JPMorgan’s Playbook for a 10-15% Correction (or Worse) — ft. Michael Cembalest

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0:02:10 Today’s number, 104,000.
0:02:13 That’s how many pounds a sperm whale
0:02:15 that washed up on a Nantucket beach this week weighed.
0:02:17 Oh, gosh.
0:02:19 Just everybody sit back and buckle up.
0:02:23 Okay, so I’ve never had an unplanned pregnancy, Ed,
0:02:25 because when my sperm finally reaches the egg,
0:02:27 the egg swipes left.
0:02:31 My sperm really aren’t like swimmers, Ed.
0:02:32 They’re more like a slow jogger
0:02:35 being chased by a lame dog.
0:02:39 My sperm are not really swimmers.
0:02:43 They’re more like an old man browsing at a Costco
0:02:46 who’s looking for gluten-free peanut butter
0:02:47 who just had cataract surgery.
0:02:49 Like, it’s well-intentioned,
0:02:51 but it’s just not going to reach the goal.
0:02:54 I could do this all day.
0:02:54 I know.
0:02:56 By the way, I made up the last one myself, Ed.
0:03:12 Ed, do you have a nickname for your sperm?
0:03:15 No, I’m not quite there yet.
0:03:18 I’m not really going to play ball with you on this one.
0:03:20 Also, let me give you a little tip.
0:03:23 Just while we’re barreling towards cancellation,
0:03:25 even Claire looks horrified.
0:03:28 It’s important and it’s fun
0:03:29 that every time you orgasm,
0:03:31 you yell out the same thing.
0:03:32 It’s really important.
0:03:35 You have to come up with the same thing.
0:03:38 This is the part where you go,
0:03:39 what do you yell out, Scott?
0:03:42 I told you I’m not playing ball with you.
0:03:44 Ed’s got space in his hands.
0:03:46 This has not seen me.
0:03:47 Joe Kernan.
0:03:50 I’m not going to do you the service
0:03:51 of asking the question.
0:03:52 Surrender Dorothy.
0:03:54 That’s what I yell out from the Wizard of Oz.
0:03:56 That’s what I yell out.
0:03:58 And my other Wizard of Oz reference is
0:04:00 I yell out,
0:04:01 I’m melting.
0:04:06 That’s why the fans tune in.
0:04:07 That’s why they tune in.
0:04:08 Next up,
0:04:08 JP Morgan,
0:04:10 chief economist for the last time.
0:04:14 How are you, Ed?
0:04:15 I’m doing well, Scott.
0:04:15 How are you doing?
0:04:16 I’m good.
0:04:18 I’ve been in LA the last few days
0:04:19 and I’m at the Beverly Hills Hotel
0:04:22 where they’re charging me $1,800 a night
0:04:24 so I can have a construction site above my room.
0:04:26 But they stopped the construction.
0:04:28 I mean, just off mic here,
0:04:30 you gave the front desk a call.
0:04:32 You asked them to stop hammering
0:04:33 and they did it.
0:04:34 Pretty baller.
0:04:35 Good stuff.
0:04:35 Yeah.
0:04:37 No, I carry a lot of weight here.
0:04:40 They’re very scared of me.
0:04:41 Number one client.
0:04:44 So what have been the highlights from LA
0:04:46 just before we get into this conversation?
0:04:48 I had a dinner last night
0:04:49 with like a small dinner with eight people
0:04:51 and I’m totally name dropping.
0:04:51 What the fuck?
0:04:53 And Larry David,
0:04:54 I sat next to Larry David.
0:04:54 Wow.
0:04:55 How was that?
0:04:58 He is literally the same person in real life.
0:04:59 It’s not a show.
0:05:00 It’s a camera falling around.
0:05:01 He showed up.
0:05:01 He’s like,
0:05:02 nice to meet you.
0:05:02 He’s like,
0:05:04 why do they serve hors d’oeuvres?
0:05:06 He like goes into a bit.
0:05:07 He’s like,
0:05:08 he’s like,
0:05:09 why would it?
0:05:09 Like,
0:05:10 I hate Uber.
0:05:11 Do you hate Uber?
0:05:13 Let me talk about Uber for,
0:05:14 it’s literally,
0:05:14 I’m like,
0:05:15 oh my God,
0:05:15 it’s the show.
0:05:17 the guy just shows up
0:05:19 and starts doing these bits.
0:05:21 But he’s very funny.
0:05:23 He seems pretty nice.
0:05:24 His wife is lovely.
0:05:25 I don’t know.
0:05:26 I enjoyed.
0:05:27 How did you wind up
0:05:28 at a dinner with Larry David?
0:05:29 I’m what you call right now
0:05:32 like an intellectual support animal
0:05:33 and that is all these rich people
0:05:34 think it’s interesting
0:05:35 to have me over for dinner
0:05:38 and talk about my book.
0:05:38 Like,
0:05:40 so I think there’s two or three
0:05:41 of these people every year
0:05:42 where they’re like,
0:05:42 okay,
0:05:43 let’s all get together
0:05:44 and,
0:05:46 and also at the dinner
0:05:47 was my role model,
0:05:48 Sam Harris,
0:05:49 and I enjoyed seeing Sam.
0:05:50 So when you get invited
0:05:52 as the intellectual support animal,
0:05:53 do you feel a pressure
0:05:55 to provide
0:05:56 the intellectual support?
0:05:56 Yeah,
0:05:57 that’s why you’re there.
0:05:58 Is that fun?
0:05:59 That sounds stressful.
0:06:00 Maybe that’s why Larry David
0:06:01 was doing his bit.
0:06:03 He was the comedic support animal.
0:06:04 The answer is yes
0:06:05 and I find as I get older,
0:06:06 they called me
0:06:08 and they were going to invite
0:06:12 this fairly famous tech couple
0:06:12 and I said,
0:06:13 no,
0:06:13 I’m like,
0:06:14 I just don’t want to,
0:06:15 I don’t want to meet them.
0:06:16 I don’t want to talk about them.
0:06:17 I don’t,
0:06:17 I don’t,
0:06:20 I find that stuff
0:06:21 is increasingly intimidating me
0:06:22 and I don’t want to,
0:06:23 I mean,
0:06:23 you know what,
0:06:24 I’d rather just stay at home
0:06:25 and watch Netflix
0:06:27 and have some big Filipino man
0:06:29 reheat my soup six or seven times
0:06:30 and just sort of
0:06:31 start peeing in bottles,
0:06:33 grow my nails really long.
0:06:37 Surrender,
0:06:38 Dorothy.
0:06:40 Should we,
0:06:42 should we get to AI
0:06:43 and the economy,
0:06:43 Ed?
0:06:46 You want to move on now?
0:06:47 Somebody gave me mushroom chocolates
0:06:48 over the weekend.
0:06:49 Just saying.
0:06:50 Just saying.
0:06:50 Okay,
0:06:51 well that explains a lot.
0:06:52 All right,
0:06:53 let’s get to the economy.
0:06:54 Okay,
0:06:55 now that we know
0:06:57 what happened in LA this weekend,
0:06:57 here is our conversation
0:06:58 with Michael Semblis
0:06:59 the chairman of
0:07:01 Market and Investment Strategy
0:07:02 for J.P. Morgan Asset
0:07:03 and Wealth Management.
0:07:04 Michael,
0:07:06 thanks for joining us again
0:07:07 on Prof.G. Markets.
0:07:07 Good to see you.
0:07:08 Morning.
0:07:11 So,
0:07:12 I’m going to dive
0:07:13 right into the questions
0:07:14 that we have
0:07:14 because we’ve only got you
0:07:15 for so long.
0:07:17 Just some context.
0:07:17 So,
0:07:18 last week,
0:07:20 we had Professor Aswath de Moderen
0:07:21 on our podcast.
0:07:22 He
0:07:23 presented
0:07:25 what we think
0:07:26 is kind of
0:07:27 the most bearish position
0:07:28 we’ve ever seen from him.
0:07:30 And this is a very
0:07:31 level-headed,
0:07:32 calm guy
0:07:34 who basically told us
0:07:34 that he thinks
0:07:35 that everything
0:07:36 in the stock market
0:07:37 is overvalued.
0:07:38 He said there’s no place
0:07:39 to hide in stocks.
0:07:40 And I’m just going to
0:07:41 play you a short clip
0:07:43 that kind of summarizes
0:07:44 what he thinks right now.
0:07:45 To the extent that there’s
0:07:46 going to be a correction,
0:07:47 there’s no place
0:07:48 to hide in stocks.
0:07:50 I can’t see a way
0:07:51 because if the
0:07:52 MAC-10 go down
0:07:53 by 40%,
0:07:54 it’s not like
0:07:55 the industrials
0:07:55 are going to hold
0:07:56 their value
0:07:56 while this happens.
0:07:57 The panic
0:07:58 that that’s going
0:07:58 to create
0:07:59 is going to ripple
0:08:00 through stocks.
0:08:01 You’re an investor
0:08:02 primarily investing
0:08:03 in stocks and bonds.
0:08:04 My advice is
0:08:05 even though
0:08:06 historically
0:08:07 you might never
0:08:08 have invested
0:08:09 in non-financial
0:08:10 asset categories,
0:08:11 this might be
0:08:11 a time
0:08:12 where you think
0:08:14 about at least
0:08:15 moving a portion
0:08:15 of your portfolio.
0:08:17 Bigger chunk
0:08:18 than ever
0:08:19 into cash
0:08:19 or something
0:08:20 close to cash
0:08:21 or maybe even
0:08:22 collectibles.
0:08:23 I’ve never
0:08:24 owned collectibles
0:08:26 but for the first
0:08:27 time in my
0:08:28 investing history
0:08:28 I’m saying
0:08:29 maybe I should
0:08:31 hold something
0:08:32 that is not
0:08:32 going to be effective
0:08:33 if inflation
0:08:34 goes to 10%.
0:08:35 There’s a market
0:08:36 and economic crisis
0:08:37 that is
0:08:38 potentially
0:08:39 catastrophic.
0:08:40 we were very
0:08:42 struck
0:08:43 by everything
0:08:44 he said
0:08:44 in that interview.
0:08:46 What do you think
0:08:47 Michael?
0:08:48 Is he right?
0:08:49 Are you concerned
0:08:50 like he is?
0:08:51 What are your reactions?
0:08:52 It’s hard
0:08:53 to react
0:08:54 to somebody
0:08:55 that doesn’t
0:08:56 have a long-term
0:08:56 track record
0:08:58 of this is
0:08:58 when I bought
0:08:59 this is when I sold
0:09:00 this is what I bought
0:09:01 this is what I sold.
0:09:02 You know
0:09:03 professors
0:09:04 are basically
0:09:05 running
0:09:06 fantasy
0:09:07 baseball teams
0:09:08 by coming out
0:09:10 intermittently
0:09:11 and telling
0:09:11 you
0:09:12 what their
0:09:12 trades are.
0:09:13 It’s not
0:09:14 real money
0:09:14 it’s not
0:09:15 real life
0:09:15 and so
0:09:16 you know
0:09:16 the purpose
0:09:17 of Morningstar
0:09:18 and Lipper
0:09:20 and a lot
0:09:20 of other
0:09:21 entities out
0:09:22 there is to
0:09:22 kind of track
0:09:22 the people
0:09:23 that actually
0:09:23 manage money
0:09:24 how do they
0:09:25 do over the
0:09:25 long run
0:09:27 and things
0:09:27 like that
0:09:28 so it’s
0:09:28 a little
0:09:29 abstract.
0:09:30 A better
0:09:31 example might
0:09:31 be the cash
0:09:32 stockpile
0:09:32 that’s
0:09:33 accumulating
0:09:33 getting at
0:09:34 Berkshire Hathaway
0:09:35 is probably
0:09:37 a much
0:09:37 better indicator
0:09:40 of people
0:09:40 that are having
0:09:41 trouble putting
0:09:41 money to work
0:09:42 because they
0:09:42 can’t find
0:09:43 any value
0:09:43 and certainly
0:09:44 it’s challenging
0:09:45 right now
0:09:45 there’s a ton
0:09:46 of dry powder
0:09:48 in private equity
0:09:48 and venture
0:09:49 that still has
0:09:49 to be put
0:09:50 to work
0:09:50 and so
0:09:51 it’s a
0:09:51 challenging
0:09:52 time.
0:09:54 His premise
0:09:55 of I think
0:09:56 I heard him
0:09:56 say 10%
0:09:57 inflation
0:09:57 like I’m
0:09:59 not prepared
0:09:59 to quite buy
0:10:00 into it
0:10:00 a 10%
0:10:01 inflation
0:10:01 forecast
0:10:02 nor am I
0:10:03 prepared to
0:10:03 buy into
0:10:05 the certainty
0:10:05 of a 10%
0:10:07 sorry a 40%
0:10:08 correction in
0:10:08 the MAG-10
0:10:09 or MAG-7
0:10:10 those stocks
0:10:11 have gone up
0:10:11 a ton
0:10:12 and a passing
0:10:13 wind could
0:10:14 cause
0:10:15 profit taking
0:10:16 right
0:10:17 similar to
0:10:18 what took
0:10:18 place earlier
0:10:18 this year
0:10:19 with the
0:10:19 dollar
0:10:20 dollar went
0:10:21 down 10%
0:10:21 it was at
0:10:22 its post
0:10:22 financial crisis
0:10:23 high
0:10:23 as soon as
0:10:24 any asset
0:10:24 falls by
0:10:25 10%
0:10:26 Nouriel
0:10:26 Roubini
0:10:26 and the rest
0:10:27 of the people
0:10:27 come out of
0:10:28 the woodwork
0:10:28 and say okay
0:10:29 this is it
0:10:29 this is the
0:10:30 big one
0:10:30 everything is
0:10:30 going to
0:10:30 go down
0:10:31 from here
0:10:31 and then
0:10:32 of course
0:10:32 the dollar
0:10:32 has been
0:10:33 flat since
0:10:33 that
0:10:34 the dollar
0:10:34 has been
0:10:34 actually
0:10:35 flat since
0:10:35 our podcast
0:10:36 in May
0:10:37 so the last
0:10:37 time I was
0:10:38 on this
0:10:38 show
0:10:39 you know
0:10:39 I think
0:10:40 people have
0:10:40 to have
0:10:40 some kind
0:10:41 of discipline
0:10:42 to recognize
0:10:42 that when
0:10:43 assets are
0:10:43 trading at
0:10:43 20 to
0:10:44 25 year
0:10:44 highs
0:10:45 they can
0:10:45 correct by
0:10:46 10 or
0:10:46 15%
0:10:48 it doesn’t
0:10:49 necessarily
0:10:50 unfold and
0:10:51 unravel
0:10:52 into the
0:10:53 big 40%
0:10:54 corrections that
0:10:54 we had
0:10:55 in 2009
0:10:56 and then again
0:10:56 in 2001
0:10:58 so it
0:10:59 sounds
0:11:00 I mean
0:11:01 we’re positioning
0:11:02 more defensively
0:11:02 than we have
0:11:04 but you know
0:11:05 what he’s
0:11:06 describing there
0:11:07 is a bridge
0:11:08 too far
0:11:08 plus our
0:11:09 normal
0:11:10 balanced and
0:11:10 conservative
0:11:11 portfolios
0:11:12 already have
0:11:13 30 to 40%
0:11:14 in cash
0:11:15 cash equivalents
0:11:16 gold
0:11:17 very diversified
0:11:18 hedge funds
0:11:19 municipals
0:11:21 some short
0:11:21 duration
0:11:22 preferreds
0:11:22 and things
0:11:22 like that
0:11:23 just to play
0:11:24 defense for
0:11:25 Aswath
0:11:26 you know
0:11:26 we
0:11:27 that clip
0:11:27 that we
0:11:28 got was
0:11:28 the most
0:11:29 bearish
0:11:30 string of
0:11:30 sentences
0:11:31 that he
0:11:32 uttered
0:11:33 otherwise
0:11:34 I think
0:11:34 if you
0:11:34 listen to
0:11:35 the full
0:11:35 podcast
0:11:36 it would
0:11:36 sound
0:11:37 less
0:11:38 hyperbolic
0:11:39 perhaps
0:11:40 but you
0:11:40 know
0:11:41 in some
0:11:41 he’s
0:11:42 concerned
0:11:44 but also
0:11:44 so is
0:11:45 everyone
0:11:45 else
0:11:46 and
0:11:47 right now
0:11:47 this
0:11:48 AI bubble
0:11:49 more and more
0:11:50 people are talking
0:11:50 about it
0:11:51 more and more
