.

32:48

That may sound silly. were like i think

32:50

i think this is this is a super

32:52

interesting question is that the. More.

32:55

That. What? You're doing is

32:57

solving and engineering problem. The

33:00

mess a he tends to do spacex

33:02

has to big advantages. One is that

33:05

he kind of doesn't. Touch. It

33:07

very much he doesn't spend much time on earth.

33:09

He has a woman named When Shot over who

33:11

runs it, runs it, who by all accounts is

33:13

excellent. Any kind of trust said to. Do.

33:15

The right thing as it runs itself,

33:18

but also. It's. Solving engineering problems

33:20

is how do we get really heavy

33:22

things up into space and he by

33:24

I can solve that problem. In

33:27

the early days of Tesla, what he

33:29

had was an engineering problem. How do

33:32

I build an electric cars Electric cars

33:34

the something that didn't really exist. He

33:36

wanted to build and links a car

33:38

that was your more powerful than bathroom

33:40

justice affordable as as as whom I

33:43

seek us and everyone's that it couldn't

33:45

be done and he did it and

33:47

that was an engineering problem and that

33:49

was his great contribution to the world

33:51

right? He showed that it could be

33:54

done. But. Then

33:56

the having shown that it could be done. Other

33:58

people's questions. The relay for they could

34:01

do it to a now they are doing it

34:03

to when they're doing it frankly just as well

34:05

if not better than he. If.

34:08

You. Go further away from engineering

34:10

problems. In to say he had take.

34:13

Boring company. He thinks it's an engineering problem

34:15

like how you build a tunnel. in fact

34:17

it says you know zoning problem enough for

34:19

added threat in the transit problem and s

34:22

trying to deal with local government problem and

34:24

he's terrible a bad and is going nowhere

34:26

and as a disaster. If

34:28

you buy Twitter, there's no engineering

34:30

at all. It's all about like

34:32

working with humans and networks in

34:34

moderation in all of this kind

34:37

of stuff. and he has no

34:39

idea how to do that. So

34:41

I think that you know. There.

34:43

Are things he's good at, but the

34:45

kinds of things that Tesla needs to

34:48

do in order to be successful going

34:50

forwards or not. Really engineering problems as

34:52

the Well does. Not sitting here Going

34:54

U S. E B

34:57

need to be technologically much more advanced in

34:59

order to be successful. No one is. Desperately.

35:03

To holding their breath waiting for yeah full

35:05

self driving cars and autonomy to arrive. I

35:07

give it comes. it comes best. For.

35:10

The time being. If.

35:12

You want to compete on he these. You've

35:14

got to compete frankly on cost and is

35:17

very hard to compete with Chinese On Caught.

35:19

In fact, it's impossible. I agree with

35:21

the top line thesis that ill and

35:23

success of companies is correlated. See whether

35:25

or not it's as engineering problem but I

35:27

believe it for exactly the opposite Really reason

35:30

that the like that as I don't think

35:32

he learns really an engineer and where he

35:34

didn't did I say i'm in that I've

35:36

you to imply that he knows that assault

35:39

these engineering problems and I don't think that's

35:41

what's happening? I think where you see him

35:43

being successful is it a very early stage

35:45

of a company when his subic his skills

35:48

are writing a check for capital intensive business

35:50

nobody else wants but money and. And

35:52

then managing shareholder expectations and then

35:54

the Mormon Church. These companies get.

35:57

The more he's not mediated

35:59

the Pr people and lawyers.

36:02

People sort of begin to understand that

36:04

he's not the best Manager says engineering

36:06

capabilities are not barely exists. It is

36:09

that an engineer by. You

36:11

know, education or trades? Or

36:14

his a yes or no. So the question. Is

36:16

is he a good Ceo is not

36:18

a good product person necessarily. And

36:21

in the things you expect to see, oh dear,

36:23

manage. Well managed. Shareholder expectations communicate

36:25

well externally, and that's where

36:28

he's shooting himself in the

36:30

foot constantly. right?

36:32

And I think the more he gets

36:34

involved in that on us afloat the

36:36

the weirder guess like as who would

36:38

say with work which is clearly a

36:40

creature of yo you on musk as

36:42

product manager or all of the crazy

36:44

back and forth insanity around with a

36:46

blue and who gets sick mother and

36:48

who doesn't the net that those kind

36:50

of product decisions when he makes them

36:52

ah that tend to work out very

36:54

badly. That said, Yo. The

36:58

model S when it came out

37:00

was as a product, genuinely revolutionary

37:02

and amazing and everyone's mind was

37:04

blown. Feeds you know the amazing

37:07

videos his spacex rocket like landing

37:09

vertically and saying up right after

37:11

going to stay feel? it's okay.

