00:00:00
welcome to the next episode of the
00:00:03
analytics at Wharton and AI at Wharton
00:00:05
series on artificial intelligence um
00:00:08
this series is or this episode is
00:00:11
probably one of the topics that we
00:00:12
should be spending a lot more time
00:00:13
thinking about which is AI and
00:00:15
Automotive in a lot of ways it's really
00:00:18
Where it All Began I'm honored to have
00:00:20
my friend and colleague John Paul
00:00:22
McDuffy speaking to us today uh John
00:00:24
Paul is a professor in the management
00:00:25
department he's Al the also the director
00:00:28
of the program on vehicle and mo IL
00:00:30
Innovation I'm going to want to ask him
00:00:31
what that is as part of the Mac
00:00:32
Institute of innovation management here
00:00:34
at the Wharton School uh so John Paul
00:00:36
Welcome to our podcast today thank you
00:00:38
glad to be here yeah so why don't before
00:00:40
we get into Ai and Automotive which is
00:00:43
the big topic of today why don't you
00:00:45
tell us I I mean I've always thought of
00:00:46
you as our innovation in automobiles guy
00:00:50
so what is the program on vehicle and
00:00:52
Mobility Innovation and what kind of
00:00:53
things do you guys
00:00:54
doing probably the best shorthand for
00:00:58
our research agenda is is the so-called
00:01:01
case technologies that are transforming
00:01:03
Mobility
00:01:05
c c for connected a for autonomous s for
00:01:09
shared Mobility business models and E
00:01:13
for electric and understanding each of
00:01:17
those Technologies and their
00:01:18
implications but also how they combine
00:01:21
because they certainly combine in some
00:01:24
cases and in other cases not um we for
00:01:27
example think that most autonomous
00:01:29
vehicles going forward are likely to be
00:01:31
electric but obviously not all electric
00:01:32
vehicles are fully autonomous and uh
00:01:35
it's a fascinating competitive space we
00:01:38
have not only the Legacy incumbents we
00:01:40
have newcomers like Tesla we have a lot
00:01:42
of other newcomers you may not have
00:01:44
heard of and then we have big Tech
00:01:46
hovering on the edges you know apple and
00:01:48
and Google
00:01:49
slwo um uh foxcon you know Apple's
00:01:54
manufacturer wants to get into the
00:01:57
autonomous vehicle business and uh you
00:02:00
know run an entirely different you know
00:02:03
iPhone type model for how this industry
00:02:06
evolves so there's just uh a lot of
00:02:09
fascinating things going on another
00:02:10
thing to say about um the history of
00:02:13
this program on vehicle Mobility
00:02:14
Innovation I got my start on all this at
00:02:17
MIT as a doctoral student with something
00:02:19
called the international motor vehicle
00:02:20
program at the time it was trying to
00:02:22
understand the transformation from
00:02:24
traditional mass production which had
00:02:26
dominated most of the 20th century
00:02:28
starting with Henry Ford to um Toyota
00:02:30
production system which uh our program
00:02:33
gave the name lean production to so why
00:02:36
was lean production supplanting mass
00:02:38
production why did it have competitive
00:02:40
advantages and everything from
00:02:41
manufacturing and product development to
00:02:43
Supply Chain management and the like and
00:02:46
when I came to Wharton um I continued an
00:02:48
affiliation with MIT for a long time uh
00:02:51
even was co-directing that program from
00:02:53
down here uh at a certain point that
00:02:56
program was about to close down at MIT
00:02:58
and I actually asked if I could move it
00:03:01
to
00:03:02
Wharton and they said sure because uh at
00:03:05
this point the program was really a
00:03:06
network of Automotive researchers all
00:03:08
over the world that we kept kind of
00:03:10
loosely coordinated sometimes we would
00:03:12
simply get together and share knowledge
00:03:13
sometimes we would do joint Global
00:03:15
research projects together and so to
00:03:18
move kind of the network Hub to Wharton
00:03:21
from MIT was not such a big deal and uh
00:03:25
program on vehicle Mobility Innovation
