Fortune Global Forum 2023: Mastering Complex Financial Markets
Jenny Johnson, President and CEO, Franklin Templeton Peter Orszag, Chief Executive Officer Lazard In conversation with: Clay Chandler, FORTUNE
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00:00 Welcome back, everyone.
00:01 Transformation, resiliency, and opportunity
00:04 have been central themes here at the Fortune Global Forum,
00:07 and there are plenty of opportunities out there.
00:09 We want to talk now about opportunities
00:11 that exist in the financial markets,
00:13 where the smart money is, asset classes to watch,
00:15 and how technology is impacting the investment landscape.
00:19 Joining us for this conversation are Jenny Johnson,
00:22 President and CEO of Franklin Templeton,
00:24 one of the largest global asset investment firms
00:27 with over $1.4 trillion US in assets under management,
00:31 and Peter Orszag, CEO of the global financial advisory
00:34 and asset management firm Lazard.
00:36 Please welcome them to the Fortune Global Forum stage.
00:39 Peter, Jenny.
00:40 (upbeat music)
00:43 Thank you.
00:48 Thank you both for being here.
00:52 It's really a delight to have two people
00:54 that have such a far-ranging view of what's going on
00:58 in the global economy,
01:00 including some of the geopolitical turmoil
01:03 and developments that we see.
01:04 And I'm gonna try to just ask you very broadly
01:08 about what you see going on, what you tell clients,
01:11 and what clients are asking you about.
01:14 But let's start with the big picture,
01:17 and maybe, Peter, give us your quick snapshot
01:20 of what you think is going on with the US economy right now,
01:22 how healthy you think it is,
01:24 and where you think it's headed.
01:27 Well, it's certainly turned out better
01:29 than many people expected, say, a year ago.
01:31 And I think fundamentally, at least to date,
01:34 and that's important because it could change,
01:36 the consumer is held up pretty well.
01:39 And that's backed up by a pretty,
01:42 actually a remarkably strong labor market.
01:44 And so what's particularly remarkable
01:47 is that despite ongoing growth
01:49 and a fairly strong consumer
01:52 and a pretty strong labor market,
01:54 inflation has come down quite substantially.
01:57 And my view is that the narrative,
02:01 at least in most of the media,
02:02 about what's happened on inflation is basically wrong.
02:06 That the way it's being presented
02:08 is the Fed has slayed inflation.
02:11 Instead, I think what's happened is the pandemic came,
02:14 inflation went up, the pandemic left,
02:17 and it took a little while,
02:19 but inflation slowly subsided in the wake of that.
02:23 And what the Fed has done has, at the margins,
02:26 maybe accelerated that a bit,
02:28 but that inflation would be roughly in the same place
02:32 even without dramatic monetary policy.
02:35 So generally speaking, I think the biggest challenge
02:39 for the US at this point is ongoing political polarization,
02:42 which just makes the fiscal policy picture challenging
02:47 and just everyday conduct of business challenging.
02:52 I was with Paul Ryan about two weeks ago,
02:54 and it reminded me that when I was budget director
02:57 for President Obama,
02:58 Paul Ryan introduced me at my confirmation hearing.
03:01 That was only 13 or 14 years ago.
03:04 That would never happen.
03:05 - That's almost impossible to imagine.
03:06 - Totally impossible.
03:07 - Yeah.
03:08 Well, just to say a little bit more about that,
03:09 I mean, how troublesome is that for the US?
03:14 How big of a, is that just--
03:15 - Look, I think we're in a world now
03:17 where with a few exceptions,
03:19 and I say that cognizant of the fact
03:21 that we just passed massive pieces of legislation
03:24 in the US, including that it's highly relevant
03:27 on the climate front.
03:30 But in general, what polarization means is
03:32 if you like inertia, that's good,
03:35 because the polarization means sort of nothing happens.
03:37 The problem becomes where things have to happen.
03:40 So the debt limit, or if there's a need to do something,
03:45 that's when the polarization becomes problematic.
03:48 And I don't really see anything that fundamentally shifts
03:51 that we've gone through cycles of polarization
03:54 in the US in the past,
03:55 but they've typically ended in civil wars
03:57 or Great Depressions.
04:00 And it's not clear what will kind of,
04:04 you know, bring the country back together again.
04:07 - Jenny, what are your thoughts?
04:08 - So I think, I was gonna ask you
04:13 what you think the Fed's gonna do on rates,
04:15 'cause as you concluded, I'll tell you what I think.