37:13

that's a really legitimately impressive product.

37:16

Did Ilan must personally design them?

37:18

Know that use? you know he?

37:20

He has enough engineering mouth to

37:23

release. kind of understand what's going

37:25

on there. So. Can we then

37:27

think about this robotic thing as the next

37:29

in the line of engineering problems that he's

37:31

tackled and try to solve? And as.basically what's

37:34

happening with this. Is it? In.

37:36

Terms of like this framing of of. Tesla.

37:39

As robotics company. And.

37:41

Understand the client city Well that's like.

37:43

Sense. Other actually building a robot they have

37:45

a you annoyed robot called optimists that

37:47

they say they're gonna really really dramatic

37:49

automation and auto companies As he announced

37:51

that that makes sense for Tesla, but

37:53

is is your talk about robots for

37:55

General Year said on Understand it at

37:57

all. Your. What's what's.

38:00

What's this robot supposed to do?

38:02

Alex? I don't fully know.

38:04

I mean it is supposed to be,

38:06

I guess. I guess it's the human

38:08

I'd robot you would imagine. You can

38:10

sort of put it into action the

38:12

same way you would like an L

38:14

M, except in the real world. So

38:16

something that's assistive, something I imagine can

38:18

do work. On but.

38:21

We've. Had like Boston Robot, Boston Robotics or

38:23

Dynamics has been doing. These were like robot

38:25

demos for a while but they're not exactly

38:27

like mass produced outside of like sometimes like

38:30

the and Y P D will buy one

38:32

and they'll be like this whole blow up

38:34

around it. Is a creepy

38:36

robot a my supermarket that. Like or

38:38

every every heirloom. yeah yeah, no I've

38:40

I've certainly had a couple of like

38:43

cute little robots in hotels so late.

38:45

deliver your room service to burn. I'm

38:47

but does. but see. the other thing

38:49

that we have to mention about. This

38:52

sort of extended. He. Longs

38:55

universe is that. He

38:58

can kind of put whatever he

39:00

likes wherever he likes this robots

39:02

that he's talking about. Is

39:04

you know may be part of Tesla

39:06

right now, but maybe it could suddenly

39:08

turn out to be part of X

39:11

A I You know if he woke

39:13

up one morning and and decided to

39:15

change his mind in s the If

39:17

part is nothing up. X Ai is

39:20

actually him. Basically

39:22

threatening. Their

39:25

borders. Tesla. And

39:27

saying like, unless you give me another

39:29

hundred billion dollars worth of pay, I'm

39:31

just gonna do all of my sexy

39:33

I stuff somewhere else He said that

39:35

quite explicitly. Yeah.

39:38

He famously brought a bunch his Tesla

39:40

engineers have to Twitter after he bought

39:42

it president for us. in the have

39:44

a Twitter engineers serve. As.

39:46

An investor in any of the lawns

39:48

companies, you kind of. Don't.

39:51

Know what you're investing in? Because of that

39:53

money could just wind up. Benefiting.

39:56

A completely different company altogether. Yep,

39:58

says is from my. Trusting

40:00

engineering.com they say that robot is

40:02

designed to be a general purpose

40:04

machines that can help humans in

40:06

various domain such as manufacturing, construction,

40:08

health care and entertainment. A

40:11

I will add my new Tesla you

40:13

know I If you want to revolutionize.

40:17

The. American economy. Robot.

40:19

That can build houses would be amazing

40:21

because the cost of building a house.

40:23

The labor cost of building a house

40:25

is not only extremely high, but there

40:27

just isn't enough labor to go around

40:29

that there's a massive labor shortage of

40:31

people who out there are skilled enough

40:33

to build a house and if we

40:35

could get a bunch of robots to

40:37

do that, that would be amazing for

40:39

making housing or affordable. Yeah, I'm

40:42

watching a video of it now and this

40:44

robot is like taking things off and assembly

40:46

line stalking and and like special compartments in

40:48

some container so. Who knows her

40:50

body. I you know, a lot of

40:52

robotics used in manufacturing. It's not like.

40:55

Yeah, but that's on like assembly lines and the

40:57

idea is that if you if you. Put

41:00

a sort of x and and a i

41:02

tip interest in it. Can. Work.

41:04

In like real world or yes,

41:07

situations like. A building. Site.

41:11

Okay as were coming towards a close I just

41:13

want to talk about this thing that as like

41:15

had and ah my prep doc with Ron John

41:17

for like months and haven't gotten around to of

41:19

but I think this is the right. Crowded.

