Howard Marks: AI, Debt vs Equity & The Next 40 Years Of Investing | Nikhil Kamath | People by WTF

0:05 Mhm.

0:52 Could you also say that indexation and ETFs have

0:55 taken over because the markets have been in an uptrend.

1:01 My answer is that indexation has taken over as it

1:04 has not because it's so good but because active management

1:09 was so bad cuz a lot of the distressed bonds

1:12 that you are in is the chance of a downside zero.

1:33 So I'll give you a little bit of a background.

1:35 So most of these are like conversations.

1:38 Most of the interviews that we do are conversations.

1:41 They're fairly free flowing and the audience

1:44 we cater to are entrepreneurs of Indian origin

1:49 who are around the age of 25 to 30.

1:52 Everyone's looking to invest in the market, trade the market,

1:57 start a business, building something from the bottom up.

2:01 So we are speaking to them.

2:02 So most of today is around that.

2:05 Can we start with a little bit about you and uh how your own journey began

2:10 from Queens to Wall Street maybe like a how

2:16 do you describe your story in your words,

2:20 your story, your origin story?

2:22 Well, um, you know, I was born in Queens, New York, as you know,

2:29 one of the what one of what are called the outer burrows.

2:33 Uh, middle class upbringing.

2:36 Uh, neither parent uh went to college.

2:40 Um, but my father was a very intelligent man and and and an accountant

2:49 and I think he did his job well.

2:52 And so we lived a uh comfortable middle class uh existence.

2:59 Um I went to the public schools of New York at a time

3:03 when you could get a good education in the public schools of New York.

3:07 Uh and I think I got one uh and uh oddly

3:11 enough u ended up taking courses in business law and then accounting

3:19 in high school.

3:20 Mhm.

3:20 and really connected with accounting, with the orderliness and the symmetry.

3:26 Uh it it it just clicked for me.

3:28 So I decided to go to uh uh business school.

3:32 I applied to Wharton as the best business school for undergraduates in America.

3:38 Uh I was told I wouldn't get in, but I did.

3:41 Uh went to Wharton as an accounting student, switched my major to finance.

3:45 Mhm.

3:50 Uh uh then went on to get a an MBA

3:53 in in accounting from the University of Chicago Business School.

3:57 Uh and uh between years of business school,

4:00 I had a job in the investment research department of City Bank,

4:04 liked it, went back.

4:05 That that's my that's my background.

4:08 Is Wharton, would you say, still one of the better schools?

4:12 No, I think it's I I I I have no

4:14 reason to believe it's not still the best undergraduate business school.

4:18 There are other good graduate schools.

4:24 Uh that are uh uh very competitive with Wharton.

4:28 Um I'm not I'm no expert.

4:31 I'm going to say that a hundred times on this tape.

4:33 Uh but uh I believe that Wharton

4:36 is still the best undergraduate business school.

4:38 And you studied Japanese literature at Wharton?

4:42 Yes.

4:42 Uh when I went to Wharton, uh there were two very enlightened requirements.

4:56 Mhm.

4:54 You had to have uh a semester of the literature of a foreign country

5:01 and you had to have a non-b businessiness concentration, what we call a minor,

5:05 right?

5:06 And you know the the wonks would take stat or economics or polyide.

5:11 What are the wonks?

5:13 Wonks.

5:14 Yeah.

5:14 Oh, you know people who just think about making money.

5:19 Okay.

5:17 Or or or or just think about numbers and business.

5:23 Uh but for my uh literature requirement for some

5:27 reason I don't remember I took Japanese studies.

5:32 Uh Japanese literature.

5:33 uh uh per I think perhaps it was the uh exoticism.

5:42 Mhm.

5:41 Uh and I just fell in love with it.

5:44 Uh and so I ended up taking a second semester

5:46 of Japanese literature and then two semesters of Japanese civilization,

5:50 then one semester of Japanese art.

5:53 And what did that teach you?

5:55 Well, it taught me a lot.

5:57 uh uh but the main thing I've carried away

6:02 have been some aspects of the Japanese uh philosophy.

6:08 Mhm.

6:07 Uh the most important one, the most relevant one is something called mujo.

6:13 Mhm.

6:13 Uh which uh literally means the turning of the wheel of the law.

6:21 And what it means in in everyday life is it means the inevitability of change.

6:33 That change is uh inevitable, unpredictable and uncontrollable.

6:42 and that we have to accommodate to it and make

6:48 the most of it rather than think we can control it.

6:54 Should one expect it?

6:56 One should.

6:57 It's inevitable.

6:58 Yes, of course.

6:59 One should expect change.

7:01 One should not expect a specific change because it's unpredictable.

7:06 I once read somewhere that a western man if it rains

7:11 for 5 days will expect it to rain on the 6th.

7:15 Whereas a East Asian person say Japanese for example if it rains for 5 days will

7:22 expect no rain on the 6th because the pendulum

7:25 like you say swings back to the mean.

7:28 Is that culturally true?

7:30 Uh I have no reason to know.

7:32 I don't know.

7:34 uh but uh what I would say is if if the probability of rain is let's say 5050

7:47 Mhm.

7:47 If some people think five days of rain means the six will rain.

7:52 Mhm.

7:51 Because of continuation of trend and some people think five days of rain means

7:57 that it won't rain the next day because the rain has all been used up.

8:03 What I think is on the sixth day it's 50/50.

8:08 The previous days do not matter for the sixth day.

8:10 Well, I mean, and in weather that may not be precisely true,

8:15 but but I'm a big believer in in the independence of events.

8:20 And I think that that's what mujo is about, independence of events.

8:24 So you know if you flip 10 a coin

8:28 10 times better example coin than rain because rain there

8:31 are some physical properties that work but uh if you

8:35 flip a coin 10 times and you get 10 heads

8:38 the probability of heads on the 11th flip is

8:40 still 50/50 because the because the trials are independent.

8:45 Do you believe in game theory

8:50 in

8:47 game theory?

8:49 Uh well, I believe that uh I believe that uh it you can strategize

8:58 and you can uh figure out uh which action has the highest expected payoff.

9:06 Mhm.

9:06 Or the highest maximum potential payoff

9:10 or the least probability of a really bad payoff.

9:15 Mhm.

9:16 And you can perhaps even strategize about what your uh opponent is going to do.

9:24 Mhm.

9:23 I I believe in strategy.

9:25 I don't know if that's exactly the same as game theory,

9:28 but I I think um I think that you know

9:34 uh I as I've suggested in my comments so far,

9:41 what I really don't believe in is predictions and uh I

9:46 believe that u given human nature and the uh dislike for ambiguity.

9:56 Uh people pursue uh predictions so that they can understand what world

10:02 they're going to be operating in and and deal with it accordingly.

10:08 Uh and if they do this to excess,

10:14 then they theorize about a world uh they'll be operating in uh which is not

10:24 as likely to obtain as they think

10:29 which means that their predictions can be misleading.

10:32 Mhm.

10:33 And um about 20 odd years ago I wrote a memo called you can predict

10:39 you can't predict you can prepare and that's

10:42 really the essence and that that you

10:44 can tell that that comes directly from the understanding of mujo um we can't

10:51 predict the future we can't control the future

10:55 but we can prepare for the future.

10:57 Now that raises an interesting contradiction in terms

11:00 if you can't predict what the future will be, how can you prepare for it?

11:04 And the answer is you can prepare for a future which is unpredictable.

11:09 So you can uh in investing you can put together a portfolio

11:14 rather than one that will produce maximum success if a certain future unfolds.

11:21 You can put together a portfolio which will do fine if several any

11:30 of several futures unfold and not too

11:34 terribly if a bunch of other futures unfold.

11:37 Now you can't prepare simultaneously for the uh

11:45 positive tale and the negative tale.

11:47 Mhm.

11:48 If you if you prepare for a a total disaster,

11:54 then you're unprepared for a great future.

11:58 If you prepare for enormous success and you're

12:02 going to maximize your outcome should this success unfold,

12:08 then you're probably totally unprepared for a very negative.

12:12 But you can prepare if you if you uh suboptimize

12:20 for a number of possible outcomes in the middle of the probability distribution.

12:25 Isn't that a bit contrarian to the coin example?

12:29 You said if you toss a coin and it comes heads 10 times.

12:35 Yes.

12:34 And I toss it the 11th time, you still expect the odds to be 50/50.

12:39 So I can't look at past data to predict the future or prepare for the future.

12:44 Then how do I prepare?

12:45 I I think that the I think that the uh

12:49 the difference is that the coin tosses are independent.

12:55 The result of the preceding coin tosses has no impact on the next one.

13:01 Mhm.

13:00 That's not true in the areas I work in.

13:02 Mhm.

13:03 Uh most importantly, uh history,

13:10 the things that have happened so far and people's

13:14 reaction to history have semi-predictable implications for the future.

13:27 Not history, but people's reactions of it.

13:29 Well, not history itself as much.

13:33 I mean, if you if you knew history, but but but well, but but but I mean,

13:40 history is the history of what people did and what people did in the past

13:48 by itself has implications for the things that will

13:53 unfold tomorrow and next month and next year.

13:56 So in a way are you saying the psychology of people mass mentality in a manner

14:02 remains the same and that can be modeled for more so than events can be.

14:10 Well of course events are largely the things that people

14:16 do which is largely the result of their psychology.

14:20 But I do think that that events

14:28 in the future are somewhat predictable because be

14:33 because the implications of past behavior are

14:38 somewhat predictable and future behavior is somewhat predictable.