0:11:52 people are googling
0:11:52 it
0:11:52 we’re now
0:11:53 beginning to see
0:11:54 a little bit of a
0:11:54 correction in
0:11:55 tech stocks
0:11:56 as we speak
0:11:57 small
0:11:58 small
0:12:01 the fears
0:12:01 of this
0:12:02 AI bubble
0:12:03 are quite
0:12:04 strong
0:12:04 or stronger
0:12:04 than they
0:12:05 have been
0:12:05 before
0:12:07 what do you
0:12:08 make of those
0:12:08 fears
0:12:09 do you think
0:12:09 that they
0:12:10 are warranted
0:12:11 and how
0:12:12 are you
0:12:13 at JP Morgan
0:12:15 thinking about it
0:12:16 something like
0:12:18 75% of all
0:12:18 of the revenues
0:12:19 profits
0:12:19 and capital
0:12:20 spending
0:12:21 since November
0:12:22 2022
0:12:23 have come
0:12:24 from 40
0:12:24 AI related
0:12:25 stocks
0:12:26 so there’s
0:12:27 almost no
0:12:27 justification
0:12:28 to spend
0:12:28 time on
0:12:28 anything
0:12:29 but this
0:12:30 and I try
0:12:31 to be an
0:12:31 even keel
0:12:31 kind of
0:12:32 person
0:12:32 but the
0:12:33 meta number
0:12:33 threw me
0:12:34 you know
0:12:35 a company
0:12:36 announcing
0:12:36 that they’re
0:12:37 spending
0:12:38 65 to
0:12:38 70%
0:12:38 of their
0:12:39 revenues
0:12:40 on capital
0:12:41 spending
0:12:41 and R&D
0:12:42 without even
0:12:43 knowing what
0:12:44 the destination
0:12:45 is
0:12:46 that they’re
0:12:47 going to
0:12:48 as an
0:12:48 investor
0:12:50 that doesn’t
0:12:51 fill me
0:12:51 with a lot
0:12:51 of good
0:12:52 feeling
0:12:52 about
0:12:53 where we’re
0:12:54 going to be
0:12:54 two years
0:12:54 from now
0:12:54 and it
0:12:55 was only
0:12:55 two years
0:12:55 ago
0:12:57 that they
0:12:57 did this
0:12:58 they reached
0:12:58 the same
0:12:59 peak of 65%
0:13:00 of revenues
0:13:01 investing in the
0:13:01 metaverse
0:13:02 which obviously
0:13:03 hasn’t really
0:13:04 turned out to be
0:13:04 anything
0:13:06 so there’s a lot
0:13:07 more behind
0:13:08 this particular
0:13:10 generative AI
0:13:10 boom
0:13:12 but the numbers
0:13:14 I put together
0:13:14 something Scott
0:13:15 I think you’d
0:13:15 appreciate this
0:13:16 that you like
0:13:16 this kind of
0:13:17 historical context
0:13:19 the tech
0:13:20 capital spending
0:13:20 in 2025
0:13:22 is equal
0:13:23 relative to
0:13:23 GDP
0:13:24 of
0:13:27 the moon
0:13:27 landing
0:13:29 the Manhattan
0:13:29 project
0:13:30 the interstate
0:13:31 highway system
0:13:32 electrification of
0:13:33 farming
0:13:34 the Triborough
0:13:34 Bridge
0:13:35 the Midtown
0:13:36 Tunnel
0:13:36 the Golden
0:13:37 Gate Bridge
0:13:38 and the Hoover
0:13:38 Dam
0:13:38 combined
0:13:41 there’s all
0:13:42 these futurists
0:13:42 hovering around
0:13:43 like bats
0:13:44 telling us
0:13:44 how all of
0:13:45 this is going
0:13:45 to magically
0:13:47 make some
0:13:48 fantastic future
0:13:49 and there
0:13:49 are surely
0:13:51 bits and
0:13:52 pieces of
0:13:52 evidence
0:13:53 here and there
0:13:54 of real
0:13:55 productivity gains
0:13:56 coming from
0:13:56 this
0:13:57 but
0:13:59 this is
0:14:00 a scarier
0:14:01 version
0:14:02 of the
0:14:03 ICT
0:14:03 revolution
0:14:03 of the
0:14:04 early 90s
0:14:05 the only
0:14:06 silver lining
0:14:06 that I
0:14:07 latch on to
0:14:09 is that
0:14:10 whether it’s
0:14:11 2001
0:14:12 or the
0:14:12 casino
0:14:13 build-out
0:14:14 airlines
0:14:15 fiber optics
0:14:16 the
0:14:18 Calpine
0:14:18 gas turbine
0:14:19 era
0:14:20 all of
0:14:20 those
0:14:20 capital
0:14:21 spending
0:14:21 booms
0:14:21 were
0:14:22 financed
0:14:22 with
0:14:23 debt
0:14:24 and
0:14:24 this
0:14:24 one
0:14:25 with
0:14:25 the
0:14:25 exception
0:14:25 of
0:14:26 Oracle
0:14:26 is
0:14:27 being
0:14:27 financed
0:14:27 with
0:14:28 internally
0:14:28 generated
0:14:28 cash
0:14:29 flow
0:14:29 but
0:14:30 that
0:14:30 simply
0:14:30 means
0:14:30 it
0:14:31 can
0:14:31 go
0:14:31 on
0:14:31 for
0:14:32 longer
0:14:32 before
0:14:32 it
0:14:32 gets
0:14:33 unplugged
0:14:33 by
0:14:33 the
0:14:33 debt
0:14:34 markets
0:14:35 it
0:14:35 doesn’t
0:14:35 relieve
0:14:36 you
0:14:36 of the
0:14:36 ultimate
0:14:37 need
0:14:37 for
0:14:37 there
0:14:38 to
0:14:38 be
0:14:38 substantial
0:14:39 profit
0:14:39 generation
0:14:40 to
0:14:41 remunerate
0:14:41 the
0:14:41 trillion
0:14:41 two
0:14:42 of
0:14:42 capital
0:14:42 that’s
0:14:42 been
0:14:43 spent
0:14:43 since
0:14:43 November
0:14:44 2022
0:14:45 I want to
0:14:46 recognize
0:14:46 I’m feeling
0:14:47 defensive
0:14:47 around my
0:14:48 colleague
0:14:48 I’ve been
0:14:48 following
0:14:49 Aswath for
0:14:49 20 years
0:14:49 and he
0:14:50 does the
0:14:50 work around
0:14:51 valuations
0:14:52 and there’s
0:14:52 few people
0:14:53 in the
0:14:53 alternative
0:14:54 investment
0:14:54 space
0:14:55 or investors
0:14:55 that at
0:14:56 least in
0:14:57 my observation
0:14:57 have been
0:14:58 more right
0:14:59 more often
0:14:59 than him
0:15:00 and it
0:15:00 did rattle
0:15:00 us
0:15:01 because he
0:15:01 generally
0:15:02 I want to
0:15:02 acknowledge
0:15:03 your point
0:15:05 it’s easier
0:15:05 to sound
0:15:05 smart when
0:15:06 you’re
0:15:06 a
0:15:06 catastrophist
0:15:07 you just
0:15:07 sound
0:15:08 smarter
0:15:08 right
0:15:09 and
0:15:10 the
0:15:11 best
0:15:11 traders
0:15:12 the best
0:15:12 historians
0:15:13 the best
0:15:13 politicians
0:15:14 have erred
0:15:15 on the
0:15:15 side
0:15:16 of asking
0:15:16 themselves
0:15:17 what could
0:15:17 go right
0:15:18 you know
0:15:19 and because
0:15:19 the markets
0:15:20 over the
0:15:20 medium and
0:15:20 long term
0:15:21 are up
0:15:21 into the
0:15:21 right
0:15:22 so I
0:15:22 want to
0:15:22 acknowledge
0:15:22 that
0:15:24 the
0:15:24 I want
0:15:25 to put
0:15:25 forward
0:15:25 a thesis
0:15:26 and have
0:15:26 you respond
0:15:27 to it
0:15:27 and that
0:15:27 is
0:15:28 if you
0:15:28 look at
0:15:28 every
0:15:29 single
0:15:29 of the
0:15:30 company
0:15:30 one of
0:15:30 the
0:15:30 companies
0:15:31 we’re
0:15:31 talking
0:15:31 about
0:15:32 they
0:15:32 have
0:15:32 all
0:15:33 had
0:15:33 years
0:15:34 where
0:15:34 they’re
0:15:34 down
0:15:35 50 to
0:15:36 70 percent
0:15:36 in that
0:15:37 12 month
0:15:37 period
0:15:38 and the
0:15:39 thing that’s
0:15:39 scary now
0:15:40 is if a
0:15:40 company like
0:15:40 NVIDIA
0:15:41 which hasn’t
0:15:41 had one
0:15:42 of those
0:15:42 years
0:15:42 I don’t
0:15:42 think
0:15:43 or maybe
0:15:43 it did
0:15:44 in 2022
0:15:45 if that
0:15:45 happens
0:15:46 to a
0:15:46 company
0:15:46 that’s
0:15:46 now
0:15:47 got
0:15:47 the
0:15:47 market
0:15:47 cap
0:15:48 of the
0:15:49 GDP
0:15:49 of
0:15:50 whatever
0:15:50 Germany
0:15:51 that
0:15:51 that
0:15:52 might
0:15:52 take
0:15:52 the
0:15:53 entire
0:15:53 market
0:15:53 down
0:15:54 that
0:15:54 essentially
0:15:55 America
0:15:55 has
0:15:55 become
0:15:56 so
0:15:56 fragile
0:15:57 because
0:15:57 it
0:15:57 is
0:15:57 now
0:15:58 a
0:15:58 giant
0:15:58 bet
0:15:59 on
0:16:00 AI
0:16:01 and
0:16:01 if
0:16:01 open
0:16:01 AI
0:16:02 which
0:16:03 you
0:16:03 know
0:16:03 I
0:16:03 mean
0:16:04 60%
0:16:04 of
0:16:05 revenues
0:16:05 on
0:16:06 CapEx
0:16:06 is
0:16:06 one
0:16:06 thing
0:16:07 as far
0:16:07 as I
0:16:07 can
0:16:07 tell
0:16:07 open
0:16:08 AI
0:16:08 is
0:16:08 about
0:16:09 at
0:16:10 you
0:16:10 know
0:16:10 30
0:16:11 times
0:16:12 30
0:16:12 times
0:16:12 the
0:16:12 revenue
0:16:13 going
0:16:13 into
0:16:13 CapEx
0:16:14 if
0:16:15 open
0:16:15 AI
0:16:15 or
0:16:15 some
0:16:15 big
0:16:16 customers
0:16:17 here’s
0:16:17 the
0:16:17 thesis
0:16:17 and
0:16:17 you
0:16:18 tell
0:16:18 me
0:16:18 where
0:16:18 I
0:16:18 got
0:16:18 this
0:16:19 wrong
0:16:19 some
0:16:20 standard
0:16:20 S&P
0:16:21 companies
0:16:21 PepsiCo
0:16:22 Caterpillar
0:16:22 P&G
0:16:23 announced
0:16:23 look
0:16:23 AI
0:16:23 is
0:16:24 great
0:16:24 but
0:16:25 we
0:16:25 are
0:16:25 out
0:16:25 over
0:16:25 our
0:16:26 skis
0:16:26 we’re
0:16:26 not
0:16:26 seeing
0:16:27 the
0:16:27 return
0:16:27 we’d
0:16:27 hope
0:16:27 for
0:16:28 we’re
0:16:28 scaling
0:16:28 back
0:16:28 the
0:16:29 investment
0:16:30 open
0:16:30 AI
0:16:30 in
0:16:30 the
0:16:31 secondary
0:16:31 market
0:16:31 trades
0:16:32 way
0:16:32 down
0:16:33 and
0:16:33 then
0:16:35 NVIDIA
0:16:37 goes down
0:16:38 60%
0:16:38 which
0:16:38 would
0:16:39 not
0:16:39 be
0:16:39 unusual
0:16:39 it
0:16:39 wouldn’t
0:16:40 look
0:16:40 cheap
0:16:41 and
0:16:41 then
0:16:42 we
0:16:42 have
0:16:42 a
0:16:43 $3
0:16:43 trillion
0:16:44 destruction
0:16:44 in
0:16:44 the
0:16:44 capital
0:16:45 markets
0:16:46 and
0:16:46 we
0:16:46 basically
0:16:47 overnight
0:16:47 have
0:16:48 flat
0:16:49 markets
0:16:49 GDP
0:16:50 growth
0:16:50 of
0:16:50 zero
0:16:51 and
0:16:53 companies
0:16:54 sneeze
0:16:54 we’re
0:16:54 not
0:16:54 catching
0:16:54 a
0:16:55 cold
0:16:55 we’re
0:16:55 catching
0:16:56 pneumonia
0:16:56 the
0:16:57 thing
0:16:57 that
0:16:58 worries
0:16:58 me
0:16:58 the
0:16:58 most
0:16:58 and
0:16:58 I
0:16:58 want
0:16:58 to
0:16:58 get
0:16:58 your
0:16:59 response
0:16:59 is
0:16:59 we
0:17:00 have
0:17:01 inadvertently
0:17:01 turned
0:17:01 our
0:17:02 markets
0:17:02 into
0:17:03 a
0:17:04 very
0:17:04 fragile
0:17:05 house
0:17:06 of
0:17:06 cards
0:17:06 there’s
0:17:06 the
0:17:07 S&P
0:17:07 490
0:17:07 and
0:17:08 there’s
0:17:08 the
0:17:08 S&P
0:17:08 10
0:17:09 and
0:17:10 right
0:17:10 now
0:17:10 everything
0:17:10 is
0:17:11 banking
0:17:11 on
0:17:11 these
0:17:11 10
0:17:12 companies
0:17:13 living
0:17:13 up
0:17:13 to
0:17:13 these
0:17:14 extraordinary
0:17:15 expectations
0:17:16 your
0:17:16 thoughts
0:17:17 I agree
0:17:17 with most
0:17:17 of
0:17:18 that
0:17:19 one
0:17:19 of
0:17:20 the
0:17:21 data
0:17:21 points
0:17:21 that
0:17:21 was
0:17:22 really
0:17:22 disturbing
0:17:22 is
0:17:22 two
0:17:23 years
0:17:23 ago
0:17:24 someone
0:17:24 talking
0:17:24 about
0:17:25 GDP
0:17:25 growth
0:17:25 would
0:17:25 not
0:17:26 have
0:17:26 mentioned
0:17:26 tech
0:17:26 capital
0:17:27 spending
0:17:27 it
0:17:27 would
0:17:27 have
0:17:27 been
0:17:27 a
0:17:27 rounding
0:17:28 error
0:17:29 last
0:17:29 quarter
0:17:30 it
0:17:30 was
0:17:30 a
0:17:30 third
0:17:31 of
0:17:31 GDP
0:17:31 growth
0:17:32 and
0:17:32 US
0:17:33 GDP
0:17:33 growth
0:17:33 would
0:17:34 already
0:17:34 be
0:17:35 1%
0:17:35 if
0:17:35 it
0:17:35 weren’t
0:17:36 for
0:17:36 this
0:17:36 tech
0:17:36 AI
0:17:37 build
0:17:37 out
0:17:38 and
0:17:38 you
0:17:38 know
0:17:38 how
0:17:39 people
0:17:39 get
0:17:39 upset
0:17:40 when
0:17:40 the
0:17:41 private
0:17:41 sector
0:17:41 gets
0:17:42 bailed
0:17:42 out
0:17:42 by
0:17:42 the
0:17:42 public
0:17:43 sector
0:17:43 I
0:17:43 would
0:17:44 argue
0:17:44 this
0:17:44 year
0:17:46 generative
0:17:46 AI
0:17:46 has
0:17:46 bailed
0:17:47 out
0:17:47 the
0:17:47 public
0:17:47 sector
0:17:48 the
0:17:50 hyperscale
0:17:50 has
0:17:50 bailed
0:17:51 out
0:17:51 the
0:17:51 Trump
0:17:51 administration
0:17:52 because
0:17:52 without
0:17:52 them
0:17:53 they’d
0:17:53 be
0:17:54 staring
0:17:54 a
0:17:54 1%
0:17:55 GDP
0:17:55 growth
0:17:55 number
0:17:55 in the
0:17:56 face
0:17:56 and
0:17:57 having
0:17:57 to
0:17:57 explain
0:17:57 it
0:17:58 which
0:17:58 they’re
0:17:59 in
0:17:59 really
0:17:59 no
0:17:59 position
0:18:00 to
0:18:00 do
0:18:00 and
0:18:01 by
0:18:01 the
0:18:01 way
0:18:01 I
0:18:01 don’t
0:18:01 think
0:18:02 it’s
0:18:02 a
0:18:02 coincidence
0:18:03 that
0:18:04 we
0:18:05 track
0:18:05 all
0:18:05 of
0:18:06 the
0:18:06 country
0:18:06 product
0:18:07 tariff
0:18:07 matrix
0:18:08 combinations
0:18:10 the
0:18:10 AI
0:18:11 infrastructure
0:18:12 about
0:18:12 70 to
0:18:13 90%
0:18:13 of those
0:18:14 imports
0:18:14 are exempt
0:18:14 from
0:18:14 tariffs
0:18:15 right
0:18:15 now
0:18:15 so
0:18:16 those
0:18:17 sectors
0:18:17 and
0:18:18 companies
0:18:18 have
0:18:18 also
0:18:18 done
0:18:18 a
0:18:18 very
0:18:19 good
0:18:19 job
0:18:20 navigating
0:18:20 however
0:18:21 one
0:18:24 in
0:18:24 order
0:18:25 to
0:18:25 get
0:18:25 their
0:18:26 imports
0:18:26 which
0:18:27 are
0:18:27 critical
0:18:27 to
0:18:27 their
0:18:28 survival
0:18:28 exempt
0:18:29 from
0:18:29 tariffs
0:18:30 so
0:18:30 yeah
0:18:31 I’m
0:18:31 nervous
0:18:31 about
0:18:32 it
0:18:32 again
0:18:33 the
0:18:34 fact
0:18:34 that
0:18:34 it’s
0:18:34 being
0:18:35 financed
0:18:35 through
0:18:35 cash
0:18:36 flow
0:18:37 means
0:18:37 that
0:18:38 we
0:18:38 don’t
0:18:38 have
0:18:38 quite
0:18:39 the
0:18:39 same
0:18:40 risk
0:18:40 of
0:18:40 a
0:18:41 sudden
0:18:42 seize
0:18:42 up
0:18:43 because
0:18:43 you know
0:18:44 bonds
0:18:44 can’t
0:18:44 be
0:18:45 placed
0:18:47 you know
0:18:47 you’re
0:18:47 not
0:18:47 going
0:18:47 to
0:18:47 have
0:18:48 a
0:18:48 hung
0:18:48 bridge
0:18:49 loan
0:18:50 that
0:18:51 gets
0:18:51 like
0:18:51 the
0:18:52 broker
0:18:52 dealers
0:18:52 in
0:18:52 trouble
0:18:53 and
0:18:53 have
0:18:53 to
0:18:53 go
0:18:53 to
0:18:53 the
0:18:53 Fed’s
0:18:54 primary
0:18:54 dealer
0:18:54 credit
0:18:55 facility
0:18:55 you’re
0:18:55 not
0:18:56 going
0:18:56 to have
0:18:56 something
0:18:56 like
0:18:56 that
0:18:58 Oracle
0:18:58 is
0:18:58 really
0:18:58 the
0:18:59 only
0:18:59 one
0:18:59 that’s
0:18:59 financing
