March 3, 2025 • 32 mins
Imagine testing a new medical device without putting someone on an operating table. Or experimenting with new autonomous capabilities without placing a car on the road.
Computer simulations have been used for decades to predict outcomes but now, digital twins create virtual environments that not only mirror their real-world counterparts but respond to real-time data to yield more confident decision making.
Yael and Mike discuss digital twins with UVA Darden Professor Sam Levy, Assistant Professor of Business Administration at Darden. Levy holds a Ph.D in Marketing from Carnegie Melon and his dissertation introduces a novel concept of “digital marketing twins” to better understand individual-level customer satisfaction.
Look for new episodes of Good Disruption on the first Monday of each month!
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Episode Transcript
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(00:00):
[Music]
the disruption a lively discussion
between UVA Darden School of Business
professors Yael grushka cocaine and Mike
Linux on cuttingedge Technologies and
practices that are challenging the
(00:22):
status quo
[Music]
hello y hey Mike how are you I'm doing
well today good it's good I'm excited to
talk to you about a topic that I
personally see a lot of excitement and
(00:42):
future for well tell me tell me more
what are we talking about have you heard
the term digital twin I have in fact I
have in fact have you have you ever
dreamed of having a
twin good question uh no and not
actually maybe yes but not a digital one
for sure but uh yeah that's a curious my
wife is actually a twin but not an
identical twin she has a brother you
(01:03):
know twin brother but yeah I don't know
I mean I had six years between myself
and my sister so the idea of having a
twin that could be kind of cool be a
different experience than I had growing
up yeah what do what would you want to
do why would you think that having a
twin would be cool like to play tricks
on your parents I don't know I mean
maybe it's like the movies a Parent Trap
and things like that exactly exactly
which one confuse them which one twin
(01:24):
their deal with there um well so so
what's a digital have you have you heard
it I mean it's everywhere these days I
think the term has become U popular in
kind of uh common use and you see it
everywhere like in what context have you
heard about digital twin I think where
I've seen it um most often for two
things one is for industrial processes
so the idea of creating a digital
(01:45):
version of an industrial process and
then some of these interesting Pilots
for cities where they've tried to create
a digital twin of know City operations I
think Singapore has one of the first out
there yeah bolognia I've heard um even
here in this area in Virginia you'll
hear of cities that have a digital twin
and so what is the idea the idea here is
(02:06):
that we can actually mimic the reality
all the systems all the different
interactions between people between
systems if you think about complex uh
traffic patterns and the like um but in
the digital space and why would that be
beneficial because we can experiment
with um decision-making we can make
changes we can kind of study phenomenon
(02:26):
without having to worry about really
making any harm or damage in reality and
even anticipate maybe steps ahead in
order to put in place corrective actions
and the like yeah and when I think about
digital twins I also think about the
notion of kind of something else we've
talked about in the past iot so internet
of things correct most of these systems
are relying on remote sensoring to
(02:47):
provide realtime data to craft the
digital twin which might be a little
different than you know simulations been
around for a long time create a
simulation of some physical uh
environment but this is now real time
data constantly updating quote unquote
the simulation yeah and so uh you and I
we've talked about this on our show
(03:09):
before we share a background related to
operations research System Dynamics
system engineering uh where in the past
maybe we've built I remember my
assignment when I was in my Master's
studies putting together a hospital
simulation with the events of patients
com systems was the best mean of course
but the shortcoming there was typically
that you could only Model A upset of
(03:30):
maybe the interactions there was no
real-time data to your point and it was
very limited in the type of problems
that you might want to solve and so now
you can run in parallel to reality you
can really run a system that is in the
in the in the you know the digital
ecosystem it's not interfering with
anything on the ground but it can help
(03:50):
you study and understand what's going on
so just to give you a sense of uh the
size of this Market this is a real thing
okay we are talking about um the Dig to
Twin Market currently is estimated
around $2 billion and it's anticipated
in the next like 5 years or so to get up
to $150 billion and the players here and
the companies that are investing in this
(04:11):
are the companies that you might imagine
maybe others that you wouldn't you know
everybody from General Electric seens
Microsoft uh uhm yeah Cisco and the like
and probably a lot of other players like
you mentioned municipalities and more um
folks that are doing it in a local level
um uh in order to improve the way that
they think about their environment
(04:32):
improve the decision making and to allow
them really to test and to and to and to
be thinking more creatively so so let me
be the cynic just for a second you know
so again the idea of like smart cities
and this idea of using these digital
twins to optimize processes Yes sounds
great in theory but like does it does it
really materialize in in enough gains
(04:53):
that it's worthwhile to do these things
well I think like anything else um I
think there are pockets of activity that
we see a lot lot of gains and a lot of
um uh real decision making and training
and and studying and developing new
products that we could have never
anticipated before and so it might not
be across the entire segment of the
(05:13):
ecosystem but you do need to model all
of that in order to get even pockets of
improvements and pockets of benefits so
for instance if you can imagine and
we'll start something small if you can
imagine um a large hospital system and
you can design a digital twin for the
hospital system are there efficiency and
U um improvements that you can kind of
derive at a hospital level potentially
(05:35):
but the real benefits might be even at a
much smaller and more myopic level in a
specific operating room with a specific
physician allowing those Physicians
allowing those residents to train and to
practice and to simulate the environment
and to be to have a much richer
environment to learn and to you know run
through a dry run or digital run of a
(05:56):
specific operation and then check where
there's going to be some issues or maybe
where they run into some pitfalls and
then maybe correct course and improve
yes we're going to see huge improvements
in the in in the future now how would we
separate out this concept from something
we've talked about in the past the
metaverse I was about to say is this is
this relate at all to the meterse could
this be put into the metaverse of course
(06:18):
it could be related and like you know
there's it's not totally dis uh
disconnected to stuff that we've talked
about in terms of AI and generative AI
these things these Technologies bleed
into each other and help improve um all
of them across the board the digital
Trend really from my understanding and
the impl you know cases that I've seen
in the implementations that I've seen is
(06:39):
really relying on this notion of
realtime data so the real benefit is
that you're mimicking reality you're not
creating a parallel reality you're
mimicking reality with all of its
complexities with all of the different
factors that intermingle and that you're
really trying to capture the pacing and
the nature of the world that you live in
in order to make here in our world
(07:00):
better decisions but you're you're kind
of learning from from the digital twin
all right does my avatar in the digital
twin World know that it's in a simulated
world or does it think it's in the real
world yeah more here you're more like
simulation you know that in you're in
