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The $1.5 Trillion of Hidden Debt Fueling the AI Boom | Robin Wigglesworth of FT Alphaville

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The $1.5 Trillion of Hidden Debt Fueling the AI Boom | Robin Wigglesworth of FT Alphaville

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1747 segments

0:00

I'm joined today by Robin Wigglesworth,

0:02

editor of Alphavville, the Financial

0:04

Times financial blog, and author of A

0:06

Fabulous Debt: The Epic Story of How

0:08

Bonds Built the Modern World. Robin,

0:10

you've been doing some work on the

0:12

offbalance sheet hidden leverage of the

0:14

hyperscalers, Meta, Google, Microsoft.

0:17

So, we're we're reading, oh, hundred

0:19

billion dollars of capex. Google's doing

0:21

that. Microsoft's doing $150 billion.

0:23

This is so much money. Oh my god. But

0:25

actually, it's looking like it's almost

0:26

guaranteed to be way way higher. you've

0:29

been looking at the numbers and just

0:31

from the first to the second quarter the

0:33

the guarantees the lease uh obligations

0:35

and and so forth off balance sheet again

0:38

went from roughly one trillion to

0:39

roughly 1.5 trillion. What are what are

0:42

your thoughts? What are we looking at?

0:43

>> No, I mean it's fascinating. I mean it

0:45

is one of the biggest capital markets

0:47

events of you know our lifetimes really.

0:51

Uh it's just I mean we've seen massive

0:54

capex booms before like the railways in

0:57

the 19th century is like the classic

0:58

parallel that people draw transformative

1:01

technology very expensive to build. Uh

1:04

what's unusual of course you know

1:06

railways back in the day used to be

1:07

almost like venture capital ideas right

1:10

they were very sort of VCish. Uh today

1:13

it's like major large uh money machines

1:16

that are doing it. of Google, Alphabet

1:18

makes lots of money, Meta makes lots of

1:20

money, Amazon makes lots of money. Uh,

1:22

and for a long time the money they were

1:24

pouring into data centers, you know,

1:26

they could just fund it from their free

1:27

cash flow. You know, out Google search,

1:30

Amazon, Facebook itself just, you know,

1:32

prints money. So, it's easy. But the

1:34

scale is just becoming so massive that

1:37

they've increasingly turned to the debt

1:39

markets. uh as we now see there are

1:43

actually some signs of indigestion like

1:44

the sheer scale of the bond sales. We're

1:47

talking multiple hundreds of billions of

1:49

dollars both last year and already this

1:51

year we've already smashed last year's

1:53

record for the hyperscalers bond sales.

1:56

Uh and they're getting more creative and

2:00

look

2:01

creativity in finance can be a good

2:04

thing. I I I find a lot of this stuff

2:05

fascinating, but it can also be quite

2:07

dangerous as you know, Jack. And you

2:10

know, first it was structuring some of

2:12

the bonds as leases. So essentially like

2:15

let's say take a great example, Meta is

2:17

building a a huge data center in

2:19

Louisiana called Hyperion. And rather

2:21

than sort of pays to build it, they're

2:24

essentially sort of they're only

2:25

investing 20%. They're buying 20% of it,

2:28

but they are guaranteeing that they will

2:30

lease that data center for 20 years. So,

2:33

and the lease payments are essentially

2:35

will cover the costs of that company

2:38

itself like a JV with Blue Owl. Uh, and

2:41

they'll sell those bonds to other

2:42

investors. But, you know, so it's

2:44

offbalance sheet. It's not doesn't come

2:46

up as as a as a bond or a debt or a loan

2:49

or for Meta, but of course, it's on the

2:51

hook for paying this lease for 20 years.

2:54

A and this, you know, has inspired a lot

2:56

of the other hyperscalers. So, we've

2:58

seen massive amounts of these kind of

3:01

lease structures happen. So that's

3:02

what's gone to $1.5 trillion.

3:05

Uh I mean up from I mean less than

3:08

trillion last year and nothing

3:10

dimminimous a few years ago. And you

3:13

know crucially

3:15

you know 500 billion or so of that you

3:18

you can see as sort of uh they the

3:20

leases have started. So you can see them

3:22

in the financial accounts. They won't

3:23

appear as debt but you'll see the

3:25

payment obligations on the balance

3:27

sheets. But a trillion dollars of that

3:29

is for leases that haven't even started

3:32

yet. And that doesn't appear except as a

3:34

footnote. So Goldman Sachs, that's where

3:36

I got the numbers from. They they did

3:38

their God's work in going through uh all

3:41

the filings to find their stuff. What I

3:43

did then was I started looking at the

3:45

purchase commitments because these

3:46

companies have also promised to buy

3:49

obviously chips and equipment, uh

3:52

cooling, stuff like that, but obviously

3:53

power as well. You know these data

3:55

centers need electricity and they need

3:57

quite often guarantees that they will

3:58

get that power and that has gone from

4:02

again also roughly $1 trillion earlier

4:05

they said to $1.5 trillion. Uh and these

4:09

are quite often payment obligations they

4:11

can't squirrel out of. So they kind of

4:12

walk talk and quack a bit like debt but

4:15

they don't actually appear as debt. And

4:18

I think it's fascinating. I maybe this

4:21

is, you know, they're betting the house

4:22

on AI and uh I hope this all works out,

4:26

but I'd feel maybe slightly more

4:28

comfortable if they structured some of

4:29

this as more plain vanilla debt and let

4:31

the debt markets do the talking.

4:33

>> Well, if they've structured it as plain

4:35

vanilla debt, we you know, Goldman and

4:37

and you wouldn't have to do all all of

4:38

this work to figure out. It's it is

4:40

really interesting. So it's really a a

4:42

transformation from a pure complete AAA

4:46

double A investment grade counterparty

4:48

like Microsoft and it is using that but

4:52

through a much less investment grade

4:54

data center developer or a neocloud to

4:57

then they're the ones who are actually

4:59

spending the money and they you know

5:00

report to investors this giant backlog

5:02

which you know core we've just reported

5:04

and that giant backlog is basically

5:05

these offbalance sheet commitments that

5:07

the Microsofts and the yeah the

5:09

hyperscalers have made. Yeah, I mean you

5:10

can say, you know, this is entirely

5:13

disclosed. I mean, these companies

5:15

aren't hiding the fact that they're

5:16

pouring hundreds of billions of dollars

5:18

into capex uh and that money has to come

5:21

from somewhere. They're not hiding this.

5:23

But I do think uh they could perhaps

5:26

have chosen a slightly more transparent

5:28

approach to this. Rather than wanting to

5:31

preserve the optics of pristine balance

5:33

sheets, they are kind of increasingly

5:36

getting creative about like how they

5:38

structure, how they pay, how they

5:39

disclose it. Just literally going

5:41

through the 10 cues to look at the

5:43

purchase commitments. You know, some of

5:44

these companies, Google for example,

5:47

actually was admirally like they don't

5:48

break it up, but that you can search for

5:50

it and find it fairly easily. On some of

5:52

the other companies, I had to spend, you

5:54

know, quite a lot of time digging it

5:56

out. It's not easy. And I think that's

5:59

unfortunately

6:00

>> who's the most complicated?

6:01

>> I guess all the others except Google.

6:03

Some of them disclose it. Uh but they

6:06

don't disclose it in a uniform way. They

6:09

use different words in different places.

6:11

So it's hard to compare one quarter to

6:13

the next quarter. And some of them, you

6:16

know, don't disclose it at all really.

6:18

They just say they have, you know,

6:20

material upcoming payments or promises

6:22

to make payments.

6:24

>> So it's a bit of a mixed bag really.

6:26

Google maybe because it's the biggest. I

6:28

mean Google has p purchase commitments

6:30

now of $800 billion.

6:33

>> So that's chips, memory, equipment,

6:35

cooling, electricity, the whole

6:37

jamboree.

6:38

>> But that's almost half the total. I

6:40

think maybe their accountants maybe got

6:42

a little bit worried and thought you

6:43

know we need to be quite transparent

6:44

about this.

6:45

>> And do you have a rough sense of over

6:47

how many years the majority of that is

6:50

scheduled for? cuz like if that's

6:52

scheduled for the next three years then

6:54

the Google capex number for expectations

6:56

is too low.

