0:00

hello. Good evening, good afternoon, good morning,

0:02

wherever you are. This is Off

0:04

the Fence, a transatlantic podcast where

0:07

three guys in Madrid and

0:09

in the New York metropolitan area get

0:12

together and try to put the world to this week we're gonna

0:14

talk about developments in banking,

0:16

in US banking and specifically Apple

0:19

offering a savings account. It's

0:21

gonna actually pay a competitive interest

0:23

rate which should be interesting given the way

0:26

deposits have been moving in the US and

0:28

and other and other developments.

0:31

But Luis, this was your topic.

0:33

Why didn't you, why don't you take it, tell us what you

0:35

think what the issues are. Thank

0:38

you Peter. I think, we discussed this topic

0:41

in previous podcasts and.

0:44

My impression is that when you're presented

0:47

with such a fantastic spread

0:49

between deposits that are

0:52

site deposit that pay nothing and federal

0:54

concentrated 5% or

0:56

500 basis points spread between what

0:59

banks pay for deposits and

1:01

treasuries short data treasuries,

1:04

there will be somebody somewhere that will take

1:06

advantage of that to take market share

1:08

in deposits and do something with it. And

1:11

we have, we, we talked about it and we couldn't,

1:15

I couldn't figure out who would be the disruptor.

1:17

Apple yesterday broke

1:20

the ice. It is, in my opinion, a much

1:22

more significant attacker than

1:24

any of the attackers that we saw in the early 2000,

1:27

such as I n g Direct or

1:29

the online brokers. Various reasons.

1:31

First of all, apple as an, as the.

1:35

Owner of the operating system

1:37

in which a lot of people's lives work

1:40

has an enormous amount of subscribers

1:43

already many of whom

1:45

use different functions that the

1:48

Apple Pay app allows them

1:51

to use. It is also

1:53

a big contender in the buy now pay

1:55

later layaway system of buying

1:57

for retail purchases. And

2:00

therefore the fact that they can get funding

2:02

at a competitive rate for them and

2:05

maybe not for them, maybe somebody else

2:08

who does the actual financial management

2:10

of both the asset and the liabilities of

2:12

these in, of this

2:14

book. I think it's quite a threat

2:16

to some lenders to

2:19

consu to some consumer lenders

2:22

in particular. Unsecured

2:24

consumer loans. One has to

2:27

believe that it won't be

2:29

too long before Amazon thinks

2:31

that maybe this is also their domain,

2:35

that they should probably get into this. And

2:38

perhaps other of the platforms

2:40

that are involved in either payments or

2:43

consumer Retail might,

2:46

will also develop their own special,

2:49

the interesting savings products

2:52

with this. I think the

2:54

one belonged before this trend moves

2:56

from west to east as these trends generally

2:58

do. And discuss with

3:00

you guys in previous podcast. I

3:03

believe that the current excitement with cyclical

3:07

Companies, specifically banks

3:09

in, in the Eurozone might be

3:11

a little bit ahead of itself because,

3:14

this would be one of the threats

3:16

that we could think of for that

3:19

markets. What do you think? I,

3:24

go ahead Alex, please. No, please,

3:26

Peter. I'm having

3:28

a look on online right now. What big

3:30

banks. Our paying is an average

3:32

rate, and it's about 0.1%.

3:36

And most if you look across

3:38

smaller banks, they're, they get to around

3:40

3%. I see you have some

3:42

special offers. PNC has an account

3:44

at 4%. But this

3:47

is a competitive move. Banks

3:50

are slow to raise deposit rates.

3:52

We know that they're a lot quicker to raise their lending

3:55

rates. It's which is natural.

3:58

Goldman Sachs, who will be servicing

4:00

this, took a stab at retail

4:02

banking in a, with a unit called Marcus that

4:05

hasn't gone so well. It hasn't

4:07

been unwound, but it's been it's been reduced

4:10

that back. Apple and Goldman

4:13

have been working together for a while. And

4:16

especially on the card side. I,

4:18

and, but this is a new and interesting move.