simulated world you have agents that
behave like you that think like you that
mimic you but it's allowing us to
(07:20):
understand really broader broader
patterns here can you like let's think
of other environments so for instance if
you're a sailing or if you're an Olympic
sport or any kind of sports team and
you're trying to get new strategies or
you're trying to play out new new play
uh uh moves um a digital twin could be
an environment that you could test
things out oh I like that idea so you
(07:42):
could take like uh the the simulations
we have for football or basketball but
then run a digital twin based on like
the biometric data of course and imagine
what's going on like imagine something
as high stakes as the Super Bowl imagine
you have a digital Trend running in the
background and that you can try out
various moves there with the same
players and the and the fatigue that
(08:02):
they're recording and the conditions on
the ground and then you can kind of take
that as an opportunity to learn from
that and be inspired for changes that
you want to make on the field I like it
I like it yeah um what would be some
downsides or some concerns you you said
you were going to be the se you know the
skeptic I know well it's hard to see in
this particular case but I guess again
you know there's always a Black Mirror
(08:23):
episode uh for this everything for and
again I guess this is the the blurring
of reality with uh the the simulated
environment and does that create you
know downsides uh of how people view the
world or how they engage in the world
and like anything else I mean some of
the the challenges that we can
anticipate if you're thinking about
(08:44):
digital twins from anything from a as
you mentioned a manufacturing plan for
let's say um you know in Ford's case if
they're thinking about the products that
they manufacture there to something like
a supermarket where you have customers
walking around and interacting with each
other and with the staff they C could be
some downsides if we um have certain
biases in these systems or if the
(09:04):
simulation helps reinforce some
negativities that then come out and play
out um we want to make sure that these
the digital twin is also used
responsibly with the same ethical
Frameworks you're not totally um you
know it's not as if all of that
automatically falls out you have to
ensure that the system is set up in a
way that ensures ethical decision-
(09:25):
making responsible uh responsible
decision- making and the like yeah it's
interesting to think about like okay in
the in the world without digital twins
how would we deal with some of these
things well some of it would be like
feet on the ground walking the actual
you know manufacturing facility and
seeing if there's issues or the like on
the floor you do we become overly
reliant on the simulation in that world
(09:48):
I think about something high stakes like
a nuclear power plant right um and where
again making a digital twin makes a lot
of sense but do we then become overly
reliant on oh everything seems fine
because my digital twin is telling me
everything's fine but then I don't know
the pipe the water pipe is leaking over
here and we're going to have overheating
uh and we just missed it because it
(10:09):
isn't censored by the um the information
flowing in and like anything if you are
a per a people's person and you believe
that you need to look somebody in the
eye to see that they're feeling okay
behaving okay able to perform okay as
opposed to watching just you know the
Monitor and seeing what it what
technically the numbers look like they
might the numbers might look like
they're fine but if you're actually
(10:31):
interacting with a real person you might
come to a different conclusion yeah are
we at this point where we need some help
yeah maybe we can talk to an expert so
it so happens that one of our colleagues
is actually an expert we are so
fortunate to have uh Sam uh Sam Levy
who's going to join us today he's a an
assistant professor here at the Darden
school and he is uh focused in the areas
of marketing so his digital twin
(10:53):
expertise comes from the area of
marketing his research investigates um
customer Analytics in different contexts
such as privacy data fusion and
pro-social behavior and he holds a PhD
in marketing from Cary melon his
dissertation introduced the novel
concept of a digital marketing twin okay
so that's a specific application where
(11:14):
he finds path of least resistance to
individual level customer satisfaction
Sam welcome welcome to our podcast hi
yeah hi Mike it's pleasure to be great
to have you Sam so Sam maybe you could
start off by just telling us what is a
digital marketing twin so sure uh
digital marketing twin in my context is
a virtual representation of a customer
(11:36):
right so the idea is to create what if
scenarios at the individual level so say
you have a customer and you're say a
telecom company and you are running a
bunch of surveys and those surveys are
very expensive you can only run them
once per customer because those surveys
are very long to feel and you have to
pay customers and they they just don't
come back so then the idea would be how
(11:58):
do you create
that virtual twin of customer where you
could basically simulate different
scenarios say if you're the Telecom
company Chang the pricing level or the
network speed would that customer switch
to a different carrier for example that
would be the idea of a customer twin
here beautiful so it's like a scenario a
more sophisticated scenario analysis
(12:21):
where we can experiment with potential
Futures and see how it plays out in our
digital twin environment is that the
idea something like that yes uh digital
twins um of customers are very
particular type of uh twins but we could
think of the twins of for example stores
right in the context of in the retail
(12:41):
right and the idea would be oh how do
you create a twin of a store here um you
know you would you start with the
shelves would you start with the
trajectories of customers there's a lot
of questions here going on I can imagine
you know when it comes to physical
assets it's easy to think about how we
simulate them in one of these
environments and again use you know uh
(13:01):
Internet of Things type of sensing to
provide us information maybe even like
what's on the shelf and the like but key
to what you were just describing is you
have to have a model of the consumer of
the individual like how do you do that
how do you simulate that individual and
how they might behave in different
scenarios so it's quite difficult
actually so in the context of the store
(13:25):
uh you would have to predict customers
moving in the stores and so people would
quickly go out of patterns if you're
using traditional machine learning
techniques so instead you would have to
use more advanced algorith more advanced
algorithms such as reinforcement
learning to be able to accommodate how
people go out of pattern when for
example you change the store layout you
(13:46):
would need to have a very robust model
of people how they move in the store so
that's quite complex the way I treat it
in my research I use a generative model
so I use a type of generative AI
algorithm to be able to predict how
people behave without the physical
context at all and so this is where it
departs from maybe what you and I
studied where it was more simulation
(14:07):
where we'd put in a distribution it was
kind of lock step you know off the shelf
and all we would do is kind of walk in
and go to the front of the line and
following up pant distribution or the
like what immediately came to my mind is
I am not a great Shopper my wife will
confirm this and uh she calls me the
lighthouse because we will go to like a
a clothing store and I will stand there
(14:30):
and she will shop and I will like look
back and forth like a lighthouse there
so Sam if you don't have the lighthouse
in your model like I I can provide you
the behavioral attributes of the the
lighthouse consumer but it makes me
think Sam so are we seeing um so for
instance are we going to see schools
like let's say Darden or UVA should we
have a digital twin thinking about the
(14:51):
students of the future and how we should
interact with them well it could be a
good idea to
understand how students would react
react in this virtual reality I I would
see a couple of challenges um I would
say the first thing to do would be to