14:44 The things that have happened, for example,

14:46 in the economy and the markets are

14:53 likely to induce certain behavior in the future.

14:57 And you can know a little bit about that.

15:00 And but when when the history and the past behavior has been extreme,

15:10 the ability to infer from that rises.

15:17 In other words, I wrote a book about uh cycles and that's

15:21 the one where I refer to my experience traveling in India

15:27 and uh cycles are induced primarily by behavior by you know uh

15:39 uh the upcycle is exaggerating overgeneralizing

15:45 the upcycle that there's a trend line.

15:47 Let's say it's GDP growth.

15:52 Mhm.

15:49 There's a trend line.

15:51 But sometimes uh the economy grows more than that.

15:58 Why?

16:00 Largely, I think because of optimism.

16:03 Because uh people are optimistic.

16:05 So they spend a lot of money.

16:09 Producers are optimistic about demand for their product.

16:12 So they build new factories and hire labor and buy machinery.

16:16 And the sum of these things produces a period of above trend growth.

16:21 Mhm.

16:23 But then when producers have more than prepared

16:32 for the coming growth and consumers have been sated by consumption,

16:44 then the then things will ease off and maybe

16:52 it results in a period of below average growth.

16:55 So, uh you know, I I wrote the book uh I was about twothirds

16:59 through writing the book and I've been studying cycles for almost 60 years now,

17:04 living through cycles.

17:05 And uh I said to myself, just why do we have cycles?

17:11 Why doesn't GDP if if the average growth in GDP is two, why isn't it always two?

17:16 Why is it three or four or one or zero or minus one?

17:20 uh and the answer is excesses and corrections and then you go to a negative

17:27 which is an excess and then it corrects back to the trend line.

17:31 So rather than thinking of cycles as ups and downs which I think most people

17:35 do think of them as excesses and corrections

17:39 excesses and corrections fluctuations around the trend line.

17:44 I mean, this is most easily seen in in in the stock market.

17:48 For example, you look at the S&P 500.

17:51 The S&P 500 has returned about 10% a year on average for the last 100 years.

17:59 But the constituents of the S&P have changed many times in that period.

18:03 But the but the average rate of return doesn't change.

18:06 Average rate of return.

18:07 I don't know about the S&P.

18:08 I know in India the indexes.

18:10 I'm a stock investor.

18:11 That's my full-time job.

18:15 Yes.

18:14 We have returned about 11% over the last couple of decades,

18:20 but we have depreciation of currency which is about 4 5% a year.

18:25 So I'm assuming the S&P would have returned 6%

18:28 if you were to ask me to wager a guess.

18:30 But I don't think it's a fair benchmark because the companies

18:33 in the S&P like the indexes back home change by virtue of market cap.

18:39 They do.

18:40 But my point is that the S&P, however constituted,

18:46 has returned an average of 10% a year for the last 100 years,

18:51 but um not steadily.

19:01 Mhm.

18:59 In the '9s, it returned 20% a year and in the as it returned zero per year.

19:08 So certainly not steady.

19:10 And not only is that an interesting phenomenon to think about, but even more so,

19:17 the fact that the return on the S&P,

19:20 which averages 10, is almost never between 8 and 12.

19:28 This is a very interesting datam.

19:30 Uh uh why is it that the norm is not the average greed and fear

19:45 excess and correction excess of optimism correction excess of p pessimism

19:52 can I ask you a small digression in a question

19:57 if humans were no longer in the picture

20:02 yes

20:00 and AI models are trading in the market

20:05 would 10% be 10% every year because there would be no optimism and pessimism.

20:11 Uh this is another of those times when I'm

20:14 going to say I'm not smart enough to know.

20:16 It stands to reason that it would

20:19 if America if if we programmed AI to find the elements and expect

20:35 uh the patterns that produced success in the past and left it alone.

20:43 And since we're told AI does not have greed or fear or optimism or pessimism,

20:50 you would think that that the returns would be uh steadier.

21:00 I think it makes it makes sense to think that.

21:03 I heard your interview a year ago

21:06 with Nikolai from the Norwegian Sovereign Fund.

21:08 He's a friend of mine.

21:10 And when he asked you where are we in the cycle,

21:15 you said somewhere near the middle.

21:21 Well, I think that was two years ago.

21:23 Okay.

21:24 Yes.

21:24 Where are we now?

21:25 Um, well, we're guess what?

21:27 It's two years later.

21:29 Mhm.

21:29 That's the only thing I know for sure.

21:31 But uh I believe that if I so I believe that interview was April of uh 20

21:38 4 and the market continued upward from April

21:48 24 through I think it's fair to say January

21:54 of 26.

21:55 So another uh 21 months took it considerably higher.

22:01 Um and uh of course the uh the economy people when you

22:09 look at the economy people think of the age of the recovery.

22:13 So the rec economic recovery is two years older.

22:15 The the the bull market in stocks went on another almost two years.

22:22 So um I would I would say that the uh

22:28 you see it it first of all as to the economy

22:31 it's very very hard to date or talk about

22:34 the age of this cycle because we had the pandemic where we did something unique.

22:42 We closed the world economy

22:45 to produce the spread to reduce the spread of the disease

22:49 and so that interfered with the normal workings

22:55 and it according to the people who make these decisions it produced a recession.

23:01 It was in a very unusual recession because it was only one quarter.

23:04 I always thought an a recession was two quarters of negative GDP growth.

23:09 Uh and of course it didn't occur for the normal

23:12 reason of excess optimism leading to a correction.

23:16 It was man-made.

23:19 Mhm.

23:19 And was readily corrected.

23:22 So the second quarter of 2020 was the worst quarter in the history of the world

23:26 uh for for us for GDP uh and then the third quarter was the best.

23:31 Mhm.

23:32 So was it really a recession in the in the functional

23:36 terms of determining cyclicality uh or or just some noise?

23:44 Uh and can I ask another question here?

23:47 I would say GDP if I were to consider

23:50 it the total produce going into the economy

23:55 and consumers consume as much as we can.

23:59 I could argue that a recession is truly when there is

24:04 more production in an economy and the consumption is organically lesser.

24:08 So do you think GDP is a good benchmark

24:10 to figure out when there is a recession or not?

24:13 Because the consumers went away then artificially.

24:19 Well, I think it's I think um I think your definition of recession isn't

24:24 isn't right because what you said was uh there's more production but that's not

24:31 less consumption.

24:32 Less consumption that is that's the main element

24:36 of of of a recession in my opinion.

24:39 Uh manufacturers are always happy to produce goods to meet demand.

24:47 The recession means primarily a reduction of demand.

24:52 So that which which discourages manufacturers from producing

25:00 and the reduced production is really what causes the recession.

25:06 Fewer people are at work uh etc.

25:09 Fewer goods are produced and consumed.

25:11 So in the p pandemic production went down

25:14 but the demand for consumption did not actually go down

25:18 but just that the markets were shut for a period

25:20 of time cuz when they opened people really consumed.

25:22 Well I mean yes the the underlying demand didn't go down but people

25:27 I mean uh in the sense that people would have liked to have parties.

25:35 They just weren't allowed to have parties.

25:37 So the demand, the psychological demand for a party was there,

25:42 but the economic demand for rental halls and bands and food

25:48 and uh party dresses and things like that uh didn't exist.

25:55 Uh so that's that.

25:57 So, but this is why I say that the GDP

26:00 was kind of artificially depressed uh by a decision of government

26:09 really.

26:09 So, so when we say that how old is this recovery?

26:14 Was that a real recession?

26:16 If that was a recession, then this recovery is now 6 years old.

26:22 If that wasn't a recession,

26:25 28 years old,

26:27 this then this recovery is 17 years old,

26:29 which is the longest in history is 10 years.

26:33 Why 17?

26:34 Because the last recession was in '09.

26:37 So it's 17 years since then.

26:40 But can you just tie recessions and cycles down to tenure time?

26:46 You can't.

26:47 And that's one of the reasons why I don't believe in predictions.

26:50 But there seems to be a norm.

26:54 Mhm.

26:54 Uh we always said that the average was eight years.

27:00 Mhm.

26:58 For a for a recovery and in the 2010s uh it went on for 10 plus years.

27:08 So we called that the longest in history.

27:10 So, so and and and normally if we have a recession uh in year one X,

27:17 we don't expect another recession two years later because recovery goes

27:23 on for some period of years which seems to average eight.

27:26 I can't explain why.

27:28 I don't think anybody can tell you why.

27:30 Maybe maybe the answer is on average eight years is long

27:33 enough from the last one to prevent to permit the next one.

27:36 Uh but uh so I don't believe you can you know somebody I wrote the book

27:43 about cycles and and somebody in England said

27:46 oh no these aren't cycles because they're not predictable

27:49 you know cycles like radio waves or sine waves

27:51 or or or something like that these are predictable and and I

27:56 think that uh patterns in the in the in the real

28:01 world the non non-scientific non- mechanical world uh certainly aren't regular,

28:09 but they do occur ups and downs.

28:11 They do it predictably,

28:13 but not necessarily predictable in time or predictable in uh amplitude,

28:20 but they certainly occur predictably.

28:23 So, if I can bring you back Howard to the question,

28:26 what question is so long ago?

28:29 Where are we in the cycle now?

28:30 Well, I think I think that the uh the recovery is uh I think

28:39 it's uh it's gone on a good while and u it's uh it's not nent,

28:49 it's not adolescent.

28:51 Uh it's but it's also not uh uh geriatric.

28:56 I think it's in middle age.

28:58 uh and and I think I think the US economy,

29:02 if that's what you're talking about, is healthy and and doing quite well.