04:18 But I'm curious.
04:19 - Wait, so I'm supposed to look this way.
04:21 - Right, they told us to look this way.
04:23 So first of all, I think that the market
04:26 is overly optimistic about rate cuts next year.
04:28 Not sure I agree whether the Fed raising rates
04:33 would have had the impact that it had.
04:35 It's definitely slowing, but you still have
04:37 something like 1.3 to 1.4 job openings
04:41 to people looking for jobs.
04:44 So that's gonna be inflationary.
04:45 So I think it's, you know,
04:46 they've done a good job getting it down
04:48 to three and a half, four, little over four.
04:51 It's gonna be really hard from here.
04:53 And I think there's signs of that.
04:54 People talk about consumer debt increasing,
04:56 but the reality is when you compare it to real wages,
04:59 it's actually not, hasn't increased that
05:01 as much as kind of historical averages.
05:04 So I think it's gonna be hard to see
05:07 rate cuts next year.
05:10 And I think the bigger issue that I don't think
05:12 gets talked about enough in the US
05:14 is the fact of our deficit.
05:16 And Ray Dalio talked about it a bit this morning.
05:18 You've gone from basically $9 trillion in debt in 2007
05:22 to 31 trillion this year.
05:24 You're gonna add 2 trillion more this year.
05:27 Our largest foreign owner of US debt
05:29 is Japan at 1.1 trillion, China next at 850 billion.
05:33 They're not gonna be increasing their exposure.
05:36 You're gonna have to keep rates.
05:37 Rates are gonna have to stay up to attract.
05:40 US is still gonna be the reserve currency,
05:42 but to attract it.
05:43 And that's gonna keep a floor
05:45 on the ability for rates to be cut.
05:47 - What's your thought about that?
05:48 I mean, debt obviously is something you know a lot about.
05:51 - So whatever you thought of the US fiscal picture
05:54 three months ago, four months ago,
05:56 you should be more concerned today
05:58 with the run-up in the long rate.
06:00 It makes the debt dynamic
06:03 just a lot more explosive and dangerous.
06:06 So two things.
06:08 One, no one really knows,
06:12 like when rates were low,
06:14 everyone assumed those would be permanent.
06:16 And so the run-up in rates was a surprise.
06:20 It shouldn't have been.
06:21 Similarly, I think there's a tendency today
06:24 to kind of do these linear projections
06:26 where you just take the recent past
06:27 and assume it's the permanent future.
06:30 Many, not all, but many of the forces
06:33 that led to low rates before COVID
06:37 are still present in the world.
06:39 Demographic changes, and we can go through some of them.
06:41 So it is possible that the long rate will come,
06:44 the question is what's the terminal rate?
06:46 It is possible that the long rate will come back down.
06:48 If it does not, the debt dynamic does not look good.
06:52 And it's back to the polarization problem,
06:55 which is it is extremely unlikely
07:00 that the US outside of a true fiscal crisis
07:03 is going to act ahead of time.
07:04 And the ideas that are being bandied about in Washington now
07:07 to have yet another commission,
07:09 it's like, oh my God, how many, really?
07:12 That's the best idea we can come up with?
07:15 - Well, and sorry if I could just add on.
07:17 I do think, I mean, there's some major macroeconomic trends
07:21 that are very inflationary, right?
07:23 I mean, the wars that are going on are inflationary.
07:25 The geopolitical split between US and China,
07:28 when people talk about supply chain,
07:30 China plus one, or near,
07:32 those are all inflationary factors
07:35 that I think are gonna get,
07:37 it's gonna be harder and harder to kind of control that.
07:40 - Well, let's talk about some of those factors,
07:43 particularly the US-China tensions.
07:48 We've heard from a number of people
07:50 over the last few days here
07:52 that they think that the global investor pessimism
07:55 about China and its prospects is overdone.
07:59 Jenny, what's your thinking on that?
08:02 - So I think it's overdone.
08:06 You're talking about the second largest economy
08:08 in the world.
08:09 You're talking about a country that produces more engineers
08:13 than anywhere else in the world.
08:16 I do think that the divide,
08:19 and somebody said it earlier this morning,
08:21 in the divide between US and China on tech,
08:23 you're gonna sort of have two tracks.
08:25 I don't think that changes.
08:27 But it's not gonna be for a variety of reasons.
08:31 You're not gonna have that high single digit,
08:34 low double digit growth.
08:35 You're gonna have a more kind of normal growth.
08:38 And it's probably gonna be held back
08:41 for the next couple of years.