00:03:26
pvmi is the opposite set of uh order the
00:03:29
initials of imvp that's a little in joke
00:03:32
for those of us in the program I like
00:03:34
that one so let me ask you um when I
00:03:37
think when I say historically I don't
00:03:39
mean back 20 years when I think over the
00:03:41
last s to 10 years I really do as I
00:03:44
opened up our podcast about I think of
00:03:46
AI and Automotive as being like that was
00:03:49
the flagship like that was going to
00:03:51
demonstrate the ability of AI to you
00:03:54
know self-driving cars I guess as you
00:03:57
may call it you put in your notes level
00:03:58
four where do things stand today like is
00:04:01
that dream are we going to be having
00:04:03
driverless cars all over the place soon
00:04:05
is level four autonomy even the goal
00:04:08
anymore yeah these are great questions
00:04:10
and I think anybody who's followed this
00:04:12
technology and has had as in in interest
00:04:14
in it knows about the hype in the sort
00:04:17
of 2016 17 maybe even a little earlier
00:04:21
phase and how it's been kind of
00:04:23
disappointing since then uh and yet
00:04:25
these days you hear about uh Cruz and
00:04:28
weo and Zo operating you know on call
00:04:32
Robo taxis in San Francisco on a limited
00:04:34
basis um you may also hear about some of
00:04:37
the controversy with uh fire truck
00:04:39
collisions and the regulation that's on
00:04:42
the books in California to limit this
00:04:44
one of the things I always think about
00:04:45
is like you know I used to work for a
00:04:47
large I could say dupond large
00:04:49
International Chemical Company and like
00:04:51
an error rate of one in a thousand one
00:04:53
and 100,000 one and a million might be
00:04:56
fine what could possibly be an
00:04:58
acceptable error rate for a product and
00:05:01
service like this like do you have to be
00:05:03
like 10 standard deviations out on the
00:05:05
safety scale like how do you even launch
00:05:07
a product where one in 100,000 error
00:05:11
rate which is great for most products
00:05:13
and service would be totally
00:05:14
unacceptable here I mean I think the the
00:05:17
right answer the candid answer is we
00:05:19
really don't know and it of course
00:05:21
depends partly on public perception and
00:05:23
what people feel is safe it depends on
00:05:26
what how Regulators think of this issue
00:05:28
and of course on the progress of the
00:05:30
technology you know the proponents uh
00:05:33
who say this is already a much safer
00:05:35
technology would say you know 40,000
00:05:39
people a year die in car accidents in
00:05:41
the US those are all cars driven by
00:05:43
humans right and that after about 50
00:05:46
years of that rate going down in the
00:05:49
last four or five years it's gone up
00:05:51
despite all the new safety technology um
00:05:55
probably distracted driving in phones is
00:05:57
is one of the big reasons there seem to
00:05:59
be two things going on at the same time
00:06:01
when a user consumer has a first
00:06:04
experience of a driverless car they're
00:06:07
like nervous for a little while then
00:06:09
they ask a lot of questions and then
00:06:11
pretty soon they get bored and they
00:06:12
start looking at their phone in other
00:06:13
words people adapt to the experience
00:06:15
very quickly and once they decide it's
00:06:17
basically looks like it's driving
00:06:20
normally they don't worry about it but
00:06:22
whenever there's a big visible accident
00:06:25
and the biggest one was the 2018 death
00:06:28
of a pedestrian Wheeling a bike by an
00:06:31
Uber car which actually had a human
00:06:34
operator there to keep an eye on the
00:06:36
autonomous vehicle she was actually
00:06:37
looking at a phone at the time um that
00:06:40
had a dramatic impact on how the public
00:06:43
felt and then suddenly you had a spike
00:06:45
and people who said I would never even
00:06:47
get in and try one of those and it put a
00:06:50
chill on many other aspects so I think
00:06:53
it's still true of a lot of Technologies
00:06:55
we accept less error from a automated
