🤯 More Data Than Tesla?! Palantir’s Partner in Auto (PLTR + WEJO)

00:00
one of the things i'm passionate about
00:01
as an investor is discovering newly
00:04
publicly traded companies that are using
00:06
advanced technology to transform our
00:08
daily lives one of the many reasons i
00:10
follow palantir so closely is because
00:12
they've invested in over a dozen such
00:14
companies but there's one in particular
00:16
that really catches my eye
00:18
ouijo is a connected vehicle data
00:20
company that uses palantir's foundry to
00:22
organize billions of data points from
00:24
over 10 million cars that are on the
00:26
road today just to be clear that's many
00:29
times more data than tesla has access to
00:31
with their fleet imagine using every
00:34
car's temperature sensors braking
00:35
patterns fog lights and windshield
00:37
wipers to predict bad weather patterns
00:40
and hazards on the road imagine seeing
00:42
the flow of traffic everywhere and
00:44
optimizing traffic lights to minimize
00:46
everyone's commute based on what's
00:48
actually happening on the road in real
00:50
time imagine giving all automakers
00:53
access to this data to help train
00:55
self-driving software to be even more
00:57
safe in even more use cases well you
01:00
don't have to imagine it because that's
01:01
exactly what we joe is building on top
01:03
of palantir's foundry in just a few days
01:06
wijo's holding its meeting to complete
01:08
its business combination with virtuoso
01:10
acquisition corp ticker symbol voso if
01:14
that merger completes wijo will begin
01:16
trading under the ticker symbol w-e-j-o
01:19
so in a market full of risky automotive
01:21
focused companies going public via spac
01:24
deals like this why should investors
01:26
believe that ouijo is special let alone
01:28
worthy of our investment to answer that
01:30
question i sat down for an exclusive
01:32
interview with wijo's founder and ceo
01:35
richard barlow if you watch this
01:37
interview until the end you'll learn all
01:39
about wejo's partnership with palantir
01:41
and clearly see wijo's market future
01:43
growth and path to profitability you'll
01:46
have a solid understanding of the use
01:47
cases for and value of connected vehicle
01:51
data and you'll also be the first to
01:53
hear some huge news that broke during
01:54
the interview about big institutions
01:57
making big investments in wijo and the
01:59
future of connected mobility full
02:01
disclosure i'm not a financial advisor
02:03
and nothing in this episode is financial
02:05
advice i took no compensation for this
02:08
interview and i currently do not have a
02:10
position in wijo although i may initiate
02:13
one in the near future based on this
02:15
research your time is valuable so i've
02:17
cut out all of the fluff and time stamps
02:19
are enabled for your convenience i hope
02:21
you enjoy and let's get right into it so
02:24
we joe is a platform of connected
02:25
vehicle data what's a connected vehicle
02:28
even mean and what kind of data are we
02:29
talking about
02:31
so there's over 225 million vehicles
02:33
globally that already have embedded
02:35
connectivity
02:36
and in fact the vast majority of new
02:38
vehicles sold every year um and vehicles
02:41
include car trucks and bikes have
02:43
embedded connectivity what is embedded
02:45
connectivity it's where a motor
02:47
manufacturer otherwise known as an oem
02:49
has installed a telecommunication unit a
02:52
modem in the fabric of the vehicle
02:55
and that and that and that on that
02:56
communication unit captures data from
02:59
sensors in car
03:01
if you think every button in your car or
03:04
truck or what have you
03:06
every dash
03:07
every image there could well be data
03:10
being generated from the outcome or from
03:12
the button press or from the display of
03:14
data in car
03:15
so
03:16
ouija has access to up to 150 sensors in
03:19
vehicle in near real time so typically
03:22
we receive data at a frequency of once
03:24
every three seconds
03:26
in a latency of less than 15 seconds
03:29
where we get this data from vehicle back
03:31
to our life back to our cloud
03:33
the data includes
03:35
location on a trend basis in fact we see
03:37
seven percent of vocal movements in new
03:39
york we see 12 vocal movements in austin
03:42
but it's not just location data on a
03:43
trend basis it's also live weather so we
03:46
have over 150 000 vehicles where we're
03:48
getting live weather data so if you
03:50
imagine a pair of we're getting data
03:51
from abs anti-lock brake we're getting
03:54