start small maybe we shouldn't start
with the school maybe we should start at
the classroom level for example right
because you know creating twins of
(15:13):
cities or creating twins of complete
virtual assembly lines are extremely
difficult uh I I there are lots of costs
involved with building the twins
Gathering high quality data for example
so where would we gather the data do we
have consent to get that data as well
right is an important question and
finally how to use different teams right
(15:34):
different teams have different type of
data right maybe um you would have to
talk to a different team who has a data
set and so the the the problems with
many organizations is they work in silos
and so you cannot really access that
data in an in an easy fashion right so
there's actually a lot of a lot of
problems here associated with building a
twins it's it's a lot of challenges to
(15:55):
build them but I can see why we're
talking about this disruption today and
this day and age where more data more
higher quality data is being collected
so we're in a new era on that Frontier
as you mentioned Sam the algorithms have
evolved we've talked about that in our
in our show and so we're seeing that the
data is meeting the algorithmic
development that allows us and the
(16:16):
censoring that you mentioned it's all
coming together to enable these digital
twins while still costly and complex
they're becoming more possible than ever
before because all the combination of
these kind of Technologies yeah I'm I'm
curious Sam are we seeing at all the
kind of
self-generation of these digital twins
what I have in mind here is again yeah
(16:38):
and I come from a generation where if
you were going to program this you would
You' be hardcoding basically the
behavior of the agents and the like um
could we basically teach the simulation
to teach itself how to create these
environments or to create these these uh
different agents that are operating in
these environments that's a good
question I would say it's probably
(17:01):
difficult right now to make these agents
teach themselves I don't think we are
there yet uh there are a lot of
companies right now that take care of
the heavy lifting right so if you want
to build a digital twin that even like
teaches itself you would for example go
to Nvidia you would use their Omniverse
platform or you would go to aw us Amazon
(17:23):
web services and they have a Internet of
Things twin maker system but you
wouldn't build it from scrp
so you would rely on third party
techniques and and products so that you
don't start from from zero and then to
the question of being able to teach
itself I I don't think we're there yet
reinforcement learning is very data
(17:43):
hungry and so we would need tons and
tons of data to reach that level of
capability let me ask you we we you
brought up briefly before um this
ethical question around uh with consent
so collecting this dat data and
capturing it with consent and you know
we heard of applications as in examples
(18:04):
in hospitals or training Physicians
where these Digital Trends are being
used are are there ethical issues that
are emerging are there concerns that
there are some uh use cases of digital
twins which are not totally legit or
that folks feeling could could go in the
wrong direction it could go both ways I
would say because you could also say you
(18:24):
could also use flip the argument and say
look the twin is build to actually
prevent ethical misconduct right you
could create the twin of a customer for
example to be able to uh to run an
experiment in an ethical way without
actually harming right the idea of a
twin is also to live in a risk-free
environment right so now the question of
collecting data with
(18:46):
consent um it's a different question
there's now a lot of uh things going on
with uh privacy um laws changing and the
and the the end of third party cookies
in in in many settings so companies are
more and more careful now in in giving
data to others but I think within the
company I think there are still
(19:07):
processes that could you know you can
still create twins within the company
you just have to be able I think the the
the challenge are more organizational
rather than ethical right now uh because
you just need to work with so many
different
teams yeah you know one thing that comes
to mind is that some companies are using
in essence digital twins to train
(19:30):
algorithms for the real world and what
comes to mind is um weo of Google Fame
who has been using virtual simulations
of some of the Cities they operate in to
actually train their autonomous vehicles
that they're running on the road and I
be CU Sam or maybe iel like what what do
we think about that idea that the it
(19:51):
could actually work the other way where
the digital twin is now in essence
training real world actors in this case
robots but training them I mean I think
to Sam's Point it's encouraging to hear
if it can prevent any casualties or
catastrophic outcomes in the real world
then why not use the digital environment
to practice right like that to the
(20:12):
degree that the digital environment is
close enough to reality I don't know but
again this is taking it and bringing it
into reality that's the that's I think
the interesting thing yeah another thing
is you have to keep these sensors and
and you know you have to have you have a
lot of upkeeping here you need to um
constantly monitor if the sensor are
(20:33):
delivering the right data so you're
right the could be ethical issues
related to the quality of the sensors
are they actually bu are they actually
faithful to um to what uh what we would
expect from them I can imagine so I
spoke to a few folks who um are dealing
with digital twins at a government or
(20:54):
municipality level and we mentioned
these earlier like cities that are using
digital twins locally um and it's
interesting because I can see a a desire
to be more efficient and to be
forward-looking so you know if you
monitor uh births in the NICU or in the
in the hospital and then you already
(21:14):
play that out several years and you
think about the distribution of your
classes in the schools and you know the
different uh you know number of students
that you can facilitate in each
neighborhood like could we get ahead of
the game and optimize some of our
planning our urban planning around these
using these digital twins but then you
have to go back and adjust it based on
(21:35):
the decisions that you make so s we seen
these digital Twins play out so they're
used decisions are made and then we go
back to the digital twin and update it
and continue to refine it so it
continues to reflect reality yeah yeah
exactly the the the digital twins should
be continuously updated based on the
decisions um we see a r a rise of also
(21:56):
more probabilistic Twi me where the idea
is that you don't just generate one
outcome you generate a range of outcome
and so you can make a better decision
based on the worst case scenario versus
the best case scenario so that you know
that aspect of decision making is I I I
think very important if you take the
example of health care where you are now
(22:16):
we are now building digital twins of
hearts for example right Hearts is a is
not a physical process there's a lot of
noise uh you know uncertainties
associated with the human body so so
that that's why we we actually need more
um to incorporate uncertainty in the
digital twin models so I I would say
(22:36):
these problemistic twins would help make
better decisions especially in
healthcare see there there's always a
Black Mirror episode and it reminds me
there is one about dating I don't know
if you've ever seen this one where you
don't know this I'm sorry for those who
maybe not seen this episode before but
um what what comes to light is that when
people are dating through an app they
(22:56):
create two digital twins of the two the
couple and they basically run a
simulation multiple times to see what
the outcomes are to then make a better
prediction of whether you are compatible
with the person that you just met you
know at at a bar or whatever it is to
see your future kids and the future you
get along with mother-in-laws right run
out the whole simulation look at the you
(23:18):
know probabilistically what the life
outcomes could be and then kind of work
back to say yeah this is a good match
you're going to have a good life or or