29:08 Uh if if the thing that preages a a downturn is excesses to the upside,

29:18 you don't find too many of those.

29:21 You know, uh when I go to a city, I I look for construction cranes in the sky

29:25 to tell me if there's a building boom.

29:27 Most booms are followed at some point by busts.

29:31 I don't see it in most of our cities.

29:34 Uh I don't see uh inventories growing.

29:39 Uh inventory growth is in in inventories grow for two reasons.

29:46 In times of uh weak consumption because

29:50 people don't buy the things that manufacturers produce.

29:53 In times of of of strong consumption, uh,

29:57 inventories grow because manufacturers amp up production

30:01 to get ahead of some anticipated surge

30:04 in demand and those often pre preage downturns

30:08 when they turn out not to be warranted.

30:11 I don't see that in some sectors.

30:13 Do you?

30:15 Well, I mean, maybe I I don't I don't

30:18 look at the data finely enough to tell you that.

30:20 In AI, maybe.

30:22 Pardon me.

30:22 in AI maybe.

30:24 Uh well, of course, we don't talk about inventory in AI,

30:27 but there's certainly a boom in capital investment for AI.

30:31 One could say hardware, energy, compute is inventory.

30:35 Yeah.

30:35 Well, I mean, look, there's massive construction of infrastructure for AI,

30:41 much like buildings.

30:43 Yes.

30:43 Yes.

30:45 Yes, that's right.

30:46 That that well, that is that is the area that's booming in our economy.

30:51 And uh I need uh people who know more about

30:54 AI than me to tell me whether it's going to bust.

30:57 You know, the question is is all

30:59 of this or is some of this construction unwarranted?

31:04 Um uh I don't think anybody can tell us that.

31:08 We do know we certainly know that it is an area of of uh of boom.

31:16 How do you stay so sharp Howard at your age?

31:18 like I have watched a whole bunch of your interviews and read a lot

31:23 of what you have written over the years as a stock market investor all my life.

31:29 Uh I think there's so much I can learn from you.

31:33 But how do you stay this sharp today?

31:37 Well, I don't think I don't I mean I'm lucky I have

31:44 genetically

31:43 Well, I I think I'm I think I'm very lucky genetically.

31:48 Okay.

31:47 Yes.

31:47 because uh my dad lived to 101 and he was quite sharp at the end.

31:52 Mhm.

31:53 Uh and uh you know uh we all face a decline at some point in time

32:02 and the only question is when does it start and and and at what

32:05 rate does it progress and uh you know you look at uh Warren Buffett,

32:12 Charlie Mer some of my uh role models and friends and uh you

32:19 know they were or are uh sharp in their mid 90s and late 90s.

32:29 So, it's just a I mean I didn't when you say how do you stay sharp?

32:34 I don't go to the gym and work.

32:36 Well, I do do a lot of puzzles and I

32:38 read and I read in fields that are not my fields.

32:43 I you know I I'm still trying to gain

32:45 knowledge and broaden myself and uh I guess I make

32:50 some effort to uh to uh plow new ground

32:54 and the question is when do you stop doing that?

32:56 At what age?

32:58 But you know I my last two memos uh I hope your audience knows about the memos.

33:04 Yeah.

33:04 But the I wrote one December 9th and one I think February 25th uh um about AI

33:14 and I had to do a lot of learning.

33:16 You changed your mind on AI as I changed my mind.

33:18 But first I first I uh challenged my mind

33:22 and uh you know I had uh for the second

33:25 one in particular I had a extensive conversation with Claude

33:30 and and and and learned a great deal.

33:33 Um and uh you know it was very exciting to be plowing new ground at almost 80.

33:41 Um, and do you think it could be ambition and hunger?

33:48 Well, it it's it's it's it's it is definitely a hunger.

33:51 It's a hunger to learn and to stay relevant and uh and to stay stimulated.

33:58 Uh when you say ambition, that that usually starts with dollar signs.

34:04 Not necessarily.

34:05 Yes.

34:05 Well, it's it's an ambition to stay relevant and and uh

34:11 and to to use your terminology to stay sharp rather than start to atrophy.

34:20 I think staying relevant is the core ambition of everyone.

34:25 The dollar is a means that people pick in the beginning, not at the end maybe.

34:33 Well, I think I think that most ambition starts with the dollar sign.

34:43 In enlightened cases, it transitions to non-dollar.

34:50 Not every case.

34:51 Now, you know, there are people are different.

34:53 You can't Mark Twain, I think it was,

34:56 uh said uh all generalizations are flawed, including including this one.

35:02 But you can't generalize.

35:04 Uh

35:06 like you say very well, I heard you speak about the role luck plays in our life.

35:12 Would you say enlightened or lucky?

35:15 The ones who transition.

35:18 Uh well, now now you're really getting

35:20 deep because now you're talking about uh determinism uh versus uh intention.

35:29 Um and I think there are some of both.

35:32 Uh I mean some people kind of I I mean these things are hard to parse

35:38 Nquille and but some people I think transition

35:42 naturally and as they grow maybe they become wiser

35:47 and they start to understand uh that that there

35:51 are things that are more important than money

35:53 or uh let's say things that are important

35:56 in addition to money and so they do it.

36:04 Let's say that naturally, not intentionally.

36:08 Mhm.

36:08 I imagine there are people who do it intentionally.

36:10 People who uh go to the mountain and meditate

36:15 and think about it and conclude, you know what,

36:18 I really should change my focus at this point in my life.

36:21 Have you a to have you been able to ever pull that off?

36:24 Like I've been fascinated with the idea of going to a mountain and arriving

36:30 at some kind of evolutionary thought in my mind

36:32 which makes me actively transition into something.

36:39 Have you been able to touch that?

36:42 Well, I I I I haven't physically gone to the mountain,

36:46 but I I think the key

36:49 uh I've done I've written the first pages of a book.

36:53 Mh.

36:53 Uh, I think the I think the the the key to all of this, to all of life,

37:05 is to behave thoughtfully with your mind engaged.

37:10 Not just uh let the river take you,

37:15 but give thought to what's going on, why is it going on, what does it mean,

37:26 why did it happen, what does it imply for me, what should I do about it?

37:33 And uh it's the that's that is I think going through life with your eyes open.

37:45 Uh and that's how you can intentionally get

37:53 to a higher stage in life by doing that.

37:57 And and the the the uh the image that comes back

38:06 to me is the image of whether you let the river

38:09 take you or whether you uh try to figure out

38:16 a a better place to get to and then try to get there.

38:22 Maybe once or twice in my life I have felt like I could see the river.

38:29 See the river?

38:30 The river.

38:30 Yes.

38:31 Can you actively?

38:34 Um I I think at this stage I I

38:37 I am able to think about this progression constructively.

38:44 When I think about my early decades, not years, decades,

38:50 and I think about uh you know what I did from let's

39:01 say I would say from the beginning of let's say high school.

39:08 Mhm.

39:10 1960.

39:10 Mhm.

39:10 until starting Oak Tree in in 95.

39:17 Mhm.

39:16 So that 35 year period which took me up to age uh let's say 49.

39:22 I was a drift.

39:27 I did not make proactive decisions.

39:31 I let the river take me.

39:33 I'm very lucky that it took me to some good places.

39:37 Mhm.

39:37 in in in several different aspects of life,

39:40 but I don't consider when I look at my behavior,

39:45 I don't consider it thoughtful or intentional.

39:49 And so I'm just lucky that I got to a good place despite my own inattention.

39:55 Uh I think that um starting Oak Tree was the biggest and maybe first I thing

40:06 I really did with intention and uh I

40:09 and I've tried to be more intentional since then.

40:12 But when I look back and I describe uh how I selected my career,

40:16 how I selected my first job,

40:18 how I decided to go to graduate school uh u and and uh

40:23 h and and and how I transitioned from from the the equities

40:29 department uh to the bond world uh in 1978 in time to uh

40:38 in time to have the benefit of the uh birth of Hayo Bonds,

40:45 it was all happen stance, serendipity, coincidence, uh passivity, you know,

40:52 I I I was not making intentional decisions in that period.

40:57 What changed?

40:58 How did you become intentional from being unintentional when you began oak tree?

41:07 Because other pe people can maybe learn from it.

41:09 Maybe I can learn how to catch it.

41:10 Well, the easy answer is that my wife pushed me uh uh

41:16 to to uh think about it more and to uh independently start oak tree.

41:25 My partner Bruce Kh uh uh and I did it together.

41:32 He he so his participation uh encouraged

41:36 mine but that that required a proactive decision.

41:41 Uh you know uh the the biggest change I made before

41:45 that professionally was moving from City Bank to TCW in' 85.

41:50 They approached me.

41:53 Mhm.

41:52 That was not proactive on my part.

41:54 Not the act of starting oak tree but from being

41:59 a drift the river to seeing the river.

42:02 Well, I think I think uh once I made

42:09 the decision to participate in the uh starting of oak tree,

42:14 I I couldn't drift anymore because now I was uh the person leading oak tree.

42:22 I had to make uh proactive decisions.

42:25 I mean, it became my job.

42:27 Uh and uh and maybe I maybe I warmed to it.

42:32 Uh maybe having made the proactive decision

42:35 to start Oak Tree and having done it, maybe I said, "Hey, this is this is good.

42:39 I think I I think that I maybe

42:42 I like leadership." And leadership is by definition proactive.

42:48 is it's it seems like an oxymoron to say he led passively.

42:54 Those two words are in opposition.