00:06:59
systems than we do from humans because
00:07:01
we feel like we kind of understand the
00:07:03
sources of human error and particularly
00:07:05
with AI you know behind the choices that
00:07:08
are being made with automated driving we
00:07:10
don't feel like we understand those
00:07:12
choices well let me ask something that
00:07:13
our listeners may be interested so we
00:07:15
all know today generative AI is the next
00:07:17
big thing or at least people are talking
00:07:19
about it chat GPT Etc if you if I think
00:07:21
about it as a statistician which I am um
00:07:24
I think it's just a extremely
00:07:26
high-dimensional prediction problem
00:07:28
isn't Ai and Automotive just a very
00:07:31
high-dimensional prediction problem and
00:07:33
so is there any reason why the I'll call
00:07:36
it General advances in AI today which
00:07:39
tend to be focused right now on prompt
00:07:42
engineering and chat GPT won't those
00:07:45
advances eventually help Ai and
00:07:48
Automotive as well or do you see them as
00:07:50
different problems you know I always say
00:07:53
what we do as academics is while the
00:07:55
jargon changes and ones like what does
00:07:58
John Paul McDuffy type into a chat box
00:08:00
and what comes back and the other is
00:08:01
what does weo build into some automated
00:08:04
AI driven vehicle to me they're both
00:08:06
high-dimensional prediction problems or
00:08:08
do you see it differently no I I think
00:08:10
the underlying technology affects both
00:08:13
and the progress of the underlying
00:08:14
technology affects both um they
00:08:17
obviously you know where where words is
00:08:19
the primary uh you know coin of the
00:08:22
realm as it is with gen versus driving
00:08:24
decisions there there's there's some
00:08:26
differences but you know I think part of
00:08:28
what happened with autonomous driving is
00:08:32
the progress was remarkably rapid at
00:08:34
first it started with Google competing
00:08:37
successfully in a DARPA you know a
00:08:40
government defense department I teach
00:08:42
that DARPA challenge in my MBA class
00:08:44
it's a great example and it was everyone
00:08:46
was astounded at how well the Google
00:08:48
software combined with about half a
00:08:50
million dollars of hardware on the
00:08:51
vehicle did and so that fueled the the
00:08:54
VC and all the other funding and and
00:08:56
everyone getting into it let's imagine
00:08:59
progress that was rapid up to 90% of
00:09:01
handling driving situations 95%
00:09:05
97% wow this thing is going to be
00:09:07
everywhere in no time at all you hit
00:09:10
that last couple of percent you know I
00:09:12
can't say exactly what it is the
00:09:13
so-called Corner cases the very rare
00:09:17
combination of events that are really
00:09:21
hard to you know either teach with
00:09:24
traditional programming here's what may
00:09:26
happen and here's what you have to do or
00:09:27
even teach with the more inductive you
00:09:30
know way of of machine learning which is
00:09:32
you have a lot of data that you train it
00:09:33
on right these very rare events by
00:09:36
definition don't happen very often and
00:09:37
how do you how do you train them you can
00:09:39
write simulations and that's what these
00:09:41
companies are doing okay let's imagine a
00:09:43
weird situation or let's find a freak
00:09:47
accident that happened in the real world
00:09:48
and let's create a create a simulation
00:09:51
for that their confidence that they've
00:09:53
figured out how to handle all those
00:09:54
simulated situations as part of where
00:09:56
they say yeah we're going to be able to
00:09:57
conquer it all but for the public and
00:09:59
for regulators and all of us who think
00:10:01
about you know climbing into these
00:10:03
vehicles uh every time one of those
00:10:06
weird situations is not handled well it
00:10:09
adds doubt to that side of the ledger so
00:10:13
um and I don't know that anybody ever
00:10:15
feels that AI is going to solve
00:10:17
everything 100% we're on warning right
00:10:20
that there's hallucination and there's
00:10:21