0:19:00 any
0:19:00 material
0:19:00 amount
0:19:01 of
0:19:01 this
0:19:01 through
0:19:01 debt
0:19:02 my
0:19:02 understanding
0:19:03 so
0:19:03 Oracle
0:19:04 is
0:19:04 raising
0:19:05 a ton
0:19:05 of
0:19:05 debt
0:19:05 and
0:19:06 it’s
0:19:07 you know
0:19:07 capital
0:19:08 markets
0:19:08 are
0:19:08 concerned
0:19:08 about
0:19:09 this
0:19:09 and
0:19:09 that
0:19:09 we’re
0:19:09 seeing
0:19:10 that
0:19:10 reflected
0:19:10 in
0:19:10 the
0:19:10 stock
0:19:11 price
0:19:11 is
0:19:11 essentially
0:19:12 underwater
0:19:12 since
0:19:13 the
0:19:16 deal
0:19:16 with
0:19:16 open
0:19:17 I was
0:19:17 announced
0:19:18 my
0:19:18 understanding
0:19:19 though
0:19:19 is that
0:19:20 the
0:19:20 other
0:19:20 big
0:19:21 hyperscalers
0:19:22 are
0:19:22 going
0:19:22 out
0:19:22 and
0:19:23 beginning
0:19:23 to
0:19:23 raise
0:19:24 debt
0:19:25 I mean
0:19:25 we just
0:19:26 saw a big
0:19:26 announcement
0:19:27 from Amazon
0:19:28 and we
0:19:28 saw that
0:19:29 from Meta
0:19:29 a couple
0:19:30 weeks ago
0:19:30 as well
0:19:31 I don’t
0:19:31 know the
0:19:31 exact
0:19:32 numbers
0:19:32 but
0:19:32 my
0:19:33 understanding
0:19:33 is
0:19:33 they
0:19:33 are
0:19:34 going
0:19:34 and
0:19:34 financing
0:19:35 this
0:19:35 with
0:19:35 a ton
0:19:35 of
0:19:36 debt
0:19:36 perhaps
0:19:37 not
0:19:37 debt
0:19:37 that
0:19:37 they
0:19:38 necessarily
0:19:38 have
0:19:38 to
0:19:39 raise
0:19:39 perhaps
0:19:39 they
0:19:39 have
0:19:40 the
0:19:40 cash
0:19:41 surplus
0:19:41 to
0:19:42 finance
0:19:43 this
0:19:43 but
0:19:44 they
0:19:44 are
0:19:44 going
0:19:45 and
0:19:45 raising
0:19:45 debt
0:19:45 and
0:19:45 it’s
0:19:46 record
0:19:46 amounts
0:19:46 of
0:19:46 debt
0:19:47 that
0:19:47 they
0:19:47 are
0:19:47 raising
0:19:48 is
0:19:48 that
0:19:49 wrong
0:19:49 of
0:19:49 the
0:19:50 40
0:19:50 companies
0:19:51 I
0:19:51 mentioned
0:19:51 as
0:19:52 AI
0:19:52 stocks
0:19:53 30
0:19:53 are
0:19:54 technology
0:19:54 what
0:19:55 you and
0:20:00 turbines
0:20:01 and
0:20:01 then
0:20:01 five
0:20:01 are
0:20:02 utilities
0:20:03 let’s
0:20:03 look
0:20:03 at
0:20:04 the
0:20:04 30
0:20:04 AI
0:20:05 stocks
0:20:05 something
0:20:05 like
0:20:06 25
0:20:06 of
0:20:06 them
0:20:07 actually
0:20:07 have
0:20:08 negative
0:20:09 net
0:20:09 debt
0:20:09 to
0:20:10 EBIT
0:20:10 ratios
0:20:11 because
0:20:11 they
0:20:11 have
0:20:11 more
0:20:12 cash
0:20:12 and
0:20:12 cash
0:20:13 equivalence
0:20:13 on
0:20:13 their
0:20:13 balance
0:20:13 sheet
0:20:14 than
0:20:14 debt
0:20:15 so
0:20:15 for
0:20:15 the
0:20:16 most
0:20:16 part
0:20:17 the
0:20:17 debt
0:20:18 is
0:20:18 not
0:20:18 an
0:20:18 issue
0:20:18 the
0:20:19 Oracle
0:20:19 is
0:20:19 an
0:20:19 exception
0:20:20 and
0:20:20 is
0:20:20 in
0:20:20 part
0:20:20 a
0:20:20 reflection
0:20:21 of
0:20:21 the
0:20:21 fact
0:20:21 that
0:20:22 Ellison
0:20:22 has
0:20:22 been
0:20:22 buying
0:20:23 back
0:20:23 stock
0:20:30 have
0:20:30 to
0:20:30 be
0:20:31 recapitalized
0:20:31 if
0:20:31 it’s
0:20:31 going
0:20:31 to
0:20:31 be
0:20:31 borrowing
0:20:32 like
0:20:32 this
0:20:33 the
0:20:33 Meta
0:20:33 deal
0:20:33 was
0:20:34 interesting
0:20:35 and
0:20:36 when
0:20:36 I
0:20:37 explain
0:20:37 how
0:20:37 I
0:20:37 view
0:20:38 it
0:20:38 you’ll
0:20:38 probably
0:20:38 say
0:20:38 well
0:20:39 that’s
0:20:39 even
0:20:40 scarier
0:20:40 than
0:20:41 if
0:20:41 it
0:20:41 was
0:20:41 meta
0:20:42 let’s
0:20:43 hear it
0:20:44 the
0:20:44 Meta
0:20:45 partnership
0:20:45 with
0:20:46 Blue Owl
0:20:46 that
0:20:46 borrowed
0:20:47 27
0:20:47 billion
0:20:47 in
0:20:47 the
0:20:48 investment
0:20:48 grade
0:20:48 bond
0:20:49 markets
0:20:49 was
0:20:49 an
0:20:50 SPV
0:20:50 with
0:20:50 a
0:20:51 strange
0:20:51 French
0:20:51 name
0:20:52 and
0:20:53 the
0:20:53 debts
0:20:53 not
0:20:54 consolidated
0:20:55 onto
0:20:55 Meta’s
0:20:55 balance
0:20:56 sheet
0:20:56 initially
0:20:56 I
0:20:56 was
0:20:57 concerned
0:20:57 that
0:20:57 that
0:20:57 was
0:20:57 some
0:20:58 kind
0:20:58 of
0:20:58 sleight
0:20:58 of
0:20:58 hand
0:20:59 it’s
0:20:59 not
0:20:59 it’s
0:20:59 worse
0:21:01 they
0:21:01 basically
0:21:02 have
0:21:02 walkaway
0:21:02 rights
0:21:03 every
0:21:03 four
0:21:03 years
0:21:04 and
0:21:04 a
0:21:04 declining
0:21:05 residual
0:21:06 value
0:21:06 guarantee
0:21:08 Blue Owl’s
0:21:08 holding the
0:21:08 bag
0:21:10 so
0:21:10 Blue Owl
0:21:11 is at
0:21:12 risk
0:21:12 if at
0:21:13 any
0:21:13 point
0:21:14 Meta
0:21:15 basically
0:21:15 says
0:21:16 you know
0:21:16 this
0:21:17 we’re not
0:21:17 getting a
0:21:17 return on
0:21:18 this
0:21:18 particular
0:21:18 data
0:21:18 center
0:21:20 complex
0:21:21 we’re
0:21:21 out
0:21:22 and
0:21:22 Blue Owl
0:21:23 and the
0:21:23 people that
0:21:23 have put up
0:21:25 the
0:21:26 money for
0:21:27 Blue Owl
0:21:27 was
0:21:28 right
0:21:28 to
0:21:29 withhold
0:21:30 consolidation
0:21:31 of that
0:21:32 obligation
0:21:32 but it
0:21:33 says
0:21:34 a lot
0:21:34 about
0:21:34 the
0:21:34 underwriting
0:21:35 discipline
0:21:35 that’s
0:21:35 taking
0:21:36 place
0:21:37 in
0:21:38 private
0:21:38 credit
0:21:39 and in
0:21:39 other
0:21:40 places
0:21:40 that are
0:21:41 financing
0:21:41 these
0:21:41 data
0:21:42 centers
0:21:42 because
0:21:42 they’re
0:21:42 the
0:21:43 ones
0:21:43 that
0:21:43 are
0:21:43 taking
0:21:43 the
0:21:43 risk
0:21:44 it’s
0:21:44 essentially
0:21:45 releasing
0:21:45 risk
0:21:46 the likes
0:21:46 of which
0:21:46 you’ve
0:21:47 seen
0:21:48 forever
0:21:48 in the
0:21:48 commercial
0:21:48 real
0:21:48 estate
0:21:49 markets
0:21:49 so
0:21:49 it
0:21:50 sounds
0:21:50 like
0:21:50 someone
0:21:51 is
0:21:51 on
0:21:51 the
0:21:51 hook
0:21:51 in
0:21:52 this
0:21:52 case
0:21:52 it’s
0:21:53 Blue Owl
0:21:53 and we’re
0:21:53 probably
0:21:54 seeing
0:21:54 equivalents
0:21:55 in these
0:21:55 other
0:21:55 debt
0:21:56 deals
0:21:56 and by
0:21:56 the way
0:21:56 we’re
0:21:57 seeing
0:21:57 this
0:21:57 kind of
0:21:57 reflect
0:21:57 in the
0:21:58 stock
0:21:58 right now
0:21:59 Blue Owl
0:21:59 is publicly
0:22:00 traded
0:22:00 and it’s
0:22:01 declining
0:22:01 this week
0:22:03 but
0:22:03 is
0:22:04 is the
0:22:04 is the
0:22:05 conclusion
0:22:05 because
0:22:06 it sounds
0:22:06 like
0:22:07 you agree
0:22:07 with
0:22:07 Aswath
0:22:08 we are
0:22:08 seeing
0:22:09 some
0:22:09 we’re
0:22:09 seeing
0:22:10 some
0:22:10 form
0:22:10 of
0:22:10 a
0:22:11 bubble
0:22:11 people
0:22:12 are
0:22:12 exuberant
0:22:13 there’s
0:22:13 a lot
0:22:13 of
0:22:13 hype
0:22:13 and a lot
0:22:14 of
0:22:14 excitement
0:22:14 that
0:22:15 isn’t
0:22:15 fully
0:22:16 tethered
0:22:16 to
0:22:16 reality
0:22:17 right now
0:22:17 when it
0:22:17 comes
0:22:17 to
0:22:18 AI
0:22:18 but
0:22:18 it
0:22:19 sounds
0:22:19 like
0:22:19 where
0:22:19 you
0:22:20 disagree
0:22:21 is
0:22:21 the
0:22:21 extent
0:22:22 and
0:22:22 scale
0:22:22 of the
0:22:22 damage
0:22:23 that
0:22:23 we’re
0:22:23 going
0:22:23 to
0:22:23 see
0:22:24 if
0:22:24 something
0:22:25 blows
0:22:25 up
0:22:26 or at
0:22:31 the
0:22:32 year
0:22:32 was
0:22:32 the
0:22:32 Trump
0:22:33 people
0:22:33 were
0:22:33 going
0:22:33 to
0:22:33 break
0:22:34 something
0:22:34 we’d
0:22:34 have
0:22:34 a
0:22:35 15%
0:22:35 to
0:22:36 20%
0:22:36 correction
0:22:37 but
0:22:37 stocks
0:22:37 would
0:22:37 end
0:22:37 the
0:22:37 year
0:22:38 higher
0:22:38 than
0:22:38 where
0:22:38 they
0:22:38 began
0:22:40 it
0:22:40 happened
0:22:40 within
0:22:41 50 days
0:22:42 of the
0:22:43 inauguration
0:22:43 they broke
0:22:43 everything
0:22:45 markets
0:22:46 went down
0:22:46 they eventually
0:22:47 came back
0:22:48 I wouldn’t
0:22:48 I wouldn’t
0:22:49 describe
0:22:49 any of the
0:22:50 subsequent
0:22:50 recovery
0:22:50 as
0:22:51 the
0:22:52 byproduct
0:22:52 of
0:22:53 administration
0:22:53 policy
0:22:54 but I
0:22:54 think
0:22:55 you and
0:22:55 Scott
0:22:56 are right
0:22:56 which is
0:22:58 it would
0:22:58 be
0:22:58 kind of
0:22:59 shocking
0:22:59 if
0:23:00 you
0:23:00 didn’t
0:23:00 have
0:23:01 some
0:23:01 kind
0:23:01 of
0:23:01 profit
0:23:02 taking
0:23:02 correction
0:23:04 in
0:23:04 2026
0:23:05 at some
0:23:05 point
0:23:06 on the
0:23:06 order
0:23:06 of
0:23:07 10
0:23:07 to
0:23:07 15%
0:23:08 it
0:23:08 would
0:23:08 be
0:23:09 I’d
0:23:09 be
0:23:10 I’d
0:23:10 be
0:23:10 really
0:23:11 surprised
0:23:11 not
0:23:11 to
0:23:11 see
0:23:12 that
0:23:12 the
0:23:13 big
0:23:13 question
0:23:14 is
0:23:15 if
0:23:15 you
0:23:15 told
0:23:15 me
0:23:15 the
0:23:16 drawdown
0:23:16 is
0:23:16 12%
0:23:17 I
0:23:17 don’t
0:23:18 really
0:23:18 have
0:23:18 to
0:23:18 make
0:23:19 any
0:23:19 substantial
0:23:20 portfolio
0:23:21 allocation
0:23:22 changes
0:23:22 here
0:23:22 we can
0:23:23 we can
0:23:23 work
0:23:23 through
0:23:24 that
0:23:24 with
0:23:24 the
0:23:24 way
0:23:24 that
0:23:24 we
0:23:24 manage
0:23:25 money
0:23:25 if
0:23:26 it’s
0:23:26 40
0:23:26 that’s
0:23:26 different
0:23:27 and
0:23:28 so
0:23:28 that’s
0:23:29 essentially
0:23:29 the big
0:23:30 call
0:23:30 that
0:23:30 asset
0:23:31 allocators
0:23:31 and
0:23:31 ERISA
0:23:32 plans
0:23:32 and
0:23:32 endowments
0:23:32 and
0:23:33 foundations
0:23:33 have to
0:23:33 make
0:23:34 are we
0:23:35 looking at
0:23:35 a 12
0:23:36 to 15%
0:23:36 correction
0:23:37 or are we
0:23:37 looking at
0:23:37 something
0:23:38 that’s
0:23:38 going to
0:23:38 end up
0:23:38 at 40
0:23:40 right now
0:23:42 with
0:23:42 a little
0:23:42 bit of
0:23:43 Fed
0:23:43 easing
0:23:44 and
0:23:45 some
0:23:46 steady
0:23:46 momentum
0:23:47 I’m
0:23:48 more
0:23:49 inclined
0:23:49 to think
0:23:49 of the
0:23:50 12
0:23:50 to 15
0:23:50 and the
0:23:51 40
0:23:53 we’ll be
0:23:54 right back
0:23:54 after the
0:23:54 break
0:23:55 and if
0:23:55 you’re
0:23:55 enjoying
0:23:56 the show
0:23:56 so far
0:23:57 send it
0:23:57 to a
0:23:57 friend
0:23:58 and
0:23:58 please
0:23:59 follow us
0:23:59 if you
0:23:59 haven’t
0:24:00 already
0:24:22 it’s
0:24:22 all
0:24:22 of
0:24:22 those
0:24:23 things
0:24:23 and
0:24:23 then
0:24:23 some
0:24:23 at
0:24:24 a
0:24:24 fraction
0:24:24 of
0:24:24 the
0:24:25 price
0:24:25 and
0:24:25 bonus
0:24:25 it
0:24:26 tastes
0:24:26 great
0:24:27 all
0:24:27 Grun’s
0:24:28 daily
0:24:28 gummy
0:24:28 snack
0:24:28 packs
0:24:28 are
0:24:29 vegan
0:24:29 nut
0:24:30 gluten
0:24:30 dairy
0:24:30 free
0:24:31 but
0:24:31 no
0:24:31 artificial
0:24:32 flavors
0:24:32 or
0:24:32 colors
0:24:33 and
0:24:33 they’re
0:24:33 packed
0:24:33 with more than
0:24:34 20
0:24:34 vitamins
0:24:34 and
0:24:34 minerals
0:24:35 made
0:24:35 with more
0:24:36 than 60
0:24:37 nutrient-dense
0:24:37 ingredients
0:24:38 and whole
0:24:38 food
0:24:39 Grun’s
0:24:39 ingredients
0:24:39 are backed
0:24:40 by over
0:24:41 35,000
0:24:41 research
0:24:42 publications
0:24:43 and the
0:24:43 flavor
0:24:43 tastes
0:24:44 just like
0:24:44 sweet
0:24:45 tart
0:24:45 green apple
0:24:46 candy
0:24:46 and
0:24:46 for a
0:24:47 limited
0:24:47 time
0:24:47 you can
0:24:48 try
0:24:48 their
0:24:48 Gruny
0:24:49 Smith
0:24:49 Apple
0:24:49 flavor
0:24:50 just
0:24:50 in
0:24:50 time
0:24:50 for
0:24:50 fall
0:24:51 it’s
0:24:51 got
0:24:52 all
0:24:52 the
0:24:52 same
0:24:52 snackable
0:24:53 packable
0:24:53 full
0:24:53 body
0:24:54 benefits
0:24:54 you’ve
0:24:54 come
0:24:54 to
0:24:55 expect
0:24:55 but
0:24:55 this
0:24:56 time
0:24:56 these
0:24:57 taste
0:24:57 like
0:24:57 you’re
0:24:57 walking
0:24:58 through
0:24:58 an
0:24:58 apple
0:24:58 orchard
0:24:58 in
0:24:59 a
0:24:59 cable
0:24:59 knit
0:24:59 sweater
0:25:00 warm
0:25:00 apple
0:25:01 cider
0:25:01 in
0:25:01 hand
0:25:02 grab
0:25:02 your
0:25:02 limited
0:25:03 edition
0:25:03 Gruny
0:25:04 Smith
0:25:04 Apple
0:25:04 Grun’s
0:25:04 available
0:25:05 only
0:25:05 through
0:25:05 October
0:25:06 stock
0:25:06 up
0:25:06 because
0:25:06 they
0:25:06 will
0:25:07 sell
0:25:07 out
0:25:07 get
0:25:07 up
0:25:07 to
0:25:08 52%
0:25:08 off
0:25:08 when
0:25:09 you
0:25:09 go
0:25:09 to
0:25:10 gruns.co
0:25:11 and
0:25:11 use
0:25:11 the
0:25:12 code
0:25:12 markets
0:25:17 support
0:25:17 for the
0:25:17 show
0:25:17 comes
0:25:18 from
0:25:18 betterment
0:25:19 nobody
0:25:19 knows
0:25:19 what’s
0:25:19 going
0:25:19 to
0:25:19 happen
0:25:19 in
0:25:20 the
0:25:20 markets
0:25:20 tomorrow
0:25:20 that’s
0:25:21 why
0:25:21 when
0:25:21 it
0:25:21 comes
0:25:21 to
0:25:21 saving
0:25:22 and
0:25:22 investing
0:25:22 it
0:25:22 helps
0:25:23 to
0:25:23 have
0:25:23 a
0:25:23 long
0:25:23 term
0:25:24 approach
0:25:24 and
0:25:24 a
0:25:24 plan
0:25:25 you
0:25:25 can
0:25:25 stick
0:25:25 to
0:25:26 because
0:25:26 if
0:25:26 you
0:25:26 don’t
0:25:27 it’s
0:25:27 easy
0:25:27 to
0:25:28 make
0:25:28 hasty
0:25:28 decisions
0:25:28 that
0:25:29 could
0:25:29 potentially
0:25:29 impact
0:25:30 performance
0:25:31 betterment
0:25:31 is a
0:25:31 saving
0:25:31 and
0:25:32 investing
0:25:32 platform
0:25:32 with a
0:25:33 suite
0:25:33 of
0:25:33 tools
0:25:33 designed
0:25:33 to
0:25:34 prepare
0:25:34 you
0:25:34 for
0:25:34 whatever
0:25:34 is
0:25:35 around
0:25:35 the
0:25:35 corner
0:25:35 their
0:25:36 automated
0:25:36 investing
0:25:36 feature