not yeah um that's interesting I can see
that that would be a case with the with
the dating apps being what they are
these days Sam we often ask this
question as Mike and I try to get our
head around uh whether this is a good
disruption or a bad disruption or no
(23:38):
disruption at all we're not going to put
you on the spot because you are an
expert on this but what what does keep
you up at night like what concerns do
you have as the use of digital twins
continues to grow and various contexts
what what do you worry about I would say
I worry about the fact that we might
become overring on it if the technology
(23:59):
keeps growing the way it is um we might
completely Overlook uncertainty
associated with the outcomes because
it's so easy to overlook Overlook at
them so yeah that's that's what keept me
up at night also the idea of digital
twins of customers and stores at the
same time so full the full thing is
(24:20):
actually very hard to do just modeling
trajectories of customers and at the
same times having um modeling the store
can be extremely difficult just because
you know you have to mix the laws of
psychology and economics to actually the
physical laws of a store which probably
very hard to do right now so I would say
(24:40):
um you know starting small is uh always
a good idea with digital twins for first
modeling the the the the shelf and then
going more uh you know scale it up so I
think the idea of scaling keeps me up at
night is is probably the most difficult
part starting small is easy and I I
would say many companies and researchers
can do that but getting getting it big
(25:03):
and getting it uh reliable is very
difficult yeah and in such a way that we
don't over rely on it to the point where
we forget that it's just a digital twin
and not reality right yeah um yeah
fascinating yeah yeah can you see what
is the use case that you're most excited
about Mike I don't know why my mind does
go to like the the Smart City model like
(25:24):
that idea like we could we could maybe
it's a systems engineer may but but like
could we really optimize traffic and all
these other processes to make cities
more efficient yeah that that has some
just intuitive appeal to me well I know
that there's a lot of interest around
manufacturing and digital twins of
manufacturing um lines in order to
anticipate of course efficiency but also
(25:46):
anticipate new use cases and to
experiment with uh new products and new
capabilities that maybe have not been
done before and so the digital twins can
provide some some clarity on that um and
they're much much much cheaper than
actually setting up a prototype uh in
reality do we do we think maybe it's a
question for you Sam do we think that
digital twins can push Innovation like
(26:08):
do are they able not only to help us
Monitor and and create efficiencies but
could it actually help us innovate in
ways that we might otherwise I would say
so it's um it's mixing so many different
aspects of Technology nowadays so AR VR
uh accelerated Computing through you
know graphical processing units um
accelerating Innovation between teams
(26:30):
right Innovation coming up from from
teams human Innovation um I would say
yes it's definitely robotics right it's
it's a big one too
so for sure it will increase Innovation
over the years just because of the large
array of uh fields that is touching
including the metaverse right this is
where the metaverse comes in maybe maybe
(26:52):
well help us out here as we wrap up to
so that our our listeners can get um um
kind of close their eyes and and imagine
it when we say a digital twin are we
looking at a screen and just seeing
numbers and vectors of kind of results
or are they act like right that's what
I'm right that's one scenario or when we
think about a digital twin are we
actually looking at like a a virtual
(27:13):
reality and almost like like you said
the metaverse like a picture an image
that is realistic like when you talk
about your marketing uh uh digital
marketing twin is there are there actual
customers that you see and they're like
drawn on the screen or are you talking
about like just numbers that is an
output on the screen so in my research I
visualized them with uh perceptual Maps
(27:34):
so it's it's more abstract but I've I'm
I've seen research where it's
trajectories of customers and you see
them moving in the screen so it gets
more physical and of course uh visual so
in in context of like the of the heart
it's very visual right um in the context
of um assembly lines and factories it is
(27:56):
also very visual you would see see the
assembly line in the screen of course
you would see the sensors delivering the
data in real time so there is a very
visual aspect if you want if you want it
if it's relevant for your context
fantastic and I've been in operations
rooms and in airports and we all know
with recent um events and tragedies that
(28:16):
we've seen around us that this is you
know all the more needed right like to
the degree that it will prevent even one
accident in the future then we should
you know try to embrace it and make it
as good as we can make it um well Sam
thank you so much for being here I'm
sure that folks will uh uh be excited
and also maybe curious as to how their
(28:37):
data and behavior gets uh modeled in a
digital uh marketing twin um but um
thank you for sharing with us your
knowledge and your expertise thank you
Sam thank you so much so Mike is this A
disruption who are we disrupting so
that's when you went there because
that's exactly where my mind's been
going I I you know we we've had so many
where we go to the the negative and the
(28:58):
downsides I I think the downsides seem
fairly limited to me here so I'm I'm
going to rule out bad disruption okay
but is it a disruption at all is it A
disruption at all and clearly a positive
addition like I had no doubt in my mind
that this is going to help us in
innumerable ways Advance you know
Society humankind humankind if you um
(29:20):
but if we take disruption you know in
the technical sense that we we tend to
use it here where is this going to
restructure markets is this going to
lead to competitive reordering because
of the Advent of this technology is it
going to change labor forces
dramatically yeah exactly all of those
things it it strikes me that maybe the
(29:41):
technology itself will not now it has
these secondary impacts that we've
talked to from these other related
technologies that could have those
impacts but I'm not sure just the nature
of digital twin itself is it's a useful
tool to help us improve process flows
and the like but I'm not sure it's going
to lead to that disruption in that in
that technical sense that we use it um
(30:02):
let me give you one or two context where
it might um so can you maybe uh just to
push on that just throughout how we
teach again thinking about medical
students you know they have to work 16
hours days in the hospitals and do their
rotations will it disrupt that kind of
industry around instruction and
education uh will it in disrupt how we
(30:26):
uh practice uh for various military
operations could there be something
there well again I think it would
definitely be a a learning tool that
would maybe improve the outcomes in any
of these applications but it seems to me
it needs to be married with something
else on the back end to have that type
of disruption so you know Sam mentioned
robotics that I could start to see that
(30:47):
are we using the digital twins again to
train real world artifacts like robots
and the like to do work all right then I
would say that could be quite disruptive
but I'm not sure the digital twin in it
self go back to my weo example right
using a simulated environment to train
the AV that's really interesting and
value ad but it's the actual AV that's
(31:07):
that's causing disruption here not
necessarily the digital twin aspect and
maybe I'm splitting hairs here no I I
get it I get it so in and of itself
digital twins may be our your ruling or
our ruling in this show is no disruption
I'm going to go with I I rarely go with
no disruption you know I'm all about
disruption unless it's married with for
instance a very exceptional advancement
(31:29):
related to uh virtual reality the
metaverse or something that takes us to
the next level where suddenly people are
living their lives and making their
decisions and and and really investing
in the digital world or the virtual