08:42 Honestly, it can be difficult at times
08:45 if you're doing business.
08:46 We've been in China for a long time with a joint venture
08:49 to find somebody who's willing to make a decision.
08:52 That's new.
08:54 We've been in the process of trying to buy out
08:57 our joint venture,
08:58 and literally we cannot figure out
08:59 who can make a decision to do that.
09:01 It's a change.
09:03 - Peter, your question.
09:05 - Here's the dilemma that I think
09:06 the Chinese authorities face,
09:08 which is, I think it's well known,
09:10 the demographic challenge.
09:11 So I'll leave that to the side.
09:12 But a few years ago, roughly maybe five or so years ago,
09:17 China went through what development economists
09:20 call the Lewis turning point.
09:21 The point at which moving people from the farm
09:23 to the factory no longer adds to productivity.
09:26 And when that happens,
09:27 you need to change the growth model
09:29 away from that simple shift
09:31 and towards technology and education in particular.
09:36 You have to advance the frontier
09:37 rather than just kind of play to the frontier.
09:40 And the fundamental tension facing the Chinese economy today
09:43 is they've started to shift in that direction,
09:45 making investments in higher education, et cetera,
09:48 but there is a fundamental disconnect
09:50 between that imperative and the desire for state control.
09:54 Because fundamental to technology and education
09:57 is the free flow of information,
09:59 at least to some degree.
10:00 And that comes headlong into the desire for state control.
10:03 So I think at the heart of the challenges
10:06 over the next five or 10 years for the Chinese
10:09 is not only a very challenging demographic picture
10:13 where the move away from one child has not worked,
10:16 the total fertility rate is still way below replacement,
10:20 but more importantly, that there is a pathway here
10:24 where you could have at least some degree of healthy growth,
10:28 but I'm not sure that it's going to work
10:30 in line with the desire for state control.
10:35 - So I know as an economist,
10:37 you've looked a lot at productivity
10:39 and you mentioned the demographic challenge.
10:43 The demographic challenge wouldn't be such a big deal
10:45 if the productivity rate was rising faster.
10:49 As you look at China's,
10:50 I don't know if you've looked at this,
10:51 but as you sort of compare China's productivity rate
10:55 relative to its level of economic maturity,
10:58 is it underperforming or about average
11:00 performing kind of where it should be?
11:01 Or what do you--
11:02 - This is an area where I think the difficulty
11:06 of not having precise economic statistics
11:08 makes it very, very challenging to have a firm view.
11:11 What I would say is I think the weight of the evidence
11:14 probably tilts towards a lot of the growth
11:16 being that sectoral shift that I mentioned,
11:19 and then very capital intensive
11:21 and often not really high--
11:24 - Very productive, yeah.
11:25 - High productive investment,
11:26 none of which is kind of all that promising
11:29 with regard to productivity being the way out.
11:31 - Huh.
11:32 Well, let's stick on this topic of productivity for a second,
11:35 come back to the United States
11:36 because one of the hot topics everyone is talking about
11:40 is AI and what does it mean.
11:42 And I remember when the big shift to the internet came
11:47 and then there was all this discussion about that
11:49 and there was all this celebration of,
11:51 oh, this is gonna make the US way more productive
11:53 and it was gonna be great for the economy.
11:54 But then economists like you, Peter,
11:57 kind of looked at the data and said,
11:58 yeah, the evidence that the internet
12:00 is really revolutionary shows up everywhere
12:03 except in the productivity statistics.
12:05 And it took a long time and many people debate
12:07 that it still hasn't had much of an impact.
12:10 Is AI gonna be like that
12:11 or is it gonna be something that shows up immediately
12:13 in improved productivity in the US economy?
12:16 - I have a view, but do you wanna?
12:17 - Well, first of all, I always find it interesting
12:19 when the economists say that productivity
12:21 hasn't improved with the internet.
12:23 I mean, the fact is you get so many more services
12:27 so much more immediately.
12:28 And we all have things that we never knew we needed
12:33 that are now at our fingertips.
12:34 So, whether you measure that in productivity
12:37 or just being able to enjoy more things in life,
12:41 I think that the internet did bring that.
12:44 And I think with technology, if you look in history,
12:46 anytime that a big breakthrough technology hits,
12:51 it takes a long time for people to understand.
12:54 The first thing that happens
12:55 is people create some efficiencies.
12:56 So, we'll see more efficiencies,
12:58 but then the demands will increase
13:00 as far as the expectation of what clients have.