all these problems with Gen that means
00:10:23
we have to really be alert this is Eric
00:10:26
bradow professor of marketing statistics
00:10:28
and data science here at the Wharton
00:10:29
School and also Vice dean of analytics
00:10:31
and we're here as part of our analytics
00:10:33
at Wharton and AI series uh we're
00:10:35
talking to professor John Paul McDuffy
00:10:37
we're talking about Ai and automation
00:10:39
today so let me ask you do you think the
00:10:41
future is that maybe Ai and automation
00:10:44
will be more widespread but it might not
00:10:46
be what your just what you call level
00:10:48
four like probably the next step for
00:10:51
there to get more mass adoption is I I
00:10:53
guess it would be level three which
00:10:55
would be yes self-driving but there
00:10:58
might also be a steering steing wheel or
00:10:59
there might be the opportunity for human
00:11:01
intervention or what do you see is going
00:11:03
to take it from let's call it what it is
00:11:05
now which is really a niche market to
00:11:07
something that might be more widespread
00:11:09
yeah a brief background on those levels
00:11:12
it's um something the Society of
00:11:14
Automotive Engineers came up with to
00:11:16
describe different levels of autonomy uh
00:11:19
level there's actually a fifth level
00:11:22
which is can go anywhere at any time in
00:11:25
any circumstances without being able to
00:11:27
connect to the internet um that's even
00:11:29
further out but level four is basically
00:11:31
in most operating conditions can operate
00:11:34
autonomously you're right level three
00:11:36
involves some handing back of control
00:11:38
between a human driver and an automated
00:11:40
system level two is stuff that we
00:11:43
already see in a lot of modern vehicles
00:11:45
Lane control selfing L control even
00:11:47
automatic braking I mean some of it's
00:11:49
becoming becoming pretty Advanced so one
00:11:53
kind of strategic I don't know if it's a
00:11:55
full divide but it's something you can
00:11:56
see the level for Stuff got all the
00:11:59
headlines and that's what weo is
00:12:01
investing on and that's what Cruz some
00:12:03
of the other prominent startups that's
00:12:05
what Tesla with Alon musk has been
00:12:06
promising for years with his you know
00:12:08
optimistically or misleadingly named
00:12:10
autopilot system um the Legacy
00:12:14
automakers and all the suppliers who
00:12:15
feed in the technologies have been
00:12:17
slowly adding the level two advanced
00:12:21
stuff and even experimenting a little
00:12:22
with level three and they can say we're
00:12:25
making a safer car they can usually
00:12:28
charge extra money for it although
00:12:30
increasingly a little bit following
00:12:31
toa's lead there's a tendency to bundle
00:12:34
all the safety stuff together and just
00:12:36
say this is the right thing to do buy it
00:12:38
you know you get it all at once basic
00:12:40
prospect theory you you bundle prices
00:12:43
together and
00:12:45
so it's kind of a question of whether
00:12:48
the slow moving up from the lower
00:12:51
levels ends up affecting more people's
00:12:54
Driving Experience sooner than the
00:12:57
promise of level four which may stay a
00:12:59
niche until it satisfies a lot of the
00:13:01
questions we have about it so who's
00:13:03
going to be you know I've thought about
00:13:05
this matter of fact I teach an
00:13:05
automotive cases I mentioned in my in
00:13:07
the NBA Corp because I I think it's such
00:13:09
a fascinating industry who's going to be
00:13:11
the winner here and let me even put on
00:13:13
my so besides being Vice of analytics I
00:13:15
am the chair of the market I'll put on
00:13:16
my marketing department hat here who
00:13:18
would I trust more to get into an
00:13:21
autonomous AI driven vehicle would I
00:13:24
trust Ford or would I trust Google and
00:13:27
to me I'm thinking
00:13:29
I want a data company I want a company
00:13:31
that's really good in Ai and Predictive
00:13:34
Analytics I mean like another way to
00:13:37
frame it is do you ever see a day where
00:13:39