6:57

>> So Google using them again they broke it

6:59

up a little bit more transparently

7:01

around $200 billion of that $800 billion

7:04

is what they call short-term. They don't

7:07

define it there but I'm pretty sure

7:09

that'll be over the next 12 months or

7:10

so. Uh so their payments are coming due

7:14

on the leases. you know, if the leases

7:16

haven't started yet, it's it's an

7:18

accounting thing. Like once a lease is

7:20

started, you actually can put a right of

7:22

use asset on your balance sheet and then

7:25

put the liability on the other side. So,

7:26

it's is transparent. This isn't them

7:29

necessarily doing something bad or

7:31

changing anything or doing anything

7:32

differently from how people have done it

7:34

forever. I just think that the scale of

7:36

it is now such that it's just it's it's

7:39

a whole new world and and maybe these

7:41

these offbalance sheet liabilities

7:44

because they are financial liabilities

7:46

that are in many cases extremely hard to

7:48

squirrel out of methods for guarantee

7:51

for example of the leases for the

7:52

Hyperion data center are incredibly

7:55

strong. Uh I don't see how they can

7:57

squirrel out of them. uh then they

8:00

should be more transparently um

8:02

disclosed and then investors can make

8:04

their minds up and you know broadly

8:06

speaking I think people are okay and

8:07

understand that they are spending this

8:09

money these companies are hiding it uh

8:11

but I wish um some of the gamesmanship

8:15

uh could maybe sort of be cut out and

8:18

then them breaking out a little bit more

8:20

obviously

8:21

>> Robin what do you make of the deal that

8:24

the memorum of understanding that Nvidia

8:26

made with five or six giantter

8:28

alternative asset management firms

8:30

Blackstone, Black Rockck, KKKR to

8:33

finance chips and recognize that these

8:36

are an investable asset class. I was

8:40

looking I asked Claude before this what

8:42

percentage of that 500 billion is going

8:43

to be debt versus equity. They said

8:46

rough my you know roughly 80% debt 20%

8:49

equity. So uh you're you're the debt guy

8:51

so this is this is good we're speaking

8:52

to you.

8:52

>> I know this this is a debt cycle. It's

8:54

not I mean there's lots of hoopla around

8:56

like the IPOs or SpaceX and Anthropic

8:59

and and OpenAI to come but really this

9:01

is a debt cycle. Uh that actually makes

9:04

me more worried. I mean fundamentally

9:05

like incredible transformative

9:07

technologies come around occasionally

9:08

and sometimes they come true. The

9:10

railways were genuinely transformative

9:12

as were canals or telegraph poles or the

9:15

internet. Uh but when these sort of big

9:17

investment cycles are primarily equity

9:19

finance you know they can break bad but

9:22

it's generally fine. I mean the end of

9:23

the dotcom bubble the stock market

9:25

dropped 50% peak the trough economically

9:28

it was a nothing burger it's kind of

9:29

hard to disentangle from the effects of

9:30

9/11

9:32

debtfueled capex cycles are very

9:35

different like even when you know the

9:37

underline premise comes true and AI kind

9:40

of transforms the world quite often they

9:42

end in tears uh on this specific deal

9:46

one thing I would say that you know it's

9:49

very easy to push out press releases

9:51

saying we're going to lend or invest S X

9:53

or Y or Z into this or that and we'll

9:57

see what actually materializes. I mean

9:59

clearly there is a lot of heat uh in

10:02

this area now and everybody wants to be

10:04

seen to be leaning into it. Uh but it's

10:07

going to be really interesting to see

10:08

what they actually do and how they

10:10

structure it because I think a lot of

10:11

these investment firms in particular are

10:13

going to be very careful about how they

10:15

protect their own balance sheets but

10:16

also the balance sheets of their

10:17

investors. Uh so I I'd urge people at

10:21

this point in the cycle to take you know

10:24

press releases

10:26

uh with a pinch maybe in a fistful of

10:29

salt.

10:31

>> Yes.

10:33

What what do you think that these AI

10:36

securities are going to look like? I

10:37

mean Larry Frink literally said I don't

10:39

know if he was on vacation these AI

10:41

securities. What's an AI security?

10:44

Well, I think it's just compute and I

10:45

think that's quite an interesting thing

10:47

and I I think this story is kind of

10:49

throwing in a lot of different things,

10:51

but it's the transformation of compute

10:53

like just kind of you can buy a lease a

10:56

certain amount of GPUs I guess or or how

10:58

you structure it that can can be turned

11:01

into an asset class. I I think it is you

11:03

know there is a a journey there. Just

11:06

because you say something is an asset

11:07

class doesn't make it so. uh when it

11:10

does become so the SEC typically has

11:12

something to say and will have a view

11:14

about it. Uh but I do think that is the

11:16

directional travel that in the same way

11:18

that like water or or commodity is a

11:22

tradable asset class at least the

11:24

futures on these things. I can see us

11:26

getting compute futures as well and and

11:29

that becoming roughly investable. Um my

11:34

my weariness is that you know just

11:36

because something is investable asset

11:37

class doesn't mean people should be

11:38

investing in it. We'll hear lots of s of

11:41

stuff about oh you need to kind of be

11:43

diversified and this is democratizing

11:45

access to whatever everybody has and you

11:49

know to paraphrase you know who was it

11:51

gobles who the Nazi propagandist who

11:54

said when I hear people talk about

11:55

culture I reach for my gun he was not a

11:58

nice man but whenever I hear people talk

12:00

about democratization of something I

12:02

tend to reach for my metaphorical gun

12:03

because it's usually a code word for

12:05

jamming something down the necks of

12:08

retail investors that not really quite

12:09

ready to digest. Um, so I actually have

12:13

great hopes for compute futures and and

12:15

and turning compute into some sort of

12:17

tradable asset class. That sounds cool

12:19

and interesting to me. Uh, but I think

12:22

there's a journey still to to make on

12:24

that. And I'd worry about what happens

12:27

along the way as well. And something

12:30

like a commercial real estate building,

12:32

yeah, it can be risky, but like you

12:34

know, a building is generally going to

12:37

hold its value and generally appreciate

12:38

over over time. So like a loan to value

12:40

of 50%. So so the you know, you lend 50%

12:44

of what the property is worth is like,

12:46

you know, pretty pretty appropriate. And

12:47

you're you're lending to pipelines of

12:49

oil infrastructure like these things

12:50

these are things we understand. And for

12:52

something like compute, yes, the market

12:54

right now is super hot. And you know,

12:56

Google is buying compute from SpaceX for

12:58

a super super high amount of money. And

13:00

on the depreciation argument, like the

13:02

Michael Bur argument, basically every

13:04

single data point of the past nine

13:05

months has has not supported the Michael

13:07

Bur depreciation point like depreci

13:16

favorable rates. Um but but just because

13:18

Michael Bur's been wrong doesn't mean

13:19

that like there's going to be a glut at

13:21

some time and that lending a trillion

13:23

dollars against this thing is a good

13:25

idea.

13:26

>> Well, fundamentally it's it's a lending

13:28

decision if you're lending towards it. I

13:30

mean the comput the chips do degenerate.

13:33

There is like there is a halflife to how

13:35

long you can keep them and you know for

13:36

example with SpaceX putting up in space

13:38

like how you going to do maintenance?

13:39

How you going to replace chips that

13:41

burnt out? Uh but I would say that look

13:44

you know finance lending officers like

13:48

they mess this up all the time but

13:50

broadly speaking that's what markets are

13:51

really good at that can be priced as

13:54

long as it's a known risk and in you

13:56

know this is very much a known risk

13:59

people can price it in. People can

14:01

adjust and sometimes they'll make a

14:02

mistake and you know they'll get their

14:04

faces ripped off and other people will

14:06

make money but that's the way of

14:08

markets. is kind of what makes this

14:10

system so dynamic. Um, so you know, I'd

14:14

be worried about the sheer amount of

14:16

money going in there and some of the

14:17

return expectations and this kind of

14:19

sense of of FOMO that seems to be

14:21

everywhere these days. Um, but that

14:24

doesn't mean that sort of the end

14:26

destination isn't the right one. It's

14:28

just, you know, how many how much money

14:31

we lose along the way and who loses it

14:32

and when. Robin, in your book, you've

14:35

been doing a lot of work on, I mean,

14:38

close to a a millennia of reading of of

14:40

financial history. I'm curious about the

14:43

trends and patterns you've noticed about

14:46

the following question, pricing power.

14:49

Whenever a new industry emerges, there

14:52

often is tremendous pricing power, as

14:54

there is in AI and semiconductors right

14:56

now. throughout your many many centuries

14:59

of of reading of history, what tends to

15:02

happen to that pricing power?

15:05

>> It tends to erode. Uh there are so many

15:08

examples of this. Um I do worry for

15:11

example this right now Nvidia is the the

15:15

picks and the shovels distributed to the

15:17

entire AI revolution. uh but it has

15:20

obviously it depends on its own supply

15:21

chain and I do wonder about the the

15:25

assumption that nobody else can create

15:28

GPUs at scale and quality of an Nvidia

15:31

ever. Uh because right now we're pricing

15:34

that in and pricing power tends not to

15:37

last forever. It's just again in a

15:40

capitalist system, people respond to

15:43

incentives and kind of monopolist like

15:46

pricing power tends to not last very

15:49

long. Sometimes it can last for a few

15:51

years. Um, but it never lasts forever as

15:55

far as I know.

15:55

>> What's the most extreme

15:58

debt cycle that you studied in the book?

16:00

the GFC to lead up to the GFC, you know,

16:03

because it was kind of the culmination

16:06

of uh a debt bubble

16:10

in every part of the world and in every

16:12

sector. Sometimes it's governments,

16:14

sometimes it's companies, sometimes it's

16:16

households.