13:02 And that will eat up some of those efficiencies
13:05 because you're gonna have to, for say the same price point,
13:09 we're providing way more services
13:11 for probably what cost us,
13:12 we probably receive less for now.
13:14 But do you measure that in productivity?
13:17 I don't know, but I measure in what our clients achieve
13:20 or get for the dollars that they spend with us.
13:23 And I think with AI, you look at it
13:26 and there are gonna be a lot of efficiencies
13:30 in our operational area, but what gets more exciting
13:32 is that next phase with technology,
13:35 which is where you break through
13:37 on the things that you didn't imagine.
13:38 None of us understood, remember the internet was gonna,
13:41 in my business, we use financial advisors
13:44 to distribute our funds.
13:47 Newsweek, I think had it or Businessweek had on the cover,
13:51 the death of the financial advisor, the death of the broker.
13:53 Well, no, it just made the broker way more efficient
13:56 and now the client expects way more services
13:58 from that broker.
14:00 And I think that that's what ultimately is the next phase.
14:04 - So, I think that just to echo some of this,
14:07 two things, three things quickly.
14:10 The first is that any big new technology,
14:14 comments that are made at this kind of stage
14:17 with regard to what then subsequently happens
14:19 are invariably wrong.
14:21 And so a huge dose of humility here
14:24 in terms of how this will play out
14:26 because if you look back at the introduction
14:29 of a whole variety of technologies
14:30 in terms of how they were predicted to affect anything,
14:34 that track record is terrible.
14:38 The second thing is, despite what I just said,
14:41 if I had to guess today, I think the impact
14:44 will be more immediate and larger
14:47 on what I'll call hard skills
14:49 and less immediate and more attenuated on softer skills.
14:54 So think in medicine that AI will more quickly
14:59 replace the radiologist than the primary care doc
15:02 where human interaction is more important.
15:06 And then the third thing is, I think a core crucial thing
15:11 that no one has a good answer to is,
15:14 what does this revolution do to what I'll call truth?
15:19 So we have not yet seen the full impact
15:24 of all of these tools being unleashed
15:27 in terms of their output being then put back
15:30 into the public domain.
15:32 As that happens, the feedback loop
15:34 from the well-known hallucination problem
15:38 will become more severe.
15:40 So places like Fortune and others
15:42 that become trusted sources of truth,
15:45 A, that's gonna be more important,
15:47 B, it's gonna be harder to do that
15:49 because you can't just click through
15:51 to the underlying website if that's then getting infected
15:55 by the output of the tools themselves.
15:59 I had a debate with one of my older kids
16:02 about one of these things came up
16:05 and said that he had a PhD, which he doesn't.
16:07 And I said, but what if in the next iteration
16:12 of these things using DALI or whatever,
16:16 there you have the certificate
16:17 and it looks completely real and so on and so on and so on.
16:21 Who's gonna tell exactly whether my son
16:25 who doesn't have a PhD actually does?
16:27 - But that's where you'll look at your data set
16:29 and say, I'm gonna go to Fortune because I trust them
16:32 and then now summarize those things, right?
16:35 So I think it actually makes the trust--
16:37 - And the burden on Fortune and other news organizations
16:40 is going to be exponentially higher
16:42 in terms of then tracing down--
16:43 - Yeah, the truth.
16:45 - The truth.
16:46 - That's right, because the temptation will be,
16:47 because this stuff is so efficient--
16:50 - Just rely on that, yeah.
16:51 - To rely on that and not to fact check.
16:53 So I mean, I hear this kind of worry
16:56 about the AI poisoning itself to death a lot
17:01 because if AI is generating more of the open data
17:05 that AI is trained on, it does create
17:07 this kind of crazy toxic feedback loop.
17:10 - But just one, the head of AI at Stanford University
17:15 shared this story about there's a professor
17:18 whose entire career was researching a single peptide
17:22 on the genome and we have about 20,000 of them.
17:25 So that was an entire career.
17:26 Now they have mapped all of the genomes
17:33 or all the peptides on the genomes, all 20,000 in one year.
17:36 So just think about that productivity improvement.
17:39 Now the reality is that's just the foundation
17:42 of trying to figure out what the medical breakthroughs
17:45 that get built on that.
17:46 But I mean, think about that productivity efficiency
17:49 that ultimately happens.