4:21

It's interesting also cause Apple has none

4:23

of the capital or I

4:25

believe liquidity concerns that

4:27

other banks might have

4:30

in, in backing up a savings account.

4:32

And this has become an issue in the last few, we, few

4:35

months as because Yeah. What sovereign

4:38

Silicon Valley Bank was was liquidity.

4:41

The liquidity can move incredibly

4:43

rapidly. And this is a huge

4:45

concern for regulators and supervisors.

4:47

Supervisors right now, I don't think,

4:50

I don't think Apple offers a deposit cause Apple

4:52

doesn't own a bank. I think the deposit

4:55

have to look into the small print is probably deposit

4:57

at Goldman Sachs or some third

4:59

party bank. Apple just

5:01

is the facilitator. I think. The marketer.

5:04

Yeah. But that's the whole point, right? So few things.

5:06

That's right. So the re what

5:08

I'm trying to say, Alex, is that the regulatory constraints

5:10

on the deposits will be with the department taking

5:13

bank will be with Goldman. That,

5:16

that's the whole question. The funny part is that Marcus offers

5:19

today 3.9% versus four

5:21

and change percent. You're not gonna your

5:24

own product are you? Yeah, of course. Exactly.

5:26

You're gonna love that. And it shows you the power of the thing.

5:29

So a few things you mentioned at the beginning,

5:31

the impetus for this and it makes sense is,

5:33

the fact that the spreads have widen significantly

5:36

and that the banks being slow to raise rates,

5:38

as Peter mentioned. Given that we've had, a

5:40

good 10 years of customers being used

5:42

to not earning anything in their deposit

5:45

accounts. This is, marketing

5:47

wise kind of an important move for a.

5:50

National player as opposed to a regional player. Regional

5:53

recently certainly have had to fight for

5:55

the deposits by paying higher rates. It's

5:57

a move that makes a whole lot of sense. And then actually like similar

6:00

to SVB in some ways, what you worry about

6:02

is with an inflow of significant

6:04

amount of savings in all one swoop

6:06

in a fairly big

6:08

marketing push by a very important

6:11

player that has a lot of customers, you

6:13

really wonder where, where those deposits

6:15

are going to go and who's taking the gap

6:17

book decision. You'd hope that it's

6:19

not Apple, as you mentioned, since they

6:22

don't have expertise, credibility,

6:24

or even a structure to do it. So as you say,

6:26

most likely somebody else is doing it. And if it's Goldman,

6:29

fantastic. It's Goldman Sachs, I mean

6:31

at the Apple. There you go. Starting

6:34

today, apple Card users can choose to grow their

6:36

daily cash rewards with savings account from Goldman

6:38

Sachs, which offers high yield annual

6:40

percent in yield, annual percentage

6:43

yield of four point 15% rate.

6:45

That's one than 10 times the national average. No

6:47

fees, no minimum deposits. Yeah. It's

6:49

a perfect marketing thing, but the argument that I'd

6:51

like to make, just to move the conversation elsewhere

6:54

is that for sure we have an opportunity

6:56

right now because we've had this, the rates back

6:58

up and expectations have been very low and it takes

7:00

a while for customers. Customers will look at this as being a good

7:02

deal. That's great, but my argument is a different

7:04

one, which is that Apple should.

7:07

Itself, long term

7:09

B, in the transaction

7:12

processing business, because one

7:14

of the main factors that keeps the wholesale

7:16

rate of transaction processing in the US at 2%

7:19

right is fraud. And

7:22

if there's one thing that the marriage of software

7:24

and hardware in your hand, has

7:27

been able to handle is biometric verification

7:29

and really getting an end-to-end between

7:31

the receiver and the sender of

7:34

a if not trusted, but. Verified

7:38

identity ecosystem, which is quite

7:40

strong. The apple has both the

7:43

physical hardware and the software

7:45

to run transactions.

7:48

Again, forget crypto, forget, how it gets handled.

7:50

But the idea is for them to be a

7:52

payment processor, given that they can ensure

7:54

what is probably the largest piece.