0:25:37 helps
0:25:37 keep
0:25:37 you
0:25:37 on
0:25:37 track
0:25:38 for
0:25:38 your
0:25:38 goals
0:25:38 their
0:25:39 globally
0:25:39 diversified
0:25:40 portfolios
0:25:40 can
0:25:40 smooth
0:25:40 out
0:25:40 the
0:25:41 bumps
0:25:41 of
0:25:41 investing
0:25:41 and
0:25:42 prepare
0:25:42 you
0:25:42 to
0:25:42 take
0:25:42 advantage
0:25:42 of
0:25:43 long
0:25:43 term
0:25:43 trends
0:25:44 and
0:25:44 their
0:25:45 tax
0:25:45 smart
0:25:45 tools
0:25:45 can
0:25:46 potentially
0:25:46 help
0:25:46 you
0:25:47 save
0:25:47 money
0:25:47 on
0:25:47 taxes
0:25:47 in
0:25:48 short
0:25:49 betterment
0:25:49 helps
0:25:49 you
0:25:49 save
0:25:49 and
0:25:50 invest
0:25:50 like
0:25:50 the
0:25:50 experts
0:25:51 without
0:25:51 having
0:25:51 to
0:25:51 be
0:25:51 an
0:25:52 expert
0:25:52 yourself
0:25:53 and
0:25:53 while
0:25:54 you
0:25:54 go
0:25:54 about
0:25:54 your
0:25:54 day
0:25:55 betterment
0:25:55 team
0:25:55 of
0:25:55 experts
0:25:56 are
0:25:56 working
0:25:56 hard
0:25:57 behind
0:25:57 the
0:25:57 scenes
0:25:57 to
0:25:57 make
0:25:57 sure
0:25:57 you
0:25:58 have
0:25:58 everything
0:25:58 you
0:25:58 need
0:25:59 to
0:25:59 reach
0:25:59 your
0:25:59 financial
0:26:00 goals
0:26:00 so
0:26:01 be
0:26:01 invested
0:26:01 in
0:26:02 yourself
0:26:02 be
0:26:02 invested
0:26:03 in
0:26:03 your
0:26:03 business
0:26:03 be
0:26:04 invested
0:26:04 with
0:26:05 betterment
0:26:05 go to
0:26:06 betterment.com
0:26:07 to learn
0:26:07 more
0:26:07 that’s
0:26:10 b-e-t-t-e-r-m-e-n-t
0:26:10 dot com
0:26:11 investing
0:26:12 involves
0:26:12 risk
0:26:13 performance
0:26:13 not
0:26:13 guaranteed
0:26:19 support
0:26:19 for this
0:26:20 show
0:26:20 comes
0:26:20 from
0:26:21 Odoo
0:26:22 running
0:26:22 a
0:26:22 business
0:26:22 is
0:26:23 hard
0:26:23 enough
0:26:24 so
0:26:24 why
0:26:24 make
0:26:24 it
0:26:24 harder
0:26:25 with
0:26:25 a dozen
0:26:26 different
0:26:26 apps
0:26:26 that
0:26:27 don’t
0:26:27 talk
0:26:27 to
0:26:27 each
0:26:27 other
0:26:28 introducing
0:26:29 Odoo
0:26:30 it’s
0:26:30 the only
0:26:31 business
0:26:31 software
0:26:31 you’ll
0:26:31 ever
0:26:32 need
0:26:32 it’s
0:26:32 an
0:26:33 all-in-one
0:26:34 fully
0:26:34 integrated
0:26:35 platform
0:26:35 that
0:26:35 makes
0:26:36 your
0:26:36 work
0:26:36 easier
0:26:37 CRM
0:26:38 accounting
0:26:39 inventory
0:26:39 e-commerce
0:26:40 and more
0:26:41 and the
0:26:41 best part
0:26:42 Odoo
0:26:42 replaces
0:26:43 multiple
0:26:43 expensive
0:26:44 platforms
0:26:45 for a
0:26:45 fraction
0:26:45 of the
0:26:46 cost
0:26:46 that’s
0:26:46 why
0:26:47 over
0:26:47 thousands
0:26:48 of
0:26:48 businesses
0:26:48 have
0:26:48 made
0:26:49 the
0:26:49 switch
0:26:49 so
0:26:50 why
0:26:50 not
0:26:50 you
0:26:51 try
0:26:51 Odoo
0:26:52 for free
0:26:52 at
0:26:53 odoo.com
0:26:54 that’s
0:26:56 odoo.com
0:27:05 we’re back
0:27:05 with
0:27:06 property
0:27:06 markets
0:27:07 I just
0:27:07 want to
0:27:08 unpack
0:27:08 something
0:27:08 you said
0:27:09 about
0:27:09 the
0:27:09 Trump
0:27:10 administration
0:27:10 would
0:27:10 not
0:27:11 have
0:27:11 nearly
0:27:11 the
0:27:11 cloud
0:27:12 cover
0:27:12 for
0:27:12 some
0:27:12 of
0:27:12 the
0:27:12 things
0:27:13 he was
0:27:13 doing
0:27:13 if
0:27:14 these
0:27:14 10
0:27:15 companies
0:27:15 were
0:27:15 off
0:27:16 the
0:27:16 S&P
0:27:16 would
0:27:16 be
0:27:17 flat
0:27:17 or
0:27:17 down
0:27:17 GDP
0:27:18 the
0:27:19 stuff
0:27:19 I’ve
0:27:19 said
0:27:19 is
0:27:19 that
0:27:19 we’d
0:27:19 actually
0:27:20 already
0:27:20 be
0:27:20 in
0:27:20 a
0:27:21 recession
0:27:22 so
0:27:23 he
0:27:23 has
0:27:23 a
0:27:23 vested
0:27:24 interest
0:27:24 in
0:27:24 this
0:27:25 boom
0:27:25 continuing
0:27:26 I
0:27:26 wouldn’t
0:27:26 say
0:27:26 this
0:27:27 bubble
0:27:27 but
0:27:27 this
0:27:27 boom
0:27:28 continuing
0:27:29 doesn’t
0:27:29 that
0:27:29 I
0:27:29 feel
0:27:29 like
0:27:30 all
0:27:30 pass
0:27:30 lead
0:27:30 to
0:27:30 the
0:27:31 same
0:27:31 place
0:27:31 and
0:27:31 that
0:27:32 is
0:27:32 the
0:27:32 government
0:27:32 will
0:27:33 back
0:27:34 the
0:27:34 debt
0:27:34 to
0:27:35 continue
0:27:35 to
0:27:35 make
0:27:35 these
0:27:36 extraordinary
0:27:37 capital
0:27:38 purchases
0:27:38 which
0:27:39 in
0:27:39 my
0:27:39 view
0:27:40 weakens
0:27:41 the
0:27:41 strength
0:27:42 and
0:27:42 integrity
0:27:43 of
0:27:44 the
0:27:44 US
0:27:44 debt
0:27:45 markets
0:27:45 or
0:27:46 treasuries
0:27:47 and
0:27:47 it’s
0:27:47 like
0:27:47 a
0:27:47 further
0:27:48 move
0:27:48 into
0:27:49 socialism
0:27:49 but
0:27:50 I
0:27:50 predict
0:27:50 he
0:27:50 is
0:27:50 going
0:27:51 to
0:27:51 back
0:27:52 these
0:27:53 companies
0:27:54 not
0:27:54 the
0:27:55 real
0:27:55 economy
0:27:56 not
0:27:56 the
0:27:56 main
0:27:56 street
0:27:56 economy
0:27:57 not
0:27:57 the
0:27:57 490
0:27:58 companies
0:27:58 in
0:27:58 the
0:27:58 S&P
0:27:58 that
0:27:58 got
0:27:58 to
0:27:59 actually
0:27:59 compete
0:28:00 in
0:28:00 the
0:28:02 but
0:28:02 he
0:28:02 has
0:28:03 such
0:28:03 a
0:28:03 vested
0:28:03 interest
0:28:03 in
0:28:04 the
0:28:04 music
0:28:05 continuing
0:28:05 here
0:28:06 that
0:28:06 the
0:28:07 government
0:28:07 will
0:28:07 step
0:28:08 in
0:28:08 and
0:28:08 offer
0:28:08 in
0:28:09 some
0:28:09 way
0:28:09 to
0:28:10 back
0:28:10 the
0:28:11 exceptional
0:28:11 capital
0:28:12 expenditures
0:28:13 so they
0:28:13 can
0:28:14 continue
0:28:14 to
0:28:14 make
0:28:14 them
0:28:14 and
0:28:15 that
0:28:15 is
0:28:15 only
0:28:16 inflating
0:28:16 the
0:28:16 bubble
0:28:16 to
0:28:17 very
0:28:17 dangerous
0:28:18 territory
0:28:18 your
0:28:19 thoughts
0:28:19 on
0:28:19 that
0:28:19 thesis
0:28:20 that
0:28:20 would
0:28:21 worry
0:28:21 me
0:28:21 if
0:28:21 I
0:28:21 saw
0:28:21 it
0:28:23 I
0:28:23 would
0:28:24 they’ve
0:28:24 done
0:28:24 a
0:28:24 couple
0:28:24 things
0:28:25 so
0:28:25 far
0:28:25 that
0:28:25 I
0:28:25 would
0:28:25 in
0:28:26 fairness
0:28:26 put
0:28:26 in
0:28:26 different
0:28:27 bucket
0:28:27 I
0:28:27 like
0:28:27 I
0:28:28 you
0:28:28 know
0:28:28 Jamie
0:28:28 is a
0:28:29 very
0:28:29 inspirational
0:28:30 CEO
0:28:31 and
0:28:31 one
0:28:31 of
0:28:31 the
0:28:31 things
0:28:32 I
0:28:32 admire
0:28:32 most
0:28:32 about
0:28:32 him
0:28:33 is
0:28:33 he
0:28:33 calls
0:28:33 it
0:28:33 like
0:28:33 he
0:28:34 sees
0:28:34 it
0:28:34 right
0:28:35 so
0:28:38 they’ve
0:28:38 done
0:28:38 a couple
0:28:39 of deals
0:28:39 with
0:28:39 MP
0:28:40 materials
0:28:40 and
0:28:41 Intel
0:28:42 both
0:28:42 of
0:28:42 those
0:28:43 deals
0:28:43 fall
0:28:44 under
0:28:44 traditional
0:28:45 industrial
0:28:46 policy
0:28:47 of
0:28:47 trying
0:28:48 to
0:28:48 rescue
0:28:49 supply
0:28:49 chains
0:28:49 that
0:28:49 are
0:28:50 rapidly
0:28:50 diminishing
0:28:50 in
0:28:50 the
0:28:50 United
0:28:51 States
0:28:51 with
0:28:51 respect
0:28:52 to
0:28:52 domestic
0:28:53 semiconductor
0:28:53 production
0:28:54 and
0:28:55 critical
0:28:55 minerals
0:28:56 I
0:28:56 think
0:28:56 you’re
0:28:56 being
0:28:56 generous
0:28:57 when
0:28:57 they
0:28:57 pick
0:28:57 one
0:28:58 company
0:28:58 and
0:28:58 take
0:28:58 a
0:28:58 stake
0:28:59 and
0:28:59 they
0:28:59 take
0:28:59 a
0:28:59 golden
0:28:59 share
0:28:59 in
0:29:00 one
0:29:00 steel
0:29:00 company
0:29:01 I
0:29:01 would
0:29:01 argue
0:29:01 that
0:29:02 is
0:29:02 not
0:29:02 structural
0:29:02 or
0:29:03 there’s
0:29:03 not
0:29:03 a
0:29:03 systemic
0:29:04 strategy
0:29:04 there
0:29:04 that’s
0:29:04 the
0:29:05 blood
0:29:05 sugar
0:29:05 of a
0:29:05 man
0:29:05 who
0:29:05 thinks
0:29:06 he
0:29:06 can
0:29:06 run
0:29:06 these
0:29:06 businesses
0:29:07 better
0:29:07 than
0:29:07 the
0:29:08 private
0:29:08 sector
0:29:09 I’m
0:29:09 going
0:29:09 to
0:29:09 disagree
0:29:09 with
0:29:10 you
0:29:10 on
0:29:10 that
0:29:10 one
0:29:10 and
0:29:11 NP
0:29:11 materials
0:29:11 in
0:29:12 particular
0:29:14 I
0:29:14 mean
0:29:14 they
0:29:15 have
0:29:15 provided
0:29:15 a
0:29:16 price
0:29:16 floor
0:29:17 on
0:29:17 neodymium
0:29:18 and
0:29:18 presodymium
0:29:19 which is
0:29:19 needed
0:29:19 by the
0:29:19 US
0:29:20 military
0:29:20 it’s
0:29:20 the
0:29:21 last
0:29:21 man
0:29:22 standing
0:29:22 in
0:29:22 that
0:29:22 sector
0:29:24 if
0:29:24 you
0:29:25 told
0:29:25 me
0:29:25 that
0:29:26 a
0:29:27 presidential
0:29:28 administration
0:29:28 had to
0:29:29 administer
0:29:30 an
0:29:30 industrial
0:29:30 policy
0:29:31 to
0:29:31 pick
0:29:31 winners
0:29:31 and
0:29:32 losers
0:29:33 this
0:29:33 might
0:29:33 be
0:29:34 the
0:29:34 last
0:29:34 administration
0:29:35 I
0:29:35 would
0:29:35 want
0:29:35 to
0:29:35 do
0:29:35 that
0:29:36 that
0:29:36 said
0:29:37 you
0:29:37 know
0:29:37 you
0:29:37 don’t
0:29:38 get
0:29:38 the
0:29:38 time
0:29:39 and
0:29:39 place
0:29:40 and
0:29:40 as
0:29:41 things
0:29:41 stand
0:29:41 right
0:29:41 now
0:29:42 China
0:29:42 has
0:29:43 a
0:29:43 chokehold
0:29:43 on
0:29:44 those
0:29:44 critical
0:29:44 minerals
0:29:45 and
0:29:46 whatever
0:29:46 administration
0:29:47 is
0:29:47 in
0:29:47 power
0:29:48 is
0:29:49 responsible
0:29:49 for
0:29:49 making
0:29:50 sure
0:29:51 particularly
0:29:51 with
0:29:51 China
0:29:52 flexing
0:29:52 its
0:29:52 muscles
0:29:52 on
0:29:53 export
0:29:53 controls
0:29:54 that
0:29:54 we
0:29:54 start
0:29:55 the
0:29:55 process
0:29:56 of
0:29:56 trying
0:29:56 to
0:29:57 rebuild
0:29:57 both
0:29:58 mining
0:29:58 and
0:29:58 processing
0:29:58 of
0:29:59 critical
0:29:59 minerals
0:29:59 if
0:30:00 you
0:30:00 can’t
0:30:00 do
0:30:00 that
0:30:00 you’ve
0:30:00 got
0:30:01 a
0:30:01 wide
0:30:01 range
0:30:01 of
0:30:01 both
0:30:02 renewable
0:30:02 energy
0:30:03 and
0:30:03 military
0:30:04 applications
0:30:04 that
0:30:04 will
0:30:04 soon
0:30:05 become
0:30:06 untenable
0:30:06 that
0:30:07 has
0:30:07 to
0:30:07 be
0:30:07 done
0:30:08 I
0:30:08 would
0:30:09 have
0:30:09 preferred
0:30:09 for
0:30:10 Eisenhower
0:30:10 to do
0:30:10 it
0:30:11 okay
0:30:12 but
0:30:12 you know
0:30:12 I’m
0:30:13 63
0:30:14 so
0:30:15 I
0:30:15 probably
0:30:15 would
0:30:16 have
0:30:16 preferred
0:30:16 for
0:30:16 George
0:30:17 Bush
0:30:17 Senior
0:30:17 to do
0:30:18 it
0:30:19 right
0:30:20 I’m
0:30:20 not
0:30:20 sure
0:30:20 I
0:30:20 would
0:30:20 have
0:30:20 wanted
0:30:21 Obama
0:30:21 to
0:30:21 do
0:30:22 it
0:30:22 those
0:30:23 I’m
0:30:23 not
0:30:23 sure
0:30:23 I
0:30:23 would
0:30:23 have
0:30:23 wanted
0:30:24 Jennifer
0:30:24 Granholm
0:30:24 to do
0:30:25 it
0:30:26 so
0:30:26 but
0:30:26 you
0:30:26 don’t
0:30:26 get
0:30:26 to
0:30:27 pick
0:30:27 your
0:30:27 time
0:30:27 and
0:30:27 place
0:30:29 Intel
0:30:29 was
0:30:29 different
0:30:30 there’s
0:30:30 there’s
0:30:30 no
0:30:31 guarantee
0:30:31 of
0:30:32 demand
0:30:33 there’s
0:30:33 no
0:30:33 price
0:30:33 floors
0:30:34 it’s
0:30:34 basically
0:30:34 just
0:30:35 a
0:30:35 cash
0:30:35 infusion
0:30:36 and
0:30:36 based
0:30:36 on
0:30:37 everything
0:30:37 I
0:30:37 understand
0:30:37 about
0:30:38 Intel
0:30:38 their
0:30:38 problem
0:30:38 isn’t
0:30:39 money
0:30:40 so
0:30:40 the
0:30:40 worst
0:30:40 thing
0:30:41 about
0:30:41 that
0:30:41 one
0:30:41 is
0:30:41 I
0:30:42 don’t
0:30:42 even
0:30:42 think
0:30:42 whatever
0:30:43 it is
0:30:43 they
0:30:43 did
0:30:44 is
0:30:44 going
0:30:44 to
0:30:45 be
0:30:45 very
0:30:45 impactful
0:30:47 but
0:30:47 I
0:30:48 do
0:30:48 expect
0:30:49 to
0:30:49 see
0:30:51 more
0:30:53 intervention
0:30:53 by the
0:30:54 administration
0:30:55 in
0:30:57 companies
0:30:57 that
0:30:58 represent
0:30:58 the
0:30:59 tail
0:30:59 end
0:30:59 of
0:31:00 surviving
0:31:00 supply
0:31:01 chains
0:31:01 whether
0:31:01 it’s
0:31:02 ship
0:31:02 building
0:31:03 or
0:31:03 anything
0:31:03 else
0:31:04 and
0:31:04 I’m
0:31:05 actually
0:31:06 I’m
0:31:06 actually
0:31:07 I’m
0:31:07 not
0:31:07 I’m
0:31:08 not
0:31:08 a
0:31:08 heritage
0:31:08 Cato
0:31:09 guy
0:31:09 so
0:31:10 I’m
0:31:10 in
0:31:11 support
0:31:11 of
0:31:11 that
0:31:11 you
0:31:12 oversee
0:31:12 and
0:31:13 kind
0:31:13 of
0:31:13 set
0:31:13 the
0:31:14 strategy
0:31:14 and
0:31:14 the
0:31:15 narrative
0:31:15 and
0:31:15 the
0:31:15 theme
0:31:15 for
0:31:15 one
0:31:15 of
0:31:42 and
0:31:43 the
0:31:44 and
0:31:44 the
0:31:45 and
0:31:45 the
0:31:45 answer
0:31:45 to
0:31:45 the
0:31:46 question
0:31:46 I
0:31:46 think
0:31:46 you’ll
0:31:47 find
0:31:47 interesting
0:31:48 because
0:31:48 it’s
0:31:48 less
0:31:49 about
0:31:49 changing
0:31:49 the
0:31:50 asset
0:31:50 allocation
0:31:52 of
0:31:52 the
0:31:52 different
0:31:53 portfolios
0:31:54 it’s
0:31:54 more
0:31:54 about
0:31:55 explaining
0:31:55 to
0:31:56 clients
0:31:56 that
0:31:57 they
0:31:58 may be
0:31:58 better
0:31:59 off
0:31:59 switching
0:32:00 from
0:32:00 balanced
0:32:01 to
0:32:01 conservative
0:32:01 or from
0:32:02 growth
0:32:02 to
0:32:02 balanced
0:32:03 when
0:32:03 we
0:32:04 define
0:32:04 the
0:32:04 risk
0:32:05 contours
0:32:05 of a
0:32:05 balanced
0:32:05 portfolio
0:32:06 there
0:32:06 are
0:32:07 certain
0:32:07 parameters
0:32:08 that
0:32:08 we
0:32:08 can
0:32:08 go
0:32:09 outside
0:32:09 of
0:32:10 but
0:32:10 usually
0:32:10 tend
0:32:10 not