the Legacy
00:13:41
automakers actually turn out to be the
00:13:43
big winners in this or is it likely to
00:13:45
be a tech company that just happens to
00:13:48
also do work in
00:13:50
automotive well I mean that's a great
00:13:52
and big question and so we may want to
00:13:54
spend a little time on it yeah well
00:13:56
that's what we're doing here on the AI
00:13:57
series Ai and we're talking about big
00:13:59
questions the um weo which is the Google
00:14:03
subsidiary doing this uh they've said
00:14:07
pretty clearly we are not going to build
00:14:08
a vehicle we're not going to have a
00:14:10
vehicle we're making a software driver
00:14:12
which we're going to sell to license to
00:14:16
the people who make the vehicles so you
00:14:18
could have a wayo driver in a Ford
00:14:21
vehicle maybe that would be a sweet spot
00:14:23
for you if you like uh or or any other
00:14:26
Legacy automaker um you have other
00:14:29
companies who are taking a different
00:14:31
approach Tesla obviously being one but
00:14:33
zuk's is a company that is now owned by
00:14:35
Amazon which is building the vehicle
00:14:38
hardware and the software and the
00:14:40
business model for they have a robo taxi
00:14:43
and also a automated Trucking like like
00:14:46
small Trucking almost delivery makes
00:14:48
sense if they're own owned by Amazon um
00:14:52
so you have all these different
00:14:54
combinations I've been increasingly
00:14:57
feeling like even though the Automotive
00:14:59
companies and the tech companies don't
00:15:00
really like each other and don't really
00:15:02
want to work together that they may have
00:15:04
to there's something about Mastery of
00:15:06
the physical realities of a vehicle that
00:15:08
the digital Giants really don't have and
00:15:10
obviously the auto companies are not
00:15:12
good at digital stuff anybody who has
00:15:14
tried the the company provided you know
00:15:17
interface in their car knows knows that
00:15:19
for sure and I I think you know we
00:15:21
instinctively think that the the the the
00:15:24
data Giants will be better at something
00:15:26
that involves so much data that involves
00:15:28
Co connectivity and the like um and
00:15:31
they've handled the control of physical
00:15:33
systems braking and steering and the
00:15:36
basic stuff really quite well but um I
00:15:39
think uh another aspect of this is the
00:15:42
computer industry It generally has been
00:15:44
a quite a modular industry you have kind
00:15:47
of clear interfaces you have Innovation
00:15:51
that's possible when people simply know
00:15:53
the the specs the interface specs the
00:15:55
apis of the people they're working with
00:15:58
the automotive industry has remained a
00:16:00
very integrated industry there's a lot
00:16:03
of interdependencies in this complex
00:16:05
multi-technology vehicle that can't be
00:16:08
predefined away it requires a lot of
00:16:11
interaction to work it through and and
00:16:13
this maybe is a clue to what you know
00:16:17
autonomous vehicles but also autonomous
00:16:18
and electric and all the other stuff
00:16:20
together it will require combining that
00:16:23
knowledge of the physical with the
00:16:25
knowledge of the digital and will force
00:16:27
some collaboration that maybe the
00:16:29
parties wouldn't choose otherwise so
00:16:32
what's I don't use the word preventing
00:16:34
but let's use that what's preventing
00:16:36
things from getting to even level five
00:16:39
autonomy is it um it would just cost too
00:16:42
much to build that's one possibility
00:16:44
like you could build it but at that
00:16:46
price just there would be such a small
00:16:48
Market another possibility is you know
00:16:50
I'll say it we can't process like it's
00:16:53
our Computing like we can't process like
00:16:55
something in a vehicle can't process
00:16:57
that much information at that quick a
00:17:00
speed it could be um we don't know
00:17:03
enough math it could be our algorithms
00:17:05
aren't good enough yet what is the big
00:17:07
stumbling block given you said 95 plus
00:17:11
percent was done in the first couple