42:57 So I think that I think that uh thank God I I rose

43:04 to the occasion and and left TCW with Bruce and and my other partners to start.

43:10 And then I think that it was I don't I don't think I ever said,

43:15 "Well, now I'm going to start taking control of my life.

43:18 Now I'm going to start acting intentionally.

43:21 Now I'm going to become a leader." I just think it was inescapable.

43:29 That's very interesting because it sounds

43:32 like you're saying entrepreneurship and taking

43:36 risk can trigger the riding the river to seeing the river.

43:42 Some people are driven entrepreneurially.

43:45 I think that entrepreneurship is the epitome of intentionality.

43:54 It's it's taking the bull by the horns as we say proactively.

44:01 So I think the entrepreneur naturally innately

44:10 does the opposite of drifting down the river.

44:14 Um and u my inclination was not entrepreneurial, you know.

44:23 Uh I I uh I liked most of what I was doing at TCW.

44:30 Uh but uh with uh with encouragement from Bruce Kh and my wife and with a little

44:42 encouragement from TCW to leave in terms of how I thought I was treated,

44:50 my inertia was overcome.

44:52 That's what I would describe.

44:54 You know, it wasn't a proactive decision.

44:56 Hey, let's let's get going.

44:58 It was the unlikely overcoming of my innate inertia.

45:06 It's a very different story.

45:07 It could be like a chicken and egg thing.

45:09 Oh, it's very chicken and egg.

45:11 Yes.

45:11 But but uh I think the important thing is for the purposes

45:17 of your audience that my departure from TCW

45:23 did not start with an entrepreneurial spirit.

45:28 And some people I know I know people who you know

45:32 who were entrepre I mean uh you know always dreamed of making

45:38 a lot of money always dreamed of running their own business chafed

45:44 at being an employee had to get out and start their own.

45:49 That that that was never descriptive of me.

45:53 So I kind of I I kind of uh did something that you might call entrepreneurial uh

46:03 despite myself is the way I would describe

46:06 it rather than because of my uh innate drive.

46:14 So Howard, my job for 20 years has been that of a stock investor.

46:18 Mhm.

46:20 You've been a debt person almost all your life.

46:25 Why would you be in an asset class which has finite upside and the downside I

46:33 guess is finite because in most cases it's

46:35 zero versus equity where you could have exponential upside.

46:40 Well, that's a great question and uh I I

46:43 haven't really been asked that question much in the past.

46:46 My answer may take a while.

46:48 Settle in.

46:51 Yeah.

46:51 Uh the the question as you pose it is an interesting question.

47:10 if you assume rationality and objectivity,

47:16 but that ignores personal, let's say, kinks.

47:24 So, I'll return to that.

47:26 But first of all, my decision to move

47:28 from I I spent nine years in the equity department.

47:31 So, it wasn't really at the very beginning.

47:32 It felt like a long time.

47:34 But my decision to move from the equity department in' 69

47:37 to to the bond department in 78 was not my decision.

47:42 And it it it it uh it wasn't because I figured out it would be better for me.

47:49 Uh the the the the city bank I joined in September

47:53 of' 69 was an investor in what was called the Nifty50.

47:58 Mhm.

47:57 The 50 supposedly best and fastest growing companies in America.

48:03 companies where nothing bad could ever happen.

48:06 There was no price too high for the stock.

48:09 And if you bought those stocks the day I got to work in September 69,

48:12 if you held them tenaciously for for five years,

48:16 you lost about 95% of your money.

48:18 95%.

48:20 Because for many of them, something did go wrong.

48:23 And for all of them, the price was too high.

48:26 The PE ratios, as I recall, were mostly between 60 and 90.

48:34 So it was a and and so that was

48:37 a real disaster for the people who invested in the nifty50

48:42 which was most of the money center banks

48:45 and I was uh director of research by the mid70s.

48:50 So I was part of the process and we hired

48:54 a new CIO who wanted to have a new head

48:57 of research and uh I helped him hire my successor

49:02 and then he said what do you want to do next?

49:05 I said I don't know I could do this I could do that.

49:09 Typically I didn't think it over very much

49:10 or or assess my strengths and weaknesses very well.

49:14 and he said, "I'd like you to move to the bond

49:16 department and start a convertible bond fund." Because he

49:19 had come from a place that had a had one

49:21 and it was very successful and we didn't have one.

49:24 And I said, "Yes." So that was the extent of my intentionality,

49:29 which is almost non-existent.

49:31 It was, you know, and and I say now,

49:34 and it may be an exaggeration, but I was lucky I didn't get fired.

49:38 Uh but American companies gave pretty much lifetime employment at that time.

49:43 Anyway, so so number one, my move to to the bond department was

49:48 not voluntary other than I could have said no.

49:53 Uh uh but number two,

49:57 you talk about the fact that bonds

49:58 have capped upside and some downside from default.

50:04 Certainly true.

50:05 But it fit me because I was brought up to be very conservative.

50:11 My parents were adults during the depression.

50:16 Now, your parents probably weren't alive during the depression

50:20 or and most people I know who are old,

50:23 their parents were children during the depression.

50:26 My parents were adults during the depression,

50:29 which meant when I grew up, what did I hear?

50:32 Don't put all your eggs in one basket.

50:34 Save for a rainy day, avoid risk, etc.

50:38 So I think Indian Indian parents live through pseudo socialism in the country.

50:43 Mhm.

50:43 Similar.

50:44 Yeah.

50:44 Not as bad.

50:45 Yeah.

50:45 Yeah.

50:46 But but um see you as as a stock

50:56 guy look at bonds and say no upside some downside.

51:04 I say predictable outcome which is achieved almost every time.

51:12 Is that true though cuz a lot of the distressed bonds that you

51:17 are in which have fairly high coupons is the chance of a downside zero?

51:24 I said almost every time.

51:27 And you know uh uh first of all uh so as I

51:34 said I moved to the bond department in 78 uh May of 78.

51:37 In a August of 78 I got the phone call

51:41 that changed my life from the head of the bond

51:42 department who said there's some guy in California named Milin

51:45 or something who deals in something called high yield bonds.

51:48 Do you think you can figure out what that is?

51:50 Because a client had asked for a portfolio.

51:53 Junk Bonds.

51:54 Pardon me.

51:54 Junk Bonds.

51:56 Milkin.

51:57 Michael Milkin.

51:58 Junk Bonds.

51:58 Yeah.

51:58 Junk Bonds.

52:02 So, so I said, "Yes."

52:07 Mhm.

52:04 I can do it.

52:06 And that put me here today, 48 years later.

52:13 Um but but um so I I if you've read Malcolm Gladwell Outliers,

52:22 you know the importance of right time, right place.

52:25 I was in the right place at the right time.

52:28 I was in that put me in high yield

52:30 bonds at the beginning of the high yield bond movement.

52:34 I've met Michael Milin and because he got unlucky later in life,

52:39 it couldn't have just been right place,

52:41 right time because it didn't go that way for him for a period of time.

52:46 Well, that's right.

52:47 But, uh, things in his existence conspired to produce a bad result for a while.

52:53 Uh, but not for me.

52:57 I got there at the beginning.

52:59 the the what Malcolm Gladwell's book is really about, Nquille,

53:02 is about it's great to be at the front of the line.

53:06 That's really what it's about.

53:07 He talks about demographic luck.

53:11 Right.

53:11 Right time, right place.

53:12 I think the question is how do you figure out where is the front of the line?

53:15 Well, you Well, I didn't.

53:17 Somebody said, "Would you please stand on that line?" And I looked around.

53:20 Then I was the first person in the line.

53:23 You see, so that's luck.

53:26 uh figuring out is is much harder.

53:30 And by the way, not all the people who got who were first in the line

53:37 were there because they figured out that that was the line to be on.

53:40 A lot of it is serendipity, you know, and not all of them did well either.

53:45 That's right.

53:46 That's right.

53:47 Not all of them did well.

53:48 But anyway, the point is I've been in Hayobonds for 48 years.

53:53 Mhm.

53:54 And in our experience,

53:56 99% of the bonds have paid interest in principle as promised.

54:01 So I think I can say almost every time.

54:05 And if you earn on average 10%, you only need it to be right 90% of the time.

54:11 No, that's not right.

54:12 Because if you if if 10% of your bonds defaulted and you lost

54:16 10% of principal and the made 10% of interest on the ones that paid,

54:20 you you had a zero return.

54:22 Yeah.

54:22 you you had to you had to re constrain

54:25 your losses to a much lower figure than 10%.

54:28 You had to be right 90 7 8 n% of the time and we were

54:36 right 99% of the time because I think we did it in an above average way.

54:40 But the point is that reliability appealed to me and uh I'm not

54:48 a futurist or a optimist and so I was well suited to it.

54:55 Um and then of course we got into the distressed

54:58 debt business when Bruce Kar joined me in' 87.

55:01 we brought out what I believe was the first distress debt

55:04 fund from a mainstream institution uh or one of them and you

55:09 know he he runs those funds he's managed about 70 odd

55:13 billion dollars since 1988 in that field by far the biggest

55:19 and uh of of of his total profits and losses well

55:27 over 90% are profits less than 10% are losses so And I

55:31 think you could say most of the time and so

55:35 the regularity the contractual nature of returns on bonds appealed to me.

55:44 Um and so this is why I've been very comfortable in that in that field.

55:50 The other thing is frankly a lot

55:54 of your success in investing is determined by other people.

56:01 If if if you go into a field that everybody likes

56:04 and they like it a lot and have bid up the price,

56:08 then your returns are not likely to be high.