live weather temperatures we're getting
03:57
super accurate location on a trend basis
04:00
so we can identify hazards on roads we
04:03
can identify black ice on roads to
04:04
within three meters of accuracy
04:06
not to pinpoint back to the consumer or
04:08
the drive the vehicle but to pinpoint
04:09
where the hazard is on the road and that
04:11
also gives us a great example of a use
04:13
case for our data that's awesome so as
04:15
vehicles become more software defined
04:17
and you know every component of the
04:19
vehicle keeps grabbing more and more
04:20
data are is wijo's platform adapting to
04:24
that use case of newer cars just
04:25
spitting out tons and tons more data
04:27
than say cars that were made 10 or even
04:29
20 years ago
04:31
yeah that's that's an excellent question
04:33
and you know you're the first person to
04:34
ask me about so um we jo's working on on
04:38
an edge approach to the processing data
04:40
as you say there are terabytes and
04:41
terabytes of data
04:43
and the communication units in the car
04:45
the the vehicle it hasn't got the
04:47
intelligence ouija has developed a way
04:50
of
04:50
of machine learning when data should or
04:53
shouldn't be sent from the car so we get
04:54
access to a much broader array of data
04:56
from vehicle
04:57
and we call it edge to edge we we've got
04:59
edge base intelligence now being
05:01
developed inside the vehicle we've got
05:03
edge-based intelligence out on the cloud
05:05
side where we can then
05:06
directly controls what data does and
05:08
doesn't set his is or isn't sent from
05:10
the vehicle in this near real-time
05:11
environment to get the ultimate outcome
05:13
for providing a better experience for
05:15
the driver for example frictionless
05:17
payments or to identify a parking place
05:20
quicker or to have a department transfer
05:22
identify hazards on the road by
05:24
prioritizing when data should or
05:26
shouldn't leave the vehicle does wejo
05:28
require like a physical device or a
05:30
plug-in or is there some other way that
05:32
they're capturing all this data no so we
05:34
um
05:35
work with most manufacturers where
05:36
they've embedded the connectivity
05:38
so there's no aftermarket device there's
05:40
no mobile software required
05:42
and you know we're unique in how we do
05:44
that our competition we broke a data in
05:47
which means they can't guarantee the
05:48
origination of data they can't they
05:49
can't guarantee the compliance
05:51
whereas we only work with oems whereas a
05:53
very clear consent process from the data
05:55
leaving the vehicle
05:56
we've got a 100 assurity of the privacy
05:59
and security of the data it's because
06:01
we're working directly with the oem not
06:03
i'm not a not a broker of data and we're
06:05
not having to rely on an
06:07
aftermarket to the vehicle so 11.9
06:10
million vehicles we see data from day in
06:11
day out is embedded connectivity in the
06:14
vehicle and so i want to pull on that uh
06:16
embedded connectivity thread for a
06:18
little bit i just read through your
06:19
investor presentation again and you guys
06:21
want to become not just the data stack
06:23
for connected vehicles but also the
06:25
communication stack can you describe
06:27
what you mean by communications and not
06:29
just data
06:30
yeah and we we announced last week that
06:33
one of our new non-executive directors
06:34
is a gentleman called larry burns larry
06:36
was the senior advisor for waymo for
06:38
over 10 years he worked directly with
06:40
sergey and larry in in getting waymo off
06:42
the ground and i met larry and you know
06:44
i said look larry you know we've
06:45
collected over 477 billion miles of data
06:49
20 times more than tesla more than 20
06:51
times more than waymo and this data is
06:53
super accurate it's super granular and
06:56
you know we've got other outcomes where
06:57
we're seeing vehicle-to-vehicle
06:58
interaction so we understand those human
07:00
behaviors before and after of a of a
07:02
potential collision and the challenge
07:04
with autonomous vehicles at the moment
07:06
is that there are more than 39 oems who
07:09
registered the dmv to to to trial
07:12
driverless cars every one of them has a
07:14
different approach for how that how the
07:15
vehicles are learning to drive in a
07:17
driverless environment
07:18
so at some point well they'll need to be
07:20
a stack as it's called where
07:23
one brand of av needs to communicate
07:26
with another brand of av they've got
07:27
they've got a differing approach to how
07:29