world in of itself exactly exactly yeah
I think so well I've learned something
like always from you and from your
(31:49):
perspective and of course from Sam today
so um well again you know always a
fascinating set of topics here uh thank
you as always to our producer Gary and
to our head researcher Becky this is
good
[Music]
disruption good disruption is a podcast
(32:11):
from the University of Virginia Darden
School of Business
[Music]
[Music]
the disruption a lively discussion
between UVA Darden School of Business
professors Yael grushka cocaine and Mike
Linux on cuttingedge Technologies and
practices that are challenging the
(00:22):
status quo
[Music]
hello y hey Mike how are you I'm doing
well today good it's good I'm excited to
talk to you about a topic that I
personally see a lot of excitement and
(00:42):
future for well tell me tell me more
what are we talking about have you heard
the term digital twin I have in fact I
have in fact have you have you ever
dreamed of having a
twin good question uh no and not
actually maybe yes but not a digital one
for sure but uh yeah that's a curious my
wife is actually a twin but not an
identical twin she has a brother you
(01:03):
know twin brother but yeah I don't know
I mean I had six years between myself
and my sister so the idea of having a
twin that could be kind of cool be a
different experience than I had growing
up yeah what do what would you want to
do why would you think that having a
twin would be cool like to play tricks
on your parents I don't know I mean
maybe it's like the movies a Parent Trap
and things like that exactly exactly
which one confuse them which one twin
(01:24):
their deal with there um well so so
what's a digital have you have you heard
it I mean it's everywhere these days I
think the term has become U popular in
kind of uh common use and you see it
everywhere like in what context have you
heard about digital twin I think where
I've seen it um most often for two
things one is for industrial processes
so the idea of creating a digital
(01:45):
version of an industrial process and
then some of these interesting Pilots
for cities where they've tried to create
a digital twin of know City operations I
think Singapore has one of the first out
there yeah bolognia I've heard um even
here in this area in Virginia you'll
hear of cities that have a digital twin
and so what is the idea the idea here is
(02:06):
that we can actually mimic the reality
all the systems all the different
interactions between people between
systems if you think about complex uh
traffic patterns and the like um but in
the digital space and why would that be
beneficial because we can experiment
with um decision-making we can make
changes we can kind of study phenomenon
(02:26):
without having to worry about really
making any harm or damage in reality and
even anticipate maybe steps ahead in
order to put in place corrective actions
and the like yeah and when I think about
digital twins I also think about the
notion of kind of something else we've
talked about in the past iot so internet
of things correct most of these systems
are relying on remote sensoring to
(02:47):
provide realtime data to craft the
digital twin which might be a little
different than you know simulations been
around for a long time create a
simulation of some physical uh
environment but this is now real time
data constantly updating quote unquote
the simulation yeah and so uh you and I
we've talked about this on our show
(03:09):
before we share a background related to
operations research System Dynamics
system engineering uh where in the past
maybe we've built I remember my
assignment when I was in my Master's
studies putting together a hospital
simulation with the events of patients
com systems was the best mean of course
but the shortcoming there was typically
that you could only Model A upset of
(03:30):
maybe the interactions there was no
real-time data to your point and it was
very limited in the type of problems
that you might want to solve and so now
you can run in parallel to reality you
can really run a system that is in the
in the in the you know the digital
ecosystem it's not interfering with
anything on the ground but it can help
(03:50):
you study and understand what's going on
so just to give you a sense of uh the
size of this Market this is a real thing
okay we are talking about um the Dig to
Twin Market currently is estimated
around $2 billion and it's anticipated
in the next like 5 years or so to get up
to $150 billion and the players here and
the companies that are investing in this
(04:11):
are the companies that you might imagine
maybe others that you wouldn't you know
everybody from General Electric seens
Microsoft uh uhm yeah Cisco and the like
and probably a lot of other players like
you mentioned municipalities and more um
folks that are doing it in a local level
um uh in order to improve the way that
they think about their environment
(04:32):
improve the decision making and to allow
them really to test and to and to and to
be thinking more creatively so so let me
be the cynic just for a second you know
so again the idea of like smart cities
and this idea of using these digital
twins to optimize processes Yes sounds
great in theory but like does it does it
really materialize in in enough gains
(04:53):
that it's worthwhile to do these things
well I think like anything else um I
think there are pockets of activity that
we see a lot lot of gains and a lot of
um uh real decision making and training
and and studying and developing new
products that we could have never
anticipated before and so it might not
be across the entire segment of the
(05:13):
ecosystem but you do need to model all
of that in order to get even pockets of
improvements and pockets of benefits so
for instance if you can imagine and
we'll start something small if you can
imagine um a large hospital system and
you can design a digital twin for the
hospital system are there efficiency and
U um improvements that you can kind of
derive at a hospital level potentially
(05:35):
but the real benefits might be even at a
much smaller and more myopic level in a
specific operating room with a specific
physician allowing those Physicians
allowing those residents to train and to
practice and to simulate the environment
and to be to have a much richer
environment to learn and to you know run
through a dry run or digital run of a
(05:56):
specific operation and then check where
there's going to be some issues or maybe
where they run into some pitfalls and
then maybe correct course and improve
yes we're going to see huge improvements
in the in in the future now how would we
separate out this concept from something
we've talked about in the past the
metaverse I was about to say is this is
this relate at all to the meterse could
this be put into the metaverse of course
(06:18):
it could be related and like you know
there's it's not totally dis uh
disconnected to stuff that we've talked
about in terms of AI and generative AI
these things these Technologies bleed
into each other and help improve um all
of them across the board the digital
Trend really from my understanding and
the impl you know cases that I've seen
in the implementations that I've seen is
(06:39):
really relying on this notion of
realtime data so the real benefit is
that you're mimicking reality you're not
creating a parallel reality you're
mimicking reality with all of its
complexities with all of the different
factors that intermingle and that you're
really trying to capture the pacing and
the nature of the world that you live in
in order to make here in our world
(07:00):
better decisions but you're you're kind
of learning from from the digital twin
all right does my avatar in the digital
twin World know that it's in a simulated
world or does it think it's in the real
world yeah more here you're more like
simulation you know that in you're in
simulated world you have agents that
behave like you that think like you that
mimic you but it's allowing us to
(07:20):
understand really broader broader
patterns here can you like let's think
of other environments so for instance if
you're a sailing or if you're an Olympic
sport or any kind of sports team and
you're trying to get new strategies or
you're trying to play out new new play
uh uh moves um a digital twin could be
an environment that you could test
things out oh I like that idea so you
(07:42):
