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0:00SARAH HANSEN: I'm Sarah Hansen. And today, I'm talking with
financial economist Andrew Lo, whose videos have been
viewed millions of times
0:06on our channel. I asked Andrew how he
makes a topic like finance accessible to everyone. ANDREW LO: It's amazing how
bad we are as Homo sapiens
0:15in managing our finances. SARAH HANSEN: We
also get personal about his own learning journey. ANDREW LO: I have
a learning issue.
0:22It's the mathematical equivalent
of dyslexia, dyscalculia. SARAH HANSEN: You do? And I get his answer
to the question,
0:28should I use ChatGPT
to plan my retirement? [DRUM ROLL]
0:33All this and more
on "Chalk Radio." Andrew, thank you so much
for being with us today.
0:39ANDREW LO: Pleasure. Thank you for having me. SARAH HANSEN: For
a long time, I've thought that if I don't have an
advanced degree in mathematics,
0:45that I probably don't
have a place in finance and probably shouldn't be
thinking about it much. But I'm wondering if you might
prompt me to think differently.
0:54ANDREW LO: Well,
not surprisingly, I have a very
different perspective as a financial economist.
0:59Someone once said that, to
a person that has a hammer, everything looks like a nail. And I'm guilty as charged.
1:05I learned early on in life
that virtually everything, at some point or another,
ends up being about money.
1:13Particularly with regard to
any kind of innovative pursuits for entrepreneurship
or career-wise,
1:20at some point or
another, you're going to have to deal with money. And then you need to speak
the language of finance.
1:26So that's what
really motivated me to learn how to speak that
and then ultimately, be able to develop new words and
sentences in that language.
1:33SARAH HANSEN: Yeah. It is a new language. And it seems like it's
something that everybody can learn, no matter where
they're starting from.
1:40ANDREW LO: Absolutely. SARAH HANSEN: You've
contributed to OpenCourseWare. Your videos have been
watched millions of times.
1:46And you seem really
good at making finance accessible
to people, no matter where they're starting from.
1:52I'm wondering what
it is that you do that allows that to happen.
1:58Have you done any reflecting
on that over the years? ANDREW LO: Well, first of
all, it's a great honor to be part of OpenCourseWare. And the fact that
it does reach people
2:05all around the world,
regardless of cost or access, is really wonderful.
2:11And the reason that I was
so pleased and honored to participate in that
is because I really
2:16feel like everybody
should have access to this knowledge, at
different levels, of course. But we all need to know
a little bit of finance.
2:24And I think that
early on, I was really drawn to this field because
of how impenetrable it was,
2:31and at the same time,
how important it is. Growing up, I just
remember always
2:37hearing my mother
talk about finances and some of the challenges
that we were facing. And I think that really made
me hyperaware of the fact
2:46that it's really a necessary
part of life-- sometimes, unfortunately, too
much a part of life.
2:51And if we don't
understand it, it's very easy to end up
being taken advantage of
2:57or not being well prepared
for the kind of things that finance helps
you deal with. SARAH HANSEN: Yeah, I
think about this a lot,
3:04when I go to the grocery
store and see cereals for over $6 and eggs
sometimes up to $8.
3:12What are some theories of modern
finance that everyday people like me could apply
or think about
3:20when I'm faced with
those situations? ANDREW LO: Wow. Well, there's a whole bunch
of ideas and important notions
3:27that we can use in
our everyday lives. One of the key notions
is that there's
3:33a trade-off among
everything that we do and everything that we purchase.
3:38Far too often, we tend
to compartmentalize. And we have a mental budget
for certain activities,
3:45as well as other budgets
for other activities. And we don't allow them to mix. But sometimes, if you can
think a little bit more
3:52broadly about the fact that we
have a given set of resources,
3:57and we have to allocate
them across lots of different
activities, and there are interesting financial ways
of making those decisions,
4:05we can actually come up
with better outcomes. One example that I think
most people are aware of is borrowing money to
buy a home or buy a car.
4:13That is basically moving money
from the future, our futures, to our present.
4:18We're borrowing. And sometimes we need
to put money today aside for our kids' college fund or
purchasing a car in the future.
4:27That's an example of moving
money from today to ourselves two years from now or
three years from now.