7:57

Of the puzzle in terms of the cost of wholesaling,

7:59

transaction processing I think

8:01

long term ease and attractive is

8:03

an attractive business. It could very well be that they wanna grow

8:06

their marketing shops at

8:08

this point, which makes sense, and turn that into

8:11

over time, building a real financial business

8:13

where they do transaction process. Not take, necessarily

8:16

back and balance sheet risk, but

8:18

just be there to make sure that you have the. Funds

8:21

in your account and that you're making an accurate payment.

8:23

That to me, I think is a long term play That would be a

8:25

significant source of growth for Apple. And

8:31

what does it mean for banks? I

8:34

don't know if you can hear me. Can you hear me? We

8:36

can. Yes. And just because of that, I'm gonna

8:38

answer it. The the problem with banks,

8:40

I think is that the physical existence

8:43

of a bank branch may have

8:45

outlived its relevance. And as

8:47

such, you have a lot of legacy

8:49

costs associated in a cost structure

8:51

that makes it fairly difficult to, to compete. Those

8:54

kind of banks are gonna be, this is not a new theme.

8:56

People have been talking about it for 20 years and it hasn't happened,

8:59

of course. But I think more and more my

9:01

sense is particularly as you get

9:03

to alternative ways of processing

9:06

transaction, which are almost

9:08

costless, it's gonna be very difficult for people to

9:10

accept the fees associated with traditional

9:13

banking. That's, and that I agree

9:15

completely. And the, and place where fees

9:17

are most obvious is

9:19

in payments. Yep. And for

9:22

years, banks basically had a monopoly

9:24

on payments. They no longer do. Yet

9:26

there's still competitive, there's still

9:29

room for new competition in payments and

9:31

and Apple is part of it. Others

9:35

are in the field and and banks are feeling

9:37

it and bank, especially in Europe. Agreed,

9:41

yep. I think if

9:44

I may say something very

9:46

obvious the big difference between. A

9:49

bank like i n g direct that

9:51

in the early nineties started collecting

9:53

deposits by paying a competitive rate

9:56

and then didn't know what to do with those deposits.

9:58

And the current situation is

10:00

that Apple also

10:02

originates an asset product, which is

10:04

the, these layaway loans. And

10:08

they are the platform where you can do both. Whoever

10:11

they partner with that runs the

10:13

both portfolios. We'll be

10:15

very keen to see that, this

10:17

works as, as well as it does into

10:20

the advantage of Apple and

10:22

the financial partner they have. And I think that

10:25

if they continue on this vein

10:27

and their opportunity for other products,

10:31

it's probably different in various parts of

10:33

the world, but, I am

10:35

going to go to meet some people

10:37

from Apple because, there is an enormous

10:39

opportunity for a brand like Apple to develop

10:42

a mass affluent independent financial advisor

10:45

business in Europe, which is

10:47

probably one of the areas of the market where there's the most

10:49

fat in the world. Yes. And

10:51

and this would be terrific for the

10:53

vast majority of the. Population

10:56

of the European Union or and other

10:58

Europeans. But what

11:01

I would also remind you of is that

11:03

a few years ago, Michael Milken, in

11:06

one of his appearances at one of

11:08

the conferences with, asked

11:10

to provide advice to some of

11:12

these platforms. And what he

11:15

told them is, don't get into finance because

11:17

the regulation will bug you down. I

11:20

talked this morning I should correct myself.