17:50 - Well, and I would imagine in our businesses too,
17:52 I mean, going forward, a lot of the somewhat manual tasks
17:57 that people conduct at the analyst associate levels,
18:00 et cetera, of pulling data from one source,
18:02 putting it into a spreadsheet, analyzing it,
18:05 that is gonna be completely different.
18:07 - Talk to me about the higher value kind of added thing.
18:10 'Cause you both are in the kind of global advisory
18:12 kind of space, you're giving big macro advice to clients
18:16 who are kind of grappling with what's going on
18:18 and where do I put my capital.
18:19 Can you automate that?
18:22 Is that something that--
18:23 - So I jokingly say like, so I have five kids
18:25 and my three daughters, I always say,
18:27 picked careers of a hardworking mom, a singer,
18:30 an actress and a documentary filmmaker.
18:32 And my son became a computer programmer
18:34 and I was like, oh, thank goodness,
18:35 somebody's gonna have a real job.
18:37 Guess what?
18:38 I think the content producers are gonna have
18:40 much more opportunities going forward
18:42 because today you go on to GitHub
18:44 and the programmers are using Copilot.
18:46 It's programming half of what their code is
18:51 and it's just gonna get better and better.
18:52 So I actually think those on the creative side,
18:55 it's just gonna be different
18:57 as far as the skill sets that are needed.
18:59 - Yeah.
18:59 I think there's gonna be, at least in our business,
19:02 there will be differences across the experience spectrum.
19:06 So I mentioned before, replacing some of the,
19:09 what I'll call manual menial work
19:11 that really highly talented people don't need to be doing.
19:15 They can be freed up to do other things.
19:17 But at the managing director level
19:19 in terms of the people who are providing advice,
19:22 we're definitely in the augmentation stage here
19:24 because a lot of what an advisor does is,
19:26 well, here's another example of another company
19:28 that's thought about something similar
19:31 or here's another situation that has some parallels
19:35 to the problems you're facing.
19:37 That's based on experience,
19:38 but to the extent that it can be augmented
19:40 by these new tools, and frankly,
19:44 we're experimenting with that now
19:46 and it's really exciting to see the potential.
19:50 - But anybody in asset management,
19:51 I think has been using at least machine learning,
19:54 probably for, I mean, we've been using it for a decade
19:57 and now I think it's the generative AI
19:59 that you're trying to figure out
20:00 and it's just gonna allow more information to be absorbed
20:05 in as you do things like analyzing earnings calls
20:09 or summarizing annual reports.
20:12 And the beauty of it is,
20:13 it doesn't notice the font size of a footnote,
20:16 which is where the interesting information is,
20:18 versus the font size of the rest of the thing
20:20 when it summarizes.
20:21 So there's actually gonna be some opportunities
20:23 to take out some bias in it.
20:24 - But this is really interesting.
20:25 I think about like IBM Watson, for example,
20:28 and first it was, well, it can't beat the chess master
20:31 and it beat the chess master.
20:32 And then, well, it can't beat the go master,
20:34 it beat the go master.
20:35 And then it kind of ran into this wall
20:38 when it came to diagnosing patients,
20:40 which was supposed to be the commercial application.
20:43 And so there are these kind of holy grail things
20:45 that still seem to have eluded AI.
20:48 And I wonder, just like pure stock picking,
20:51 could you assign an algorithm to just say,
20:53 look, go pick a basket of stocks for my clients
20:56 and trust it?
20:57 Or is that never gonna happen?
20:58 We got 30 seconds to answer that, but what do you think?
21:03 - Here's where it becomes interesting.
21:04 And I used to do consumer lending.
21:06 And consumer lending, you looked at,
21:08 when we did auto loans, it was down payment.
21:11 There were six things that you looked at, FICO score.
21:13 We now have a team that does machine learning
21:16 and buys loans.
21:18 And they found that if you didn't capitalize
21:21 the name of your employer,
21:22 and these three other micro signals exist,
21:25 you were 25% more likely to charge off.
21:28 So it's going to make you better.
21:32 But on the other hand, as a consumer lender,
21:34 when I literally one time saw somebody finance,
21:37 a grandmother was buying a Corvette,
21:38 I said, you didn't think it was fraud?
21:41 I mean, so there's that overlay
21:42 that you could never fully put into the models,
21:45 which is why I think the human hybrid
21:47 is a much better way to go.
21:49 - I think we're gonna have to leave it there.
21:51 We're right out of time, but this is really fascinating.
21:54 Thank you both for coming to join us
21:55 and share your insights.
21:57 Appreciate it. - Thank you.
21:58 (audience applauding)
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