56:11 If they if you go into a field where

56:14 everybody else has been blind to the merits and says,

56:17 "I wouldn't touch that with a 10-ft pole." and you figure

56:20 out that it's good and it turns out to be good.

56:23 Uh that's how you get an exceptional return with low risk.

56:27 Uh so um I think that uh for the most part well I

56:36 was lucky at I should say at the time I made these decisions

56:41 uh actively or passively took these steps shall we say u u I

56:48 went into fields other people didn't like you you use the term junk bonds

56:53 a derogatory term nobody talks about junk stocks right But I mean junk

56:58 bonds are much more predictable than stocks and yet they called them junk.

57:01 We'll call them distress bonds.

57:03 Yes.

57:03 But that was a that that was a bias, right, that made them available to me cheap

57:09 and and that was that was a very good thing

57:11 and and I could see that and take advantage of it.

57:14 Can you contextualize distressed bonds?

57:18 Let's say the US Treasury, US government borrows at 4%.

57:22 Let's say I read somewhere that Google is borrowing 100red-year paper at 6%.

57:27 Which sounds crazy and we'll get to that.

57:30 What kind of companies are in this distress bond

57:36 ecosystem and what rate of return are they paying?

57:38 Rate of interest.

57:39 Well, let's we have to clarify.

57:42 Oak tree and I h have two businesses.

57:46 One we perform we call performing credit and one we call opportunistic credit.

57:51 That's the new euphemism for distressed.

57:55 Mhm.

57:57 So, wouldn't you benefit from calling it junk so you have

57:59 a higher margin because more people would stay out of it?

58:04 Yes, if they listen to me if I was that influential,

58:07 but and and that's the right kind of kind of uh counterinking.

58:12 But uh on the other hand,

58:15 a big part of our business now is making

58:17 loans to companies that need what we call rescue loans.

58:22 Mhm.

58:22 They don't like to be associated with the word distress.

58:26 Fair.

58:25 So we're more likely to get their business if we call it opportunistic.

58:33 Fair.

58:33 Um so uh you know in the performing credit area

58:44 we lend money to companies that the world thinks has

58:51 a let's say three four five six% of not paying us

58:59 prob 3456% probability of not paying us.

59:04 Mhm.

59:07 We lend them money if we think it's 1 or 2%.

59:13 So if the world thinks they're 5% likely to not pay us

59:19 and we conclude that they're only 2% likely to not pay us,

59:23 then the world requires them to pay an a rate

59:28 of interest which is commensurate with their 5% probability of non-payment.

59:33 Which means if we're right and it's only two,

59:35 we're getting something for nothing.

59:37 And that thing is called excess return.

59:39 So, so that's what we do.

59:42 That's the essence of lending money.

59:45 I mean, why would you lend money to a company

59:47 that has a nonzero probability of paying you back?

59:51 And the answer, Mike Milin's answer was you can demand

59:54 a rate of interest which is compensatory or more than compensatory.

1:00:00 And when you get when you get payment in life

1:00:04 for doing something which is more than compensatory that's called excess return.

1:00:12 So is the skill set at Oak tree calculating

1:00:16 that spread and how how do you do that?

1:00:19 Well, when you say the spread, the the the the skill set at Oakry, the the base,

1:00:24 the most important fundamental skill set is predicting

1:00:29 the probability of default better than other people.

1:00:34 That's that's where

1:00:36 if if we don't have that, that's

1:00:38 the necessary condition for for superior performance.

1:00:42 And is this skill set company specific or are you able to call

1:00:47 the larger cycle in the market and hence you do it better?

1:00:51 H no that skill set is separate from the latter

1:00:56 thing you talked about is what we call macro.

1:01:00 Mhm.

1:00:59 That skill set is not is micro.

1:01:03 We have great analysts who look at companies.

1:01:07 We have a framework for analysis.

1:01:10 The framework is essential, necessary, but not sufficient.

1:01:16 The nec the this what makes it sufficient

1:01:19 is having brilliant people to operate the procedure.

1:01:26 The procedure itself is nothing without superior implementation.

1:01:31 But we have both.

1:01:33 And so uh over the last 40 years on average

1:01:37 something like 3.6 6 or 3.7% of all high

1:01:40 yield bonds have gone into default every year

1:01:45 and our default rate has been uh roughly uh a third.

1:01:55 That's our superiority and that is

1:01:58 a casebycase bottomup uh superior implementation.

1:02:08 And when you say superiority, not the process of the people running the process,

1:02:15 do you mean attention to detail?

1:02:16 Do you mean thinking out of the box?

1:02:19 No.

1:02:20 Um why why do we have a lower default rate?

1:02:24 Mhm.

1:02:24 Well, I think we have institutionally more experience than anybody else.

1:02:31 We've been doing it longer.

1:02:32 We have a uh a a a work environment

1:02:38 that allows people to stay in that job for their lifetime.

1:02:41 You know, when I started at City Bank in' 69,

1:02:44 every analyst goal was to become a portfolio manager

1:02:49 because that's where the that's where the luster was.

1:02:52 So, so and as long as that's the case,

1:02:55 then on the analytical side, you don't build up institutional e excellence

1:03:00 because it's people are always trying to get out of that job.

1:03:04 You want people to build up excellence and stay in the job

1:03:06 so that you can get the benefit of their excellence

1:03:10 and and uh we set up that system.

1:03:13 So, you know, you can be a lifetime analyst

1:03:16 and be very successful if you're great at it.

1:03:19 So, that's one important thing.

1:03:21 So we have people who've been doing it for years and years,

1:03:24 decades and we have the benefit of their expertise.

1:03:28 Uh we have the institutional experience which gives us stability in times

1:03:33 of rocky uh psychology when people get too excited or too depressed.

1:03:39 We tend not to.

1:03:40 That's a great help.

1:03:41 Mhm.

1:03:41 We have a a process which enumerates a eight areas of investigation

1:03:48 and many subsidiary questions within that and everybody has to follow it.

1:03:53 Now people might say when I when I started doing something

1:03:55 like this at City Bank as director of research, people said, "Oh,

1:03:58 you're taking away our creat creativity." But we we

1:04:03 we told them that a process they had to follow,

1:04:08 but they could follow it creatively.

1:04:10 But the process consisted of covering the bases.

1:04:14 You got to cover the bases consistently.

1:04:16 You got to ask the same questions

1:04:17 of every company every time rather than you know

1:04:21 in in the equity world where you live you'll get

1:04:25 a research report it says buy this company great management

1:04:28 doesn't talk about the product buy this company

1:04:31 great product doesn't talk about the management

1:04:32 buy this company uh great tax shelter

1:04:35 doesn't talk about the product or the management

1:04:38 I felt that it was important to cover every

1:04:40 base every time and when when you when you

1:04:45 look at a at at a at a portfolio and you see a a a stock that's down 80%.

1:04:53 It's usually because something wasn't covered.

1:05:00 Mhm.

1:04:58 And because the the the analysis was

1:05:02 not disciplined enough to touch all the bases

1:05:06 and again you know uh you're talking

1:05:10 about the difference between stocks and bonds.

1:05:12 That's one of the differences, right?

1:05:15 uh we're we're we're bean counters.

1:05:18 We're green eye shade types, but at least we should do it well and consistently.

1:05:22 Uh the equity investor has imagination

1:05:26 and foresight and entrepreneurial spirit uh

1:05:30 and but doesn't do it as consistently as we do our analysis.

1:05:36 If you had to begin a new career today for the next 40 years, yes,

1:05:41 would you pick debt over equity again

1:05:43 for me or for or for the average watcher?

1:05:46 Let's say for the average watcher.

1:05:50 Um, well, I think that I think that we

1:05:54 were very fortunate in getting into some debt

1:05:57 markets before they were discovered and before people understood

1:06:01 them and we benefited from people's uh uh antagonism toward those markets.

1:06:09 Uh those opportunities are in the past.

1:06:13 Um so where's the front of the line today?

1:06:16 Well, I mean, who is the person in the investment business

1:06:20 today who will find the most success in the next 10 years?

1:06:26 The answer is, in my opinion, the person who best understands AI

1:06:32 and its capabilities and implications.

1:06:38 So, but

1:06:40 or is a contrarian and doesn't believe in AI.

1:06:42 Pardon me.

1:06:43 Or he could be a contrarian and bet against AI.

1:06:46 Well, that's what I said.

1:06:46 I said best understands.

1:06:48 I didn't say is most in favor of.

1:06:51 So if if AI is going to disappoint, the expectations are very high.

1:06:57 And when you, as you know from being a stock picker,

1:07:01 when you go into something where the expectations are very high,

1:07:07 it's very easy to lose money if those uh uh expectations aren't rewarded.

1:07:16 Mhm.

1:07:15 So yes, maybe maybe uh if you're the person who best understands AI today,

1:07:21 maybe you'll make a lot of money

1:07:22 by understanding that it's overdone and betting against it.

1:07:26 The current expectation with AI is almost dystopia or utopia.

1:07:33 Yes.

1:07:33 Which is crazy, I think.

1:07:37 Well, I don't know.

1:07:37 If you read my memos Yeah.

1:07:39 Uh especially the last one, you know that I was shocked

1:07:44 uh by my experience with AI and and Claude and what it

1:07:50 But you also said you haven't fired anyone, nor do you intend to because of AI.

1:07:56 Uh, I believe I don't know enough to be confident in this,

1:08:10 but I believe that AI's excellence is in discovering past patterns,

1:08:21 extrapolating them, and applying them with discipline.