16:17

In this case, actually, governments

16:19

weren't for the most part lovering that

16:21

much up. Uh they were doing a little

16:23

bit, but but but it wasn't too bad. Uh

16:25

but it was pretty much everywhere. You

16:27

know, everybody thinks that, you know,

16:29

our banks were uniquely terrible or our

16:31

politicians or our government was

16:33

uniquely effectless, but in reality, it

16:35

was a global phenomenon and the scale of

16:39

it was just kind of wild. And also, you

16:41

know, one of the reason when bonds and

16:43

debt becomes particularly dangerous is

16:47

when essentially it's been so long since

16:49

a previous crisis that you treat it as

16:51

money or money like I mean it's kind of

16:53

one of the USPS the ultimate selling

16:55

points of bonds originally was that it

16:57

was kind of a you could use it as

16:59

collateral as money uh for certain

17:02

things. it was kind of because it's you

17:03

know government bonds you quite often um

17:06

and then over time people started using

17:09

high-grade corporate bonds like IBM or

17:11

Microsoft very solid you know you can

17:13

use that as collateral for loans uh but

17:16

then of course you know in in the 2000s

17:18

people started using as back securities

17:20

and initially those were super solid as

17:22

well and eventually you know we take

17:24

things too far and you know they were

17:26

not the equivalent to money in fact you

17:28

couldn't trade them and some of them

17:29

were close to worthless and that I think

17:32

is what really transforms almost like a

17:34

a humdrum market downturn or an economic

17:38

recession into something nasty like

17:39

really bad is not when you invest in

17:43

something that's risky and it blows up

17:44

in your face. That's fine. That's just

17:47

risk and reward. They're part of it. If

17:48

I invest in junk bonds, look, if they

17:51

break bad, if they default, you know, I

17:53

can't complain about that. Maybe I did

17:54

something stupid. Maybe the company did

17:56

something stupid. That's fine. is when

17:58

you invest in something you think is

17:59

super safe or you base your entire kind

18:01

of investment strategy or the business

18:03

model of the bank around something that

18:05

you thought was super safe proved not to

18:08

be so and that's what happened I think

18:10

in 2008 it wasn't just the scale of the

18:13

debt bubble it was how people treated it

18:16

that's what transformed it into such a

18:19

horrific uh financial disaster but it

18:22

probably isn't my favorite crisis

18:24

because there are so many to to choose

18:25

from like really demented on

18:28

We'll get into some some demented ones.

18:30

So, a principle you're saying is

18:31

basically financial crises are caused

18:33

not by perceived risky assets, but by

18:35

assets that are perceived to be safe,

18:37

but that that are risky. You mentioned

18:39

junk bonds. Now, the more polite term of

18:41

course is high yield. And it's it's

18:43

funny obviously like the the real action

18:45

of risky credit lending was junk bonds

18:47

when it was, you know, invented in the

18:49

1980s, but now like all of that risky

18:52

lending has a lot of it has migrated

18:53

from the high yield bond market to the

18:55

private credit market. So, the high

18:56

yield bond market is, you know, so

18:58

so-called, you know, safe relative to

19:00

the private credit market. I'm sure the

19:01

private credit people would disagree.

19:03

What do you make of the rise of the the

19:06

private credit asset class? And what

19:07

have you made of the the jitters in the

19:12

market? And I'm I'm curious to what

19:13

degree do you think they are real versus

19:15

uh just just headlines and not much sub

19:17

substance to them?

19:18

>> Oh, they're real. Uh I I've been

19:21

borderline obsessed with private credit

19:22

for a long time and you know I've had

19:24

many arguments with people in the

19:25

industry and and the nuance I I think

19:28

it's a fantastic asset class. I think

19:30

it's fantastic idea. I hope it bring

19:32

grows and grows because I actually think

19:34

it does derisk the financial system.

19:36

That's not just marketing from uh from

19:38

from the executives in the industry. I

19:40

think it's great if we take basically

19:42

these bundles of risks which is what

19:45

every loan constitutes and that is in

19:48

the investment ecosystem like in the

19:50

non-bank system. I think that's a a

19:51

great thing but as we know like whenever

19:54

people get over optimistic people do

19:57

dumb uh people invest have

20:00

invested way too much money in private

20:03

credit based on very backward-looking

20:05

numbers and the illusion of safety or

20:09

just like frankly the the the lack of

20:10

volatility which is just an artifice

20:13

because of the sort of lack of

20:14

marktomarket accounting. Uh so yeah high

20:18

yield frankly does I mean it's not safe

20:21

but it's far more solid I'd say now than

20:24

it ever has been. I mean the ratings is

20:26

an obvious way to look at it. Like over

20:28

half the market is double B now. Uh but

20:30

just generally the quality even beyond

20:32

ratings I think is far more solid and it

20:34

throws off cash. It's the technicals

20:36

been great. Uh and private credit has

20:38

picked up all the dicey stuff and I

20:41

think that's great. That's where it

20:42

should be. But that does mean there's

20:44

been dicey stuff happening there. I

20:46

mean, for me, the the the real wakeup

20:49

moment was when I was still in the

20:51

United States and like I started getting

20:53

cold calls and offers for private credit

20:56

lines. Me, I mean, as a journalist. I

20:58

mean, that's just astonishing, right? I

21:00

mean, nobody should lend in any money to

21:02

any journalist ever.

21:03

>> You Oh, to to lend you money, not not

21:05

for you to be an investor. Really? Okay.

21:06

>> People were offering me term loans, you

21:08

know, needing you 20 30,000 million

21:10

working capital 304

21:13

percentage points above liable. I mean,

21:15

incredible sprays. I mean, I didn't

21:16

actually take them up on it, but I just

21:18

thought when we're getting to that kind

21:20

of spray and prey kind of approach to

21:22

origination. Like, there was so much

21:24

money flooding into private credit. And

21:26

it's it's a very kind of how do you find

21:27

the borrowers? How do you find

21:29

highquality borrowers? Well, actually,

21:30

in the end, you don't need to find high

21:32

quality borrowers. You just need to find

21:33

borrowers to take the money so you can

21:35

earn fees on it. It's very similar to

21:37

what we saw in 2008. Not in scale, of

21:39

course, but the idea that you just want

21:40

to make mortgages because that's how you

21:43

got paid. you got paid by sourcing

21:45

mortgages and making them and then you

21:47

know hopefully the risk is worn by the

21:48

next guy. So I think in private credit

21:51

too much money flooded in too quickly.

21:53

It has been deployed you know a lot of

21:55

it still dry pad hasn't been deployed

21:57

but it was in some cases deployed poorly

22:00

and there is a default cycle that is

22:03

probably going to be far worse than what

22:05

the backward-looking numbers look like.

22:07

you know, private credit looks great

22:10

if you look at the the the historical

22:12

data because, you know, frankly,

22:15

you're not looking at the market today

22:17

then. Now, it's just a very different

22:18

market. Um, but that's again, that's

22:22

part and parcel parcel of finance. We

22:24

want these things to happen. You want

22:26

cycles. You want ups and downs. You want

22:28

people to learn their lessons, and they

22:30

will. and and hopefully at some point

22:32

private credit will dust itself off,

22:34

learn from this in the same way that

22:36

securitization has and come up with a a

22:39

better mousetrap afterwards and and that

22:42

is actually a good thing that will stick

22:43

around for a long time.

22:44

>> What is the issue with the mousetrap?

22:47

What and what could be better about the

22:48

mouseetp

22:55

and the leverage. So, I mean, two of

22:57

those two, three things that will, you

22:59

know, blow up anybody. Uh, I think

23:03

I do think you can make a case that

23:06

private credit could be sold to retail

23:08

investors, but it has to be done

23:11

exceptionally carefully. And I don't

23:13

believe in sort of semi-liquid

23:16

offerings. Like if you're going to

23:17

invest in an illquid asset class that

23:19

like touts liquidity as one of its main

23:23

selling points, do not do it even in a

23:26

semi-liquid format. You know, if you

23:28

invest in loans with a 5year tener, then

23:30

you should be locked up for 5 years. Uh

23:33

because retail investors, we know

23:34

whatever they say, whatever, you know,

23:36

retail investors can be like people like

23:38

me or people are worth, you know, quite

23:40

a few million or even billion, but

23:42

people pull their money out when they're

23:44

afraid. uh and these structures aren't

23:46

built for that. So I hope more credit

23:50

migrates from the banking system and

23:52

into the non-bank financial system that

23:54

the non-bank financial system you know

23:56

private credit firms, bond funds and so

23:58

on uh lock up investor money for a bit

24:01

longer. I don't think I mean we've built

24:03

an entire financial system around the

24:05

idea that like one day liquidity is some

24:08

sort of god-given human right and it it

24:10

isn't and it shouldn't be. It's actually

24:12

dangerous. I think even mutual funds

24:14

should have you know my personal view

24:16

should have longer lockups. You should

24:17

not be able to pull your money out daily

24:19

because it actually leads to suboptimal

24:22

outcomes for both you the investor and

24:25

the fund manager because they have to

24:26

make decisions knowing that you know

24:29

money can go in and out on any given

24:31

day. Uh and private credit that's

24:33

particularly acute. So a better

24:35

mousetrap you know there are many sort

24:37

of small little fiddles. I would prefer

24:40

levered like investment vehicles that

24:42

invest in in highly levered companies

24:44

not be levered themselves. So BDC

24:47

>> zero to zero leverage.