00:17:13
years what's preventing this last 3% cuz
00:17:17
I think I'm ready like I'm ready for
00:17:18
level five no I'm saying I'd get into
00:17:20
level five sure I would yeah
00:17:23
um there are new Chips coming along you
00:17:27
know Tesla's designing it own chip
00:17:29
Nvidia it turns out the video processing
00:17:31
chips are very good for the so-called
00:17:33
Sensor Fusion Sensor Fusion is what they
00:17:36
call where you take the camera data the
00:17:39
regular radar data and the lar the laser
00:17:42
laser radar data and you combine them to
00:17:44
get that 360 picture of what's really
00:17:46
going on you have to have both accurate
00:17:49
information about distance things are
00:17:50
away from you and what they are right
00:17:52
you need to know is this a car is this a
00:17:54
bike is this a person is this a a
00:17:56
traffic you know piece of construction
00:17:58
equipment whatever see when you say that
00:18:00
I have to admit if this wasn't professor
00:18:02
John Paul McDuffy of the Wharton
00:18:04
Management Department this was just a
00:18:05
general AI lecture I'd say so this could
00:18:08
be a store using facial recognition or
00:18:11
this could be I mean just if you even
00:18:13
think about the language you just use
00:18:15
this is the problem of AI engines today
00:18:18
and it just happens the application area
00:18:20
in me in my view in automotive is really
00:18:22
interesting and cool and possibly
00:18:23
lifechanging yeah so you know and and
00:18:27
you were asking the various constraints
00:18:29
on it yeah they're making progress on
00:18:32
the technical constraints um for sure
00:18:35
there's the regulatory issue of how much
00:18:39
testing in real life to allow so as I
00:18:42
said there's a lot of simulated testing
00:18:43
going on uh most of the testing is on a
00:18:48
rather small scale so far it's rolling
00:18:50
out kind of state-by-state City by city
00:18:52
for the robo taxis um there's also
00:18:55
people working on automated Trucking and
00:18:58
uh trucks are much bigger which
00:18:59
potentially a I mean people don't know
00:19:01
this I teach this also in this same
00:19:02
lecture maybe you'll correct me wrong
00:19:05
I've made the claim that the trucking
00:19:06
industry is the biggest industry in the
00:19:08
US
00:19:09
today um that's interesting you're
00:19:12
obviously combining every everything
00:19:14
that moves Goods at any levela I don't
00:19:17
know if if uh but what I've also
00:19:19
commented on is let's imagine level five
00:19:21
automated truck driving happens I also
00:19:24
think about all the ancillary Industries
00:19:26
like you know truck drivers gener a lot
00:19:28
of income for hotels and restaurants and
00:19:30
everything else so we have a long
00:19:32
conversation about let's call it related
00:19:34
Industries like if Ai and Automotive
00:19:37
takes off there's so many other
00:19:39
industries that would be impacted some
00:19:41
positively some not yeah you know
00:19:44
there's a company named Aurora that's
00:19:45
working um it it it took over the
00:19:48
autonomous vehicle stuff that Uber was
00:19:50
doing it's working closer with Toyota
00:19:52
they're focusing on trucks right now and
00:19:54
they're focusing on Long Haul uh
00:19:57
distances I think most people imagine
00:20:00
that that is the part of trucking that's
00:20:03
the best application the local stuff
00:20:05
which is the delivery where you're in
00:20:08
cities and you're having to stop and put
00:20:10
things on people's doorsteps or put it
00:20:12
you know in a in a some kind of box
00:20:14
that's actually a lot more complex to do
00:20:17
right um and those those are also the
00:20:19
jobs that are more local I mean they're
00:20:22
huge shortages of truck drivers the Long
00:20:24
Haul truck driving life is not a very
00:20:26
desirable occupation in terms of being
00:20:28
away from home and and health and the
00:20:30
like and so but it it it reminds me of
00:20:33
another distinction I wanted to make you
00:20:35
know we don't know if the autonomous
00:20:37