1:08:25 and with let's say uh calculations or logic

1:08:31 which is almost always correct and not subject

1:08:35 to psychological ups and downs but I think

1:08:39 there's more to investment excellence than that it's

1:08:43 the innovation of new patterns it's seeing

1:08:46 the potential of things that have never you know

1:08:49 how if if if what if what AI does is uh pattern matching, shall we say?

1:09:01 Mhm.

1:09:01 Pattern completion from

1:09:03 Yes.

1:09:05 Mhm.

1:09:04 Then how how will it deal with something where there's no pattern?

1:09:09 Something that's that's brand new.

1:09:11 And you know, I wrote a memo.

1:09:13 Is there such a thing?

1:09:14 Well, look, I wrote a memo n or 10 years ago called investment without people.

1:09:22 investing without people and it was pretty early.

1:09:26 I so it had three levels indexation and passive investing,

1:09:33 algorithmic or systematic investing, AI and machine learning.

1:09:40 And my revisionist memory or I should say my my memory which could

1:09:47 be revisionist tells me that in that memo I said I posed some questions.

1:09:54 I may not opposed them but I wish I had if I didn't.

1:09:58 Can AI sit down with five business plans and figure out which one is Amazon?

1:10:08 Mhm.

1:10:09 Can AI sit down with five CEOs and figure out which one is Steve Jobs?

1:10:18 That goes beyond pattern recognition.

1:10:22 I would bet not.

1:10:23 Well, I would bet not, too.

1:10:26 So, if that's true, if we're right, that means there's still a role for people.

1:10:32 M and but before you go on to the next question I have to interject something.

1:10:38 Neither can most people.

1:10:41 So so what that says is there is

1:10:43 a role for exceptional people even in an AI world.

1:10:52 And that's my conclusion.

1:10:54 I hope I'm right.

1:10:55 I hope to be one of them.

1:10:57 Uh I hope my firm is is is still one of them.

1:11:00 But if that's true, if there are things that AI can't do that some people can,

1:11:09 then that's what I want to keep doing.

1:11:12 Now, you look, you're a stock picker.

1:11:16 When I went to graduate school at University of Chicago in, let's say,' 66,

1:11:21 no, no, 68, the professor said most mutual funds underperform the S&P.

1:11:31 So, and they charge high fees,

1:11:33 so why don't they just buy one share of every stock in the S&P?

1:11:36 There were no index funds or concept of indexation

1:11:40 that came along in 74 primarily with Jack Bogle.

1:11:44 And now most mutual fund equity capital is run by passive or index.

1:11:54 Why?

1:11:56 Low fees.

1:11:58 Is that the only reason?

1:12:00 No.

1:12:01 It it also the active management didn't work.

1:12:06 They were they were unsuccessful and charged high fees for it.

1:12:10 But even if they had low fees,

1:12:14 even if they charge the same fee as the index fund,

1:12:17 if the passive decisions are inferior, indexation is still better than active.

1:12:23 So it's not the low fees that was that exa that exacerbated the problem.

1:12:28 It was really So my answer is that indexation has

1:12:32 taken over as it has not because it's so good,

1:12:37 but because active management was so bad.

1:12:42 and um but I I believe that people can do some things that AI can't.

1:12:49 Could you also say that indexation and ETFs have taken over because the markets

1:12:55 have been in an uptrend largely in a more volatile time active might make sense.

1:13:02 In a depression, active might make sense.

1:13:04 You know what?

1:13:07 The bad times create an opening

1:13:12 for active management, but it's still hard.

1:13:16 And not many people can do it well.

1:13:20 Why do bad times create an opening?

1:13:24 Because panic drives down, let's say, stock prices.

1:13:28 Mhm.

1:13:28 But the same panic causes most people to panic,

1:13:31 which means that most people can't stop up and buy in the panic.

1:13:35 And there's a there was a guy named Wally Demer who

1:13:38 was an oldtime trader who had some great quotes and he turned

1:13:41 them into a little book and but his greatest quote of all

1:13:45 is when the time comes to buy you won't want to.

1:13:49 So this this idea that upheaval creates opportunity is logically correct but not

1:14:00 realistic because most people's psychological impediments prevent

1:14:05 them from taking advantage of the upheaval.

1:14:08 You see

1:14:09 maybe you mix active plus AI together to try and take emotion out of it.

1:14:13 Well that that could work.

1:14:17 That could work if if Claude could call you or send you

1:14:22 a message which says this is one of those opportunities we talked about.

1:14:25 Don't be stupid.

1:14:27 Get your ass in gear and buy something.

1:14:29 Don't hide in your in your in your uh in your hole.

1:14:34 That could that could be a good thing.

1:14:36 Yes.

1:14:37 But but I still think that I still think Nquille that when you get into areas

1:14:45 which are not connected what I would call

1:14:48 mechanically where where where physical laws are not everything.

1:14:54 Mhm.

1:14:57 That means that human psychology plays a role in developments.

1:15:02 And that tells me that people who are superior at dealing

1:15:07 with human psychology can get an advantage and I think that includes investing.

1:15:16 But not everybody.

1:15:17 It always comes back to not everybody.

1:15:20 And you know there's a there's a phrase in in our world,

1:15:23 a friend of mine in England used it as the title for a book.

1:15:26 Simple but not easy.

1:15:29 Our job is simple.

1:15:30 We got to find the best managers,

1:15:32 the best companies, the best ideas, the best products.

1:15:34 My son is a venture capitalist.

1:15:36 Does a great job.

1:15:37 He finds the best founders.

1:15:40 The task is easy.

1:15:40 I just said it to you in 10 seconds.

1:15:43 It's just not easy because what makes you superior at finding founders?

1:15:48 what makes the better founders available to you rather than somebody else.

1:15:55 Uh and Char Charlie Munger once said to me,

1:15:57 "Putting together simple but not easy." Charlie, when I finished my first book,

1:16:04 The Most Important Thing, I had lunch with Charlie and as I got up to leave,

1:16:08 he said, "Just remember, none of this is easy.

1:16:11 Anybody who thinks it's easy is stupid." That was one of the great things

1:16:15 anybody ever said to me because Charlie said it in his typically brusk way.

1:16:19 But it it can't be easy to be smarter and more

1:16:27 disciplined than all the other smart people who are trying.

1:16:32 That's what the efficient market hypothesis says.

1:16:35 They I've never seen it written down that way.

1:16:38 All these smart, highly motivated, educated, numerate,

1:16:43 computer literate, interwired people are trying to get rich.

1:16:51 So where is the $10 bill?

1:16:52 Nobody has picked up right now.

1:16:55 Well, I think it's in it's it's around AI, but but everybody's excited about AI.

1:17:03 So if if your excitement level and by that I

1:17:07 mean your insight level is average you don't have an edge

1:17:15 your perform and you get involved in AI

1:17:18 your performance will be average you will go along

1:17:23 with the tide if it works you'll be schmic

1:17:26 if it doesn't work superior it all comes down

1:17:33 you boiled down our conversation to this point.

1:17:36 It all and the things Charlie said and and and so forth.

1:17:40 It all comes down to superior insight and not you know we have an author

1:17:46 in America a guy named Garrison Keeler and he wrote a book called Lake Wiggon

1:17:51 and Lake Wiggon was a fictional community in uh I think uh Wisconsin if I'm

1:17:58 not mistaken and he said in lake

1:18:01 in Lake Wiggon all the children are above average.

1:18:06 Well, we know that there is no place where

1:18:09 all the children or all the investors are above average.

1:18:13 So I think my my own vision of the world

1:18:17 which is not a scientific vision is that in fields

1:18:25 where human nature is involved and the future is

1:18:29 unpredictable and things don't operate according to mechanical rules,

1:18:34 there is still a place for superior insight.

1:18:40 But you will not have success without superior insight.

1:18:48 How would most of my investing is in India?

1:18:51 I'm 39 39 years old now.

1:18:54 I know you've deployed a certain amount of capital,

1:18:57 I think 4 billion to India recently, but you haven't written a memo about it.

1:19:02 My assumption is India is growing at say 7%

1:19:08 or 6% it will continue to do so for a period

1:19:11 of time GDP per capita will go up so consumer

1:19:15 will do well uh energy will do well energy consumption

1:19:20 goes up as GDP per capita goes up maybe

1:19:23 above $5,000 would I be okay in just staying allocated

1:19:29 to equity say 80% present for the next 20 30

1:19:33 years and if so is there a sector that you like

1:19:39 well I I I've been cautioning you about things

1:19:43 I don't know much about here I can be more

1:19:46 uh more uh emphatic I don't have any idea what

1:19:51 the right se what the best sector in India is right

1:19:54 I will not uh hold myself out

1:19:56 as as knowing anything about the Indian stock market Okay.

1:20:01 I read that during co you lived with your son Andrew in a house.

1:20:09 Yes.

1:20:08 I'm sure Andrew has learned so much from you.

1:20:12 Is there one thing that you learned from Andrew?

1:20:14 Oh, I've learned so much because and this is how we started the conversation.

1:20:18 You said to me, "How do you stay sharp?" Yeah.

1:20:20 and and uh you know uh speaking with young people if you stay if if

1:20:27 you speak if I speak to my contemporaries how am I ever going to learn anything?

1:20:32 Speaking with young people is how you learn and they they know

1:20:38 stuff you don't know because they are still learning and they learned more

1:20:41 recently and hopefully you have something you can give them in exchange

1:20:45 which is experience that they don't have because they haven't lived 80 years.

1:20:49 Uh but Andrew is essentially your age.

1:20:53 Uh and I wrote a memo after that experience.