how they'll how they want to drive
07:31
themselves but they'll need to
07:32
understand about who should go first who
07:33
should turn left who should turn right
07:35
so ouija has already built the machine
07:37
learning around around the uh around how
07:39
humans behave and that's an environment
07:41
we now want to enable the avs
07:43
communicate with each other as well
07:44
having larry is a great validation of
07:46
the data that we've built is a great
07:48
validation of our tech we've already
07:49
built and the next stage now is to
07:50
provide the communication to enable
07:52
these differing av
07:53
companies together
07:55
that's incredible so as more and more
07:56
autonomous vehicle companies come online
07:58
and solve this autonomous navigation
08:00
problem in different ways my guess is
08:02
that includes different types of sensing
08:04
modalities too right for example tesla
08:07
is vision only but waymo solves things
08:09
using radars and lidars as well and
08:12
you're able to make sure that they can
08:13
talk to each other because you're
08:14
standing in the middle and offering that
08:16
communication service by first what
08:19
getting everything into a universal data
08:21
set and then saying hey here's what
08:22
people here's what different cars are
08:24
taking in terms of actions regardless of
08:26
the exact data format or set or sensing
08:29
modality that gets you to those actions
08:31
is that fair yeah exactly and and beyond
08:33
that we built a massive data asset of
08:35
over nine piece bytes of data over 12
08:37
trillion data points where we've already
08:39
machine learned how vehicles communicate
08:41
with each other we've already learned
08:42
how humans communicate or how humans
08:44
behave in an interaction with a vehicle
08:47
so
08:48
the insights from that data asset will
08:50
share with the av industry so for us
08:52
we're saying at the moment abs are
08:55
limited to certain cities there's
08:57
certain states in america there needs to
08:58
be nationwide coverage you know even
09:00
tesla will say that that their their
09:02
autonomous vehicles behave better or
09:04
drive better on the west coast compared
09:06
to the east coast because of the data
09:07
they've got available at the moment we
09:09
can address that problem with a huge
09:10
data asset we've already built and we're
09:12
getting from more and more vehicles
09:13
every day
09:14
so it starts with saying we've got a
09:16
massive data asset where we can license
09:17
chew insights to help your own avs drive
09:20
better and be more applicable in more
09:22
states and more cities and then beyond
09:24
that we can then enable you to
09:25
communicate with with your peers as well
09:29
that's incredible yeah that's that seems
09:31
like such a useful data asset just even
09:33
from the machine learning and training
09:35
algorithm standpoint right just being
09:37
able to say hey you're developing a new
09:38
autonomous vehicle we have tons of
09:40
driving data that we can and driver
09:43
behavior data that we can share with you
09:44
to help you train up your machine
09:46
learning algorithms yeah and ultimately
09:48
everything we do we we have this mention
09:50
we say data for good you know the data
09:52
we hold is around ultimately having
09:54
safer roads less congestion less
09:57
emissions
09:58
that's our mantra business is that you
10:00
know we all we look at use cases where
10:02
we don't cynically identify the higher
10:04
rebroker highest rebroker of data to
10:06
maximize value data that's not our model
10:08
our model is to actually
10:10
do some good for for society to actually
10:13
help reduce deaths on the road you know
10:15
there are over 130 road workers who died
10:17
last year from um from from having from
10:19
having incidents with with drivers not
10:22
seeing these the temporary construction
10:23
work on their own we want to reduce that
10:24
number we want to enable abs to better
10:27
communicate and to be more more more
10:29
commercially available so there are less
10:31
deaths on the road that's the benefit of
10:33
our that's the benefit of we do as a
10:34
business and doing good and the benefit
10:36
of the data asset we built we're working
10:38
with purdue university we're the largest
10:39
automotive university uh in in the us if
10:42
not globally and we've helped them
10:44
reduce their research time from two to
10:46
three years to 45 minutes by providing
10:49
this live dynamic day traffic you know
10:51
where we see
10:52
vehicles in 95 center roads every day in