could take like uh the the simulations
we have for football or basketball but
then run a digital twin based on like
the biometric data of course and imagine
what's going on like imagine something
as high stakes as the Super Bowl imagine
you have a digital Trend running in the
background and that you can try out
various moves there with the same
players and the and the fatigue that
(08:02):
they're recording and the conditions on
the ground and then you can kind of take
that as an opportunity to learn from
that and be inspired for changes that
you want to make on the field I like it
I like it yeah um what would be some
downsides or some concerns you you said
you were going to be the se you know the
skeptic I know well it's hard to see in
this particular case but I guess again
you know there's always a Black Mirror
(08:23):
episode uh for this everything for and
again I guess this is the the blurring
of reality with uh the the simulated
environment and does that create you
know downsides uh of how people view the
world or how they engage in the world
and like anything else I mean some of
the the challenges that we can
anticipate if you're thinking about
(08:44):
digital twins from anything from a as
you mentioned a manufacturing plan for
let's say um you know in Ford's case if
they're thinking about the products that
they manufacture there to something like
a supermarket where you have customers
walking around and interacting with each
other and with the staff they C could be
some downsides if we um have certain
biases in these systems or if the
(09:04):
simulation helps reinforce some
negativities that then come out and play
out um we want to make sure that these
the digital twin is also used
responsibly with the same ethical
Frameworks you're not totally um you
know it's not as if all of that
automatically falls out you have to
ensure that the system is set up in a
way that ensures ethical decision-
(09:25):
making responsible uh responsible
decision- making and the like yeah it's
interesting to think about like okay in
the in the world without digital twins
how would we deal with some of these
things well some of it would be like
feet on the ground walking the actual
you know manufacturing facility and
seeing if there's issues or the like on
the floor you do we become overly
reliant on the simulation in that world
(09:48):
I think about something high stakes like
a nuclear power plant right um and where
again making a digital twin makes a lot
of sense but do we then become overly
reliant on oh everything seems fine
because my digital twin is telling me
everything's fine but then I don't know
the pipe the water pipe is leaking over
here and we're going to have overheating
uh and we just missed it because it
(10:09):
isn't censored by the um the information
flowing in and like anything if you are
a per a people's person and you believe
that you need to look somebody in the
eye to see that they're feeling okay
behaving okay able to perform okay as
opposed to watching just you know the
Monitor and seeing what it what
technically the numbers look like they
might the numbers might look like
they're fine but if you're actually
(10:31):
interacting with a real person you might
come to a different conclusion yeah are
we at this point where we need some help
yeah maybe we can talk to an expert so
it so happens that one of our colleagues
is actually an expert we are so
fortunate to have uh Sam uh Sam Levy
who's going to join us today he's a an
assistant professor here at the Darden
school and he is uh focused in the areas
of marketing so his digital twin
(10:53):
expertise comes from the area of
marketing his research investigates um
customer Analytics in different contexts
such as privacy data fusion and
pro-social behavior and he holds a PhD
in marketing from Cary melon his
dissertation introduced the novel
concept of a digital marketing twin okay
so that's a specific application where
(11:14):
he finds path of least resistance to
individual level customer satisfaction
Sam welcome welcome to our podcast hi
yeah hi Mike it's pleasure to be great
to have you Sam so Sam maybe you could
start off by just telling us what is a
digital marketing twin so sure uh
digital marketing twin in my context is
a virtual representation of a customer
(11:36):
right so the idea is to create what if
scenarios at the individual level so say
you have a customer and you're say a
telecom company and you are running a
bunch of surveys and those surveys are
very expensive you can only run them
once per customer because those surveys
are very long to feel and you have to
pay customers and they they just don't
come back so then the idea would be how
(11:58):
do you create
that virtual twin of customer where you
could basically simulate different
scenarios say if you're the Telecom
company Chang the pricing level or the
network speed would that customer switch
to a different carrier for example that
would be the idea of a customer twin
here beautiful so it's like a scenario a
more sophisticated scenario analysis
(12:21):
where we can experiment with potential
Futures and see how it plays out in our
digital twin environment is that the
idea something like that yes uh digital
twins um of customers are very
particular type of uh twins but we could
think of the twins of for example stores
right in the context of in the retail
(12:41):
right and the idea would be oh how do
you create a twin of a store here um you
know you would you start with the
shelves would you start with the
trajectories of customers there's a lot
of questions here going on I can imagine
you know when it comes to physical
assets it's easy to think about how we
simulate them in one of these
environments and again use you know uh
(13:01):
Internet of Things type of sensing to
provide us information maybe even like
what's on the shelf and the like but key
to what you were just describing is you
have to have a model of the consumer of
the individual like how do you do that
how do you simulate that individual and
how they might behave in different
scenarios so it's quite difficult
actually so in the context of the store
(13:25):
uh you would have to predict customers
moving in the stores and so people would
quickly go out of patterns if you're
using traditional machine learning
techniques so instead you would have to
use more advanced algorith more advanced
algorithms such as reinforcement
learning to be able to accommodate how
people go out of pattern when for
example you change the store layout you
(13:46):
would need to have a very robust model
of people how they move in the store so
that's quite complex the way I treat it
in my research I use a generative model
so I use a type of generative AI
algorithm to be able to predict how
people behave without the physical
context at all and so this is where it
departs from maybe what you and I
studied where it was more simulation
(14:07):
where we'd put in a distribution it was
kind of lock step you know off the shelf
and all we would do is kind of walk in
and go to the front of the line and
following up pant distribution or the
like what immediately came to my mind is
I am not a great Shopper my wife will
confirm this and uh she calls me the
lighthouse because we will go to like a
a clothing store and I will stand there
(14:30):
and she will shop and I will like look
back and forth like a lighthouse there
so Sam if you don't have the lighthouse
in your model like I I can provide you
the behavioral attributes of the the
lighthouse consumer but it makes me
think Sam so are we seeing um so for
instance are we going to see schools
like let's say Darden or UVA should we
have a digital twin thinking about the
(14:51):
students of the future and how we should
interact with them well it could be a
good idea to
understand how students would react
react in this virtual reality I I would
see a couple of challenges um I would
say the first thing to do would be to
start small maybe we shouldn't start
with the school maybe we should start at
the classroom level for example right
because you know creating twins of
(15:13):
cities or creating twins of complete
virtual assembly lines are extremely
difficult uh I I there are lots of costs
involved with building the twins
Gathering high quality data for example
so where would we gather the data do we
have consent to get that data as well
right is an important question and
finally how to use different teams right