4:33So this notion of being
able to move money around and making sure that you don't
lose a lot of it in the process
4:40is part of the
language of finance. And that's something that
all of us can benefit from. SARAH HANSEN: Yeah.
4:45It's so interesting,
the idea of having more flexible understanding
of your resources
4:53and how they might
move across categories. ANDREW LO: I mean, I
think, at the heart of it, it's really all about control.
4:59But I think not enough
people understand how finance works so that
ultimately, it ends up
5:05ruling their lives, as opposed
to allowing them to use finance to achieve the kind of
goals that they really want.
5:12SARAH HANSEN: So,
Andrew, before the show, we asked you to respond to a
question, which we will now
5:18reveal the answer to. So the question was,
should I, Sarah Hansen,
5:23use ChatGPT right now
to plan my retirement? What's our answer?
5:30Not yet. OK. Please explain. ANDREW LO: Not yet. Not yet, because
large language models
5:36are not yet ready
to be able to do delegated financial
decision making for us.
5:42Turns out that
saving for retirement is one set of activities. But then spending
while in retirement,
5:47that's a whole different
set of activities. And many of us have
become good at saving. But we've been such
good savers that we
5:55forget that at some
point, we need to spend. And we have to spend
at the right rate,
6:01for the right things,
at the right times. So what my colleagues and I
are trying to understand now
6:07is not only what those
optimal decisions are-- I think we now have a lot
of information about that.
6:12There's been a lot of
research done on that. But the more challenging issue
is, how do we communicate that to people who don't have
any finance background,
6:19and moreover, can't afford to
hire the financial advisors that some of the
higher-net-worth individuals
6:25have access to? So we're working on
an AI solution, an AI
6:30financial advisor,
that can satisfy the most important criterion
of financial advice
6:36that regulators impose, which is
something called fiduciary duty. A fiduciary is somebody
who is appointed
6:42to help you further your
goals, will look out for your best interests,
above and beyond their own.
6:48And so, very often,
people get a little bit concerned about financial
planners and other people in the financial industry
because they don't
6:54want to be taken advantage of. And there are many good
financial planners out there. But not everybody
can afford one.
7:00So the question is, can
you-- for those people who can't afford financial
planning and for the financial
7:06institutions that don't want
to spend their resources on financial advice to those who
aren't going to generate enough
7:12commissions for them-- for these people, can we come up
with financial advice from an AI
7:18platform that satisfies the
definition of a fiduciary, some program that you
can trust to look out
7:25for your best interest? We're not there yet. However, I believe
that everybody should use large
language models to help
7:31them think through retirement
issues more seriously. For example, ask ChatGPT,
what are the biggest issues
7:38that I need to focus
on for my retirement? How much time do I have? What kind of ideas, and
products, and services
7:43should I make use of? How do I get better
financial information? How do I learn more
about these problems? All of those things,
I think ChatGPT
7:50can do a pretty good job at. But I wouldn't let it make
your decisions for you. SARAH HANSEN: Real
question for you.
7:55I know, a lot of times, when
I put in a prompt that's within my domain of expertise,
some of it will be good,
8:03but some it's kind of garbage
and not all that accurate. So as a person who
doesn't know finance, how do I know I can trust the
answers that are coming back,
8:11even about those general
type of questions? ANDREW LO: Well,
so that's really why you need to
have additional time
8:17and effort employed in
thinking through these issues, not just by yourself, but
with friends and family.
8:23So we need human
support for thinking about how large language
models can work, either well,
8:28or in some cases, not so well. And in any case, if you are
planning for your retirement, at some point or
another, you're going
8:34to have to engage with people
from the financial institutions. They will be able to
help deal with some of the so-called
hallucination problems
8:41with these large
language models. But the answer is like
anything else that we do. How do you know that your
doctor is always giving you
8:47the best information? Doctors are very highly trained,
but they do make mistakes. So get a second opinion.
8:52Get a third opinion. Talk to your family members. Ultimately, you're
going to be responsible for your own decisions.
8:57But the more
informed you can be, and the more you can check the
accuracy of the information--
9:03and that information can come
from humans or from chatbots-- I think those are the ways that
we've developed for making sure
9:09that we make the best decisions
possible to get a better handle on this. SARAH HANSEN: Right. And I assume part of the
idea would be to start this
9:18in your 20s, if you can. Start learning and start
using these applications to really begin to save.