1:20:56 Uh three generations living under one roof was of great value.

1:21:02 And I wrote a memo called something of value.

1:21:05 And the other reason I chose that title is

1:21:06 because we mostly talked about something called value investing.

1:21:10 Uh but I get so much from him and he

1:21:15 pushes me to see things that I don't wouldn't otherwise see.

1:21:20 And he's also intolerant of of my not uh staying sharp and moving ahead,

1:21:29 you know, how how do you how do you keep moving ahead at 80?

1:21:32 That's really the question.

1:21:33 But I I don't think it's impossible.

1:21:36 But a a a great example, I mean the the example that immediately comes to mind

1:21:43 uh and I think it's a it's a great one is

1:21:45 that he observed and I wrote in the memo the memo

1:21:48 was something of value January of 21 as I recall.

1:21:55 He pointed out it was almost

1:21:57 revelatory readily available quantitative information about

1:22:03 the present cannot hold the key to success because everybody has it.

1:22:11 Success in investing is doing better than others.

1:22:14 In investing is a is a funny field because it's really

1:22:18 easy to be average and it's really hard to be above average.

1:22:22 But readily available quantitative information about the present is not going

1:22:26 to make you above average for the simple reason that everybody has it.

1:22:30 And and I talk in the memo about that one

1:22:33 of the reasons Buffett was able to steal the march

1:22:35 on everybody else and become Buffett is because nobody he he

1:22:40 he sorted out and he worked it when nobody else did.

1:22:46 And you know when I was starting off in this business in the research world,

1:22:51 think about your process.

1:22:53 Let's say I said, um, you know, I went into a store today.

1:22:56 I bought a product.

1:22:57 I like the product.

1:22:58 I think maybe that company has a future.

1:23:00 I'd like to learn about it.

1:23:01 How would you get information?

1:23:04 Think about what you would do if you had

1:23:06 that experience this morning here in New York City.

1:23:11 What did we do then?

1:23:14 You know what we did then?

1:23:17 We wrote a letter to the company and asked it for a copy of the annual report.

1:23:24 It took a week for them to get that letter.

1:23:28 They put it at the bottom of the pile and they took the letter

1:23:31 off the top of the pile and they sent that person an annual report.

1:23:35 It took a week or two to get down

1:23:38 to my letter and then they took an annual report, put it in an envelope,

1:23:44 addressed it to me, put a stamp on, put it in the mailbox,

1:23:46 and it took a week for it to get to me.

1:23:49 Now, I exaggerate, but the point is it took a month

1:23:54 to get an annual report so that I could start studying the company.

1:23:57 There was no online.

1:23:59 There was no wiki or whatever source you might use.

1:24:05 Uh the only alternative was there were there were books called Moody's manuals

1:24:09 and they were each book was this thick.

1:24:12 No, this thick.

1:24:14 Take it off the shelf.

1:24:15 It had a paper called onion skin, the thinnest of paper.

1:24:18 And it had tiny print.

1:24:20 Tiny print.

1:24:21 You needed glasses.

1:24:23 And it it they had the financials, the current financials on every company.

1:24:29 Not much narrative, nothing, no discussion of the future,

1:24:33 but just the financials of every company.

1:24:36 That's all you could get until your annual report came in the mail,

1:24:40 then maybe you could start figuring out the business and its potential.

1:24:44 So, uh, you could if you were Buffett and if you sat in your office

1:24:50 in Omaha and read the Moody's manual when

1:24:53 nobody else was willing because it was stoaltifying, you could get an advantage.

1:24:59 Is that what he did?

1:25:00 I think Well, that's an ex that's my that's my illustration of what he did.

1:25:04 Maybe he did more.

1:25:06 I don't know.

1:25:07 But I'm no one to pay you a compliment, Howard.

1:25:09 But if I could Yes.

1:25:12 I think albums are always

1:25:15 I think the nimleness and the willingness you have to change

1:25:20 at 80 is by far the most impressive thing about you.

1:25:27 And when I speak to really successful people,

1:25:30 I think this trait over all others kind of makes them who they are.

1:25:36 Well, I I appreciate that comment.

1:25:38 I hope it's true.

1:25:39 Uh but it makes sense, doesn't it?

1:25:43 Because the ability to change your mind and change your thinking and learn

1:25:48 new things is an is is an example or a component of superior insight,

1:25:56 which is what you have to be.

1:25:57 This is a competitive game.

1:25:59 Uh it's like golf.

1:26:02 You say, "Today I shot 74." Mhm.

1:26:05 I tell you I shot 74.

1:26:07 Mhm.

1:26:07 You have no idea if I won or lost.

1:26:10 The only thing that matters is what did everybody else shoot, right?

1:26:13 If I tell you last year I made 13% in my stock portfolio, is that good or bad?

1:26:19 Well, it's pretty good.

1:26:20 The average return on the S&P is 10, but of course the S&P was up 18 last year.

1:26:25 So 13 stank.

1:26:27 So this is a competitive game.

1:26:31 You don't have to.

1:26:32 It's not a matter of being right or wrong.

1:26:34 It's a matter of being more right than the other person or less wrong.

1:26:38 Are you able to change because you have been able to consciously remove ego?

1:26:46 Uh well, I think that helps.

1:26:48 Mhm.

1:26:50 Because ego if you have too much and the wrong

1:26:53 ego would probably I would imagine tend to make somebody say,

1:26:59 "I've always done it this way.

1:27:01 It's worked in the past.

1:27:03 I'm an enormous success.

1:27:04 You know, I'm worth this much money.

1:27:06 So, why should I change my approach?

1:27:09 But you have to keep changing.

1:27:11 Uh, innovate or die.

1:27:14 What could what could trigger it?

1:27:16 Like, if we were to advise people watching this, if you were to Yeah.

1:27:20 How can they trigger the realization that they need to constantly change?

1:27:25 Well, you know, it's a funny thing.

1:27:26 This has been a theme for me for the last nine months.

1:27:30 I say this every time I get a question like yours, Nquille.

1:27:34 The the really hard questions are the ones that start with the word how.

1:27:40 How can I learn the need to evolve and grow?

1:27:48 How can I learn to see things better, more clearly than other people?

1:27:54 How can I learn to be a credit analyst

1:27:58 who better understands the default probability than anybody else?

1:28:05 How can I become a what I call a second level thinker?

1:28:10 Understanding things at a higher level and better

1:28:13 than other people and all the hows.

1:28:17 I can't tell you how how can I remain sharp at 80.

1:28:21 I can't tell you.

1:28:23 I can tell you what you have to do.

1:28:28 You have to do all the things that I

1:28:30 mentioned in order to be a superior investor.

1:28:33 I just can't tell you how to do it.

1:28:36 And in my first book,

1:28:37 uh it's it's funny when when when I sat when I had the idea for the first book,

1:28:42 which is called the most important thing.

1:28:43 It has 21 chapters.

1:28:44 Each chapter says the most important thing is and then it's

1:28:47 a different thing because there are so many things that are important.

1:28:50 And I told Columbia about the book

1:28:52 and they wanted to publish the book Columbia University.

1:28:55 And so they said give us a sample chapter

1:28:58 and I wrote out a chapter called says is each chapter

1:29:01 says the most important thing is and this one says

1:29:03 second level thinking and I wrote the chapter about second.

1:29:06 I had never really thought about it before.

1:29:09 It just kind of struck me and I wrote it down.

1:29:13 Um uh and and what it says is it it's pretty simple.

1:29:19 If you think the same as everybody else, you'll act the same as everybody else.

1:29:22 If if you act the same as everybody else,

1:29:24 your investments will perform the same as everybody else.

1:29:28 So to perform better, you have to deviate from the herd.

1:29:32 Seems clear.

1:29:33 Do you think how do you do it?

1:29:35 How do you do it?

1:29:36 And uh I said in the book asking how to how

1:29:43 to become I I can tell you the importance of second

1:29:46 level thinking but asking me how to become a second level

1:29:50 thinker is like in basketball we say you can't coach height.

1:29:55 All the coaching in the world will not make your team taller.

1:29:59 So can you I can tell you that you must

1:30:03 become a second level thinker to be successful as an investor.

1:30:06 I just can't tell you how if I were to draw a extrapolation that second

1:30:14 level thinking is also contrarian thinking in a way.

1:30:19 Mhm.

1:30:19 Do you think AIs which are predictive

1:30:21 in nature can be modeled to be contradictory tomorrow?

1:30:27 I but that's the challenge you see because second level

1:30:31 thinking or contrarian thinking is not just different from the herd.

1:30:38 Mhm.

1:30:37 That's not enough.

1:30:39 It has to be different from the herd and better.

1:30:42 So you can say you could I I I don't know how

1:30:46 how to program AI but I assume you can say to Claude

1:30:53 every answer you give me on every company has to be different from the consensus

1:30:58 or the opposite of consensus or well the opposite is is pretty categorical

1:31:02 but let's say has to deviate from the consensus

1:31:05 I don't want to and no consensus thinking is welcome here you can say that

1:31:10 you can you can make it be different.

1:31:13 Can you make it be better if if it because you see this is the catch because

1:31:23 the people you compete against in the investment arena are also intelligent,

1:31:31 educated, literate, computer able, highly motivated.

1:31:40 They're pretty good.

1:31:43 M much much of the time they're as close to right as you can get.

1:31:50 So if you believe that consensus is as close to right as most people can get,

1:31:55 then if you tell Claude, I only want non-conensus thinking,

1:32:01 then it has a high probability of being wrong.

1:32:05 That's not a good thing.