11:22

I exchanged WhatsApps

11:25

this morning with one of the most senior

11:27

people that I have

11:30

access to WhatsApp at eight o'clock in the morning

11:32

in finance. He was the chairman of a bank

11:34

in Spain. He was the vice chairman of one of the

11:36

top four investment banks for Europe. And

11:39

I asked him what, where he thoughts about this,

11:41

and one of the things he said is, this

11:44

company was doing very

11:46

well, UN, until or

11:48

has done very well. Let's hope they don't want to get

11:50

into the MOAs of being regulated

11:53

as a financial entity. And

11:55

I, and my, what I understand

11:57

is great about this deal with Goldman

11:59

Sachs is that Goldman Sachs already pays all the fixed

12:01

costs of being regulated as a financial entity

12:04

and if they can get the additional

12:06

business, Of being

12:09

Apple's joint venture partner in this,

12:12

everybody's better off and

12:15

including the public probably. So I think

12:17

it's a, it's an incredible, incredibly

12:20

I think it is as

12:23

material to the banking to the

12:25

retail banking world as the cash

12:28

management account in 1973 was

12:30

to the retail banking world

12:32

when Marin h introduced that. I

12:35

think you're right. And there's something else about it, which

12:37

is in recent years

12:39

I have heard an argument in banking,

12:41

which goes something like this. A bank does three

12:43

things. It takes deposits, it

12:46

lends money, and

12:48

it makes payments. It affects

12:51

payments. The payments business

12:53

it's going it's, if it's not gone, it's

12:55

going. Others have entered. You

12:57

don't really need a banking license to do it. They're

13:00

companies that can do it without the legacy. The

13:03

lending business is also is

13:05

also going private equity

13:07

and others are now lend money. Amazon

13:11

lends money to small and medium enterprises

13:13

and securitize to to fund it.

13:16

There's a lot going on in the lending space. Buy

13:18

now, pay later is done by a

13:20

non-bank, bank financial institution

13:23

or can be done. So there's a lot going on

13:26

in that space. But deposits,

13:28

taking deposits is where the regulation

13:31

kicks in when you're a deposit taking institution,

13:33

and thus others weren't gonna go into it.

13:36

The, they may try to create Anna,

13:38

the instruments that look like deposits, feel

13:40

like deposits, but they're not gonna be able to offer

13:43

customers guaranteed deposits because they don't

13:45

wanna be regulated. As Luis has just pointed out.

13:48

This seems to be a way in.

13:52

You, this partnership with Goldman Sachs

13:54

to to get a big tech into

13:57

that space, into the deposit taking space.

13:59

And these will be deposits because I'm,

14:01

they'll be guaranteed by the F D I C. So

14:05

I, I agree. It's, it

14:07

this is potentially a big move. I

14:10

agree. I think what's interesting about here is

14:12

you have two best of breeds, honestly, in, in

14:15

my mind, between Apple being knowledgeable

14:18

on the software hardware side, consumer

14:20

loyalty, privacy brand

14:23

and obviously Goldman Sachs on probably

14:26

regulatory management and all different financial

14:28

capabilities. So you have two best of breeds,

14:31

which for the moment early are,

14:34

have compatible. And corresponding

14:37

strengths in trying to address this. My,

14:39

my question would be long term, I

14:41

think to both your points, how

14:45

forward integrated into this, does Zapp want

14:47

to be long term? Right now it's easy.

14:49

It's a marketing deal. That's no problem. Longer

14:52

term, it's gonna be a business for them. But if it is, it's

14:55

a completely different business than what they do and

14:57

would be, enormously risky. And I think for

15:00

the investment base, for Warren Buffet, for everybody

15:03

else around would be viewed as something that

15:05

to due to gingerly. So it's gonna be very interesting

15:07

to see. And agreed. I think

15:09

Warren Buffet will probably not

15:11

be around long enough to see that, but

15:14

cuz just statistically speaking

15:16

but. He does like

15:18

banks and he does like insurance products.

15:20

And imagine Apple being hub

15:22

for banking and insurance products and

15:25

Warren Buffett sitting at the helm. I cannot

15:27

think of a more powerful platform to steward

15:30

retail products ever in

15:32

the history of mankind. Yes, agreed.

15:35

Completely agree. Again and

15:37

probably fairer, I think even for everybody

15:39

around, interestingly enough. That's

15:42

one would hope. But I can, let me tell you

15:44

what's happened in banking in a small

15:46

backward countries such as Spain, 20

15:49

years ago, there were, Peter

15:51

would probably know the numbers better, but

15:54

probably about 80, 90 banks

15:56

of national, of them

15:58

with branches in Madrid. Yeah.

16:02

Barcelona and some of the largest cities. And

16:05

banking services were competitively

16:07

priced because there was a significant amount

16:09

of competition. Nowadays

16:13

we're down to a handful of banks

16:15

that could, with large market shares.