24:48

>> Yeah. So zero leverage ideally. I mean

24:51

again term leverage if if a BDC sells a

24:55

10-year bond and invests in some similar

24:58

maturity assets. Look, it's not ideal

25:01

but it's fine. You know

25:02

>> for the public BDCs, you know, I've done

25:04

done a little bit of research and a lot

25:06

of it is termed. The BDC is just one

25:08

more transparent slice of the private

25:10

credit industry that you know quite a

25:12

lot of institutional investors that put

25:13

money into private credit.

25:15

>> As the returns started falling because

25:18

there was capital gushing in certain

25:21

return expectations, they lever up their

25:23

investments in these funds and you know

25:26

again done judiciously, done carefully

25:30

with no recourse. Maybe that's smart,

25:32

but it makes me feel uncomfortable when

25:35

you basically kind of lever up an

25:36

investment in a highly levered vehicle.

25:38

Anyway, so that's on the more on the

25:40

institutional side. I hope nobody's

25:43

borrowing money from the brokerage and

25:44

yoloing into BDC's, but you know, to

25:47

each of the road.

25:48

>> And also, there's a reflexive dynamic

25:50

you referenced of that when money floods

25:52

into an asset class, it makes returns

25:54

look really good. So the private credit

25:56

loans that were made in 2018, a ton of

25:58

money flooded into in 2022 to refinance

26:01

those loans. So defaults were very close

26:03

to zero. So so even if on a fundamental

26:05

basis nothing changes, defaults will you

26:08

likely be higher be as inflows go down

26:10

which they look like they are going to.

26:12

>> I mean that's very apparent in in the

26:14

equity market, right? inflows will uh

26:17

encourage you know will push the asset

26:19

classes up and that in BDCS like I say

26:22

the or in private credit there's a

26:24

different nuance it's not like that the

26:26

loan value will suddenly go to the moon

26:28

suddenly because there's more money

26:29

going in but yes it will give the

26:31

private credit fund manager far more

26:34

flexibility in how they manage humps

26:37

along the road bumps along the road uh

26:40

but you know only to a certain extent

26:42

it's one of the reason why we've seen

26:44

the increase in payment payment in kind

26:46

is because some of these companies like

26:48

payment in kind is a completely viable

26:50

and acceptable and important tool in

26:52

many cases. It is the right one to use

26:54

for companies growing very quickly but

26:56

you know don't want to send cash out the

26:58

door right then but I think it's

27:01

unambiguous that lots of private credit

27:03

funds have been using pick as a way of

27:08

deferring the pain essentially. Uh the

27:11

danger is of course a lot of these

27:12

companies and this is where for example

27:15

the default cycle comes in. It's not

27:16

just the fact that the defaults have

27:18

been kept probably artificially low

27:19

because of the money coming into the

27:21

market but also the recovery rates are

27:24

are are somewhat I'd say fantastical

27:27

rate uh the assumptions. So typically

27:29

let's say in a high yield bond you might

27:31

get 70 80 cents on the dollar. uh

27:33

depends on where you are in the cap

27:34

structure of course, but people have

27:36

penciled in um I suspect unrealistic

27:40

recovery rates when a lot of these

27:42

companies are not going to have any

27:43

recoveries whatsoever. Let's say if

27:45

they're in the software industry uh

27:47

where you know there there are no plants

27:50

and factories and roads and trucks,

27:52

right? It's just if the company isn't

27:54

good, it blows up and there's nothing

27:56

there for you as a creditor. So, it's

27:58

going to be fascinating to watch. I so I

28:01

I tend to be on private credit stepping

28:03

back. I tend to be on those guys that I

28:05

think it's it's going to be a bad

28:06

default cycle. It's started already, but

28:09

like it's getting masked, but it's not

28:11

going to be catastrophic. And the asset

28:13

class deserves to survive and thrive

28:17

once more once it's been through a few

28:19

of these.

28:19

>> Definitely. And you know, in a in a

28:21

crisis, I think some of these public

28:22

BDCs are probably going to go to 30

28:24

cents or 40 cents of net asset value.

28:26

And and for for investors with the

28:28

stomach, there could be opportunity

28:29

there. Yeah, completely. I mean, you

28:32

know, buying, you know, dollars for

28:34

pennies is is a a classic way of of

28:36

making a killing. The problem is when

28:38

you time it, of course, and when the

28:40

BDC's, you know, go further because it's

28:42

people always think something can fall

28:44

can't fall any further than it always

28:45

can, unfortunately. But, you know, I'm

28:48

not yoloing into BDC's go that way.

28:50

Robin, everything we we've we've talked

28:53

about the offbalance sheet, debt, the

28:55

hyperscalers, Nvidia, very, you know,

28:57

murky unclear what's going to look like,

28:58

private credit. What themes are present

29:01

in the in there that are present

29:04

throughout the history of the rise of

29:05

the bond market and in debt, uh, that

29:07

you you wrote about in your book. Well,

29:09

in fabulous debt, I talked a lot about

29:10

how, you know, we we we associate bonds

29:14

with safety, and quite often that is

29:16

true, but sometimes that safety can lull

29:19

pe people into a false sense of safety,

29:21

and they do stupid stuff. Uh, and also

29:24

bonds are just as maybe not just as, but

29:28

are also susceptible to these kind of

29:30

bouts of mania that that we see in the

29:33

stock market most obviously. Um so

29:36

whenever a transformative new technology

29:39

comes that typically manifests itself in

29:42

both the stock and the bond market and

29:44

sometimes the most dangerous development

29:46

happens in the in the bond market. A

29:48

classic case where were the canals and

29:50

banks of the United States in the early

29:53

uh 18th 19th century. So after

29:55

independence the US was rebuilding

29:57

itself. It was building canals. All

29:59

these states were borrowing money for

30:01

banks. New York famously started with

30:03

the Eerie Canal which was just a

30:05

transform. It was like the Apollo

30:07

program of the era and it was a huge

30:09

success and they sold lots of bonds and

30:11

both the investors and the state made a

30:12

killing out of it. But that encourage a

30:14

d debt bubble a bondishness bubble that

30:17

ended up you know half the United States

30:19

being in default like the individual

30:21

states all bankrupt uh and and that was

30:24

you know quite a nasty crisis that took

30:27

some time.

30:27

>> So it was the states the government

30:29

state governments that built the canals

30:30

and they were the borrowers. Okay. Yeah,

30:31

typically. So they looked at what New

30:34

York had done with the Erie Canal and

30:36

then borrowed a lot of money on their

30:37

own balance sheets because obviously

30:38

they had very little debt because the

30:40

the United States as a federal

30:42

government had assumed all the

30:43

post-revolution debts. Uh they sold lots

30:46

of bonds to investors in Britain and the

30:48

Netherlands and Italy and France and

30:50

Germany and some of the United States

30:52

and they you know started banks, they

30:54

built canals, they started gingerely to

30:58

industrialize but they just borrowed too

31:00

much money. There's actually a great

31:01

scene in in A Christmas Carol by Charles

31:04

Dickens where Ebene the Scrooge, it's

31:07

not unfortunately in the uh the Muppets

31:09

version, which is my favorite. I watch

31:10

it with my kids every Christmas. Uh but

31:12

where Ebenezer Scrooge has a nightmare

31:15

and he wakes up in a cold sweat cuz he's

31:17

had a nightmare that all his securities

31:19

have been transformed into United States

31:21

securities, which was Dickens's joke

31:24

about how US bonds had then by then like

31:27

all been become worthless. like half the

31:29

states pretty much had all defaulted and

31:32

some of them never repaid back their

31:34

debts ever. Uh so at the time, this is

31:36

in the 1840s, the US was synonymous with

31:40

I guess like Argentina today, like a

31:42

country that just defaults all the time.