(15:34):
different teams have different type of
data right maybe um you would have to
talk to a different team who has a data
set and so the the the problems with
many organizations is they work in silos
and so you cannot really access that
data in an in an easy fashion right so
there's actually a lot of a lot of
problems here associated with building a
twins it's it's a lot of challenges to
(15:55):
build them but I can see why we're
talking about this disruption today and
this day and age where more data more
higher quality data is being collected
so we're in a new era on that Frontier
as you mentioned Sam the algorithms have
evolved we've talked about that in our
in our show and so we're seeing that the
data is meeting the algorithmic
development that allows us and the
(16:16):
censoring that you mentioned it's all
coming together to enable these digital
twins while still costly and complex
they're becoming more possible than ever
before because all the combination of
these kind of Technologies yeah I'm I'm
curious Sam are we seeing at all the
kind of
self-generation of these digital twins
what I have in mind here is again yeah
(16:38):
and I come from a generation where if
you were going to program this you would
You' be hardcoding basically the
behavior of the agents and the like um
could we basically teach the simulation
to teach itself how to create these
environments or to create these these uh
different agents that are operating in
these environments that's a good
question I would say it's probably
(17:01):
difficult right now to make these agents
teach themselves I don't think we are
there yet uh there are a lot of
companies right now that take care of
the heavy lifting right so if you want
to build a digital twin that even like
teaches itself you would for example go
to Nvidia you would use their Omniverse
platform or you would go to aw us Amazon
(17:23):
web services and they have a Internet of
Things twin maker system but you
wouldn't build it from scrp
so you would rely on third party
techniques and and products so that you
don't start from from zero and then to
the question of being able to teach
itself I I don't think we're there yet
reinforcement learning is very data
(17:43):
hungry and so we would need tons and
tons of data to reach that level of
capability let me ask you we we you
brought up briefly before um this
ethical question around uh with consent
so collecting this dat data and
capturing it with consent and you know
we heard of applications as in examples
(18:04):
in hospitals or training Physicians
where these Digital Trends are being
used are are there ethical issues that
are emerging are there concerns that
there are some uh use cases of digital
twins which are not totally legit or
that folks feeling could could go in the
wrong direction it could go both ways I
would say because you could also say you
(18:24):
could also use flip the argument and say
look the twin is build to actually
prevent ethical misconduct right you
could create the twin of a customer for
example to be able to uh to run an
experiment in an ethical way without
actually harming right the idea of a
twin is also to live in a risk-free
environment right so now the question of
collecting data with
(18:46):
consent um it's a different question
there's now a lot of uh things going on
with uh privacy um laws changing and the
and the the end of third party cookies
in in in many settings so companies are
more and more careful now in in giving
data to others but I think within the
company I think there are still
(19:07):
processes that could you know you can
still create twins within the company
you just have to be able I think the the
the challenge are more organizational
rather than ethical right now uh because
you just need to work with so many
different
teams yeah you know one thing that comes
to mind is that some companies are using
in essence digital twins to train
(19:30):
algorithms for the real world and what
comes to mind is um weo of Google Fame
who has been using virtual simulations
of some of the Cities they operate in to
actually train their autonomous vehicles
that they're running on the road and I
be CU Sam or maybe iel like what what do
we think about that idea that the it
(19:51):
could actually work the other way where
the digital twin is now in essence
training real world actors in this case
robots but training them I mean I think
to Sam's Point it's encouraging to hear
if it can prevent any casualties or
catastrophic outcomes in the real world
then why not use the digital environment
to practice right like that to the
(20:12):
degree that the digital environment is
close enough to reality I don't know but
again this is taking it and bringing it
into reality that's the that's I think
the interesting thing yeah another thing
is you have to keep these sensors and
and you know you have to have you have a
lot of upkeeping here you need to um
constantly monitor if the sensor are
(20:33):
delivering the right data so you're
right the could be ethical issues
related to the quality of the sensors
are they actually bu are they actually
faithful to um to what uh what we would
expect from them I can imagine so I
spoke to a few folks who um are dealing
with digital twins at a government or
(20:54):
municipality level and we mentioned
these earlier like cities that are using
digital twins locally um and it's
interesting because I can see a a desire
to be more efficient and to be
forward-looking so you know if you
monitor uh births in the NICU or in the
in the hospital and then you already
(21:14):
play that out several years and you
think about the distribution of your
classes in the schools and you know the
different uh you know number of students
that you can facilitate in each
neighborhood like could we get ahead of
the game and optimize some of our
planning our urban planning around these
using these digital twins but then you
have to go back and adjust it based on
(21:35):
the decisions that you make so s we seen
these digital Twins play out so they're
used decisions are made and then we go
back to the digital twin and update it
and continue to refine it so it
continues to reflect reality yeah yeah
exactly the the the digital twins should
be continuously updated based on the
decisions um we see a r a rise of also
(21:56):
more probabilistic Twi me where the idea
is that you don't just generate one
outcome you generate a range of outcome
and so you can make a better decision
based on the worst case scenario versus
the best case scenario so that you know
that aspect of decision making is I I I
think very important if you take the
example of health care where you are now
(22:16):
we are now building digital twins of
hearts for example right Hearts is a is
not a physical process there's a lot of
noise uh you know uncertainties
associated with the human body so so
that that's why we we actually need more
um to incorporate uncertainty in the
digital twin models so I I would say
(22:36):
these problemistic twins would help make
better decisions especially in
healthcare see there there's always a
Black Mirror episode and it reminds me
there is one about dating I don't know
if you've ever seen this one where you
don't know this I'm sorry for those who
maybe not seen this episode before but
um what what comes to light is that when
people are dating through an app they
(22:56):
create two digital twins of the two the
couple and they basically run a
simulation multiple times to see what
the outcomes are to then make a better
prediction of whether you are compatible
with the person that you just met you
know at at a bar or whatever it is to
see your future kids and the future you
get along with mother-in-laws right run
out the whole simulation look at the you
(23:18):
know probabilistically what the life
outcomes could be and then kind of work
back to say yeah this is a good match
you're going to have a good life or or
not yeah um that's interesting I can see
that that would be a case with the with
the dating apps being what they are
these days Sam we often ask this
question as Mike and I try to get our
head around uh whether this is a good
disruption or a bad disruption or no
(23:38):
disruption at all we're not going to put
you on the spot because you are an
expert on this but what what does keep
you up at night like what concerns do
you have as the use of digital twins
continues to grow and various contexts