41:21

Talk about it with and that is

41:24

sort of when it's time to get

41:26

off their Donna treadmill and retire and

41:28

whether retirement is still gonna be. A

41:31

things are just set it up. There was

41:33

as reddit posts where this person are

41:35

posted and. They. Said I After the

41:37

first two or three million a paid off

41:39

home and a good car, there's no difference

41:42

in quality of life between you and Jeff

41:44

Bezos. Basically. Like the sooner than

41:46

you figure this out, the happier you're going

41:48

to be. And time as the currency of

41:50

life, not money, And. The.

41:52

Desire Austin Reef is the founder of

41:54

Morning Bruce said he posted this and

41:57

he like summarize the responses and he

41:59

said it's funny. Everyone I know who

42:01

has two to three million dollars think the

42:03

magic number is ten million and everyone I

42:05

know was ten million. Think the magic numbers

42:07

twenty five million and everyone I know who

42:09

is twenty five million Six, the magic number

42:11

is one hundred million. So busy to saying

42:14

I know average people. that's really think that's

42:16

kind of a humble brag. but it is

42:18

I I guess like let me turn it

42:20

over those the slate money crew on this

42:22

one. Where. Do you think

42:24

about this and and do I mean

42:26

I guess like were so so The

42:28

first the first and we need to

42:30

ask is like you know the let's

42:32

let's be clear about defining ah times

42:34

more with what with defining here. Is.

42:40

How. Much money? can you

42:42

be happy? Living

42:45

on. In the

42:47

absence of any income. How

42:49

much money you need to have in

42:51

order to retire comfortably and have basically

42:53

the same standard of living is Jeff

42:55

Bezos to within in and five percentage

42:58

points says days as. So

43:01

and as that's that's an interesting question.

43:03

But one of the. One

43:06

one of the ways that you need to the next

43:08

question that you need to ask is. How

43:11

much money are you making right

43:13

now in income? Because

43:15

to your point, Alex about the hedonic treadmill.

43:18

The whole point of the hit on a

43:20

treadmill is that you. Are

43:23

a little bit unsatisfied with your current

43:25

income and you want a little bit

43:27

more income and that is not a

43:29

function of. Well, thats a function of

43:32

income. Now of lump sum of cash

43:34

of an amount of wealth will generate

43:36

a certain amount of income. And for

43:38

our purposes, let's just say four percent.

43:41

Let's just say that you know a

43:43

lump sum of cash will generate. A

43:45

certain amount real income in perpetuity of roughly

43:47

four per cents. If you have a million

43:49

dollars, thou give you forty thousand dollars a

43:52

year in. Real. Income

43:54

in perpetuity. so. If.

43:57

You have amassed your million dollars

43:59

of wealth by earning one hundred

44:01

and fifty thousand dollars a year.

44:03

And then you retire with a

44:05

million dollars and suddenly have to

44:07

zone forty thousand dollars a year.

44:10

That's a major decrease in your

44:12

standard of living. If however, you

44:14

just graduated from college and you inherited

44:16

a million dollars he own you've never

44:18

had forty thousand dollars you to live

44:20

on the new suddenly have to forty

44:22

thousand dollar your income stream then it's.

44:25

An. Increasing a fan of living and you can probably

44:27

do that so I think. It's.

44:29

That there's two variables here, right? It's

44:32

not just the question of how much

44:34

money as enough is Also, how much.

44:36

Income. Or you use to.

44:38

And if you can reach that

44:41

point where the amount of money

44:43

you have. Divided. By

44:46

twenty five will is equal

44:48

to your current income. Then

44:50

I think you're happy than retire. And.

44:53

Let me just talk about like this for

44:55

time and question overall because more more we

44:57

see that are social systems are overburdened and

44:59

I think a big political issue of for

45:01

the next couple years is gonna be whether

45:03

these things like social security and he to

45:06

kick in at the age as they do

45:08

and years. As one quote from Ben Shapiro

45:10

he said no one of the United States

45:12

should be retiring at sixty five years old.

45:14

Frankly, I think retirement self as a

45:17

stupid idea monsieur some sort of problem. In

45:20

and we already said that I'm who sit

45:22

down with I s he has enough money

45:24

for as I said that good of society

45:26

Yeah. I'm full first,

45:28

Indian, and thirty percent of people retire

45:30

between ages of sixty two and sixty

45:33

four. And. A

45:35

man. A bunch of people retire at sixty

45:37

fast. The people retire I think earlier than

45:39

they. Think they're gonna retire? I

45:41

don't really know them superyachts talking about for

45:44

meaningful. When to retire isn't

45:46

really. they don't have as much agency

45:48

and making that decision. I think then

45:50

someone like a Bench Shapiro A is

45:52

imagining it. You know you. You.