31:44

But you know, those canals were

31:45

valuable. Most of the states dusted

31:47

themselves off and we've never had like

31:50

quite that violent a spate of of state

31:52

or or kind of municipal defaults in the

31:55

United States since then. Same thing in

31:57

the 19th century and late 19th century

31:59

with the railway mania. I mean that was

32:01

just massive. I mean if you talk about

32:03

AI data centers today, you know, that's

32:06

a few trillion dollars, but the

32:09

equivalent if you scale it the size of

32:11

the US economy in the 1870s and 1890s

32:15

and 189 and so on to the present day,

32:19

we're talking it's the equivalent

32:20

railways issued the equivalent around

32:22

$10 trillion of bonds. It was the

32:26

biggest cap explosion in history. And

32:29

you know, a lot of those railways went

32:30

bust and investors quite often again in

32:33

in in England and in the Netherlands and

32:36

France and Germany and Spain and

32:38

Denmark, they lost their shirts, but the

32:40

railways were still there. And that

32:42

literally like physically napped kind of

32:45

knitted together the United States and

32:47

kind of transformed the economy. Um,

32:50

which goes to show that these mania look

32:53

the very painful after the 1871

32:57

financial crash when lots of railways

32:59

went bankrupt. It caused the collapse of

33:01

a bank called Jay Cook which was kind of

33:03

it was the equivalent of JP Morgan going

33:04

bankrupt today overnight. It was it was

33:07

catastrophic at the time and it caused

33:10

what was you know long called the Great

33:12

Depression until the actual Great

33:13

Depression happened and we now call the

33:15

downturn in the 18 uh70s the long

33:18

depression. Uh but it still transformed

33:21

the United States because all those

33:22

railway waves were still there and

33:26

uh you know I think it shows mania and

33:29

and financial crisis although painful

33:32

sometimes they're a good thing that like

33:34

a weird thing is that the optimal number

33:36

of financial crisis is arguably not zero

33:39

as painful as they are to live through.

33:41

>> That's an that's an interesting argument

33:43

probably. I mean I mean basically to to

33:44

to guarantee that there would be no

33:47

financial crisis, you'd have to have

33:48

regulation speculation basically be

33:50

banned. And I could see I could see the

33:52

definitely the downsides of that. Robin,

33:54

I understand how someone could get into

33:57

a mania about a stock. They buy the

33:59

stock at 100 and it goes to 900 and they

34:01

get extremely emotionally very excited,

34:03

but just in terms of I can't wrap my

34:06

head around a mania, a credit mania. I

34:08

understand they exist, but like the idea

34:10

of earning uh sofur plus 4% on a risk or

34:14

thing, it just doesn't, you know, it

34:15

doesn't really get my my my my blood

34:17

pumping, you know? Maybe something's

34:18

wrong with me.

34:19

>> No, I mean, sadly, there's never been

34:22

there have been meme bombs. Uh but there

34:25

aren't any meme bombs around today. I

34:26

guess maybe TLT is the closest.

34:28

>> Oh, yeah.

34:29

>> Or the levered version of TLT. Um, no.

34:33

So, back in the day, most bonds were

34:35

actually perpetual bonds.

34:37

>> Mhm. Uh so they were quite you know they

34:40

lasted until the government or sometimes

34:42

a company uh paid them back. Governments

34:44

especially issued perpetuals and they

34:46

were quite often sold at a discount. So

34:48

they were sold at let's say 90 cents on

34:50

the dollar uh at an interest rate of

34:53

four but then of course

34:54

>> 90 or 1 n

34:55

>> 90 yeah or 60 cents on the dollar or

35:00

whatever right. Uh but they were sold at

35:02

a discount which is why you could have

35:03

price appreciation for the bond as well.

35:06

And you know this is a different era.

35:08

People didn't have Bloomberg terminals.

35:10

It was you know quite difficult for even

35:12

some smart bankers to calculate

35:15

literally what was should be the right

35:17

price for this bond. So you'd see bonds

35:20

trade way above par. We see that in

35:22

modern day but like it just shows that

35:24

the bond could go up and down a lot. So

35:27

people could get quite excited and in an

35:30

era where you know what else could you

35:32

buy to make money. So, let's say if

35:33

you're in uh Change Alley, it's kind of

35:36

the Wall Street of of Britain in the

35:38

17th and 18th century, and you're buying

35:41

a bond for a newly independent Latin

35:44

American country. Well, you might be

35:46

buying, let's say, a Brazilian bond,

35:48

this new fantastical country you've

35:49

never heard of called Brazil, but you

35:52

know, the banks are saying it's

35:54

fantastically full of potential. You're

35:56

buying that at, let's say, 50 cents on

35:59

the dollar. Well, and then you're also

36:01

getting the coupon. Maybe you're also

36:02

getting the equivalent of 10 cents a

36:04

dollar on interest all the time. So

36:06

you're getting that plus the price keeps

36:08

going up because everybody else is

36:09

discovering this new country called

36:10

Brazil. So that's why you can get

36:12

wrapped into it. I mean in the 19th

36:14

century there was a famous fraudster

36:16

called Gregor McGregor that literally

36:18

invented an entire country so you could

36:20

sell a bond and he just took the money

36:22

and ran to France. Uh but you know

36:24

people didn't know better back then.

36:27

>> Fraud fraud is the business that has the

36:28

highest profit margin.

36:30

>> Yes. Very much so. So if you can get

36:31

away with it, Gregor McGregor made out

36:33

like a bandit. But to be fair, like I

36:35

mean his efforts, you know, there are

36:37

frauds and then there are frauds. This

36:39

guy invented an entire country. He

36:41

invented a capital, a coat of arms, an

36:44

entire system of government, geography.

36:47

Had maps made. He had songs made. He

36:49

just basically invented an entire

36:50

country out of cloth and managed to

36:52

trick hundreds of people to literally

36:53

move to this country and also invest in

36:56

the country's bonds. But they ended up

36:58

at something called actually the

36:59

Mosquito Coast. And most of them died

37:01

there sadly. So you know quite tragic

37:04

end but you know the joke is that the

37:06

difference between tragedy and comedy is

37:08

time. So hopefully after 200 years we

37:10

can we can laugh at the the debacle of

37:13

pouet and Gregor McGregor.

37:16

>> Yeah I I don't think I want to go to the

37:18

mosquito coast.

37:19

>> No it's not nice. It's somewhere in

37:21

Guatemala now. But that's where he

37:23

invented this country of of of golden

37:25

honey everywhere apparently.

37:28

Robin, one thing when you mentioned that

37:30

how many of the canal bonds went bad,

37:32

how many of the railroad bonds went bad,

37:34

but ultimately like, okay, the old man

37:36

and the family made the loans and

37:37

eventually the grandson was able to like

37:39

recover 70 cents on the dollar cuz he

37:40

held it. It just made me think that just

37:42

people holding the bonds like in their

37:45

closet drawer and then eventually being

37:47

paid back. that is much more stable than

37:49

like a highly sophisticated financial

37:51

institution holding these securities on

37:53

leverage which is what BDC's are

37:55

basically even though it is a lot of it

37:57

is term debt.

37:58

>> Yeah. I mean the reason why we always

38:00

call like financial crisis back in the

38:01

day used to be called panics because

38:03

usually it was banks that held these

38:05

loans these bonds and and even though a

38:07

bond was you know is technically

38:09

designed it's supposed to be tradable

38:11

quite often when everybody wants to sell

38:13

and nobody wants to buy well good luck

38:15

trading it and there was no deposit

38:17

insurance they had deposited money they

38:19

borrowed money themselves so that's why

38:21

you know banking crisis and panics you

38:23

know they they were intertwined for a

38:25

long time uh now it is different. But

38:28

yes, sometimes if you buy something

38:30

unlevered, you know, you can lose money,

38:32

but you can only lose what you put into

38:34

it. Uh, and that's why leverage is so so

38:37

dangerous and, you know, has shown that

38:40

again and again and again in every major

38:42

and minor market cycle.

38:45

>> So earlier you talked about the great

38:46

financial crisis 2008 GFC, but you said

38:49

it wasn't one of your favorites. What is

38:51

one of your favorites in the book

38:53

>> and why?

38:54

>> Oh god, it's like choosing my favorite

38:56

child. I know. You know, it's it's very

38:58

difficult. I mean, I do like Gregor

38:59

McGregor and Poyet. I mean, it's just

39:01

incredible, right? But I mean, 1873, the

39:05

the railway man crash is kind of epic

39:08

because it was epic in size. It was epic

39:11

in in its casualty. Jay Cook was he was

39:14

the John Pay point Morgan before John

39:16

Pay Morgan. He was Titanic. He was the

39:18

guy that bankrolled the North's victory

39:20

in the Civil War. Um and suddenly he

39:24

just went bankrupt out of the blue cuz

39:26

he he'd gone over his skis on on

39:29

transcontinental railway bonds. He'd he

39:31

decided against his better judgment

39:33

initially to back one of these big

39:35

companies and and it just soured on him.

39:38

So because the mix of like the the

39:42

enormous ambition of these

39:43

transcontinental railways cuz they

39:44

weren't just like one they were like a

39:46

series of Apollo programs all happening

39:48

at the same time and it did genuinely

39:50

transform the United States into what we

39:52

know now know today. It used to be kind

39:54

of a coastal country. It was like north

39:56

and on the eastern coast and the west

39:58

coast and it went up and down but

39:59

suddenly it became a country that

40:01

changed its axis. It was west to east.