16:17

I think the top five banks in Spain probably have,

16:20

I don't know, 50, 60% market share. Maybe probably

16:23

know the numbers better. I'd say more. More

16:26

so what's happened as

16:28

a result of the great financial crisis and the European.

16:31

And Nike Crisis is that with an enormous consolidation.

16:35

And as a result of that consolidation, people

16:39

expected that regardless

16:41

of where the ECBs set

16:43

the deposit rate they

16:46

would have Sorry.

16:49

They would, they would be able to pay 0%

16:52

for deposits. This is the argument

16:54

that nine, nine out of 10

16:56

bank analyst had to buy European banks,

16:59

that the jaws would

17:01

work exceptionally well in this cycle,

17:03

that there would be no increase

17:06

in the funding costs from deposits, and then you would

17:08

get all the benefits of. With

17:11

loans and the reason they could be sand

17:13

about such a strange

17:16

prediction was that there's

17:18

so much less competition. And

17:20

they tested that in the UK market, which

17:22

is a very concentrated market. And

17:25

the, until the hiccups of October

17:27

and even after that, there hasn't been a lot of

17:30

these termination of bank deposits in spite of

17:32

everything. I think that

17:34

the svb and the other two banks

17:37

with the plus the pretty sweet situation

17:39

may have changed that, sense

17:41

of, safety of having

17:43

a bank deposit for many people. And then

17:46

this message from Apple, and if I'm right,

17:49

and Amazon comes out within

17:51

the next few weeks and has its own deposit

17:53

product with some other financial

17:55

partner I, we are

17:57

going to have another financial

18:00

crisis in the making within a couple

18:02

of years I and

18:04

focused on deposits. I think that's one scenario.

18:07

I think you're right that there are fewer competitors,

18:09

so less competition. Another

18:11

element of what's going on in Europe is

18:13

the quantitative quantitative easing,

18:15

which is still going on. The money

18:18

supply is the

18:20

market is still a wash in liquidity,

18:23

and it's not until June

18:25

that the European Central Bank is going to

18:27

actually go into quantitative tightening mode.

18:30

Let's see if that, let's see if that changes

18:32

the dynamic on deposits. May I just

18:35

say something on that, which is that

18:38

part of the qualitative quantitative

18:40

typing in Europe has. It's

18:42

too complicated for a normal person to understand,

18:45

which is the reduction in tlt r o

18:48

Yeah. Funding and that is going quite

18:50

fast, right? So Yeah. And

18:53

then it was one of the key

18:55

programs for providing

18:57

liquidity to the banking system. So

19:02

maybe we can shift to a less

19:05

financial topic.

19:08

Let's look at ai. Oh,

19:10

fun. What,

19:13

Alex you wanna give us an update on how we're going

19:15

and getting to artificial general intelligence?

19:17

Agi, so in a godlike manner,

19:20

so enormously

19:23

a rapid sub update. I think

19:25

everybody's seen, obviously stable diffusion six

19:27

months ago DLI and so forth. On the image

19:29

side, everybody a couple of months ago on the G P

19:32

T three, the three five, then four

19:34

side. And it's important to understand

19:37

more or less, what happened. The

19:39

most important thing to think about, I think, and understanding

19:42

landscape is that for years people

19:44

have been publishing papers, working very

19:46

hard at trying to understand how

19:48

in a lab, how to use artificial intelligence.