vehicle is going to be a individual
00:20:39
ownership model or it's only going to be
00:20:41
a Fleet model yeah that's what that get
00:20:42
to affordability and that gets to you
00:20:45
know how do you manage the upkeep and
00:20:49
maintenance uh you know Uber famously
00:20:51
under its founding CEO Travis clonic
00:20:54
said you know this is existential for us
00:20:56
to have to be able to move to a
00:20:58
autonomous vehicles because we can't
00:21:00
afford to do the human driver model and
00:21:04
achieve all the goals we want what's
00:21:06
your what's your forecast am I am I
00:21:08
going to be owning an autonomous driving
00:21:10
vehicle or is there just going to be a
00:21:11
fleet of vehicles out there that drive
00:21:13
up to my home anytime I want it yeah I I
00:21:16
think the the full autonomy model
00:21:21
probably works economically best as a
00:21:24
Fleet
00:21:25
model um you know remember though we
00:21:28
talked about the automation creeping up
00:21:30
from level two through level three so
00:21:33
level three is probably still a
00:21:34
personally owned vehicle right so um so
00:21:37
then is there a niche for somebody who
00:21:39
really wants their own vehicle that they
00:21:41
can then not
00:21:43
drive or or or or not or will or will it
00:21:46
all be or will it all be supplied now
00:21:48
there's also the economics of density
00:21:50
and so if you live in a place where you
00:21:53
can guarantee relatively rapid service
00:21:55
from a robot taxi you would need to but
00:21:57
if if you're way out the country you're
00:21:59
probably not going to have that ready
00:22:01
service available so then maybe you are
00:22:03
a candidate to be the one who owns it
00:22:05
maybe you have to be really wealthy to
00:22:06
afford it because the scale is not in
00:22:08
the personal ownership it's in the it's
00:22:10
in the fleet um so you know the the the
00:22:16
the there's some other issues about
00:22:18
Fleet the companies that are doing the
00:22:21
software for these vehicles they don't
00:22:24
know from running fleets and and
00:22:26
repairing vehicles and dealing with Duty
00:22:28
Cycles and cleaning up after the last
00:22:30
user and stuff like this so the minute
00:22:32
they contract that out that's a chunk of
00:22:34
their profits but that's uh that's
00:22:36
another story so in the last like two or
00:22:38
three minutes we have let me ask you um
00:22:40
first it's a two-part question but it's
00:22:42
really the same
00:22:43
question what are the open research
00:22:45
questions from you as an academic in
00:22:48
this area and secondly um maybe we'll
00:22:51
make a date 10 years from now you and I
00:22:53
are sitting here what are we going to
00:22:54
talk about has happened over the last 10
00:22:56
years in the AI and a Automotive space
00:22:59
one of the big research questions I'm
00:23:01
looking at is really about
00:23:03
the the organization and structure of
00:23:05
the industry and of competition in this
00:23:07
in this space so it's clearly expanding
00:23:09
Beyond Automotive to be a Mobility kind
00:23:12
of space it's clearly Beyond just firm
00:23:15
competition to be ecosystems and what's
00:23:17
going on with ecosystem
00:23:19
competition uh there is a vision of
00:23:23
where this Mobility stuff goes which is
00:23:26
around uh very modul systems uh open
00:23:29
source software uh we haven't talked a
00:23:32
lot about it because it hasn't shown up
00:23:33
so much yet but um for example I
00:23:35
mentioned foxcon before uh they've
00:23:38
organized a
00:23:39
Consortium of firms suppliers and the
00:23:42
like they want to have uh open source
00:23:45
software for autonomous vehicles they
00:23:47
want a contract manufacturing model for
00:23:49
making the vehicles they want a
00:23:51
completely commoditize vehicle design
00:23:54
where you start with a basic skateboard
00:23:56
and then you put different kinds of and
00:23:58
it's it's right out of the digital
00:23:59
Playbook of what we've seen happen in it
00:24:03
uh there are people who fervently
00:24:04
believe in that and part of what they