1:32:07 In a way, AI in investing could work if everyone in investing uses AI.

1:32:16 No, I don't think so.

1:32:18 Because if if if everybody uses AI and let's assume everybody's AI is the same.

1:32:26 Not same, different.

1:32:28 Well, are they?

1:32:29 That's the question.

1:32:30 it if if you know if that so that's that maybe that's what my partner Bruce

1:32:38 Kh the the recovered lawyer would call a gating question.

1:32:44 Mhm.

1:32:44 Are some AI models smarter than others since they all have super IQs

1:32:51 computing power and they all are trained on the same history.

1:32:55 Are some smarter than others?

1:32:57 I don't know.

1:32:59 I need somebody to tell me.

1:33:01 But if if they're all the if they're all equally

1:33:04 smart and you say to the This is an interesting thing.

1:33:13 So they're all equally smart.

1:33:15 You say, everyone says to all of them, write out a process

1:33:22 that will produce investment success.

1:33:26 Theoretically, they all write the same one.

1:33:28 Mhm.

1:33:28 Because they are all equally smart and have the same training.

1:33:34 Mhm.

1:33:33 And if they all follow it perfectly, they all produce the same result.

1:33:38 So nobody there's so there's no superiority.

1:33:42 But then everybody makes 10%.

1:33:44 Nobody makes 8 or 12, right?

1:33:46 But then but then what what do you get paid for making eight 10%.

1:33:51 But AI doesn't need to be paid.

1:33:53 Yeah.

1:33:53 But who employs AI?

1:33:57 M that person wants to get paid.

1:33:59 Nobody's do nobody's employing AI for fun, right?

1:34:03 They're employing AI because they want to get rich,

1:34:08 getting rich in investing comes from superiority, not from averageness.

1:34:14 How do you get superiority?

1:34:17 Mhm.

1:34:17 I don't want to ask you this question, but but you will.

1:34:20 You can choose not to answer it.

1:34:22 Yes.

1:34:22 Do you think the world is going to socialism and AI is triggering that?

1:34:28 I don't I don't consider myself a futurist.

1:34:33 I don't engage in that kind of that's

1:34:35 that kind of thinking is what we call blue sky.

1:34:40 Mhm.

1:34:38 And uh I am partial to what Albert Einstein said on the subject.

1:34:44 He said something like I don't think about the future.

1:34:46 It'll come soon enough.

1:34:50 But do I think about the past?

1:34:52 I think about the past to learn from it.

1:34:54 And I I try to I try to uh I'm I'm great on these quotes.

1:35:00 And my favorite on this subject is from Mark Twain

1:35:03 who supposedly, but I think didn't really say it.

1:35:06 Supposedly said, "History does not repeat, but it does rhyme."

1:35:11 There are certain themes that re

1:35:13 that rhyme from instance to instance in history.

1:35:17 Mhm.

1:35:20 And um they mostly have to do with human nature.

1:35:28 And I think human nature doesn't change incredibly slowly.

1:35:33 I mean, when we talk about fight or flight,

1:35:36 people are still talking about the the the the stone

1:35:41 age man and the and the watering hole, right?

1:35:43 Mhm.

1:35:44 Well, that was millions of years ago.

1:35:47 These things are still relevant.

1:35:49 So, so the point is that that uh uh I think I think you can learn from history.

1:35:57 Mhm.

1:35:56 And what you what you mostly learn are the lessons of human nature.

1:36:01 Mark Twain said that and Napoleon said

1:36:04 history is written by the winners of tomorrow.

1:36:07 So, we don't even know how accurate the history we have access to really is.

1:36:11 Well, except except for we have the numbers financial history.

1:36:16 Yes.

1:36:16 Financially, when you have nu numerical history,

1:36:19 it's probably not that subjective,

1:36:22 but maybe not the psychology that led to the numbers.

1:36:25 Right.

1:36:25 Right.

1:36:26 Well, I mean, I just read Andrew Ross Sorcin's new book 29.

1:36:30 It's a great book.

1:36:31 Yeah.

1:36:32 I liked it.

1:36:32 I have a memo coming out this week talking about private credit.

1:36:36 Uh and I referred to it in that in there.

1:36:38 What's happening with uh private credit?

1:36:40 But let me just finish this thought.

1:36:41 Okay.

1:36:42 Uh so you know he explains the events that wrote that led

1:36:47 up to the great crash rather vividly and gives you picture uh

1:36:53 and and I think there's a lot to be learned from that and I

1:36:56 try to convey some of the uh lessons of 1929 in this new memo

1:37:02 which is coming out in 2026.

1:37:05 Um, I hope that the history that Andrew describes is the real

1:37:11 history because if not the lessons are will not be that valid.

1:37:16 That's all I can say.

1:37:18 Any large last words, Howard, to our audience, any piece of advice?

1:37:24 Well, I I what I what I always say to people about investing,

1:37:27 Nikil, is that investing is a puzzle.

1:37:35 challenging puzzle investing I define as positioning

1:37:40 your capital to take advantage from f of f future developments but I also

1:37:45 say that future developments can't be predicted

1:37:49 accurately so you need superior insight to get

1:37:54 it more right than most people but you're not

1:37:59 going to get it right all the time if you have to be right all the time.

1:38:04 If there's something in your makeup that rec that says you're

1:38:07 going to be unhappy if you're not right all the time, don't become an investor.

1:38:11 Uh TB in his book Fooled by Randomness

1:38:15 talks about the difference between investing and dentistry.

1:38:18 He says if you go to dental school and you learn how

1:38:20 to fill a cavity and you fill the cavity that way every time,

1:38:23 you'll be successful every time.

1:38:25 So if you have to be successful every time,

1:38:27 become a dentist or or an engineer, a civil engineer.

1:38:32 You say, "I want to build a bridge

1:38:34 from here to there." He does his calculations.

1:38:36 How much steel?

1:38:37 How much concrete?

1:38:38 Every time the bridge stands, but but it's not that interesting in my opinion.

1:38:47 And so investing deals with this fascinating puzzle

1:38:52 that you're trying to solve to a better degree than others,

1:38:58 even though there are no laws that work.

1:39:01 Absolutely.

1:39:02 Mhm.

1:39:03 Fascinating.

1:39:04 And I love it.

1:39:05 Um it's still a puzzle.

1:39:06 It's still a puzzle.

1:39:08 I'm trying to uh peel like the onion.

1:39:11 Um but you're never done.

1:39:13 And that makes it interesting.

1:39:15 Um and and and and so that's the kind of person who should be uh engaged in it.

1:39:23 It's interesting that you resonate with Talib because I

1:39:27 somehow when I read your stuff and his stuff,

1:39:31 I find you guys on the opposite ends of the spectrum.

1:39:34 I think Talib when his he ran his fund, he was buying way out of the money call

1:39:41 options and put options hoping for blacks won events.

1:39:45 And you in a way are doing the opposite of that wherein

1:39:49 you're buying debt in distress hoping that the percentage of things

1:39:56 being black swans are lower than what the market perceives.

1:39:59 Right.

1:39:59 I well I I in that sense I think we are different.

1:40:03 uh I mean I said earlier I believe in putting together a portfolio

1:40:08 that will do well in the main portion of the probability distribution.

1:40:15 He's advocating protection that can be bought cheaply because it only

1:40:22 deals with the tail event which rarely happens.

1:40:25 So people underestimate the value

1:40:27 of the of the of the uh of the uh tail protection.

1:40:33 Uh but uh so you can buy you can cheaply buy

1:40:40 protection against something that is unlikely to happen which in theory is

1:40:43 a good thing but since it happens so rarely uh that the value

1:40:49 of that uh underpriced tail protection doesn't become apparent very often.

1:40:55 Even he uh compares in the book as I recall

1:41:00 I only read it 22 years ago or something like that.

1:41:03 Uh I read all his books.

1:41:05 I love him.

1:41:06 Yeah.

1:41:06 But he even he says that investing is like Russian

1:41:12 roulette but rather than having a bullet in the six chambers,

1:41:19 there are 100 chambers in the gun.

1:41:21 So the bullet rarely goes off.

1:41:24 Mhm.

1:41:24 And I I I don't think you can I mean,

1:41:28 so you could spend 1% of your money to buy tail protection every year.

1:41:33 Uh but I think it's I think it's that's going to be your whole existence.

1:41:38 It's going to be a dull existence because

1:41:40 the because the the uh explosion will happen so infrequently.

1:41:45 And he also said that even CO was not an explosion.

1:41:49 He said it's not a black swan event.

1:41:51 So if CO wasn't the question is what is

1:41:55 well it's yes I mean um you know obviously CO was not the first pandemic

1:42:04 but there it it was I I would say it

1:42:06 was the first pandemic of the modern era and it was

1:42:09 the first pandemic that that was uh that encountered uh the digital

1:42:13 economy and um many many and globalization many many other things.

1:42:19 Uh so it it was kind of unique or or certainly

1:42:23 innovative in a lot of ways that and and um I I I would guess we could find we

1:42:30 could say some people dealt with it better and worse.

1:42:34 Mhm.

1:42:33 Which is to me the definition of what makes something interesting.

1:42:37 The ability to deal with it better or worse.

1:42:41 Thank you Howard for taking the time today.

1:42:43 This was incredibly interesting and I feel like I'm learning so much from you.

1:42:49 Well, and I I have learned from speaking with you and I've enjoyed it

1:42:52 and your your your uh uh questions were in many cases novel and provocative.

1:42:59 Thank you.

1:43:00 Thank you for doing this.

1:43:01 Cheers.