19:51

And at the Genesis, this is back

19:54

20 15, 20 16, OpenAI was

19:56

built to try to do this independently

19:58

from the larger companies. So again,

20:00

Google, Facebook Microsoft many

20:02

people, one after. And Google

20:04

has two dedicated teams

20:06

on ai. Very powerful teams both going

20:09

after it. And the idea

20:11

when Dali was first released,

20:14

which was a shot across the bow to all

20:16

the larger companies. Back

20:18

in September and then when G

20:20

P T three was released and quickly

20:23

upgraded. What happened was

20:25

you now started to have an open

20:27

source, a set of tools that

20:29

people could use. The pricing of the a

20:31

p i of open AI was so

20:33

low or could remain so low that it provides

20:36

for an enormous amount of capability

20:38

for people to go and try things and to basically

20:41

offer. AI ish

20:43

or AI products, which are really just

20:45

the GPT APIs, meaning

20:48

that the ability to feed

20:50

onto G P T, whatever it is that you're doing. So

20:52

you've seen all kinds of other companies

20:54

larger companies come out with tools,

20:57

those that were working on AI before. So things

20:59

like Adobe and obviously Google

21:01

and everybody else has come up with stuff. And then those that

21:03

have just resold their bulk

21:05

access or wholesaling access to

21:07

to open ai. The

21:09

fact that it's open source is enormously

21:11

powerful because what it does

21:14

is that it takes away from people

21:16

in lab coats the control that they had

21:18

in some very interesting interviews

21:20

of Eric Schmidt from a couple years

21:22

ago, who was adamant that

21:24

all of his AI stuff, for all the reasons

21:27

you know, that ethicist will, will bring

21:29

up, should be kept very controlled

21:31

and inside the lab and so on, so forth. That

21:34

cat is. That Legion of cat

21:36

is completely out of bag by now. So

21:38

what you're seeing is that you're the

21:40

main limita. You're seeing a lot of development

21:42

in all kinds of different ways. Most of the activity

21:45

on GitHub, which is the place where developers

21:47

particularly open source of developers, share code.

21:49

It's now all ai all

21:51

the time, 24 hours a day which

21:53

is quite interesting. So you're starting to see a lot

21:55

of things being developed. The thing to think

21:57

about right now, I

21:59

think just to summarize it and I'll open it up, is

22:01

there are three things I think there are worth thinking about.

22:03

The first thing is that these

22:06

models have been trained a certain particular

22:08

with certain particular set of data. Some of it

22:10

cooperated again with a cutoff

22:12

date like about a year ago. I. But that's

22:15

not the future of models. Models are going to be

22:17

nightly builds. It's going to be something in

22:19

which, Morgan Stanley or

22:21

any organization where they have an internal

22:23

one or an external one facing customers

22:25

are going to have the, to retrain

22:28

or to cont continuously train on new information.

22:30

These master models that they're gonna have that're

22:33

gonna make available to customers or make available

22:35

to employees in order to be able to find

22:37

pretty much anything that you have as institutional

22:39

knowledge in your company. And

22:41

that's a gonna be a. Fairly large business that's

22:43

gonna require a lot of effort and handholding

22:46

because again, you're democrat democratizing a

22:48

a technology. The second thing is that

22:50

right now there's this concept of

22:52

how many tokens, or let's say how many words

22:54

you can feed. Into

22:57

a thread. So Che

22:59

g p t, for example, tells you that they are threads and

23:01

people and that the threads will remember what you said.

23:03

That's not entirely true. Basically, the way

23:05

to think about it is anywhere between, in the beginning 2000

23:08

words, which are really tokens, but let's just say words

23:10

for simplicity. 2000 words is

23:12

its memory, meaning that if you give it. 10

23:15

times 200 words. So if you ask it a question

23:17

for 200 words, then get a response

23:19

and ask it again, another question, or remember

23:21

the first 200 words and actually

23:23

the answer they gave you. So it has a little bit of memory

23:25

for a short period of time, but one of the

23:27

biggest problems out there is to try to give,

23:30

its more short. Term or long-term memory.

23:32

And so you've seen this development of what

23:34

they call vector databases, which

23:36

are basically ways in which you can add

23:39

some memory to the model with having to

23:41

retrain it. If you train the model,

23:43

you add permanently the data to the model.

23:45

That's a good thing, but very expensive, very complicated.