9:24ANDREW LO: Exactly. Many people don't realize
that the decisions that they make early on can have just
tremendous consequences 30, 40
9:32years from now. We now know that about health. We know that if we don't eat
right, even as a teenager, we can affect our health
when we get to middle age.
9:39The same thing is true
about our finances. If we make bad financial
decisions early on, it could end up having
repercussions far beyond what we
9:47typically are able to measure. And so that's really the purpose
of having some kind of support,
9:52some kind of advice that
can actually look 40, 50 years ahead of a teenager.
9:58SARAH HANSEN: It's kind of
interesting, because at least in the US, or at least
in the cultural circles
10:04in which I grew up, it was sort
of taboo to talk about money-- how much you make, how
much other people make,
10:10how you're budgeting. Have you found that
in this context? ANDREW LO: Absolutely. And that's actually
one of the reasons
10:16why you want to
have a fiduciary. You want to have
somebody you can talk to about your deepest
secrets, your financial goals,
10:24your constraints, the concerns
that you have about losing your job or not being
able to find a job, and be able to get honest advice
about what to do about it.
10:32Many of us have good friends
that we can rely on for that. But if you don't
have a friend who has financial expertise,
what do you do?
10:39So the hope is that maybe
a platform, a reliable AI platform that is able
to serve as a fiduciary,
10:47can really help individuals
with all of their concerns. SARAH HANSEN: Yeah. That's going to be a
game changer for sure.
10:53What are some of the dangers,
pitfalls, or challenges that might come
with leveraging AI
11:00to give financial advice
to people who do not have financial expertise?
11:07ANDREW LO: Well, any great
tool can easily be abused. And I think we have to
worry about the fact
11:12that if we have a financial
planning tool that is AI driven, can that be abused?
11:18What if we were to ask this AI
to engage in illicit activities for our financial betterment?
11:25Is that something that
we should be able to do? Or is it something that the
AIs should guard against?
11:32You get into all sorts of
tricky ethical questions about how these tools are used. And so I think that
those of us who
11:38are developing the
technologies really require spending
more time thinking about the ethical dimensions.
11:44That's not something
that you typically focus on in your research. But for us now, given how far
AI has come and what it can do,
11:52we have to be much
more proactive about thinking about these
unintended consequences. SARAH HANSEN: Yeah, and just
how fast everything is changing.
11:59The technology is so fluid
and so fast, and honestly, not all that transparent
most of the time.
12:04ANDREW LO: Yeah. That's a concern that I think
most people don't really appreciate yet. It's true that there's
always been technology
12:11that has made advances,
and as a result, has created winners and losers. When the horse-drawn buggy
gave way to railroads and then
12:19eventually to automobiles,
people lost their jobs. And people had to be retrained.
12:24But there is a big difference
between that technology and the current set
of technologies.
12:29And it's exactly what you said. It's about speed. The fact is that right now, AI
is moving at such breakneck pace
12:36that within a very
short period of time, we can actually have large parts
of our population unemployed.
12:44And the hope of retraining
them in time for them to make a difference
for their lives can be very challenging, given
how quickly things are moving.
12:52So I think that's the one
concern that AI researchers are thinking about. We don't have any
good answers yet.
12:59And one of the answers
that's been proposed, which is to slow down
the rate of progress-- that's almost never possible.
13:05Even if you want to do it,
even if you have legislation, there'll be people
that will refuse to abide by that, because
legislation is not going to stop
13:13that kind of innovation. It's not going to stop people
from having ideas and wanting to implement them. SARAH HANSEN: Yeah.
13:19The idea of people being put
out of work with the advancement of these technologies
is hugely concerning,
13:24and I know one that you're
concerned about, because it seems like, in
everything you do, it all comes back to people
and the impact on people.
13:34I'm wondering where
that drive comes from. Where did your interest
in leveraging finance
13:41to positively impact
people's lives come from? Because it didn't have to. There's a lot you
can do with money
13:47and not worry about
positively impacting people. So tell us a little
bit about that. ANDREW LO: Well, yeah.