10:54
america you know we see as i mentioned
10:55
to you before we see vehicles on sev we
10:57
see seven center vehicles moving on new
10:58
york we've got statistically relevant
11:00
huge volumes of data that ultimately is
11:03
improving safety on the roads
11:05
and then beyond that microsoft's a
11:07
customer
11:08
they're a customer on multiple levels
11:10
now we've uh we've made two press
11:12
announcements the last couple of weeks a
11:14
couple weeks ago we announced that we're
11:15
now licensing the data asset to uh to um
11:18
to work with microsoft and and and help
11:20
improve additional functionality within
11:22
their mapping product
11:23
and then today we've announced a much
11:25
broader strategic relationship around as
11:27
you as you're offering where in fact
11:29
microsoft are introducing two oem
11:30
clients
11:31
so
11:32
we we are a very much a broad data
11:34
ecosystem and just go back to the
11:37
beginning about why i said ouija
11:40
most oems now globally are providing or
11:42
embedding connectivity in their vehicle
11:44
but they don't all have a data ecosystem
11:47
the new encoding evs
11:49
you know the likes of riview and the
11:50
likes of tesla they all have their own
11:51
data ecosystem we're all trying to build
11:54
their own proprietary approach we're
11:55
democratizing the approach to access to
11:57
data in vehicles that's and that's and
11:59
that's and that's our purpose in this
12:00
world on a date of a good basis
12:04
that's huge i love that i'm gonna pull
12:06
on that thread a lot during this
12:08
interview but just to hammer that home i
12:10
think democratizing that data is super
12:12
important and building that ecosystem
12:14
for cars to connect and cars to connect
12:16
back to all the different use cases for
12:18
people that could use that data i think
12:21
is super important you mentioned a lot
12:23
of other use cases in your investor
12:25
presentation and i'd like to kind of
12:27
understand how we should be thinking
12:28
about those use cases as investors so
12:31
for example
12:32
do you do stuff do you do different
12:34
things for people who have one car
12:36
versus people who manage a fleet of
12:38
commercial vehicles do you think about
12:40
things you know residential versus
12:42
commercial drivers how do you parse this
12:44
data into different broad buckets so we
12:47
so there's there's many there's many
12:49
sort of answers to that so in terms of
12:51
the broad use cases
12:53
uh we're working with one of the world's
12:54
largest e-commerce companies you
12:55
probably guess who they are
12:57
uh and uh they in the case study they
13:00
reported back to us we identified 30
13:02
more delivery routes so they're
13:04
delivering more parcels with less
13:05
congestion less emissions what an
13:07
incredible outcome for the use of our
13:08
data
13:10
and
13:10
that and that's an example of a large
13:12
fleet leveraging our data asset
13:14
and in fact beyond that how we work as a
13:16
business is that we work with most
13:18
manufacturers where we say look you know
13:20
we we can define hundreds of
13:21
marketplaces hundreds of use cases
13:24
we'd like exclusivity in those
13:25
marketplaces before before we build
13:27
before we build demand the exclusivity
13:30
depends on the oem not having a
13:31
pre-existing commercial relationship in
13:33
place
13:34
but we then go around and we either
13:36
market make or we disrupt in the
13:37
marketplaces so fees as i just mentioned
13:40
to you but also then remote diagnostics
13:42
roadside assistance end-to-end insurance
13:45
in fact in terms of end-to-end insurance
13:47
one of our new pipe investors is sampo
13:49
sampo are a 110 billion dollar japanese
13:52
insurer and we're working with them to
13:54
build and build insurance products in
13:55
japan for in in their home market uh one
13:57
of our previous private investors was
13:59
hella hella six billion euro tier 1
14:01
manufacturer and we've helped with them
14:04
build a whole suite of new products
14:05
about identifying faults in cars and
14:08
beyond that which is which is where
14:10
there's an interesting connection
14:11
between remote diagnostics and insurance
14:13
we work with hello to identify um damage
14:15
to vehicles after a collision
14:17
we've actually pinpointed damage to
14:19
within 12 centimeters of a panel in our
14:21
trials so a great example of a blended
14:24
marketplace is a blend of outcomes but
14:27
ultimately what it means for the
14:28
consumer or the drive of the vehicle is
14:30