what what do you worry about I would say
I worry about the fact that we might
become overring on it if the technology
(23:59):
keeps growing the way it is um we might
completely Overlook uncertainty
associated with the outcomes because
it's so easy to overlook Overlook at
them so yeah that's that's what keept me
up at night also the idea of digital
twins of customers and stores at the
same time so full the full thing is
(24:20):
actually very hard to do just modeling
trajectories of customers and at the
same times having um modeling the store
can be extremely difficult just because
you know you have to mix the laws of
psychology and economics to actually the
physical laws of a store which probably
very hard to do right now so I would say
(24:40):
um you know starting small is uh always
a good idea with digital twins for first
modeling the the the the shelf and then
going more uh you know scale it up so I
think the idea of scaling keeps me up at
night is is probably the most difficult
part starting small is easy and I I
would say many companies and researchers
can do that but getting getting it big
(25:03):
and getting it uh reliable is very
difficult yeah and in such a way that we
don't over rely on it to the point where
we forget that it's just a digital twin
and not reality right yeah um yeah
fascinating yeah yeah can you see what
is the use case that you're most excited
about Mike I don't know why my mind does
go to like the the Smart City model like
(25:24):
that idea like we could we could maybe
it's a systems engineer may but but like
could we really optimize traffic and all
these other processes to make cities
more efficient yeah that that has some
just intuitive appeal to me well I know
that there's a lot of interest around
manufacturing and digital twins of
manufacturing um lines in order to
anticipate of course efficiency but also
(25:46):
anticipate new use cases and to
experiment with uh new products and new
capabilities that maybe have not been
done before and so the digital twins can
provide some some clarity on that um and
they're much much much cheaper than
actually setting up a prototype uh in
reality do we do we think maybe it's a
question for you Sam do we think that
digital twins can push Innovation like
(26:08):
do are they able not only to help us
Monitor and and create efficiencies but
could it actually help us innovate in
ways that we might otherwise I would say
so it's um it's mixing so many different
aspects of Technology nowadays so AR VR
uh accelerated Computing through you
know graphical processing units um
accelerating Innovation between teams
(26:30):
right Innovation coming up from from
teams human Innovation um I would say
yes it's definitely robotics right it's
it's a big one too
so for sure it will increase Innovation
over the years just because of the large
array of uh fields that is touching
including the metaverse right this is
where the metaverse comes in maybe maybe
(26:52):
well help us out here as we wrap up to
so that our our listeners can get um um
kind of close their eyes and and imagine
it when we say a digital twin are we
looking at a screen and just seeing
numbers and vectors of kind of results
or are they act like right that's what
I'm right that's one scenario or when we
think about a digital twin are we
actually looking at like a a virtual
(27:13):
reality and almost like like you said
the metaverse like a picture an image
that is realistic like when you talk
about your marketing uh uh digital
marketing twin is there are there actual
customers that you see and they're like
drawn on the screen or are you talking
about like just numbers that is an
output on the screen so in my research I
visualized them with uh perceptual Maps
(27:34):
so it's it's more abstract but I've I'm
I've seen research where it's
trajectories of customers and you see
them moving in the screen so it gets
more physical and of course uh visual so
in in context of like the of the heart
it's very visual right um in the context
of um assembly lines and factories it is
(27:56):
also very visual you would see see the
assembly line in the screen of course
you would see the sensors delivering the
data in real time so there is a very
visual aspect if you want if you want it
if it's relevant for your context
fantastic and I've been in operations
rooms and in airports and we all know
with recent um events and tragedies that
(28:16):
we've seen around us that this is you
know all the more needed right like to
the degree that it will prevent even one
accident in the future then we should
you know try to embrace it and make it
as good as we can make it um well Sam
thank you so much for being here I'm
sure that folks will uh uh be excited
and also maybe curious as to how their
(28:37):
data and behavior gets uh modeled in a
digital uh marketing twin um but um
thank you for sharing with us your
knowledge and your expertise thank you
Sam thank you so much so Mike is this A
disruption who are we disrupting so
that's when you went there because
that's exactly where my mind's been
going I I you know we we've had so many
where we go to the the negative and the
(28:58):
downsides I I think the downsides seem
fairly limited to me here so I'm I'm
going to rule out bad disruption okay
but is it a disruption at all is it A
disruption at all and clearly a positive
addition like I had no doubt in my mind
that this is going to help us in
innumerable ways Advance you know
Society humankind humankind if you um
(29:20):
but if we take disruption you know in
the technical sense that we we tend to
use it here where is this going to
restructure markets is this going to
lead to competitive reordering because
of the Advent of this technology is it
going to change labor forces
dramatically yeah exactly all of those
things it it strikes me that maybe the
(29:41):
technology itself will not now it has
these secondary impacts that we've
talked to from these other related
technologies that could have those
impacts but I'm not sure just the nature
of digital twin itself is it's a useful
tool to help us improve process flows
and the like but I'm not sure it's going
to lead to that disruption in that in
that technical sense that we use it um
(30:02):
let me give you one or two context where
it might um so can you maybe uh just to
push on that just throughout how we
teach again thinking about medical
students you know they have to work 16
hours days in the hospitals and do their
rotations will it disrupt that kind of
industry around instruction and
education uh will it in disrupt how we
(30:26):
uh practice uh for various military
operations could there be something
there well again I think it would
definitely be a a learning tool that
would maybe improve the outcomes in any
of these applications but it seems to me
it needs to be married with something
else on the back end to have that type
of disruption so you know Sam mentioned
robotics that I could start to see that
(30:47):
are we using the digital twins again to
train real world artifacts like robots
and the like to do work all right then I
would say that could be quite disruptive
but I'm not sure the digital twin in it
self go back to my weo example right
using a simulated environment to train
the AV that's really interesting and
value ad but it's the actual AV that's
(31:07):
that's causing disruption here not
necessarily the digital twin aspect and
maybe I'm splitting hairs here no I I
get it I get it so in and of itself
digital twins may be our your ruling or
our ruling in this show is no disruption
I'm going to go with I I rarely go with
no disruption you know I'm all about
disruption unless it's married with for
instance a very exceptional advancement
(31:29):
related to uh virtual reality the
metaverse or something that takes us to
the next level where suddenly people are
living their lives and making their
decisions and and and really investing
in the digital world or the virtual
world in of itself exactly exactly yeah
I think so well I've learned something
like always from you and from your
(31:49):
perspective and of course from Sam today
so um well again you know always a
fascinating set of topics here uh thank
you as always to our producer Gary and
to our head researcher Becky this is
good
[Music]
disruption good disruption is a podcast
(32:11):
from the University of Virginia Darden
School of Business
[Music]
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