13:53I have to say that that
was really not something that I had planned. Much of my early
career was focused
13:58on applying mathematical and
statistical models to investment problems, developing
trading strategies,
14:04risk management policies,
various kinds of systemic risk measures. It was really all geared
around the amazing impact
14:12that this technology could
have on the field itself, on finance, making
finance better.
14:18And in some of the things
that I did in practice on the commercial side-- started up my own asset
management company
14:24and ultimately implemented
some of these strategies for various kinds of
hedge funds and investors.
14:30And at some point-- I mean, it was very satisfying. But at some point, I started
to wonder whether or not
14:36this was it, this was
what I was meant to do.
14:42And around that time-- it
was about 20 years ago-- a number of friends and
family all developed cancer
14:48at the same time. Within seven years,
six people close to me all died, including my mother. I'd really never dealt
with death up close
14:56and personal before that. And so it was a big wake up
call, and through that process,
15:01realized that finance plays a
pretty big role in cancer drug development.
15:07I was pretty naive at the time. I just thought that if
there was a patient in need, and there was some
great technology that
15:12could help that patient,
that somehow, magically, money would just come sprinkling
down and develop the drug.
15:17But when I looked into it
and talked to my colleagues here in Cambridge, it
became very obvious to me
15:23that that was not
the case, that there were many examples of good drugs
that could help lots of patients
15:29that will never, ever see
it to market because there was not enough financing to
bring that to the patients.
15:36So that's when I started
thinking about how finance could play a role in that. And it was actually
totally selfish.
15:42I wanted to figure out how
to help my friends and family and how to get
better drugs to them. But in doing so, it just made
me realize the power of finance
15:50and the responsibility
that we have, given that we understand
the kind of things
15:56that other people
don't about how to finance certain
kinds of projects, about how to bring money
to a particular area
16:02and really make it go
faster, that we also have a responsibility to
take some of these ideas
16:09and implement them
for those really high-impact,
high-societal-impact kinds of pursuits.
16:16So I started with that. And then one thing
led to another. And I began thinking more
broadly about how finance
16:22impacts society in
general, and realizing that there are
many things that we can do to further all
sorts of different goals--
16:29climate change,
energy transition. There are so many
different issues that really are crying out
for new business models
16:36and financing strategies. And these are well-known tools
that all financial economists know.
16:41But we haven't really spent time
thinking about the applications. So that's been a
really rewarding aspect of my current focus.
16:47SARAH HANSEN: Yeah. Could you give an example
of how a different business model might radically change
the climate trajectory
16:54that we're on? ANDREW LO: Sure. Let's talk about
energy transition, because clearly, we
are not in the process
17:01of transitioning very quickly. SARAH HANSEN: That's right. ANDREW LO: In fact,
right now, about 82%
17:06of the world's energy usage is
in the form of fossil fuels. I think that might be down
from 83% or 84% 10 years ago.
17:15It's not enough. And we're not making a
difference fast enough. A number of people who know
a lot more about the issues
17:22than I do have said
that we're not really, right now, in the
business of transitioning,
17:27because it's not as if we're
declining our usage of energy. If anything, it's growing. So what we need to
do is to come up
17:33with new sources of
energy, energy addition, as opposed to energy transition. SARAH HANSEN: Oh,
that's interesting.
17:38ANDREW LO: And so what
do you do with that? Where do you go? There are three totally
green sources of energy
17:44that my colleagues
tell me right now that are not in the renewable space.
17:50Obviously, solar, wind,
hydro is important. But they're not always
there when we need it.
17:55We need to develop better
battery technologies. And that's all happening. That's good, but
again, not enough.
18:01So if we want to speed up the
process of energy addition, we need new sources
of green energy. And there are three.
18:06There's nuclear fission,
nuclear fusion, and geothermal. And all of these should
be areas that we pursue.
18:15And so imagine if
we could support all sorts of different
investment projects in all three areas and really
make a concerted effort
18:24to develop these new
sources of green energy. That's an example of
what finance can do.
18:29But we need to develop
the political will to be able to do that. And even part of that can
be hastened by finance,
18:37because if you can show that
governments can actually save money in the long run by
investing in these, if they can
18:43get a good rate of return
on these investments, they're much more likely to be
able to write the checks that we need now.
18:48SARAH HANSEN: Yeah. It really does all
come down to money. ANDREW LO: Unfortunately,
in most cases,
18:54that seems to be true. But fortunately, the world
has become wealthier.