00:24:06
say is it's too damn expensive for every
00:24:09
single company to come up with its own
00:24:11
vehicle its own software for you to have
00:24:14
a competition even between wh Mo's
00:24:16
software and Apple's software for
00:24:18
example is just a lot of why not have
00:24:22
one open source that can also maybe be
00:24:24
vetted and and and that we can have more
00:24:27
oversight than we would from a private
00:24:29
big tech company um my basic skepticism
00:24:33
around this big research question is
00:24:35
this fundamental fact that the
00:24:37
architecture of both the product and the
00:24:38
industry has been up until now much more
00:24:42
integrated because of all these kind of
00:24:43
interdependencies some of which are
00:24:45
really based in physics and and physical
00:24:48
realities I don't expect that to change
00:24:51
so much that this modular Vision really
00:24:53
has the potential to happen that some of
00:24:56
its it or big Tech proponents would
00:24:58
would like so that's kind of one big
00:25:00
research question I'm looking at I guess
00:25:02
in 10 years we might be looking to see
00:25:03
what the outcome is because there are
00:25:05
some people who think that even if what
00:25:08
I said is true the brains are going to
00:25:12
be somebody's software right somebody's
00:25:14
software is going to be so good that it
00:25:16
wins yep and it's either a monopoly or
00:25:19
maybe it's an oligopoly you know maybe
00:25:20
we end up with Android versus iOS you
00:25:23
know maybe there's just two autonomous
00:25:26
driving you know and and electric and
00:25:29
everything uh competitors in the world
00:25:31
and everyone else just has to bow down
00:25:34
and you know uh oh yes you know I used
00:25:37
to be BMW but now I'll make your car you
00:25:39
know oh oh oh oh big Tech Overlord um
00:25:43
who knows that uh may be possible
00:25:46
remember that this is a super
00:25:47
competitive super Global and super low
00:25:50
margin industry and these big tech
00:25:52
companies are not used to that and I
00:25:54
sort of question when I think about
00:25:56
Apple having an Apple car are not
00:25:58
whether they could do it and do a very
00:26:00
good job at it but whether they really
00:26:02
want to be in that business right and
00:26:04
even if they pull back to say we just
00:26:05
want to do the software um even that is
00:26:08
a big Challenge and we'll pull them away
00:26:10
from a lot of the other things that
00:26:12
they're doing way Mo's committed to it I
00:26:13
think weo will will stay in you've got
00:26:15
to be well financed so we may have a
00:26:18
couple winners but it wouldn't surprise
00:26:20
me given what I've said before that
00:26:22
you've got a couple of um essentially
00:26:26
alliances between y Auto and big Tech
00:26:29
some of those alliances Thrive and win
00:26:32
some of them fail and lose so we have
00:26:33
kind of a ShakeOut that leaves a few
00:26:36
Legacy automakers and big Tech
00:26:37
contenders on the floor and a few
00:26:39
winners that have figured out how to
00:26:41
combine their uh complimentary
00:26:43
capabilities let's say well John Paul
00:26:45
I'd like to thank you for joining me
00:26:47
today on the analytics at Wharton AI at
00:26:49
Wharton sirusxm podcast series obviously
00:26:52
we've been talking about AI in autom in
00:26:54
Automation and automotive and what's
00:26:56
amazing to me was was I'm sure there
00:26:58
were times in your career where you've
00:27:00
thought I'm in this old Legacy industry
00:27:02
but now's not one of them it's got to be
00:27:04
a great let's be honest it's got to be a
00:27:05
great time to be you right now come on
00:27:07
it's a great time to be me I'll say it
00:27:10
I'll agree well thank you for joining us
00:27:12
and this uh again this has been Eric
00:27:14
bradow professor of marketing and
00:27:15
statistics and data science byen here of
00:27:18
analytics at the Wharton School uh
00:27:19
please stay with us and join us for our
00:27:21
next episode of the analytics at Warden
00:27:23
AI at Warden AI
00:27:26
series