0:32:11 to
0:32:11 want
0:32:11 to
0:32:11 do
0:32:12 and
0:32:12 so
0:32:13 if
0:32:14 we’re
0:32:14 really
0:32:15 feeling
0:32:15 that
0:32:16 the
0:32:16 risk
0:32:16 return
0:32:16 of
0:32:16 a
0:32:17 growth
0:32:17 portfolio
0:32:17 is
0:32:18 changing
0:32:19 we
0:32:19 would
0:32:19 rather
0:32:19 have
0:32:19 the
0:32:20 student
0:32:20 body
0:32:22 change
0:32:22 from
0:32:23 being
0:32:23 pre-med
0:32:23 to
0:32:24 pre-law
0:32:24 and
0:32:25 migrate
0:32:25 down
0:32:26 to
0:32:26 the
0:32:26 balanced
0:32:27 portfolio
0:32:27 risk
0:32:28 rather
0:32:28 than
0:32:28 have
0:32:28 to
0:32:29 turn
0:32:29 the
0:32:30 balanced
0:32:30 portfolio
0:32:30 or
0:32:30 the
0:32:31 growth
0:32:31 portfolio
0:32:31 into
0:32:32 something
0:32:32 it
0:32:32 isn’t
0:32:33 because
0:32:33 there’s
0:32:33 always
0:32:33 going
0:32:33 to
0:32:34 be
0:32:34 people
0:32:34 with
0:32:36 generational
0:32:36 money
0:32:37 or
0:32:38 quite
0:32:38 frankly
0:32:40 the
0:32:40 city
0:32:41 of
0:32:41 Chicago
0:32:42 Cook
0:32:43 County
0:32:44 Illinois
0:32:44 New
0:32:44 Jersey
0:32:46 plans
0:32:46 that
0:32:46 are
0:32:46 kind
0:32:47 of
0:32:47 on
0:32:48 paper
0:32:48 in
0:32:49 extreme
0:32:49 distress
0:32:50 their
0:32:51 inclination
0:32:52 is to
0:32:52 take
0:32:53 a lot
0:32:53 of
0:32:53 risk
0:32:54 and
0:32:54 so
0:32:55 I
0:32:55 don’t
0:32:55 want
0:32:55 to
0:32:55 change
0:32:56 what
0:32:56 a
0:32:56 growth
0:32:56 portfolio
0:32:57 does
0:32:57 because
0:32:57 there
0:32:58 are
0:32:58 people
0:32:58 that
0:32:59 are
0:32:59 going
0:33:00 to
0:33:00 allocate
0:33:00 to
0:33:00 that
0:33:00 and
0:33:01 have
0:33:01 every
0:33:01 right
0:33:01 to
0:33:02 expect
0:33:02 the
0:33:02 growth
0:33:03 portfolio
0:33:03 to
0:33:03 be
0:33:04 positioned
0:33:04 the
0:33:04 way
0:33:04 a
0:33:04 growth
0:33:05 portfolio
0:33:05 looks
0:33:06 so
0:33:06 part
0:33:06 of
0:33:06 our
0:33:07 job
0:33:07 is
0:33:07 to
0:33:07 explain
0:33:08 to
0:33:09 whoever
0:33:09 wants
0:33:09 to
0:33:10 listen
0:33:11 that
0:33:11 the
0:33:12 risk
0:33:12 return
0:33:13 is
0:33:13 skewing
0:33:13 because
0:33:13 of
0:33:13 where
0:33:13 the
0:33:14 valuations
0:33:14 are
0:33:14 so
0:33:14 you
0:33:15 might
0:33:15 want
0:33:15 to
0:33:15 move
0:33:16 down
0:33:16 the
0:33:16 portfolio
0:33:17 chain
0:33:17 what
0:33:18 does
0:33:18 a
0:33:18 balanced
0:33:19 or
0:33:19 more
0:33:20 defensive
0:33:20 portfolio
0:33:21 look
0:33:22 like
0:33:22 for
0:33:22 those
0:33:23 just
0:33:23 at
0:33:23 a
0:33:23 high
0:33:23 high
0:33:24 level
0:33:25 general
0:33:25 level
0:33:26 like
0:33:26 what
0:33:26 does
0:33:26 that
0:33:27 portfolio
0:33:27 look
0:33:27 like
0:33:28 bonds
0:33:28 versus
0:33:29 stocks
0:33:29 you’re
0:33:30 talking
0:33:30 about
0:33:30 30 to
0:33:31 40 percent
0:33:31 of the
0:33:32 portfolio
0:33:32 being
0:33:33 in some
0:33:34 combination
0:33:36 of cash
0:33:37 short-term
0:33:39 A1 P1
0:33:39 commercial
0:33:40 paper
0:33:42 municipals
0:33:43 for
0:33:43 taxable
0:33:44 clients
0:33:45 super
0:33:46 diversified
0:33:48 hedge
0:33:48 funds
0:33:49 like
0:33:50 30 to
0:33:50 40
0:33:50 of
0:33:51 them
0:33:52 where
0:33:52 like
0:33:53 the
0:33:53 vol
0:33:53 turns out
0:33:54 to be
0:33:54 something
0:33:54 like
0:33:55 5 or
0:33:56 6 percent
0:33:58 and
0:33:59 some
0:34:00 preferred
0:34:00 stock
0:34:01 things like
0:34:01 that
0:34:01 so
0:34:02 you’re
0:34:02 kind of
0:34:02 taking
0:34:02 a lot
0:34:03 of the
0:34:03 directional
0:34:03 beta
0:34:04 risk
0:34:04 out
0:34:04 of
0:34:04 it
0:34:06 you
0:34:07 would
0:34:08 you know
0:34:08 you’re
0:34:08 obviously
0:34:09 positioning
0:34:09 much
0:34:09 more
0:34:10 defensively
0:34:11 healthcare
0:34:12 is
0:34:12 trading
0:34:13 at
0:34:13 like
0:34:14 the
0:34:14 cheapest
0:34:15 valuation
0:34:15 on record
0:34:16 relative
0:34:16 to itself
0:34:17 and relative
0:34:17 to the
0:34:17 market
0:34:18 so
0:34:19 if
0:34:19 you’re
0:34:19 going
0:34:19 to
0:34:19 take
0:34:20 a
0:34:20 position
0:34:21 someplace
0:34:22 go
0:34:23 someplace
0:34:23 where
0:34:23 you
0:34:23 can
0:34:23 be
0:34:23 paid
0:34:24 for
0:34:24 the
0:34:24 risk
0:34:24 a little
0:34:24 bit
0:34:25 better
0:34:25 you know
0:34:25 so
0:34:25 stuff
0:34:26 like
0:34:26 that
0:34:26 how
0:34:27 is
0:34:27 tech
0:34:28 valued
0:34:28 at
0:34:29 large
0:34:29 right
0:34:29 now
0:34:29 in terms
0:34:29 of
0:34:30 paying
0:34:30 you
0:34:30 for
0:34:30 the
0:34:31 risk
0:34:31 the
0:34:31 first
0:34:32 thing
0:34:32 people
0:34:32 tend
0:34:32 to
0:34:32 do
0:34:32 is
0:34:33 say
0:34:33 I’m
0:34:33 going
0:34:33 to
0:34:33 look
0:34:40 here
0:34:40 staples
0:34:41 but
0:34:42 for
0:34:42 a
0:34:42 hundred
0:34:42 years
0:34:43 people
0:34:43 have
0:34:43 been
0:34:43 adjusting
0:34:44 PE
0:34:44 ratios
0:34:45 for
0:34:45 growth
0:34:46 like
0:34:46 for
0:34:46 earnings
0:34:46 growth
0:34:47 and ROE
0:34:48 and ROA
0:34:49 and capital
0:34:49 efficiency
0:34:50 so
0:34:51 if
0:34:51 and one of the
0:34:52 things we do
0:34:52 and we share
0:34:53 this in all
0:34:53 our publications
0:34:54 is we plot
0:34:56 both stocks
0:34:57 and sectors
0:34:57 and industries
0:34:58 with
0:34:59 return on
0:35:00 equity
0:35:01 on the
0:35:01 x-axis
0:35:02 and either
0:35:03 PE or
0:35:03 price to
0:35:04 book on
0:35:04 the y-axis
0:35:05 and so
0:35:07 there’s a lot
0:35:07 more
0:35:08 consistency
0:35:09 to the way
0:35:09 the market’s
0:35:09 valued
0:35:10 once you
0:35:10 adjust
0:35:10 for
0:35:10 earnings
0:35:11 growth
0:35:11 now you
0:35:12 may think
0:35:12 the slope
0:35:12 of the
0:35:12 curve
0:35:13 is too
0:35:13 steep
0:35:13 and I
0:35:14 wouldn’t
0:35:14 disagree
0:35:14 with you
0:35:15 but to
0:35:15 just
0:35:15 kind
0:35:15 of
0:35:16 look
0:35:16 at
0:35:16 PE
0:35:17 differentials
0:35:17 is
0:35:17 really
0:35:18 missing
0:35:18 the
0:35:18 point
0:35:19 that
0:35:19 even
0:35:20 before
0:35:20 this
0:35:21 generative
0:35:21 AI
0:35:21 boom
0:35:22 and we
0:35:22 roll
0:35:22 back
0:35:22 the
0:35:22 clock
0:35:23 2019
0:35:24 the
0:35:24 tech
0:35:25 sector
0:35:25 had
0:35:25 double
0:35:26 the
0:35:26 margins
0:35:26 of
0:35:26 the
0:35:27 rest
0:35:27 of
0:35:27 the
0:35:27 market
0:35:28 they
0:35:28 had
0:35:29 become
0:35:29 the
0:35:29 things
0:35:30 that
0:35:30 the
0:35:30 tech
0:35:31 investors
0:35:31 of
0:35:31 2000
0:35:32 would
0:35:32 eventually
0:35:33 dream
0:35:33 they
0:35:33 would
0:35:33 be
0:35:34 which
0:35:34 is
0:35:35 highly
0:35:35 capital
0:35:35 efficient
0:35:36 low
0:35:37 employment
0:35:37 businesses
0:35:38 with
0:35:38 huge
0:35:39 operating
0:35:39 margins
0:35:40 so
0:35:41 that’s
0:35:41 what
0:35:41 they
0:35:41 were
0:35:42 even
0:35:42 before
0:35:42 this
0:35:42 whole
0:35:43 AI
0:35:43 boom
0:35:43 started
0:35:44 yeah
0:35:44 something
0:35:44 that
0:35:44 I
0:35:45 was
0:35:45 thinking
0:35:45 about
0:35:45 after
0:35:45 we
0:35:46 spoke
0:35:46 with
0:35:46 Aswath
0:35:46 I
0:35:47 mean
0:35:47 his
0:35:47 his
0:35:48 core
0:35:49 valuation
0:35:49 tool
0:35:49 is
0:35:49 the
0:35:50 equity
0:35:50 risk
0:35:50 premium
0:35:51 which
0:35:51 basically
0:35:52 he’s
0:35:52 taking
0:35:52 the
0:35:53 rate
0:35:53 of
0:35:53 return
0:35:54 on
0:35:54 stocks
0:35:54 minus
0:35:55 the
0:35:56 risk
0:35:56 free
0:35:56 rate
0:35:56 and
0:35:57 he
0:35:57 sort
0:35:57 of
0:35:57 plots
0:35:58 that
0:35:58 out
0:35:58 yeah
0:35:59 and
0:36:00 you
0:36:00 know
0:36:01 you’re
0:36:01 not
0:36:01 a
0:36:01 fan
0:36:02 it
0:36:03 doesn’t
0:36:03 have
0:36:03 any
0:36:03 nuance
0:36:03 to
0:36:04 it
0:36:04 it
0:36:04 doesn’t
0:36:04 look
0:36:04 at
0:36:05 earnings
0:36:05 growth
0:36:05 and
0:36:05 more
0:36:05 it
0:36:06 doesn’t
0:36:06 differentiate
0:36:06 between
0:36:07 sectors
0:36:07 and
0:36:08 you know
0:36:09 and
0:36:09 it’s
0:36:10 I
0:36:10 understand
0:36:10 why
0:36:10 people
0:36:10 want
0:36:10 to
0:36:11 look
0:36:11 at
0:36:11 it
0:36:11 over
0:36:11 the
0:36:11 really
0:36:12 long
0:36:12 run
0:36:12 it
0:36:12 can
0:36:12 be
0:36:13 a
0:36:13 helpful
0:36:13 tool
0:36:14 yeah
0:36:14 well
0:36:14 I
0:36:15 think
0:36:15 it’s
0:36:15 helpful
0:36:16 when
0:36:17 you’re
0:36:17 trying
0:36:17 to
0:36:17 take
0:36:17 a
0:36:18 bird’s
0:36:18 eye
0:36:18 view
0:36:18 and
0:36:18 understand
0:36:19 where
0:36:19 we
0:36:19 are
0:36:20 sentiment
0:36:20 and
0:36:20 the
0:36:21 thing
0:36:21 that
0:36:21 kind
0:36:21 of
0:36:22 surprised
0:36:22 me
0:36:23 is
0:36:23 you
0:36:23 know
0:36:24 we’re
0:36:24 at
0:36:24 around
0:36:25 3.7%
0:36:26 by that
0:36:26 measure
0:36:26 and
0:36:27 you know
0:36:27 to be
0:36:27 fair to him
0:36:28 he says
0:36:29 anything below
0:36:30 4%
0:36:30 is a little
0:36:30 bit
0:36:31 dangerous
0:36:31 but
0:36:31 we’re
0:36:32 also
0:36:32 nowhere
0:36:32 near
0:36:32 where
0:36:33 we
0:36:33 were
0:36:33 in
0:36:34 99
0:36:34 when
0:36:34 it
0:36:34 was
0:36:35 around
0:36:35 2%
0:36:36 and
0:36:37 you
0:36:37 know
0:36:37 you
0:36:37 you’re
0:36:38 someone
0:36:38 I
0:36:38 mean
0:36:39 you
0:36:39 were
0:36:39 a
0:36:39 managing
0:36:40 director
0:36:41 during
0:36:41 the
0:36:42 dot-com
0:36:42 boom
0:36:43 and bust
0:36:44 you
0:36:44 were
0:36:45 the
0:36:45 chief
0:36:45 investment
0:36:46 officer
0:36:46 of
0:36:46 JP
0:36:46 Morgan
0:36:47 during
0:36:47 the
0:36:48 2008
0:36:48 financial
0:36:49 crisis
0:36:49 so
0:36:50 you’ve
0:36:50 seen
0:36:50 these
0:36:50 cycles
0:36:51 before
0:36:51 and
0:36:52 I
0:36:52 think
0:36:52 what
0:36:52 we’re
0:36:52 describing
0:36:53 here
0:36:53 is
0:36:53 like
0:36:53 how
0:36:53 do
0:36:53 we
0:36:54 put
0:36:54 this
0:36:54 in
0:36:54 the
0:36:54 context
0:36:55 of
0:36:55 not
0:36:56 just
0:36:56 tech
0:36:57 versus
0:36:58 industrials
0:36:58 versus
0:36:59 consumer
0:37:00 but
0:37:00 how do
0:37:01 we
0:37:01 put it
0:37:01 in
0:37:01 the
0:37:01 context
0:37:01 of
0:37:02 all
0:37:02 of
0:37:02 history
0:37:03 and
0:37:03 what
0:37:03 does
0:37:03 this
0:37:04 look
0:37:04 like
0:37:04 compared
0:37:05 to
0:37:05 previous
0:37:06 crises
0:37:06 and
0:37:06 previous
0:37:07 cycles
0:37:07 I
0:37:08 couldn’t
0:37:08 sleep
0:37:10 in
0:37:10 March
0:37:10 of
0:37:11 2008
0:37:11 like
0:37:11 I
0:37:12 couldn’t
0:37:12 sleep
0:37:13 the
0:37:14 things
0:37:14 that
0:37:14 we
0:37:14 were
0:37:14 seeing
0:37:14 on
0:37:15 a
0:37:15 daily
0:37:15 basis
0:37:15 were
0:37:16 so
0:37:16 bad
0:37:19 and
0:37:19 we
0:37:19 knew
0:37:19 that
0:37:20 it
0:37:20 was
0:37:21 infecting
0:37:22 the
0:37:22 entire
0:37:23 network
0:37:24 of
0:37:24 the
0:37:24 financial
0:37:24 system
0:37:25 and
0:37:25 we
0:37:25 went
0:37:26 underweight
0:37:26 both
0:37:27 stocks
0:37:27 and
0:37:28 high
0:37:28 yield
0:37:29 the
0:37:29 only
0:37:29 mistake
0:37:29 we
0:37:30 made
0:37:30 is
0:37:30 we
0:37:30 didn’t
0:37:30 do
0:37:30 more
0:37:31 of
0:37:31 it
0:37:31 just
0:37:32 to
0:37:32 show
0:37:32 you
0:37:32 how
0:37:33 hidden
0:37:33 those
0:37:33 risks
0:37:34 were
0:37:34 JP
0:37:35 Morgan
0:37:35 which
0:37:35 is
0:37:35 an
0:37:35 exceptionally
0:37:36 run
0:37:36 bank
0:37:37 bought
0:37:38 a
0:37:38 couple
0:37:41 summer
0:37:42 for
0:37:42 its
0:37:42 own
0:37:42 balance
0:37:42 sheet
0:37:43 three
0:37:43 months
0:37:43 before
0:37:44 they
0:37:44 went
0:37:44 to
0:37:44 conservatorship
0:37:45 right
0:37:45 so
0:37:45 that’s
0:37:46 how
0:37:46 hidden
0:37:47 the
0:37:48 depth
0:37:48 of
0:37:48 the
0:37:49 problems
0:37:49 were
0:37:51 2001
0:37:52 was
0:37:53 easier
0:37:54 because
0:37:55 the
0:37:55 companies
0:37:55 weren’t
0:37:56 making
0:37:56 any
0:37:56 money
0:37:57 and
0:37:57 so
0:37:57 as
0:37:58 long
0:37:58 as
0:37:58 you
0:37:58 had
0:38:00 some
0:38:00 degree
0:38:00 of
0:38:01 discipline
0:38:01 and
0:38:02 support
0:38:02 from
0:38:02 your
0:38:02 investment
0:38:03 committee
0:38:03 to
0:38:04 go
0:38:04 through
0:38:04 a
0:38:05 period
0:38:05 of
0:38:05 temporary
0:38:06 underperformance
0:38:06 as the
0:38:06 market
0:38:06 was
0:38:07 rocking
0:38:08 you
0:38:08 did
0:38:08 fine
0:38:09 right
0:38:10 and so
0:38:10 that
0:38:10 in a way
0:38:11 that was
0:38:11 the
0:38:11 easiest
0:38:12 one
0:38:14 I
0:38:14 remember
0:38:16 we had
0:38:16 this
0:38:17 annual
0:38:17 MD
0:38:17 meeting
0:38:18 and
0:38:19 the
0:38:19 chairman
0:38:19 at the
0:38:20 time
0:38:20 invited
0:38:20 a
0:38:21 company
0:38:21 to
0:38:21 come
0:38:21 speak
0:38:21 to
0:38:22 us
0:38:22 in
0:38:22 1999
0:38:23 he
0:38:23 invited
0:38:23 a
0:38:24 CEO
0:38:24 to
0:38:24 come
0:38:24 on
0:38:24 stage
0:38:24 and
0:38:25 address
0:38:25 all
0:38:25 the
0:38:25 MDs
0:38:26 and
0:38:26 he
0:38:26 was
0:38:26 the
0:38:27 CEO
0:38:27 of
0:38:27 a
0:38:27 company
0:38:27 called
0:38:28 the
0:38:29 globe.com
0:38:29 so
0:38:29 I
0:38:29 pulled
0:38:30 up
0:38:30 the
0:38:31 globe.com
0:38:31 on
0:38:32 Bloomberg
0:38:32 and I
0:38:32 hit
0:38:33 DBS
0:38:33 and
0:38:34 it
0:38:34 said
0:38:34 this
0:38:35 company
0:38:35 has
0:38:35 no
0:38:36 business
0:38:36 model
0:38:41 invited
0:38:41 a
0:38:41 guy
0:38:41 who
0:38:42 runs
0:38:42 a
0:38:42 company
0:38:42 with
0:38:42 no
0:38:43 business
0:38:43 model
0:38:43 to
0:38:43 address
0:38:44 us
0:38:44 I
0:38:44 think
0:38:45 we’ll
0:38:45 go