45:55

Got laid off some your last job because

45:57

you're too expenses. Near company. Would

45:59

rather. Hire someone thirty years younger than you, So

46:01

that's what happens. another son, you're out of work,

46:04

and. You. Know you're sixty one years old

46:06

and known wants to hire sixty one year old

46:08

anymore. So. You're consulting and you're

46:10

basically retired or you get hurt on

46:12

the job. There's so many people. You.

46:15

Know without college degrees that are doing some

46:17

kind of physical labor and they'd bodies can't

46:19

make it to sixty five, or six to

46:21

seven, or. And I think been

46:23

all says sisters incapable. Of imagining the

46:25

lives of people who not like color

46:27

elites are. You know when you look

46:30

at people here retire earlier than sixty

46:32

five we have. our people don't even

46:34

have retirement plans and they they end

46:36

up during. It's is because the work

46:38

is exhausting. Interview for your during a

46:41

job where you have to do hard

46:43

labor or even in positions where you're

46:45

on your feet all day in retail

46:47

for five maybe six days a week.

46:51

And. And I think some of this when when

46:53

been says he. Doesn't think people should retire

46:55

as a key is reflecting as sentiment

46:57

that's a little bit political which is

46:59

that your work is inherently good. And.

47:02

Everyone should strive to work and and as that

47:04

are the reality as a lot people work and

47:06

really crappy jobs that make the misses her paw

47:08

so it's a you sort of have to ask

47:10

yourself who benefits are not. And

47:13

then in terms of how much how much you need, I

47:15

think. And Felix is written about

47:17

this like. No one really

47:19

knows how much they need in retirement like it's

47:22

a real big mystery. Like you get to that

47:24

end it and you have a lump sum of

47:26

money. but then like. You. Don't know one

47:28

of the. The parts of the equation which

47:30

is like how long you're gonna live it's kind of

47:32

a mystery and hope that the bass but you also.

47:35

Need the money to last and so that best

47:37

number you is reach said think it's There's a

47:39

lot of anxiety there in terms of like making

47:41

that decision like I'll. Miss. That bringing

47:43

in money and like. Hope. What

47:46

I have lasts for the next twenty

47:48

thirty years or something. And and arms

47:50

to be clear offer like the people with

47:52

twenty five Really goofy. maybe the hundred million

47:55

like this. That point the a bit just

47:57

to be cleared. Those people that do not

47:59

think they need a hundred money and because

48:01

they are worried about burning through that twenty

48:03

five million does the people. They may be

48:06

the hundred million because at that point you

48:08

start becoming more ambitious in terms of. How

48:11

much money you want to have When you

48:13

die and you want to leave money for

48:15

your family and your kids, You wanna leave

48:17

money to charity? Wanna You know? you want

48:20

a certain amount of wealthy, one a certain

48:22

amount of legacy? It's a little bit like

48:24

once you have twenty five million, there is.

48:27

Almost. Zero chance you're just gonna

48:29

spend Ill. And

48:31

that's remembering the government. yeah on not

48:33

semiconductors. do we think the government that

48:35

has borrowed against social Security but brow

48:37

Reagan about to see like a war.

48:40

On. Retirement as they try to figure

48:42

out a way to raise the retirement

48:45

age and he a can avoid Alvin.

48:47

That won't happen because Source Our Social

48:49

Security enjoys enormous bipartisan support And and

48:51

they arrived yet people republican specifically who

48:54

would rather that not be the case

48:56

because it makes it hard to kill

48:58

entitlements generally by it's it's it's. A

49:01

given that their face is is.

49:03

When it most rapidly aging segments

49:05

of the population it's gonna be

49:07

very difficult to get anything past

49:10

politically that would actually in out

49:12

a take put a dent in

49:14

social Security as a program. I

49:18

think that raising the retirement and that's

49:20

something I could see happening other it's

49:22

happen in other countries. People hate it

49:24

and they. They have happened in that hazard and.

49:26

Prior have as happen in this country

49:28

Yes and it does. I mean it

49:31

makes us at sends people do live

49:33

longer and unfortunately put more poor people

49:35

on low income people don't really know

49:37

that much longer. Sell. I'm not

49:39

sure about it as a policy. Overall, Okay

49:43

are we talked about a I Tesla

49:45

and the Died Treadmill and says pretty

49:47

diverse but super fun our conversation So

49:49

thank you to the Slate Money crew

49:52

the cohosts Felix, Emily and Elizabeth. Great

49:54

getting a chance to speak with you

49:56

about this stuff and I really can't

49:58

wait to hang out. In your neck

50:00

of the word sometimes. Ambience: Has

50:03

been salix like clouds. Thanks again thinks

50:05

everybody's I will be back on Friday

50:07

with runs on way to break down

50:09

the new two weeks news. ah until

50:11

then next to. Us.