18:59And largely, that's happened
because of industrialization and the innovations
in technology
19:04that we have pioneered. But the wealth is
not equally spread. There's definitely
winners and losers.
19:10And I would argue that the
wealth is not necessarily being allocated in the
most efficient way.
19:16If we were to change
some of that allocation, we could actually get better
outcomes for everybody. Nobody has to lose.
19:22SARAH HANSEN: So it all sounds
so logical when you explain it. But then you introduce humans.
19:29Talk to me about humans. ANDREW LO: Human behavior is one
of the most interesting and most
19:35confounding aspects of what I do
in finance, because we can write
19:40down all these
wonderful equations that predict various kinds
of opportunities for financial investments.
19:47But at the end of the day,
you're talking about people. And people can react. Sometimes they can overreact.
19:53For example, during
the pandemic, the stock market lost
something like 30%
19:59in a matter of a few days. And if most of your retirement
is tied up in the stock market,
20:05that was a really big hit. So a number of people
responded by what?
20:10By pulling out their money
from the stock market, putting it in cash. That, in and of itself, is not
necessarily a bad decision.
20:18The problem is that many
of these individuals, they took too long
to put the money back into the stock market.
20:23And they missed
the rebound, which actually happened just a few
weeks after that 30% decline.
20:28The market went right back up. And if you had taken a vacation
between middle of March 2020
20:34and middle of June 2020--
if you had taken a vacation and done nothing,
you would not even have noticed that the
stock market moved.
20:41It just went down
and then back up. So I think that understanding
these kind of dynamics
20:47is important, because
we often allow ourselves to engage in behaviors that can
be really counterproductive.
20:54And that's part of what I do
as a finance professional, is to understand how human
behavior factors into this.
21:01And it's amazing how bad
we are as Homo sapiens
21:06in managing our finances. And that's one of the
reasons why I do what I do. And I'm really excited about it. SARAH HANSEN: Yeah.
21:12You have a hypothesis. What is it called? ANDREW LO: So my hypothesis is
the Adaptive Markets Hypothesis.
21:19And it was developed
in contrast to one of the most popular
theories in my field, called the Efficient
Market Hypothesis.
21:26That's a theory that says that
markets, always and everywhere, reflect all available
information.
21:32And that means that
the prices that you see are generally correct.
21:37And it turns out that the
efficient market hypothesis is not wrong. It actually works
most of the time.
21:43But it's not complete. It doesn't work all the time. And for the times
when it doesn't work, where human behavior
overwhelms the rationality
21:51that you typically
see in the markets, that's where you
need to understand how the mechanics
of human behavior
21:57interacts with the beautiful
logic of financial markets. That's the adaptive
markets hypothesis.
22:02SARAH HANSEN: Yeah. So as a fellow
human, what can I do to counteract my own
irrational tendencies?
22:09ANDREW LO: Well,
the first step is to recognize that
this is an issue. So if you recognize that
we all have this problem,
22:17that's the first
step in recovery, in trying to understand
how to deal with it. The second step is to do
scenario analyses in our brain.
22:27One of the most magnificent
gifts of human evolution is the ability to engage
in abstract thought,
22:34in hypotheticals. We can hold in our mind
all sorts of "what ifs." Of what if I decided
not to go to work today?
22:40What would happen then? What if I decided to
become a doctor, a lawyer? What if I decided
to save money so
22:47that my kids can go to college? Those kinds of "what ifs," we
can often work out in reasonable
22:53detail in our heads and then
pick the "what if" that is the most attractive. So right now, if we're not
in the middle of a financial
23:01crisis, we can do
"what if" and say, what if it turns out that the
stock market crashes by 20%?
23:08Given my age, given
the likelihood that it's going to come
back, because based on the last hundred
years of history,
23:14when the markets go down,
eventually they go back up-- it takes a while. But we can actually see
when things are recovering.
23:20What if I decided not
to sell out everything? What if I decided to wait
and see how things go?
23:26What if I were to wait two
years, three years, five years? Can I afford to wait? By doing those kinds
of "what if" analyses,
23:33we can go a long way towards
preparing for these kinds of events. And this is where financial
planners are really helpful.