0:38:45 underweight
0:38:46 now
0:38:46 right
0:38:46 I
0:38:46 mean
0:38:46 so
0:38:47 that
0:38:47 was
0:38:47 pretty
0:38:48 easy
0:38:49 this
0:38:49 this
0:38:50 is
0:38:51 a
0:38:51 harder
0:38:52 one
0:38:53 because
0:38:54 you
0:38:54 know
0:38:54 in
0:38:54 many
0:38:54 ways
0:38:55 Google
0:38:55 is
0:38:55 one
0:38:55 of
0:38:55 the
0:38:55 most
0:38:56 successful
0:38:56 companies
0:38:57 in
0:38:58 the
0:38:58 history
0:38:58 of
0:38:59 the
0:38:59 markets
0:39:00 I
0:39:00 personally
0:39:01 think
0:39:01 their
0:39:01 AI
0:39:02 related
0:39:02 language
0:39:02 model
0:39:03 products
0:39:03 are
0:39:03 better
0:39:03 than
0:39:04 everybody
0:39:04 else
0:39:04 and
0:39:05 I
0:39:05 think
0:39:05 they’re
0:39:05 the
0:39:05 long
0:39:05 term
0:39:06 winners
0:39:06 in
0:39:06 this
0:39:06 race
0:39:07 and
0:39:07 so
0:39:08 I
0:39:08 have
0:39:08 a
0:39:08 lot
0:39:08 of
0:39:09 respect
0:39:09 for
0:39:09 what
0:39:10 that
0:39:10 company
0:39:10 has
0:39:10 accomplished
0:39:12 you
0:39:12 know
0:39:12 I
0:39:13 have
0:39:13 similar
0:39:14 feelings
0:39:14 about
0:39:14 what
0:39:18 but
0:39:19 like
0:39:19 Microsoft
0:39:20 signed
0:39:20 a
0:39:20 deal
0:39:21 recently
0:39:22 with
0:39:23 Constellation
0:39:23 Energy
0:39:24 to turn
0:39:25 back on
0:39:25 a nuclear
0:39:26 power
0:39:26 plant
0:39:26 in
0:39:27 Three Mile
0:39:27 Island
0:39:29 They’ve
0:39:29 agreed to
0:39:30 pay
0:39:31 something
0:39:31 in the
0:39:31 neighborhood
0:39:31 of
0:39:32 $130
0:39:32 a
0:39:33 megawatt
0:39:33 hour
0:39:33 now
0:39:34 most
0:39:34 of
0:39:34 your
0:39:34 listeners
0:39:35 don’t
0:39:35 know
0:39:35 what
0:39:35 that
0:39:36 means
0:39:37 that’s
0:39:37 double
0:39:38 wholesale
0:39:38 power
0:39:38 prices
0:39:39 for
0:39:40 an
0:39:40 average
0:39:41 20
0:39:41 year
0:39:42 PPA
0:39:42 agreement
0:39:43 the
0:39:43 other
0:39:44 risk
0:39:44 that
0:39:44 we
0:39:44 need
0:39:44 to
0:39:45 acknowledge
0:39:45 is
0:39:45 we’re
0:39:46 getting
0:39:46 closer
0:39:47 to a
0:39:47 power
0:39:48 wall
0:39:49 that
0:39:49 will
0:39:50 prevent
0:39:50 open
0:39:51 AI
0:39:51 from
0:39:51 getting
0:39:52 anywhere
0:39:52 near
0:39:53 like
0:39:54 they’ve
0:39:54 announced
0:39:55 partnerships
0:39:55 with
0:39:56 Broadcom
0:39:57 Oracle
0:39:58 AMD
0:40:00 and
0:40:01 NVIDIA
0:40:02 that would
0:40:02 require
0:40:03 30
0:40:04 gigawatts
0:40:05 of power
0:40:06 which is
0:40:06 the
0:40:06 equivalent
0:40:07 of
0:40:08 16
0:40:08 Hoover
0:40:09 dams
0:40:09 like
0:40:10 that’s
0:40:10 just
0:40:10 not
0:40:10 going
0:40:10 to
0:40:10 happen
0:40:12 so
0:40:12 what
0:40:13 we’re
0:40:13 trying
0:40:13 to
0:40:13 figure
0:40:13 out
0:40:14 is
0:40:14 how
0:40:15 much
0:40:15 of
0:40:15 what’s
0:40:15 in
0:40:15 the
0:40:16 price
0:40:16 of
0:40:16 this
0:40:17 whole
0:40:17 AI
0:40:18 boom
0:40:18 is
0:40:18 the
0:40:19 expectation
0:40:19 that
0:40:20 these
0:40:20 announcements
0:40:20 are
0:40:21 actually
0:40:21 going
0:40:21 to
0:40:21 come
0:40:21 to
0:40:22 fruition
0:40:22 under
0:40:23 some
0:40:23 three
0:40:23 to
0:40:23 five
0:40:24 year
0:40:24 time
0:40:24 frame
0:40:25 because
0:40:25 it’s
0:40:26 impossible
0:40:26 yeah
0:40:27 it seems
0:40:28 it seems
0:40:28 kind of
0:40:29 obvious
0:40:29 to almost
0:40:30 everyone
0:40:30 that I
0:40:31 mean if
0:40:31 you look
0:40:31 at the
0:40:31 numbers
0:40:32 when you
0:40:32 see
0:40:33 the
0:40:33 amount
0:40:33 of
0:40:34 power
0:40:34 that
0:40:34 it’s
0:40:34 going
0:40:34 to
0:40:35 take
0:40:35 to
0:40:35 build
0:40:35 out
0:40:36 AI
0:40:36 the way
0:40:36 open
0:40:36 AI
0:40:37 would
0:40:37 like
0:40:38 it’s
0:40:38 like
0:40:38 oh
0:40:39 yeah
0:40:39 that’s
0:40:39 that’s
0:40:39 not
0:40:40 possible
0:40:41 and
0:40:41 you know
0:40:41 one
0:40:42 of the
0:40:42 things
0:40:42 that
0:40:42 we’ve
0:40:42 been
0:40:42 talking
0:40:43 a lot
0:40:43 about
0:40:44 recently
0:40:44 is that
0:40:44 crazy
0:40:45 interaction
0:40:46 between
0:40:47 Sam Altman
0:40:47 and Brad
0:40:47 Gerstner
0:40:48 I don’t know
0:40:48 if you
0:40:48 saw this
0:40:49 where Brad
0:40:50 Gerstner
0:40:50 says
0:40:51 you know
0:40:51 you’re
0:40:51 going to
0:40:52 spend
0:40:52 you’re
0:40:52 making
0:40:52 13
0:40:53 billion
0:40:53 dollars
0:40:53 in
0:40:53 revenue
0:40:54 you plan
0:40:54 to spend
0:40:55 one and a half
0:40:55 trillion
0:40:56 dollars
0:40:56 over the
0:40:56 next few
0:40:56 years
0:40:57 how
0:40:57 are you
0:40:57 going
0:40:58 to pay
0:40:58 for it
0:40:59 to which
0:40:59 Sam Altman
0:41:00 responds
0:41:01 if you
0:41:01 want to
0:41:02 sell your
0:41:02 stock
0:41:02 you can
0:41:03 be my
0:41:03 guest
0:41:04 there are
0:41:04 plenty of
0:41:05 other
0:41:05 buyers
0:41:05 out there
0:41:06 who would
0:41:06 love to
0:41:06 buy
0:41:06 open
0:41:07 AI
0:41:07 stock
0:41:08 it
0:41:09 sounds
0:41:09 like
0:41:09 what
0:41:09 happens
0:41:10 when
0:41:11 there’s
0:41:11 a
0:41:11 syndicated
0:41:12 loan
0:41:14 distribution
0:41:14 now
0:41:14 on
0:41:15 Wall
0:41:15 Street
0:41:16 there’s
0:41:16 so
0:41:16 much
0:41:17 demand
0:41:17 that
0:41:18 if
0:41:18 you
0:41:19 press
0:41:19 the
0:41:19 button
0:41:20 that
0:41:20 says
0:41:20 on
0:41:20 the
0:41:20 syndicate
0:41:21 call
0:41:21 I
0:41:21 have
0:41:21 a
0:41:21 question
0:41:22 about
0:41:22 the
0:41:22 documents
0:41:23 you
0:41:23 get
0:41:24 disconnected
0:41:24 like
0:41:25 if
0:41:25 you
0:41:25 have
0:41:25 a
0:41:26 question
0:41:26 on
0:41:26 the
0:41:26 docs
0:41:26 you
0:41:27 don’t
0:41:27 need
0:41:27 to
0:41:27 be
0:41:27 part
0:41:27 of
0:41:28 the
0:41:28 syndicate
0:41:29 so
0:41:31 I would
0:41:31 agree
0:41:31 with you
0:41:31 those
0:41:31 are the
0:41:32 signs
0:41:32 that
0:41:33 are
0:41:33 kind
0:41:33 of
0:41:33 telling
0:41:33 me
0:41:34 that
0:41:34 we’ve
0:41:34 entered
0:41:34 into
0:41:34 a
0:41:35 period
0:41:35 where
0:41:36 the
0:41:37 risk
0:41:37 taking
0:41:37 isn’t
0:41:38 there
0:41:39 that
0:41:39 the
0:41:39 underwriting
0:41:40 has
0:41:40 gotten
0:41:40 sloppy
0:41:42 when
0:41:43 we
0:41:43 look
0:41:43 at
0:41:43 like
0:41:44 in
0:41:44 the
0:41:45 innards
0:41:45 of
0:41:45 the
0:41:45 private
0:41:46 credit
0:41:46 markets
0:41:47 private
0:41:48 credit
0:41:48 five
0:41:49 years
0:41:49 ago
0:41:50 was
0:41:50 substantially
0:41:51 different
0:41:51 than
0:41:51 leveraged
0:41:51 loans
0:41:52 in
0:41:53 really
0:41:53 boring
0:41:53 ways
0:41:54 having
0:41:54 to do
0:41:54 with
0:41:54 maintenance
0:41:55 covenants
0:41:56 IP
0:41:57 blockers
0:41:57 and
0:41:57 all
0:41:57 sorts
0:41:58 of
0:41:58 stuff
0:41:58 and
0:41:59 over
0:41:59 that
0:41:59 five
0:41:59 year
0:42:00 period
0:42:00 it’s
0:42:01 converging
0:42:01 rapidly
0:42:02 towards
0:42:02 the
0:42:03 leveraged
0:42:03 loan
0:42:03 market
0:42:03 which
0:42:04 basically
0:42:04 doesn’t
0:42:05 apply
0:42:05 much
0:42:05 of
0:42:05 an
0:42:06 underwriting
0:42:07 wall
0:42:07 at
0:42:07 all
0:42:08 you
0:42:08 know
0:42:08 so
0:42:08 we’re
0:42:09 kind
0:42:09 of
0:42:10 preparing
0:42:10 for
0:42:12 a
0:42:14 profit
0:42:14 taking
0:42:15 spark
0:42:16 that
0:42:17 comes
0:42:17 from
0:42:18 things
0:42:18 we
0:42:18 might
0:42:18 not
0:42:18 be
0:42:18 able
0:42:18 to
0:42:19 anticipate
0:42:20 a
0:42:21 violent
0:42:22 correction
0:42:23 in
0:42:24 overbid
0:42:24 assets
0:42:26 and
0:42:26 then
0:42:26 some
0:42:27 period
0:42:27 of
0:42:27 kind
0:42:27 of
0:42:28 recovery
0:42:28 and
0:42:28 calm
0:42:29 that
0:42:30 follows
0:42:30 in a
0:42:31 previous
0:42:31 life
0:42:31 Michael
0:42:31 I
0:42:32 used
0:42:32 to
0:42:32 take
0:42:32 boards
0:42:33 and
0:42:33 management
0:42:33 teams
0:42:34 outside
0:42:34 and
0:42:34 do
0:42:34 scenario
0:42:35 planning
0:42:35 as
0:42:36 an
0:42:36 exercise
0:42:36 for
0:42:37 how
0:42:37 they
0:42:37 allocate
0:42:37 their
0:42:38 capital
0:42:38 and
0:42:38 if
0:42:38 I
0:42:38 were
0:42:38 going
0:42:39 to
0:42:39 do
0:42:39 this
0:42:40 for
0:42:41 a
0:42:41 bank
0:42:41 or
0:42:41 anyone
0:42:42 else
0:42:42 there’s
0:42:42 a
0:42:42 couple
0:42:43 scenarios
0:42:43 I
0:42:43 want
0:42:43 to
0:42:44 outline
0:42:44 and
0:42:44 you
0:42:44 tell
0:42:44 me
0:42:45 if
0:42:45 they’re
0:42:45 realistic
0:42:46 if
0:42:46 you
0:42:46 think
0:42:46 they
0:42:46 might
0:42:47 happen
0:42:47 and
0:42:47 if
0:42:47 so
0:42:47 how
0:42:47 to
0:42:48 respond
0:42:49 the
0:42:49 first
0:42:50 scenario
0:42:50 is
0:42:51 all
0:42:51 right
0:42:52 built
0:42:52 into
0:42:52 these
0:42:54 valuations
0:42:54 of
0:42:54 these
0:42:54 AI
0:42:55 companies
0:42:55 is
0:42:56 what
0:42:56 I’ve
0:42:56 read
0:42:56 the
0:42:57 assumption
0:42:57 that
0:42:57 they’re
0:42:57 going
0:42:57 to
0:42:57 be
0:42:58 able
0:42:58 to
0:42:58 find
0:42:58 or
0:42:59 inspire
0:43:00 across
0:43:00 their
0:43:01 clients
0:43:01 three
0:43:02 to
0:43:02 five
0:43:02 trillion
0:43:02 dollars
0:43:03 incremental
0:43:03 revenues
0:43:04 or
0:43:05 efficiencies
0:43:06 now
0:43:06 I
0:43:07 don’t
0:43:07 see a lot
0:43:07 of
0:43:07 AI
0:43:08 moisturizer
0:43:08 out
0:43:09 there
0:43:09 what
0:43:09 I
0:43:09 do
0:43:09 see
0:43:09 is
0:43:10 companies
0:43:10 saying
0:43:11 okay
0:43:12 we’re
0:43:12 cutting
0:43:13 our
0:43:13 legal
0:43:13 expenses
0:43:14 we’re
0:43:14 cutting
0:43:14 compliance
0:43:15 there’s
0:43:15 real
0:43:15 savings
0:43:15 and
0:43:16 efficiencies
0:43:17 which
0:43:17 is
0:43:17 sort
0:43:17 of
0:43:17 latin
0:43:17 for
0:43:18 layoffs
0:43:19 and
0:43:20 if
0:43:20 you
0:43:20 think
0:43:21 that
0:43:21 160
0:43:22 million
0:43:22 people
0:43:22 in
0:43:22 the
0:43:23 nation
0:43:23 work
0:43:24 if
0:43:24 half
0:43:24 of
0:43:25 them
0:43:25 are
0:43:25 in
0:43:25 industries
0:43:26 and
0:43:26 mean
0:43:26 to
0:43:26 AI
0:43:27 chiropractors
0:43:28 you know
0:43:29 welders
0:43:30 80
0:43:30 million
0:43:30 are
0:43:31 vulnerable
0:43:32 if
0:43:32 you
0:43:32 were
0:43:33 to
0:43:33 not
0:43:34 inspire
0:43:34 which
0:43:34 I
0:43:34 have
0:43:35 not
0:43:35 seen
0:43:35 a ton
0:43:35 of
0:43:36 incremental
0:43:37 revenue
0:43:37 growth
0:43:37 from
0:43:38 AI
0:43:38 products
0:43:39 but
0:43:39 but
0:43:40 all
0:43:41 efficiencies
0:43:41 say
0:43:41 a
0:43:42 trillion
0:43:42 a
0:43:42 year
0:43:43 $100,000
0:43:44 average
0:43:44 load
0:43:44 per
0:43:44 job
0:43:45 you’re
0:43:45 talking
0:43:46 about
0:43:46 a
0:43:47 destruction
0:43:47 in
0:43:47 the
0:43:47 labor
0:43:48 market
0:43:48 of
0:43:48 10
0:43:49 million
0:43:49 jobs
0:43:50 a
0:43:51 year
0:43:52 across
0:43:52 the
0:43:52 80
0:43:52 million
0:43:53 jobs
0:43:53 that
0:43:53 are
0:43:53 vulnerable
0:43:54 or
0:43:54 a
0:43:55 12.5%
0:43:56 labor
0:43:57 destruction
0:43:57 per
0:43:57 year
0:43:57 in
0:43:58 certain
0:43:58 industries
0:43:58 which
0:43:59 may
0:43:59 not
0:43:59 sound
0:43:59 like
0:44:00 a lot
0:44:00 but
0:44:00 that’s
0:44:00 chaos
0:44:01 or
0:44:03 these
0:44:03 companies
0:44:04 valuations
0:44:04 get
0:44:04 cut
0:44:05 in
0:44:05 half
0:44:06 I
0:44:07 see
0:44:07 this
0:44:07 as
0:44:07 a
0:44:07 pretty
0:44:08 definitive
0:44:08 fork
0:44:08 in
0:44:09 the
0:44:09 road
0:44:09 either
0:44:10 pretty
0:44:10 much
0:44:10 chaos
0:44:11 in
0:44:11 the
0:44:11 labor
0:44:11 markets
0:44:11 for
0:44:15 just
0:44:15 down
0:44:16 their
0:44:16 expectations
0:44:16 and
0:44:17 valuations
0:44:18 what
0:44:19 are
0:44:19 your
0:44:19 thoughts
0:44:19 on
0:44:19 that
0:44:20 scenario
0:44:20 those
0:44:20 are
0:44:20 some
0:44:21 important
0:44:21 questions
0:44:23 that
0:44:24 says
0:44:24 it
0:44:24 all
0:44:26 the
0:44:26 first
0:44:26 kind
0:44:26 of
0:44:26 thought
0:44:27 piece
0:44:27 that
0:44:27 open
0:44:28 AI
0:44:28 published
0:44:28 after
0:44:29 GPT
0:44:29 was
0:44:29 released
0:44:30 was
0:44:30 a
0:44:31 piece
0:44:31 that
0:44:31 kind
0:44:31 of
0:44:32 jumped
0:44:32 out
0:44:32 and
0:44:33 said
0:44:34 generative
0:44:34 AI
0:44:34 is
0:44:34 going
0:44:34 to
0:44:35 be
0:44:35 complementary
0:44:36 to
0:44:36 workers
0:44:37 and
0:44:37 not
0:44:37 destructive
0:44:38 and I
0:44:38 remember
0:44:38 thinking
0:44:39 uh oh
0:44:40 it’s
0:44:40 going
0:44:40 to be
0:44:40 bad
0:44:41 because
0:44:42 they
0:44:44 were
0:44:44 ready
0:44:45 to go
0:44:45 out of
0:44:45 the
0:44:46 gate
0:44:46 with
0:44:46 a
0:44:47 paper
0:44:47 to
0:44:47 defend
0:44:48 the
0:44:48 labor
0:44:48 market
0:44:49 of
0:44:49 this
0:44:49 stuff
0:44:50 like
0:44:50 right
0:44:50 away
0:44:51 to try
0:44:51 to get
0:44:51 out
0:44:57 in
0:44:57 certain
0:44:57 industries
0:44:58 you know
0:44:58 the
0:44:58 other
0:44:59 thing
0:44:59 that
0:44:59 happened
0:45:00 and I
0:45:00 wrote
0:45:01 about
0:45:01 this
0:45:02 a
0:45:02 couple
0:45:02 months
0:45:03 ago
0:45:03 there
0:45:03 was
0:45:03 a
0:45:04 couple
0:45:04 of
0:45:04 papers
0:45:04 that
0:45:05 came
0:45:05 out
0:45:05 there
0:45:05 was
0:45:06 this
0:45:06 chart
0:45:06 showing
0:45:07 that
0:45:07 the
0:45:09 unemployment
0:45:09 rate
0:45:09 for
0:45:10 reaching
0:45:10 college
0:45:10 graduates
0:45:11 for
0:45:11 the
0:45:11 first
0:45:11 time
0:45:11 in
0:45:11 about
0:45:12 60
0:45:12 years
0:45:12 is
0:45:13 higher
0:45:13 now
0:45:13 than
0:45:14 the
0:45:14 overall
0:45:14 unemployment
0:45:14 rate