23:47

But the idea is that you want to have kind of a memory

23:50

buffer. The way to think about it is like if you have 10 years

23:52

of income statements or

23:54

50 product PDFs or anything

23:56

like that you really want those to be available,

23:59

not, to, to j to your

24:01

AI to share G B T. In

24:03

order to be able to answer correctly, again,

24:06

because these generalized models, can

24:08

answer generalized things, not specific things

24:10

about your knowledge. And then the third piece,

24:12

which is fascinating is, which has just started

24:14

the last couple of weeks, is this concept. There's

24:17

a very famous project called Auto G

24:19

P T, which has been Having

24:21

a lot of activity and recently, but basically is

24:23

the idea of user agents. So the idea is that if

24:26

you prompt a model meaning

24:29

you write it a

24:31

job description saying you are an expert

24:33

at marketing and you know everything

24:36

there is to do about the four Ps

24:38

and you know everything about all

24:40

this stuff, and you give it a persona, then

24:42

it's going to answer in that particular way. You

24:44

can replicate that in a bunch of different ways where you

24:46

spin up multiple models and

24:48

you give them different personas. So you can create

24:51

yourself a c e O persona,

24:53

which is, or a team leader persona

24:55

who is in whose job it is

24:57

to achieve certain goals. And then you

24:59

have it interact with other models who

25:02

are specialized in finance and operations

25:04

and marketing and HR

25:06

and whatever it is, or whatever the components

25:08

of your project is. And then you just,

25:11

let it loose, give it a, a goal,

25:14

and you just watch, while they have

25:16

all kinds of conversations about trying to

25:18

accomplish the goal at hand, again, it's very

25:21

early. But this gives you an insight as to how

25:23

you can get really

25:25

deep insight and thoughtful insight

25:27

from, from the systems, soon enough. So

25:29

to close it out, this is enormously

25:32

early. It is impressive.

25:34

It has made people realize this is not 10 years

25:36

from, 10, 10 years away, or 20 years away.

25:39

And it is just as

25:41

exciting as it is. Scary. That's all.

25:43

I'll say fascinating time.

25:46

Fascinating time. What do you think about the

25:48

petition to Pause? So

25:51

I think it is a admirable view.

25:53

There's a couple of interesting interviews.

25:55

Lex Frigman has a couple interviews, which are interesting,

25:58

if any, was interested on the

26:01

reasons for why you should take

26:04

the time to pause it. It

26:06

is laudable. It is never gonna happen. Okay.

26:11

I feel like I am the ludite, but

26:13

I talked to Chad GBT often

26:15

and I have a subscription. And

26:18

so far from its own account,

26:20

it tells me that, not

26:22

in the exact same words, but that it's a very

26:25

methodical librarian and

26:29

it doesn't have the ability to do

26:32

any original thinking

26:34

beyond what it can find in

26:36

the library and

26:39

what I thought would happen. And

26:42

what is happening with chat GBTs. Two

26:44

different things I thought that we were gonna go. So

26:47

this reminds me a little bit of what my friends

26:49

were doing out of engineering school in

26:51

the late eighties, early nineties, that

26:53

they were doing artificial

26:55

intelligence as an ency effort

26:58

to a lot of information into

27:01

databases so that a

27:03

processor could use the databases to come

27:05

out with answers that were

27:07

stock answers to stock question. I

27:11

thought when Anna and I were roommates

27:13

in New York and he introduced me to

27:17

a game called Sin City, that apparently

27:20

he developed some organizational

27:23

skills from having small

27:26

now I forget the words, but this is, goes

27:28

back to the early work of the Yes. Mathematic.

27:34

Yep. Which is that you

27:36

have, from similar initial conditions,

27:39

these, how do you call these agents?

27:41

I forget. Alex, these agents that

27:43

would find different paths to

27:46

do different things. You can call 'em agents. Agents.