23:40They have a lot more
sophisticated "what ifs" than you and I might be
able to come up on our own. And there, I think, we
can get AI to play a role,
23:48because AI now, with
large language models, are remarkably good at coming
up with all sorts of interesting
23:54"what ifs." And so to be forearmed is
to be-- to be forewarned
24:00is to be forearmed. SARAH HANSEN: We have a lot of
YouTube comments pointing out that you tend to make
finance very accessible
24:09through your lectures. And I'm wondering if
you could articulate what it is you think you do
that opens the door for students
24:17in your classes and around
the world when they watch your OpenCourseWare videos. ANDREW LO: Well, first of all,
thank you for that observation.
24:23I'm really humbled
and grateful for that. If that's true,
it's because I often
24:31develop my lectures with an eye
towards being a student myself. I was a high school student in
New York City at the Bronx High
24:38School of Science. It's a school that
specializes in STEM, and a wonderful
education that money
24:45didn't need to buy because
it's public school. And I learned more from my
classmates in Bronx Science
24:51than I think I did
from any other time in my educational career. It's a phenomenal school. But one of the things that I
learned from Bronx Science that
24:57wasn't quite right-- and
this is my own fault-- I identified
intelligence with STEM.
25:04So if you were smart
at Bronx Science, that meant you were good
at science and math.
25:09I mean, we had history,
English, social studies, all of those other fields. But those weren't the real focus
of the kids that went there.
25:17You went there because you
wanted to study science, math, and engineering. And it wasn't until I got to
college that I was completely
25:25disabused of that notion. And it happened
because I met somebody who was completely innumerate.
25:32I mean, he wasn't even able
to take the introductory math class in college and needed
my help to get through it.
25:40And so I would tutor him
every once in a while. And he would eventually learn. But it took him a while.
25:46And it was never easy. He really sweated it
out, and ultimately, I think, passed with
a C or something.
25:52SARAH HANSEN: I may or may
not have been that person, but go ahead. ANDREW LO: Well, the
thing about that person
25:59is that he was one of the
smartest people I ever met in college. And I didn't expect that.
26:05And let me explain
what I mean by that. We'd be sitting at dinner. And no matter what the
topic of conversation was--
26:13politics, religion,
music-- anything that you would talk about
other than mathematics,
26:20he was just incredibly
well informed, articulate, and extraordinarily analytical.
26:27And so that really
threw me for a loop, because again, analytical,
math, but not in his brain.
26:33So he ultimately
went to law school, became a very, very
well-respected attorney.
26:39And he could argue
anybody into the ground because he had a mind
sharp as a razor blade.
26:47How is it possible
that somebody could be so smart and yet innumerate? That was a big
wake-up call for me.
26:53And it pointed out the fact
that there are different kinds of intelligence. So up until recently,
when we think
27:00about artificial
intelligence and computation, it's all computational,
quantitative.
27:06For the very first time-- and this is why I think
large language models are such a breakthrough--
we are now confronted
27:11with a very different kind
of artificial intelligence. This is intelligence that is
like my friend in college.
27:18It becomes analytical, but in
a way that uses language, not numbers and equations.
27:24And so I think that
large language models is a different type of AI
than what we're used to.
27:30That's what makes
it so powerful. But it does have
some weaknesses, just like my friend,
who, if you asked
27:36him to prove a very simple
theorem in calculus, he'd sweat bullets doing it. But he was incredibly
intelligent in other ways.
27:44And so I think that opens up
all sorts of new insights for me in terms of my own
students and myself,
27:50and how we think
about intelligence, and sometimes how limited we are
in thinking about intelligence.
27:55We measure intelligence with
tests, reading comprehension, vocabulary, and
mathematical problems.
28:02But I wonder if there's
many forms of intelligence that we are not capturing
with those tests,
28:08and there are whole
swaths of society that we have somehow
neglected and underappreciated because they have an
intelligence that we don't
28:15know about, we can't
measure, but we really could benefit from. SARAH HANSEN: Well, I
couldn't agree with you more.
28:20I used to be an
elementary school teacher. So I saw the ways in which
schools pinpoint intelligence,
28:26and measure it, and leave
out large groups of people. So we're on the same page there. ANDREW LO: Well, speaking
of elementary school,
28:33that was one of the important
and formative experiences of my life. It turns out that,
in retrospect, I
28:42have a learning issue. It's the mathematical equivalent
of dyslexia, dyscalculia.