0:45:15 rather
0:45:15 than
0:45:15 lower
0:45:16 so
0:45:16 the
0:45:16 first
0:45:17 two
0:45:17 pieces
0:45:17 that
0:45:18 come
0:45:18 out
0:45:18 say
0:45:18 has
0:45:18 nothing
0:45:18 to do
0:45:19 with
0:45:19 generative
0:45:22 like
0:45:22 who
0:45:22 wrote
0:45:23 this
0:45:23 piece
0:45:23 and
0:45:24 as
0:45:24 you
0:45:24 unravel
0:45:25 the
0:45:25 threads
0:45:26 they’re
0:45:26 Silicon
0:45:27 Valley
0:45:27 think
0:45:27 tanks
0:45:28 and
0:45:29 so
0:45:30 then
0:45:30 I
0:45:30 kind
0:45:30 of
0:45:30 went
0:45:31 to
0:45:31 David
0:45:31 Auteur
0:45:32 and
0:45:32 Darren
0:45:33 Osamoglu
0:45:33 at
0:45:33 Harvard
0:45:33 at
0:45:34 MIT
0:45:35 and
0:45:36 John
0:45:37 Berjolfson
0:45:37 at Stanford
0:45:38 you get
0:45:38 a different
0:45:39 answer
0:45:40 which is
0:45:41 yes
0:45:41 it’s
0:45:42 kind
0:45:42 of
0:45:42 looking
0:45:43 like
0:45:43 generative
0:45:44 AI
0:45:44 is
0:45:44 beginning
0:45:45 to
0:45:45 affect
0:45:47 those
0:45:47 young
0:45:48 college
0:45:48 graduates
0:45:49 so
0:45:50 we’re
0:45:51 starting
0:45:51 to
0:45:51 see
0:45:51 that
0:45:52 we
0:45:52 made
0:45:52 this
0:45:53 pyramid
0:45:54 of
0:45:55 things
0:45:55 about
0:45:55 AI
0:45:56 and
0:45:56 the
0:45:56 bottom
0:45:56 of
0:45:56 the
0:45:57 period
0:45:57 is
0:45:57 the
0:45:57 most
0:45:58 ubiquitous
0:45:58 thing
0:45:58 you
0:45:58 can
0:45:58 find
0:45:59 which
0:45:59 is
0:45:59 like
0:46:00 futurist
0:46:01 forecasts
0:46:01 and
0:46:02 all
0:46:02 sorts
0:46:02 of
0:46:02 stuff
0:46:03 and
0:46:03 then
0:46:03 as
0:46:03 you
0:46:03 go
0:46:04 a little
0:46:04 narrower
0:46:06 it’s
0:46:06 study
0:46:07 lab
0:46:08 studies
0:46:08 of
0:46:08 AI
0:46:08 doing
0:46:09 non
0:46:09 business
0:46:09 related
0:46:10 things
0:46:10 they’re
0:46:10 great
0:46:11 right
0:46:11 it’s
0:46:12 it plays
0:46:12 chess
0:46:13 it plays
0:46:13 go
0:46:14 it plays
0:46:15 it can
0:46:15 do
0:46:15 all
0:46:16 sorts
0:46:16 of
0:46:16 non
0:46:16 business
0:46:17 related
0:46:17 things
0:46:18 extremely
0:46:18 well
0:46:18 it can
0:46:19 play
0:46:19 your
0:46:19 wordle
0:46:19 for
0:46:20 you
0:46:20 right
0:46:21 then
0:46:22 now
0:46:23 let’s
0:46:23 see
0:46:23 how
0:46:23 does
0:46:23 it
0:46:23 do
0:46:24 in
0:46:24 actual
0:46:24 business
0:46:25 related
0:46:25 tasks
0:46:26 pyramids
0:46:27 getting
0:46:27 narrower
0:46:27 but
0:46:28 there
0:46:28 are
0:46:28 some
0:46:28 studies
0:46:29 out
0:46:29 there
0:46:29 showing
0:46:29 this
0:46:29 in
0:46:30 a
0:46:30 lab
0:46:30 setting
0:46:31 then
0:46:31 you
0:46:32 want
0:46:32 like
0:46:33 surveys
0:46:34 of
0:46:34 adoption
0:46:35 but
0:46:35 then
0:46:35 you
0:46:35 get
0:46:35 those
0:46:35 kind
0:46:36 of
0:46:36 bland
0:46:37 you
0:46:37 know
0:46:38 cream
0:46:38 of
0:46:39 chicken
0:46:39 soup
0:46:40 pieces
0:46:40 from
0:46:40 McKinsey
0:46:45 right
0:46:45 and
0:46:45 you
0:46:46 don’t
0:46:46 really
0:46:46 learn
0:46:46 anything
0:46:47 from
0:46:47 that
0:46:47 and
0:46:48 then
0:46:48 you
0:46:48 keep
0:46:48 going
0:46:48 up
0:46:48 the
0:46:49 pyramid
0:46:49 to
0:46:50 hardcore
0:46:51 stuff
0:46:51 where
0:46:52 what
0:46:52 are
0:46:52 the
0:46:53 revenue
0:46:54 and
0:46:54 profit
0:46:55 and
0:46:56 productivity
0:46:57 consequences
0:46:57 of doing
0:46:57 this
0:46:58 stuff
0:46:58 there’s
0:46:58 only
0:46:59 a
0:46:59 handful
0:46:59 of
0:47:00 those
0:47:00 and
0:47:01 what
0:47:01 you
0:47:01 can’t
0:47:01 find
0:47:01 at
0:47:02 all
0:47:03 is
0:47:03 the
0:47:04 top
0:47:04 of
0:47:04 the
0:47:04 pyramid
0:47:05 which
0:47:05 are
0:47:06 pathways
0:47:07 explicit
0:47:08 pathways
0:47:08 to
0:47:09 profitability
0:47:09 for
0:47:09 generative
0:47:10 AI
0:47:10 adoption
0:47:11 for
0:47:11 the
0:47:11 hyperscalers
0:47:14 we’ll be
0:47:15 right back
0:47:15 and for
0:47:15 even more
0:47:16 markets
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0:47:41 Odoo
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0:47:59 thousands
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0:48:00 businesses
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0:48:00 made
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0:48:01 so
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0:48:23 of the
0:48:24 phoenix
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0:49:55 Profty Markets
0:49:56 I think China’s pissed
0:49:58 off I think they’re sick
0:49:59 of trying to figure out
0:50:01 the US-China relationship
0:50:03 plan their economy and
0:50:04 they’re diversifying away
0:50:05 from us 24% of their
0:50:06 exports use to come to the
0:50:08 US not 17 but they really
0:50:10 feel as if they have I
0:50:12 believe they feel we have
0:50:13 declared economic war on
0:50:14 them and that they are
0:50:15 going to punch back and
0:50:16 one way they might punch
0:50:18 back or what I would do
0:50:19 if I were she we run
0:50:21 companies for profit they
0:50:22 sort of run companies for
0:50:24 control and geopolitical
0:50:25 advantage and if I were
0:50:28 them I would begin a
0:50:29 government sponsored drive
0:50:31 of capital and strategy to
0:50:34 essentially engage in what
0:50:35 in the 80s they did with
0:50:37 steel but with AI and
0:50:39 engage in massive AI
0:50:40 dumping and that is create
0:50:43 a ton of LLMs that have
0:50:44 near parity technically to
0:50:46 US LLMs and then dump
0:50:48 them into the market you
0:50:50 know kind of these open
0:50:52 weight open source LLMs that
0:50:53 basically do what China
0:50:55 does and that is offer 95
0:50:57 97% of the premium high end
0:51:00 US product for 5% of the
0:51:02 price and then essentially
0:51:05 cut go for the jugular just
0:51:07 flood the market with open
0:51:09 weight LLMs and AI products
0:51:12 for near free that takes
0:51:14 down massively erodes the
0:51:15 margin power of these
0:51:17 companies takes them down
0:51:18 and thereby takes the US
0:51:21 economy down your thoughts
0:51:23 so the outlook for this year
0:51:25 has four major risks number
0:51:27 one is the power constraint
0:51:28 wall number two is the thing
0:51:30 you just said which is the
0:51:31 China basically scales the
0:51:33 moat on its own as as
0:51:35 recently as three years ago
0:51:36 that scenario you just
0:51:36 described would have been
0:51:37 impossible
0:51:40 China is the world leader in
0:51:41 a lot of things you know
0:51:43 fission they’ve made more
0:51:45 fusion advances they’re ahead
0:51:46 in genetic testing of certain
0:51:49 new drugs they have there’s a
0:51:51 really cool thing that looks
0:51:53 at the complexity of exports
0:51:55 it’s I think the it’s one of
0:51:56 the Harvard labs that does it
0:51:58 they’ve converged now with the
0:52:00 United States so the complexity
0:52:02 in the industrial complexity and
0:52:03 sophistication of their exports
0:52:05 has matched that of the United
0:52:07 States but as recently as five
0:52:09 years ago they couldn’t figure
0:52:11 out the semiconductor thing
0:52:15 and through a combination of
0:52:18 ingenuity and theft they have
0:52:21 now acquired a lot of things
0:52:23 there’s a long list of people
0:52:25 that used to work for for you
0:52:28 know either TSMC or ASML that
0:52:30 are now working for Huawei and
0:52:31 Huawei has announced that next
0:52:34 year they expect to sell to your
0:52:37 points got a chip cluster that
0:52:39 matches or exceeds the
0:52:41 performance of what you can get
0:52:44 from NVIDIA now it’s got more
0:52:47 it matches the power the chip on a
0:52:49 per chip basis it’s only let’s say
0:52:50 a third is powerful but they say
0:52:52 fine we’ll just use three times as
0:52:55 many chips and so that’s they’re
0:52:57 trying to use a combination of
0:52:59 brain and brawn to do that I at
0:53:02 that kind of still feels two years
0:53:04 away but China’s on a really
0:53:06 long-term journey and I would
0:53:07 agree that that is very much
0:53:09 explicitly their goal I want to
0:53:11 talk more about the your 2026
0:53:15 outlook so you mentioned that the
0:53:17 number one risks or the number one
0:53:20 risk is power constraints we don’t
0:53:22 have enough power to build all the
0:53:23 things that we want to build
0:53:25 particularly AI you said number two is
0:53:28 China what else should we know in
0:53:30 terms of risks well the two other
0:53:33 ones is the the more that China is
0:53:38 able to rely on its own chip
0:53:40 ecosystem the that means that number
0:53:43 three risk has to be Taiwan because
0:53:47 you know the Taiwan essentially was
0:53:50 always protected by the fact that
0:53:52 China relied 90 percent for its
0:53:54 advanced chips on Taiwanese output you
0:53:56 know the the day we wake up and that
0:53:58 number is below 50 percent I think
0:54:00 you have to start to reevaluate some
0:54:02 of the geopolitical contingencies
0:54:05 around this whole thing there’s
0:54:07 great work CSIS is the best think
0:54:08 tank in the world on this kind of
0:54:12 stuff and they showed us some data
0:54:14 something like 85 percent of Chinese
0:54:16 military assets are in the Taiwanese
0:54:19 strait they have been partnering with
0:54:22 Russia to do drills on how to drop
0:54:25 heavy equipment out of specially designed
0:54:28 parachutes from helicopters and you
0:54:31 know that this is this is moving in
0:54:33 another direction in another clear
0:54:36 direction here and you get you don’t
0:54:37 know when you don’t know how you don’t
0:54:38 know why and maybe it’s a quarantine
0:54:41 maybe it’s a blockade maybe it’s not a
0:54:43 full-out invasion but there are going to
0:54:46 be some challenging times for the West
0:54:50 within the next five years as it relates
0:54:53 to its almost exclusive reliance on
0:54:55 this you know NVIDIA ASML TSMC
0:54:57 partnership then in the fourth one is
0:54:58 the stuff we’ve been talking about
0:55:01 which is this kind of collective
0:55:05 metaverse moment where investors in
0:55:08 aggregate say like okay it’s not a
0:55:09 waste of money but the are we don’t we
0:55:11 can’t see the ROI we’re going to take
0:55:14 profits until we have a clearer vision
0:55:18 of of where this is going I’m still
0:55:20 digging through it there was a for every
0:55:22 good paper there’s a negative paper
0:55:24 right so that for every good paper on
0:55:26 AI adoption there was one that you should
0:55:28 read it it came from this group called
0:55:31 MIT Nanda N-A-N-D-A yeah the 95% one
0:55:34 yeah yeah like you know I read through the
0:55:37 details on that one that those are a lot
0:55:39 of companies that that kind of tried it
0:55:40 played around with it and said you know
0:55:41 what we’re just going to keep doing
0:55:43 things the way we’re doing it the other
0:55:46 thing that the outlook mentions there
0:55:48 are three forces that will define next
0:55:52 year’s market they are according to your
0:55:54 report AI we’ve discussed that I think
0:55:56 everyone understands that global
0:55:59 fragmentation and inflation describe
0:56:00 global fragmentation and inflation for
0:56:03 us let me just summarize by saying like
0:56:05 the Fed is is facing a peculiar fork in
0:56:07 the road here it’s not a violent
0:56:10 differential but there’s a meaningful
0:56:13 differential right now between we look at
0:56:15 the PMI surveys right the ISM and PMI
0:56:18 surveys of prices paid those are going up
0:56:21 the PMI surveys on labor they’re going
0:56:24 down right so what’s a federal reserve to
0:56:26 do when you have a dual mandate that are
0:56:27 going in opposite directions
0:56:31 historically the Fed has almost never cut
0:56:33 rates when prices paid surveys are going
0:56:35 up why would they right I mean you’d be
0:56:40 easing into an inflationary environment it
0:56:41 looks like they’re going to do that and
0:56:45 it’s a it’s a huge gamble the gamble is
0:56:47 that the inflationary increase is
0:56:49 tariff related and once it makes its way
0:56:51 through the system it will not ignite
0:56:53 some kind of wage price spiral and
0:56:54 inflation will be coming down by the
0:56:56 spring and the Fed will be vindicated but
0:56:59 that’s that’s a big bet and that’s the bet
0:57:01 that’s you know that we’re looking at as
0:57:03 we’re heading into you know the end of the
0:57:05 year and early next year and the markets
0:57:07 are pricing in a couple of cuts here
0:57:11 because of that I’m I’m less worried
0:57:14 about tariffs than immigration policy as
0:57:15 it relates to this stuff we’re talking
0:57:19 about I think the president is less
0:57:22 committed to some of these things than he
0:57:25 appears to be there they’re already
0:57:27 adding massive numbers of products to the
0:57:30 exemption list of tariffs so I think the
0:57:31 tariff stuff will turn out to be less
0:57:36 damaging but the the latest data from
0:57:39 the Fed is that 50,000 payroll growth is
0:57:42 is inflationary like the Nehru of
0:57:45 inflation for wages is 50,000 right so
0:57:48 they yeah they shut the border we all
0:57:50 understand what political forces they
0:57:52 were reacting to but the United States
0:57:55 needs people to grow and if if if you get
0:57:58 wage inflation at 50,000 payroll growth you