40:04

uh you could actually travel from you

40:06

know California to Maine in a few days

40:09

at least or at least a week rather than

40:11

months it would take before. So I think

40:13

the mix of both the economic impact the

40:19

political

40:21

uh importance you know this really did

40:23

transform it kind of united the United

40:25

States physically for properly for the

40:27

first time and you know how nasty it

40:30

ended it was a a gigantic financial

40:32

crisis that we don't remember that much

40:34

these days but you know it was it was

40:36

huge almost everywhere lots of companies

40:39

went bankrupt in the United States is

40:41

expressions like hobo came from that era

40:45

um because there were so many homeless

40:47

people and and soldiers, unemployed

40:49

soldiers also after the civil war that

40:51

lost their employment at the railway

40:53

lines. Um so I think that's probably my

40:57

favorite, but you know change

40:59

>> a hobo on the railroad tracks. You can't

41:01

have that if there's no railroads.

41:03

>> No, exactly.

41:04

>> It kind of seems to me like as

41:06

speculative as data center buildout is

41:08

once the data centers are built, they

41:09

are producing revenue now. Seems to me

41:11

that railroads back then were a little

41:14

bit more expected like to actually

41:16

literally you have to have a guy putting

41:18

the the wooden tack in and then you know

41:20

foot by foot across the entire country

41:23

and before the and then it has to be

41:25

built then the train has to be built.

41:26

You have to market it before the revenue

41:28

built like that does seem to be a

41:30

greater endeavor than building a data

41:31

center which is now very very difficult

41:33

and takes time and tons of capital of

41:35

course but uh it seems a little easier.

41:38

>> Yeah. I mean, don't forget I think the

41:40

the difference between railways in

41:42

Europe and the United States is an

41:44

interesting one because like in Europe,

41:46

railways connected existing towns. Like

41:49

you built a railway from Liverpool to

41:51

Manchester for example or from Berlin to

41:54

Paris. In the United States, railways

41:56

built towns. It created entire towns. It

41:59

created entire states. Bismar, the city

42:03

is literally only main named Bismar as a

42:05

marketing gimmick for the company that

42:08

built that railway line, the Northern

42:10

Pacific, uh, as a marketing gimmick to

42:12

appeal to German investors because the

42:14

charts of Germany at the time was called

42:15

Auto Fon Bismar. Um,

42:19

and you know, these were in the middle

42:20

of nowhere. I mean, as you know even

42:22

better than me, I mean, the United

42:23

States is a vast country and back there

42:26

very little of it was settled. So, you

42:29

know, it's incredibly hard work. I mean,

42:32

obviously, this is manual. People had to

42:36

literally hammer down the nail. They had

42:38

to dig out the grow, the roads. You have

42:40

to keep it smooth as well, right? So,

42:42

it's some backbreaking work. Then there

42:44

are all the the ravines, the mountains,

42:46

the forest, everything you have to go.

42:48

And this in the middle of nowhere, it

42:50

was lethal. Like thousands of people

42:52

died.

42:54

As much as the data center construction

42:56

is is is pretty epic today, I am not

42:58

aware of sort of mass casualties in the

43:01

in the construction of a data center in

43:03

New York yet and and and you know this

43:05

was

43:07

um you know the equivalent of building

43:09

the pyramids essentially very epic,

43:12

hugely dangerous and incredibly lethal

43:15

but you know transformative in the long

43:17

run.

43:18

>> Yes. And you're using the word epic in

43:20

the British sense or the way the British

43:22

people use the word great. Like it

43:24

doesn't mean that it's a good thing. It

43:25

just means it's big at scale.

43:27

>> Oh yeah. Yeah. No, I mean I think the

43:28

railways are good. Pyramids, you know, I

43:30

mean lots of slave labor there as well.

43:32

I mean in in in the railways there was

43:35

you know a lot of it was free workers

43:38

but you know not always. and they were

43:41

treated incredibly shabily, especially

43:43

like lots of workers were imported from

43:45

China for example and would you know

43:47

basically killed in the thousands. Lots

43:49

of Irish workers uh and you know it was

43:54

you know a positive thing in the long

43:56

run but you know not quite up to modern

44:00

labor standards put that way. Uh but

44:02

yes, epic in the

44:05

titanic country transforming projects

44:09

that unfortunately do sometimes always

44:11

have a darker side as well.

44:15

>> So uh a few months ago I interviewed the

44:18

Ilio Leoad Aamemed the author of a book

44:20

of 1873. A few months later I'm

44:23

listening to the Microsoft earnings call

44:24

and CEO Sachin Della says you know we at

44:26

the Microsoft the executive team we're

44:28

reading in 1973 so we're thinking about

44:30

this. So let's say in a few months the

44:32

next Microsoft call um you know the the

44:35

team they they say we're we're we've

44:36

been reading a fabulous debt epic story

44:38

of how bonds built the modern world.

44:40

What are some lessons that you think

44:42

they should know? The people who are

44:44

spending hundreds of billions of dollars

44:46

borrowing hundreds of billions of

44:47

dollars and probably according to the

44:49

off you know balance sheet lease

44:50

commitments it's going to be over a

44:51

trillion uh trillion and a half as you

44:53

say. What are what are the lessons that

44:55

that they should know?

44:57

>> Well's book is is phenomenal. Uh, it's

45:00

really good. You know, I take I tackle

45:02

the railway mania and the US. My book is

45:05

a bit more US- ccentric. His is is more

45:07

global and focus maybe a bit more on

45:08

Europe and the Grunder Crack in in in

45:10

Europe, which is spectacular.

45:12

>> Um, but I hope people realize that bonds

45:17

are an incredibly powerful financial

45:20

technology. It's kind of the financial

45:23

technology. It's kind of loans 2.0. I

45:25

mean, they were both both banks and

45:26

bonds were born in Renaissance Italy a

45:28

thousand years ago. Uh, but it's only

45:30

now really that the bond market has I

45:32

I'd argue supplanted the banking system

45:35

as the dominant credit engine of the

45:38

global economy. And the reason why

45:41

actually some of the basic building

45:42

blocks haven't changed that much over

45:44

the hundreds of years is because it's

45:46

incredibly powerful. You know, it's

45:47

fixed in interest so you can calculate

45:49

things easily and it's tradable and that

45:52

gives you and it's decentralized. It's

45:53

kind of the original, it's the OG

45:55

decentralized finance because you can

45:56

sell not just a one or two banks, borrow

45:58

for a couple a club of banks. You can

46:00

sell bonds to thousands even millions of

46:02

investors indirectly. That's why you can

46:04

pull individually tiny pieces of savings

46:07

into one big gushing river. And that's

46:10

kind of what the hyperscalers are doing.

46:12

So I hope like a Microsoft uh or any of

46:15

these CEOs and CFOs reading it would

46:18

realize that actually bonds you can

46:20

iterate on the fund fundamental

46:23

technology and people are and do but it

46:26

still works and that transparency the

46:30

the sobriety that comes with doing

46:32

something through public fixed income

46:34

markets rather than leases

46:37

uh opaque financing uh arrangements uh

46:41

private credit loans loans negotiated,

46:43

you know, off the side. Um, that comes

46:46

at a cost. That that that's flexibility.

46:48

That's great. But if you have big

46:51

projects like the railways, the most

46:54

valuable thing to do is to just sell

46:57

bonds. The bond market is supremely able

47:01

to to handle that. uh and and has shown

47:04

that again and again and again ranging

47:06

from you know Napoleonic wars, canals,

47:08

railways and AI centers to today and I'd

47:12

much rather that goes into the public

47:14

fixed income markets than than than stay

47:16

in the shadows.

47:18

>> Why is it in the shadows? And you talk

47:20

about this flexibility, you know, the

47:21

private property people, they say, "Oh,

47:22

our borrowers love flexibility." I don't

47:24

even really know what that actually

47:26

means.

47:26

>> I agree. I mean, flexibility, this

47:29

sounds great. Like, yeah, you want

47:30

flexibility? Yes, definitely. You want

47:31

freedom? Yeah, definitely. But in

47:33

practice, it comes at a cost.

47:35

>> I mean, broadly speaking,

47:38

>> if Microsoft wants to, let's say, sell

47:40

10 billion, wants to borrow 10 billion

47:42

to build a new data center, what is the

47:45

cheapest way for a large mainstream

47:48

public company to do so? Is it to sell

47:50

to like 10 private credit firms or maybe

47:53

a handful of private credit firms to do

47:55

it without a rating doing it quickly? We

47:57

can do opt opportunistically that way.

47:59

Yeah, sure. But you're definitely going

48:01

to pay a lot less to borrow by just

48:04

issuing a plain vanilla for general

48:06

purposes corporate bond. And the reason

48:08

why they aren't doing that is because

48:10

they want to maybe obscure how these

48:14

companies have become kind of been gone

48:17

from being lean mean cash machines into

48:19

being capex hungry utilities. And maybe

48:23

that pays off. Uh I mean the returns of

48:26

some of these data centers are pretty

48:27

phenomenal right now. Um, but they're

48:30

not doing it for purely financial

48:32

reasons. Uh, and I think flexibility is

48:35

probably a convenient excuse

48:38

um to hide, you know, that this is more

48:42

about making them seem healthier than

48:45

they really are. Yeah. I I think one

48:48

thing that like Coreweee, you know, Neil

48:50

Cloud is doing is delayed draw term

48:52

loans. So, oh, you don't have to

48:53

actually borrow the money until you need

48:55

it. So, it's like it's like a credit

48:56

line. Um yeah, I mean the the real king

48:59

of debt I would say is is cororeweave

49:00

that that there's just reported. I I've

49:02

never seen a bigger gap between ibbita

49:05

and net income loss. It is quite extreme

49:08

and it's it's it's a little railway

49:10

railway like what what do what do you

49:12

make of uh just I mean core's massive

49:15

massive borrowings?