28:48SARAH HANSEN: You do?! ANDREW LO: Yes. SARAH HANSEN: Wow. That is so surprising. ANDREW LO: Well, people say
that because of what I do now.
28:55Yeah. But it was definitely the case
that math was my worst subject. And for an Asian growing up
in New York City in the 1970s,
29:03that was not easy. SARAH HANSEN: Wow. ANDREW LO: So it wasn't until
I went to the third grade
29:10that I finally found a teacher
that recognized something in me,
29:16Mrs. Barbara Ficalora, third
grade, PS 13 in Queens. And she knew that I was
struggling with math.
29:24But yet, she also saw
that I was really curious. I would always enjoy
going to the library.
29:30Once a week, I would take
out a stack of science books, and read them, and be
really interested in that.
29:35And so she tried to boost
my confidence by making me the class scientist.
29:42Now, I didn't know that
that position existed. I certainly didn't apply for it. But what it meant was
that I got to demonstrate
29:48one of these science experiments
that I was concocting at the back of the room every
free period I could get,
29:54and just talk to the whole class
about how to make a battery out
29:59of lemons and magnets. And I think it was
that experience that
30:04allowed me to get through
my elementary years, despite the fact
that other teachers
30:09that I had who were
not so supportive-- were pretty discouraging
to me and to my mother.
30:16Back then, there
was no diagnosis of ADHD or dyscalculia. So, I basically soldiered on
until I went to high school
30:26at that Bronx High School
of Science I mentioned. That was in the 1970s, when New
York City was undergoing this
30:32radical experiment
called the New Math. I don't know if you've
heard about that. But it was a widely
renowned failure,
30:39because it was replacing the
basic concepts of algebra, geometry, and trigonometry with
all these mathematical concepts
30:48of groups, rings,
fields, isomorphisms, and other transformations. And while, mathematically,
it was more rigorous
30:55and intellectually
more pleasing, most of the New York
City school teachers were not prepared to
teach in this way.
31:02And so, for most schools,
it was really a failure. But for me, it
was night and day.
31:10I came from being a C student in
math to an A student overnight. SARAH HANSEN: Because it was
more analytical and less focused
31:17on-- ANDREW LO: Numbers. No numbers. To this day, I have a hard
time memorizing numbers.
31:22I never memorized the
multiplication tables. I still have trouble
with 6 times 7. I have to actually do the
calculation in my head.
31:29And it takes me a little
bit longer than most people. But when you replace
numbers with equations,
31:34that was like a huge relief. It's like wearing shoes that
are two sizes too small.
31:39And then you take
them off, and you put on shoes that are just
exactly the right size. It felt wonderful. And because I
struggled as a student
31:46with my own learning
issues, I could tell the difference between
good teaching and bad teaching.
31:52And of course, for a while,
I blamed it on myself. But then, once I learned a
bit more about my own learning
31:58issues, I began to understand
what certain things would allow me to see a concept
versus other things
32:06that would confuse me. Now, when I write
my lectures, I often have to step into the
student's role and ask myself,
32:14if I didn't know anything about
this, what would be the fastest way to get me to have some
kind of a grasp of what
32:21it is that I'm trying to teach? And again, looking at
it from my own lens. The other part is that
I've had the great gift
32:28of having a number of
really incredible teachers. When I was a high
school student, my calculus teacher, Mrs.
Henrietta Mason, was amazing.
32:36When I was a college student,
Saul Levmore, Sharon Oster, Herb Scarf, amazing. As a graduate student,
Andy Abel, Jerry Houseman--
32:45I remember all of
these teachers' names. It's because they just
made a huge impact on me.
32:51They changed my life. And so part of why I spend time
on teaching-- and I think this
32:56is another aspect. Some of my colleagues are
so focused on research that they feel like
they can't afford
33:02to spend time on teaching. But because I remember what
it was like as a student when faculty didn't take the
time to try to explain something
33:11in a way that would be
more understandable, I decided to spend the time
on working on my lectures.
33:16Someone once said that
great writing is not writing so that other
people can understand.