0:58:01 have a labor supply problem and so they’re
0:58:02 they’re gonna have to they’re gonna have
0:58:06 to make a change soon on that one or
0:58:08 else they’re gonna have bigger problems
0:58:11 yeah the report also says it says tariffs
0:58:13 are here to stay no matter what the
0:58:16 Supreme Court rules what are you thinking
0:58:18 about in terms of the Supreme Court ruling
0:58:20 on tariffs right now you know Gorsuch has
0:58:23 been kind of tilting is showing you I don’t
0:58:25 know the poker analogy because I don’t play I
0:58:28 don’t gamble but what whatever is it the
0:58:30 thing where you hold your cards and people
0:58:32 can see what you have or something I mean
0:58:34 he’s he’s been showing his cards a little
0:58:39 during during the hearing and I think he and
0:58:44 Barrett will side with the liberals and that
0:58:47 would reduce the effective tariff rate from
0:58:50 like 15 to 7 and then they would try to
0:58:52 replace two or three percent of them with
0:58:55 some other clauses but a bunch of them get
0:58:57 defanged and are not so easy to replace
0:58:59 do you think there might be a ruling where
0:59:01 they have to pay back the tariffs to the
0:59:03 companies the tariffs were imposed on I do
0:59:05 over some period of time and you know they
0:59:07 would make that as difficult as possible and
0:59:10 you know that it would be extremely messy
0:59:15 you know it would be you know think think
0:59:17 about the messiness of the PPP loans as
0:59:19 people started to kind of clean up after
0:59:22 them I mean it’s it’s it’s a messy thing to
0:59:25 do and of course the the doge people cut a
0:59:27 lot of the staffing as it relates to the
0:59:30 enforcement and tracking of this stuff so
0:59:31 one of the reasons the tariffs haven’t been
0:59:34 quite as much of an impact on the economy is a
0:59:36 lot of companies are recategorizing goods so
0:59:39 so that they fall under different tariff
0:59:41 buckets that they may not necessarily
0:59:44 supposed to apply to but if you’ve gutted
0:59:46 the enforcement of the people that look at
0:59:48 that then that’s what companies are going to
0:59:54 do so you know earlier in the year a lot of
0:59:56 people were trying to convince me that
1:00:00 there was this holistic vision that they
1:00:02 had about what they were trying to
1:00:06 accomplish and I’m I’m I’ve struggled with
1:00:08 that because I see too many policies that
1:00:10 conflict with each other like if you’re
1:00:11 really trying to attract foreign direct
1:00:15 investment why would you expel all of those
1:00:16 South Korean factory workers there’s just
1:00:20 too many policies that don’t seem to be
1:00:22 aligned with what the administration says
1:00:24 its goals are so which has given me less
1:00:26 confidence that that they that there’s a
1:00:30 a playlist here that is all adding up to some
1:00:33 big coherent grand vision you’ve got to work
1:00:37 over time to justify how it all makes sense and
1:00:40 how it all plays into a specific strategy and
1:00:43 then which leads you to believe okay that’s
1:00:46 not what’s happening this is this is policy
1:00:48 by tweets if you have time on the weekend and
1:00:51 you really want to you know read something
1:00:54 interesting the Washington Post had an
1:00:58 editorial recently from every single living
1:01:02 prior surgeon general about what they thought
1:01:06 about the leadership at HHS and uh I’m not
1:01:07 going to go into any more detail
1:01:12 check it out on our own time um just
1:01:15 final question for me Scott might have one
1:01:19 as well um we’ve talked about some kind of
1:01:21 concerning stuff on this podcast and you said
1:01:24 that you thought it was and correct me if
1:01:26 I’m wrong you thought it would be unlikely
1:01:29 to see some sort of profit taking event to
1:01:32 the tune of 10 to 12 to 15 percent next
1:01:34 year no that’s the one I thought was likely
1:01:37 I misspoke that’s what I meant that is a
1:01:39 likely scenario you that’s almost your base
1:01:42 case for next year and perhaps there is a
1:01:46 more shocking event like 40 percent but
1:01:47 you’re thinking you know you’re going to
1:01:50 see 10 12 15 percent correction um for for
1:01:53 someone who has kind of a regular portfolio a
1:01:56 regular investor who is you know dollar cost
1:01:58 averaging into the market into the S&P they’ve
1:02:01 been doing that for a few years maybe they
1:02:03 haven’t looked at bonds too closely in the
1:02:06 past what is your general advice for those
1:02:10 people going into 2026 how should they think
1:02:12 about their portfolio and how should they
1:02:14 invest right now well you know based on what
1:02:16 we’ve just discussed it would make sense to
1:02:20 start accumulating some dry powder to take
1:02:22 advantage of whatever opportunities may
1:02:24 exist a lot of times when you get sell-offs
1:02:26 like that a bunch of things sell off all of a
1:02:30 sudden you start seeing industrial and
1:02:32 utility preferred stock sell off two to three
1:02:35 points which is can be the equivalent of 50 60 80
1:02:40 basis points so you know um having having some
1:02:45 spare cash and and and credit to be able to
1:02:46 draw on when these things happen can be
1:02:49 helpful um a lot of these things tend to be
1:02:52 very v-shaped right if you look back at almost
1:02:54 every single one of these corrections including
1:02:56 the one that happened last year there’s this
1:03:00 rapid violent unwinding of risk uh i think a
1:03:03 lot of the hedge fund the big risk parity funds
1:03:06 are kind of um partially responsible for that but
1:03:09 then it tends to snap back where it comes back
1:03:11 roughly at the same speed at which it declined
1:03:15 in in that regard individual investors have an
1:03:17 advantage over some of the large institutional
1:03:19 investors i mean the average the average
1:03:21 endowment or foundation meets quarterly
1:03:24 they’re they’re not even set up to respond to
1:03:27 some of these things yeah so um i think it’s you
1:03:30 know in retail investors have at least the
1:03:32 flexibility to try to act when these things
1:03:35 happen make the bull case for 2026
1:03:38 you start seeing more concrete evidence of
1:03:44 of ai adoption that companies are willing to pay
1:03:51 for and you you start seeing productivity benefits
1:03:53 that are based equally on revenues and not
1:03:57 entirely on the backs of lower rates of hiring
1:04:05 um china starts to cut back on its excess production policy they announced this
1:04:10 involution campaign a few months ago that’s designed to make chinese companies
1:04:13 more profitable by telling them to stop overproducing i’ll believe it when i see
1:04:20 it um you get fiscal stimulus in europe in part because trump is basically forcing
1:04:23 them to defend themselves uh another policy that i think is long
1:04:29 overdue um europe agreed in 2006 to spend two percent of gdp on defense and it took them until
1:04:37 2021 to finally get there and uh so you’re starting to see more defense spending in europe which is
1:05:07 stimulative and um and then in the us the fed is vindicated where inflation starts to roll over aided and abetted by an enormous amount of multi-family and single-family real estate supply uh pushing down housing-related inflation and and the administration figures out through some combination of relaxation in certain regulations and things like that
1:05:18 to to to ramp up u.s oil and gas production so energy prices come down so that’s that’s kind of your your bold case
1:05:22 michael semblis is the chairman of market and investment strategy for jp morgan asset and wealth management
1:05:37 a global leader in investment management and private banking with six trillion dollars of client assets under management worldwide he’s responsible for the development of market and investment insights across the firms institutional funds and private banking businesses michael joined jp morgan
1:05:59 in 1987 he’s previously served as chief investment officer for the firm’s global private bank and head strategist for emerging markets fixed income and you can discover more of his insights by reading eye on the market or listening to his podcast by the same name michael uh always really insightful uh really appreciate your time thank you good to see you michael thank you very much good to see you scott
1:06:17 ed what’d you think uh i thought i was very he knows everything which is so it’s so helpful um i mean he’s kind of overseeing
1:06:24 one of the largest portfolios in terms of aum in the world so i mean he’s got so much data
1:06:35 um i i’m i think i agree with him on pretty much everything he said i mean our reactions to aswell’s interview i thought it was really striking but
1:06:42 i didn’t feel quite as bearish um as as professor de modem was when we interviewed him i feel like
1:06:47 michael’s sentiment is more where i’m at right now in terms of valuations
1:06:50 um where i’m kind of expecting
1:06:53 some sort of correction but not something that is kind of
1:06:57 gonna cause a major major crisis
1:07:02 so yeah i thought that was informative made me feel
1:07:08 maybe a little better i think he’s very measured because he the last thing he wants to do is spook
1:07:13 you know the two trillion dollars in value or in assets they oversee but he sounded
1:07:21 he sounded measured and a little bit i don’t want to say nervous but and i might be it might be just
1:07:25 confirmation bias but whether it’s sam altman or alex kirk getting defensive or demotering
1:07:31 saying aswath is the least panicked person i’ve ever met
1:07:36 and and then michael sort of michael seems
1:07:38 really measured
1:07:41 not to a fault but really measured like
1:07:45 okay i don’t want to i don’t want to spook all the capital i oversee but
1:07:47 i’m having a tough time
1:07:49 defending this market
1:07:55 um and i think part of the reason he’s in the position he is is this guy is just
1:07:58 he’s very measured he just doesn’t scare easily right but
1:08:00 i don’t know it
1:08:03 and again i i can’t figure i can’t suss out
1:08:07 like when i hate everyone around me i know it’s my depression
1:08:15 like when i literally hate everybody um i know it’s my depression speaking and i’m pretty convinced
1:08:18 now we’re in for a serious pretty rocky road in the next 12 months
1:08:26 and i can’t i can’t suss out my old man boomer anger from the actual data but the data i just think looks
1:08:32 i think if the market goes down 40 percent if the nasdaq goes down 40 percent in the next 90 days or 100
1:08:34 we’re going to be like well of course it fucking did
1:08:39 i think it’s going to be like the most obvious thing in the rearview mirror that it happens so
1:08:43 but anyways back to michael i i like him because he’s measured and just
1:08:47 i like that he pauses and thinks about questions he wants to answer them
1:08:49 answer them correctly
1:08:52 i appreciate that too yeah and and you know
1:08:55 what he said foots with what you’re saying i think he
1:09:00 he is anxious and kind of expressed that i mean he said that his base case
1:09:03 is a 10 to 12 to 15 percent correction which is
1:09:08 kind of a big deal in and of itself i mean the question is do you think it’s going to be like that
1:09:14 or do you think it’s going to be 40 which again he didn’t rule out he said that it was unlikely he
1:09:20 doesn’t think it’s going to happen but he didn’t fully rule it out i think that the the part that
1:09:27 he’s gotten right is that there is crazy amounts of leverage and debt that’s being accumulated but
1:09:34 only in certain contained spaces so he mentioned oracle i’ve talked about how open ai they haven’t
1:09:40 gotten there yet but they’re gonna have to raise huge amounts of debt in order to support the amount
1:09:47 of um ai that they want to build and spend on over the next few years uh he mentioned blue owl and the
1:09:54 idea that meta is raising debt but using these spvs to sort of protect themselves so i think where he’s
1:10:01 probably right is that the debt and the leverage that we’re seeing isn’t quite as systemic as we’ve seen
1:10:07 in previous crises which is probably going to be a good thing it means that whatever correction we see
1:10:14 is going to be at least more contained than it has been in previous crises i think that’s quite a a sound
1:10:21 analysis the question then becomes well how much more debt are we going to see in the pipeline i mean
1:10:27 is it just oracle or are we going to start seeing crazy amounts of debt from amazon and microsoft and
1:10:34 meta are they going to ramp it up um that that that’s sort of the next question but in terms of
1:10:41 his views on like what the implosion will look like um i think i think he’s pretty spot on
1:10:50 this episode was produced by claire miller and allison weiss and engineered by benjamin spencer
1:10:55 our research team is dan shalon isabella kinsel chris no donahue and mia silverio drew burrows is our
1:10:59 technical director and katherine dylan is our executive producer thank you for listening to
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