49:16

>> Yes, it's heavily indebted. Um

49:20

there is in every cycle one or two or

49:23

maybe a handful outliers. Look, I'm not

49:26

worried about like Facebook and Alphabet

49:28

or Amazon going bust.

49:30

>> Yeah.

49:30

>> Uh, you know, they they Yeah. If they

49:32

take all these liabilities on balance

49:34

sheet, like it's not great for

49:35

investors. Uh, I worry about the the

49:37

financial hangover, but like it is

49:40

fundamentally different in that this is

49:41

not the error. These companies do have

49:44

solid real products and they're just

49:46

shoveling all that money and a bit extra

49:48

into AI. And even if AI somehow goes to

49:51

zero or nothing happens, I think it's

49:54

manageable. It's okay. But there will be

49:57

of course in any cycle a few extreme

49:59

outliers that just like borrowed way too

50:01

much money, did too many dumb things.

50:03

We're kind of too invested in this or

50:05

didn't have any other products or or

50:08

fallbacks. Essentially, people are still

50:10

going to be going on YouTube uh even if

50:12

Google wastes a few hundred billion

50:14

dollars on on on AI data centers. uh and

50:18

that's going to save them with a

50:20

coreweave or some of these other

50:21

companies. Do they have that backup? I

50:24

mean, maybe crypto mining, I don't know,

50:26

but

50:27

I'd worry about those essentially uh

50:30

more than I do uh the big hyperscalers.

50:33

There are a few of the hyperscalers that

50:35

look a little bit diceier, but uh

50:37

broadly speaking, they're probably

50:39

probably okay. Yeah, you uh perhaps

50:42

referring to Oracle definitely the most

50:44

indebted relative to to its its revenue.

50:47

>> Yeah, Oracle in a hyperscaler terms

50:50

there's the rest and then Oracle. Oracle

50:53

is not like a tiny bad company or

50:56

anything like that, but it's just it's

50:58

not doesn't have nearly the financial

51:01

and corporate heft of the others. And

51:03

you know, it's clearly the weakest of

51:05

the of the litter.

51:06

>> How long do you think this capex bubble

51:09

burst? You know, I I think we all know

51:10

that this is not going to be infinite.

51:12

Trees don't go to the sky. There will

51:13

be, you know, a bust a correction. Like,

51:16

do do you think it, you know, it's going

51:18

to be soon or in a few years?

51:23

>> I I mean, obviously, I have no clue.

51:25

>> People signing the check don't know.

51:26

Yeah.

51:27

>> Yeah. It's it's like you say, trees

51:29

don't grow to the sky. Capex bubbles can

51:31

continue for a long time until it

51:33

becomes very obviously unmanageable. Uh

51:36

right now there are a lot of people in

51:38

that industry and this is maybe both

51:39

what worries me but also can keep the

51:41

show going for a lot longer. There are a

51:43

lot of people now with a vested interest

51:44

in keeping this going like that that AI

51:48

industry has become remarkably

51:52

incestuous

51:53

with just an incredible tangle of

51:57

financing agreements, co-investments,

52:00

supplier and customer relationships that

52:04

kind of bind it all together but also

52:06

can kind of keep things going for a long

52:08

time. they all have an interest in kind

52:09

of managing this and that makes me

52:13

worried about what the Den Moore funding

52:15

looks like. Um, but it also means it can

52:20

continue for a while longer. And then it

52:22

I guess you know the the chicken answer

52:26

it just comes down to the technology

52:27

like to what extent AI genuinely is

52:30

transformative. Is it glorified chat

52:32

bots or is it going to cure cancer? Is

52:33

it, you know, going to put, you know,

52:35

people in Mars? And, you know, where we

52:38

fall on that spectrum is probably what's

52:40

going to decide uh just how much those

52:42

investments pay off. But the scale is is

52:46

pretty astonishing now.

52:48

Okay. So, as as someone who is a

52:50

journalist and is talking to people all

52:51

the time and is very well informed,

52:54

what are you hearing about how the

52:57

revenue is at OpenAI and Anthropic? I

52:59

think that literally like over half of

53:02

what matters is is that is that topic.

53:04

Are are you are you hearing good things

53:05

or bad things or medium things?

53:07

>> So I haven't spoken to anybody directly

53:09

about uh the revenues at OpenAI and

53:11

Anthropic. So I only know what my

53:13

colleagues have have reported uh in the

53:15

paper. Uh I think I think it's broadly

53:18

understood that anthropic looks

53:19

financially a lot healthier than OpenAI.

53:22

Um and that's one of the reasons why

53:25

they're probably going a little bit more

53:26

aggressively for an IPO now.

53:29

Um

53:32

but I'd question with private companies

53:36

how real sometimes revenue is and not

53:38

like fending numbers but just like I

53:40

mean if you just look at the the net

53:43

income of some of the hypers the public

53:45

companies now look at how much is

53:48

actually classified as other income

53:51

which is essentially revaluations of

53:52

their investments like yes

53:54

>> a lot of the money that Microsoft and

53:56

Google and Amazon have

53:58

are basically marking up the value of

53:59

their stakes in anthropic, open AAI and

54:02

SpaceX and other companies. Uh if you

54:05

take that away, then some of those

54:07

earnings look a little bit not bad, but

54:10

definitely not as good.

54:12

>> Yes.

54:12

>> Uh and with like OpenAI and revenue, how

54:15

much of that is actually cash like free

54:18

cash by rules, everything? And you know,

54:22

until I've seen the accounts, I don't

54:23

know. Even when we've seen the accounts,

54:25

sometimes it's hard to know. Uh, but I

54:27

can tell you I'm really looking forward

54:29

to the S1's for OpenAI and Anthropic.

54:31

That's gonna be a popcorn moment for me.

54:35

>> Definitely. Robin, how durable do you

54:38

think the credit raging agencies are?

54:40

So, Moody's, S&P, Fitch, the former two,

54:43

which are are publicly traded companies

54:44

and up until recently were viewed by,

54:47

you know, the Compound Bros, the hedge

54:49

funds as these extremely durable

54:50

businesses. Their valuations have fallen

54:52

a lot because, oh, AI could displace

54:55

them. I just wonder, you having spent so

54:57

much time researching and doing and and

54:59

and writing this this book, just your

55:02

insight on the value or lack thereof,

55:04

like is just just a sticker that really

55:06

isn't that valuable um you know, over

55:08

time? Like do you think in there's a

55:11

giant credit cycle, are people going to

55:13

be like, "Oh my god, I need my Moody's

55:14

rating before I buy it."

55:15

>> So that's a great question actually and

55:18

uh because I I spent a lot of time, a

55:20

depressing amount of time uh writing the

55:22

book thinking about this. There's an

55:23

entire chap just on the history of the

55:24

rating agencies and it is kind of weird

55:27

like how many cowpies they've stepped in

55:29

over the years and and how they endure

55:32

and I think I think that's the secret to

55:36

answering your question that yes I don't

55:38

know about 2070 that's a long way off

55:40

but I think people will be shocked at

55:44

the resiliency of their business model

55:47

because people don't actually pay S&P

55:50

and Moody's and Fitch for their credit

55:53

work. It's not like if you're the CIO of

55:56

PIMCO and you sit there, well, I'm I'm

55:58

going to look at what Moody's says about

56:00

this bond. I mean, you care about the

56:01

rating agent uh the rating, but you

56:04

know, for for investment mandate

56:06

reasons. Uh but the credit work you do

56:08

yourself and that's clearly like with AI

56:10

like a lot of that is happening. A lot

56:12

of that happened before the current

56:14

excitement about large language models

56:17

like I've been covering AI for I mean

56:19

before it was cool you know natural

56:21

language processing and machine learning

56:23

I used to cover quants all the time uh

56:25

and it was fascinating to see how people

56:27

were learning to you know automate the

56:30

ripping apart of a credit perspectus and

56:33

putting their end your own models and

56:35

then automating all that. Uh and this

56:37

was 10 years ago. Uh but the rating

56:40

matters not as because of like you want

56:44

Moody's to tell you what to think of

56:45

this investment because they famously

56:47

don't try to give investment advice.

56:48

They just give a probability of default.