27:49

Agents, yeah. And I thought that was far more interesting

27:51

cause they would come up with their own solution by

27:53

playing the game. And when you get to. The

27:56

game of chess. For instance, there were agents

27:58

that came up with a way to

28:00

play chess that was systematically

28:02

unbeatable. Yep. Some

28:04

people said, okay chess is a, is a

28:06

simple game because there's always an optimal solution

28:09

to any position. Let's try

28:11

a different game. And they try and

28:13

go, which is a Japanese game

28:15

of occupation, of a territory from

28:17

the enemy. You might

28:19

be committed with that game has

28:21

white beads and black beads and

28:24

it is, they nobody thought

28:26

that it would take, that it would be easy

28:28

to program a computer to play

28:30

Go. And I think it was within

28:33

like 36 hours a program

28:35

was able to be the world. Yeah. And

28:37

I thought for the, let

28:42

me interrupt you just for one second. Just to give you one

28:44

piece of insight, which is helpful cuz I remember

28:46

we, we financed a bunch of companies that were trying to do this

28:49

kind of stuff about 20 years ago, and it was a

28:51

mess and it was very much rule-based

28:53

and so on, so forth. But I would pause at the fall, the following

28:55

thing to try to reconcile the two views. Your

28:58

brain essentially learns the same

29:00

way as a language learning model. Let's

29:02

simplify, right? So a deep learning model, and

29:05

the reason I say that is because you as a

29:07

child, experience certain things.

29:09

So for example, if you put your hand. Onto

29:12

a hot stove, you will

29:14

associate or existing nerve

29:16

endings that were built earlier, even in

29:18

your life about moving your hand. And

29:21

then you will make a connection with the fact

29:23

that touching the stove, obviously

29:26

creates heat. And I,

29:28

if you were to be able to live as

29:30

many lives as. A

29:32

language learning model does, which is

29:34

millions and millions in lives in parallel, right?

29:37

So it's touching millions of stoves and

29:39

in certain particular ways, in every possible way. What's

29:42

happening is, which is interesting, is, and this is your point,

29:44

I think Compared to the way

29:46

people thought it would be done, which is by,

29:48

making sure that we understand all the rules, that we're a good

29:50

librarian, we know what section of the

29:53

library to go and look for whatever, where

29:55

the knowledge is stored. It turns out that language

29:58

these models have are matrices

30:00

of numbers and weights. That's all they

30:02

are. They're literally the equivalent of

30:05

these connections that if

30:07

this happens there, then

30:09

I should go this way, not that way.

30:11

Kind of thing. Super simple in

30:13

some ways, but at a level that computing

30:15

has only been able to make available

30:18

in the last couple of years. Let's just stuff that would've

30:20

been massively too big

30:22

to do. So the argument I would make just

30:24

in finishing is that

30:27

we have in some ways replicated

30:30

with the way the human mind. Develops

30:33

at the early age and even later stage

30:36

in matrices

30:38

in a mathematical algorithm, train

30:41

them. And so I would

30:43

argue that it

30:47

is able to learn. At

30:50

least as well as a human can. And I

30:52

know that's a very powerful

30:55

statement. We may not see it today but I think

30:57

the argument is that, that, we're,

31:00

the way that it comes

31:02

up with the way it thinks is very

31:04

similar to the way the brain, I

31:07

think. I think also if you, Luis,

31:09

you're probably using it a lot for our,

31:12

the, for the types of issues and topics that

31:14

we get into and are curious about, and.

31:16

And talk about and discuss. If

31:18

you ask it to write a short poem in the style

31:21

of Elliot about

31:24

the restaurant across the street, I think you'd find

31:26

you'd get a, you'd get blown away by

31:28

by what it can do stylistically. And

31:31

and, do I say it creatively?

31:34

Yeah. Yeah. Bloomberg trained

31:36

the model and when you look at the paper, they

31:38

just did, released it last week. And what's funny about

31:40

it is that the Bloomberg data, their

31:42

propriety data the Crown rules, those things are

31:45

the most important thing. Only counter

31:47

for 78 basis points for less than 1%

31:49

of the training data they put into it. So there's

31:52

a wall to go before you see something, but when

31:54

that model gets trained with actual Bloomberg

31:56

data, it'll be a site to see. I

31:58

think Think

32:05

that's, I think we're gonna, we're gonna call it

32:07

a day and

32:12

call it and say thanks to everyone. Thanks

32:15

very much. Thanks, Luis. And

32:18

take care, Alex. Thanks for organizing this.

32:20

Yes, thank you very much. Thank you. Bye.

32:23

Talk soon. Bye. Take care.