33:22It's writing so that other
people cannot possibly misunderstand. SARAH HANSEN: Oh,
that's a good one. ANDREW LO: Yeah.
33:27That's a very difficult
standard to adhere to. But I think that's the
same with teaching.
33:33Teaching is lecturing so
that students cannot possibly misunderstand.
33:38And that does take a
little bit more time. But when you are able to land
a concept with an audience,
33:45there is no better feeling,
from my perspective. It is like, I don't
know, gymnasts
33:50hitting a perfect
landing, ice skaters being able to do a triple
axel without falling.
33:55For me, that is just an
incredible feeling, a rush, that I can communicate something
that somebody didn't understand.
34:04And now their faces light up. And I get it now. And they will forever-- from that point,
they will forever
34:09have that to use
and to benefit from. And also, one of the things
that makes me particularly
34:17grateful to OpenCourseWare is
that it is the great equalizer. There are so many people that
can't learn on a schedule
34:26and that have all
sorts of issues that don't allow them to
excel in a classroom with 30
34:31other kids. And yet they're
perfectly intelligent, in some cases,
super intelligent.
34:36But they have these challenges. OpenCourseWare gives them
a platform, at least, to be able to learn
at their own pace,
34:43to be able to stop the
video, to think about it, to start it up again
when they're ready. And there's nobody looking
over their shoulder,
34:49seeing how well they're doing
relative to their competitors. It changes the learning
field, and I think,
34:55gives opportunities
that weren't available. So I can't tell you how honored
I am to be part of this, and to be part of the
institution that came up
35:01with this platform, and
basically gave knowledge away to the rest of the world. SARAH HANSEN: You brought
something with you today.
35:08ANDREW LO: I did. So when I was asked to
come on this program, you asked me to bring a
meaningful memento, something
35:15small that I can carry. And I have to tell you, that
was a terrible assignment. It took me a long time, because
I-- what should I bring?
35:22Little art objects that
my kids made that are dear to me, my high school diploma?
35:28But I decided to bring
the most important reason for where I am today. And this is a
picture of my mother.
35:35SARAH HANSEN: Oh, lovely. ANDREW LO: So I mentioned that
we grew up in New York City.
35:41We were a single-parent
household. She worked one job
with overtime to be
35:50able to raise three
kids by herself-- I was the youngest of three-- and instilled in me and
my brother and sister
35:56a love of learning, and the
importance of hard work, and focusing on the
longer game as opposed
36:04to short-term issues,
which is ironic, because most of her
young life as a mother
36:10was focused on trying
to make ends meet. I would remember conversations
about finance pretty much
36:18every single month. And it was tough, because we
did not grow up with a lot.
36:23And she was divorced. And that was a time
when it was very
36:29difficult to survive as a
single woman with three kids. But she did an amazing job.
36:36And so I owe her a lot. SARAH HANSEN: So if she
was here with us now,
36:42what would she say to me
about balancing the ends meet
36:48with the long game? ANDREW LO: Yeah. Well, I don't know that her
advice would be something that anybody would or
could do, because she
36:56sacrificed her career for us. She was a lawyer
by training, left
37:03China when the Communist
Revolution took place, then married someone who was
not ideal, an abusive husband,
37:14took a long time for her to
see that and get divorced, as she did. It was a very messy divorce.
37:21And because he was
a foreign national and ended up being a
diplomat, he was also-- the best thing
about that marriage
37:27was that he was an absentee
father, because he was abusive when he was around.
37:33And so she dedicated
her life to her kids
37:40and held a menial
secretarial position, despite the fact that she was
educated in law from China,
37:47and ultimately, I think,
really put the three of us
37:53through school so that we could
have the careers that we do now. I don't know that that's
the right advice in general
38:00for women that are
in that position, because I think there has
to be more of a balance.
38:05But for her, it was
just really important for her kids to reach the career
goals that she set out for us.
38:12SARAH HANSEN: What's her name? ANDREW LO: Julia Yao Lo. SARAH HANSEN: That's lovely. Thank you.
38:18Thank you for being here. I really enjoyed
getting to know you. And you've completely
changed my perspective
38:24on what's possible in
terms of my relationship to mathematics and to finance.
38:30ANDREW LO: Good. I am honored to have
played that role. Thank you. [MUSIC PLAYING]