56:52

The Moody's rate I mean the Moody the

56:54

credit rating agency ratings are a lot

56:57

better than people think. Like there are

57:00

outliers when people say well this

57:02

company was rated a half a year ago and

57:04

it went bust. But they are they are

57:08

highlighted because they're actually

57:09

pretty rare. Broadly speaking, the the

57:12

letterbased model as a signify of s

57:17

chance of default is actually pretty

57:19

accurate. Like even the financial

57:21

crisis, all those shoddy securitized

57:24

monstrosities that were given AAA

57:26

ratings. Well, actually even quite a lot

57:28

of the AAA tranches ended up being money

57:30

good. they traded down maybe to 20 cents

57:33

on the dollar, but a lot of those

57:35

actually were pretty okay.

57:37

>> Uh, and AAA companies, AAA governments,

57:40

you know, tend to, there aren't that

57:41

many of them around these days, but it

57:43

it tends to work. And I think the reason

57:45

why the rating agencies actually endure,

57:47

will continue to endure is because this

57:50

phrase I once came across somebody in

57:52

the industry use, but he talked about

57:54

like the need for a language of credit

57:57

like we need shortand. We're humans. We

57:59

we both very smart and very stupid at

58:02

the same time. And we like these

58:03

shortorthands. We like rules of thumb.

58:06

We like simplistic models. And it's just

58:10

nice to have something like this is a

58:11

single B, that's a double A, that's a

58:14

triple C. And the reason why the rating

58:18

agencies despite having like very

58:20

different, they talk up all their

58:21

difference, they still have basically

58:23

the same letters as well. And that's

58:26

because it gives us a cohesive language

58:29

to talk about credit. And sometimes it's

58:32

wrong. Like all language can be, all

58:34

models don't work. You know, it's just

58:36

like um there's a famous British

58:39

statistician who said that all models

58:40

are wrong, but some are useful. The

58:42

rating agency models are not as wrong as

58:44

people think and it's still pretty

58:46

useful. And as much as you can automate

58:48

all sorts of cool with AI, I think

58:51

that will endure and and the craving for

58:55

just a brand, a name like Amoody's or an

58:58

S&P uh is is going to stay there. And in

59:01

fact, in places like the United States

59:03

is enshrined in law. Uh despite all the

59:06

controversy around the financial crisis,

59:07

you know, the nationally recognized

59:10

rating agency designation is is still

59:12

there. It's still in the books. And

59:14

that's why, you know, it's kind of one

59:17

of the most stubborn oligopies in the

59:21

history of business probably.

59:22

>> And if you're an insurance company

59:24

buying something, you h you you have to

59:27

buy something, a certain percentage of

59:28

your assets have to be investment grade,

59:30

even if the rating is totally wrong. And

59:33

also, I think of the the Charlie Mer um

59:37

anecdote about how he was in World War

59:38

II. And I think he was he was tracking

59:40

the weather and he ultimately was saying

59:42

to his superior like, "Hey, my forecasts

59:44

are really bad. You I shouldn't you

59:46

shouldn't be asking me for these

59:46

forecasts." And that the military people

59:48

said, "We need these forecasts for our

59:50

military planning." So even though the

59:52

forecasts are wrong, we still need them.

59:53

>> We still need them. We still need

59:55

something like that. Yeah. I mean, it's

59:56

like so many things in finance that look

59:58

weird. I mean, in the world really, they

60:00

look weird or dumb or or dangerous. is

60:04

quite often like you still come to this

60:05

kind of well if it didn't exist we'd

60:07

have to invent it. Ratings

60:10

as weird and dumb as they sometimes can

60:12

seem we still need something like that

60:14

and if they didn't exist we'd have to

60:16

reinvent them all over again. Uh the

60:18

insurance issue is quite interesting

60:20

drawing back to private credit there of

60:22

course there is always a danger of

60:24

shopping around for the greatest rating

60:28

and broadly speaking the big three have

60:30

done a pretty good job over time. not

60:33

always but over time to as much as you

60:36

know they could be a little bit more uh

60:38

commercial let's say certainly before

60:40

the financial crisis broadly speaking

60:42

not letting the standards arose too

60:45

comically far but clearly there I I I do

60:48

worry about so-called private label

60:50

credits that insurance companies are are

60:52

getting on private credit loans uh and

60:56

saying they're investment grade when

60:57

really the reality is I suspect a lot uh

61:02

iffier.

61:03

>> What does an investment grade private

61:05

loan really really mean?

61:08

>> Uh I think most of the big serious uh um

61:12

insurance companies are very aware of

61:14

this issue and are aware of it and if

61:16

they do use private label label credit

61:19

ratings um that they take it with a

61:23

pinch of salt or they know the the

61:24

issues they be dragons maybe. Uh but

61:27

there are also a lot of private uh

61:30

insurance companies that are owned by

61:31

private equity.

61:32

>> Yes.

61:33

>> And those private equity insurers

61:35

companies sometimes own also some of

61:37

these private label companies. And I do

61:39

worry about that they the the tangled

61:43

private capital ecosystem of private

61:46

credit, private equity, private ratings

61:48

and private equity in owned insurance

61:50

companies. I think that is something

61:52

that could at some point bear watching

61:54

as well. Have you looked into these

61:56

things called funding agreementbacked

61:58

notes?

61:59

>> No, but it sounds amazing. Tell me more.

62:02

>> It is basically when an insurance

62:05

company, probably a like a life

62:07

insurance company

62:09

issues debt, but the debt that they're

62:12

issuing, they can call it a policy, a

62:14

life a life insurance policy.

62:16

>> Yeah.

62:16

>> Yes. No, actually, I do remember reading

62:18

about this and I was delighted to learn

62:20

about it.

62:22

It shows that there is nothing more

62:24

creative on this planet as a financial

62:27

engineer who wants to optimize risk and

62:29

reward and game the system to do so. Um,

62:33

is it you know

62:36

one of the the dangers of of my job

62:39

journalism and and your job and I guess

62:41

everybody's job is that that we we look

62:43

very much backwards and it's always

62:46

cooler to seem pessimistic and and cool.

62:48

or this is the next big thing and this

62:50

is the next co or whatever.

62:53

>> And you know, luckily those kind of

62:55

crashes like 2008, they don't happen

62:57

very often. Uh I actually have like

63:00

literally on in front of my desk, I have

63:02

a little cartoon that shows it's from

63:04

2008 that shows like somebody going onto

63:07

an airline and the captain comes across

63:09

the tano saying, "Oh, you know, there's

63:11

a bit of turbulence. Uh buckle up." And

63:14

there's a passenger who screams, "Oh my

63:16

god, we're all going to die." And the

63:19

passenger next to him says, "Look, just

63:20

it's a financial journalist. Don't

63:22

worry." He's just panicking. And I just

63:24

have it there in front of my desk to

63:26

remind myself and not always thinking

63:27

everything is the next 2008. Not

63:29

everything is a big crisis. Uh so these

63:32

these notes, look, I think it's the

63:35

optics are bad. The fundamentals are

63:37

probably not great. Is it going to be a

63:40

disaster? Probably not because, you

63:42

know, Yeah.

63:45

It might be bad, but you know, we can't

63:47

have reward without risk. You can't make

63:49

money without losing money. That's kind

63:51

of what keeps the the train on the

63:52

roads. And you know, people sometimes

63:56

create stupid things, invent new things,

63:58

game the rules, and they get their faces

64:03

ripped off. It blows up in the next

64:05

downturn. Uh but the good inventions,

64:07

they survive and they evolve and they

64:09

thrive. I mean, securitization is one of

64:11

them. Securization was a a dirty word

64:15

not that long ago, like I just a few

64:17

years ago. And now we're looking, even

64:19

the Europeans are talking about it like,

64:21

"Oh my god, we wish we had America's

64:24

mortgage back security market. Oh my

64:26

god, that would be amazing." And I

64:28

remember when even American politicians

64:30

were were badmouthing it. Um, stupid

64:33

things happened in 2008, but we learned

64:35

from it. And you know, at some point,

64:39

um, we'll realize what was really stupid

64:44

that we're doing right now, what was

64:46

actually just fine and what was just

64:48

moderately stupid. Uh, and then I get to

64:50

write a book about it a few years after

64:52

that. So, you know, it's all it's all

64:54

gravy for me as a financial journalist.

64:56

>> People should buy the book, A Fabulous

64:58

Debt, the Epic Story of How Bonds Built

65:01

the Modern World. Buy it for yourself.

65:03

Buy it for your your kid. buy for your

65:06

parents, grandparents. Thanks so much.

65:08

>> No, thanks for having me on, Jack.

65:12

>> Thank you. Just close the door.

Interactive Summary

Robin Wigglesworth, editor of FT Alphaville and author of 'A Fabulous Debt', discusses the massive surge in off-balance sheet leverage used by major tech companies ('hyperscalers') to finance AI infrastructure. He compares the current 'capex mania' to historical debt-fueled cycles, such as the 19th-century railway boom. Wigglesworth analyzes how these companies use creative structures like off-balance sheet leases to manage optics, explores the rise of the private credit asset class, and reflects on why financial crises, while painful, are often part of a necessary evolution in capital markets.

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