that then they is that then if they're
14:31
involved in a collision then the vehicle
14:33
can actually do its own reporting of
14:35
damage the parts of the vehicle can be
14:37
actually be ordered from from the from
14:39
the air from the manufacturer before the
14:42
vehicles even arrive back in the body
14:43
shop
14:44
so a great example of how we're
14:46
democratizing access to data for all
14:48
oems we're actually improving the supply
14:51
chain we're reducing insurers cost by
14:54
virtual factors there's less claims
14:56
leak so a great end-to-end example i can
14:59
go on and on and on we've got over 200
15:01
different marketplaces as i said a lot
15:03
have this this degree of exclusivity
15:05
unless the oem has got a pre-existing
15:06
relationship in place we've got
15:08
incredible partners from the likes of
15:10
heller the likes of sampo and then
15:12
interesting another partner buyers is a
15:14
palantir palantira or a significant pipe
15:16
investor of ours um i was on their
15:18
earnings call yesterday i believe um and
15:21
they've done some great pr recently
15:23
shouted about this this joint
15:24
relationship parents are introducing new
15:26
clients just every day i mean
15:27
um i can't even talk about what we were
15:29
doing yesterday that was a full looking
15:31
statement um but it's um but we're doing
15:33
some really interesting commercial um
15:35
work work with parents here as well and
15:37
what they've got is you know they've
15:38
invested over three billion dollars in
15:41
in an incredible distribution you know
15:42
they've done an incredible job of
15:44
building distribution with air buses six
15:46
thousand suppliers but they built
15:47
distribution insurance they built
15:49
distribution and automated erp we can
15:51
now leverage that on a commercial basis
15:54
where we can
15:55
actually beat market that's incredible
15:57
yeah so let me ask the most moonshot
16:00
question first and then we'll dive into
16:01
your relationship with palantir if
16:03
that's okay sure um so right now you
16:05
guys are connected vehicle data
16:07
marketplace right we're talk we're
16:09
talking primarily about transportation
16:10
vehicles on the road so let me let me
16:12
ask two questions here first does that
16:15
include motorcycles
16:17
uh includes two two wheelers uh it
16:19
includes trucks as well so any former
16:22
vehicle that's got embedded connectivity
16:23
we we class as being a part of our
16:26
connected vehicle data asset
16:28
that's awesome and so in the future we
16:30
expect electric vertical takeoff and
16:33
landing vehicles to become at least
16:35
somewhat of a mode of transportation in
16:36
the future do you guys will you guys
16:38
plan on connecting with those types of
16:40
vehicles or are we talking more about
16:42
just ground vehicles in general no i
16:44
mean we we have a saying that we're
16:46
where we want to be by 2030 as a team
16:48
and team weejos
16:50
told me that what they want to do is
16:51
they want to put cars on mars by 2030.
16:54
so
16:55
so if we need to have embedded
16:57
connectivity from receiving from spacex
17:00
or blue blue origin or any other space
17:02
provider or any autonomous helicopter
17:05
provider we'd like to take the data we
17:07
you know there needs to be standard in
17:09
industry there is no standard we're
17:10
becoming the standard in industry what
17:12
we've shown is the incredible ability to
17:14
process huge volumes of data in near
17:16
real time and show an ability to
17:19
communicate back to the vehicle
17:21
communicate back to signage
17:23
and we and for us any form of vehicle
17:26
any form of mobility vehicle we'd like
17:28
to take the data and we are doing 17
17:30
billion times a day
17:32
that's huge yeah it's so exciting to see
17:34
that you guys are thinking three steps
17:36
ahead focusing not just on vehicles on
17:38
the roads today but vehicles in the sky
17:40
and maybe even in space tomorrow that's
17:42
definitely a great long-term road map
17:44
let's talk a little bit about your near
17:46
term roadmap so we understand that you
17:48
have a deep relationship with palantir
17:50
can you explain to us sort of the nature
17:51
of that relationship and walk us through
17:53
some of the steps
17:55
that they help you with the data ingest
17:57
processing monetization or anything else
17:59
you feel they're critical to in your
18:00
business
18:02
yeah so so we just superpower is speed
18:04
uh it's ability to take huge volunteer
18:06
data you know we say we've ingested over
18:08
12 trillion data points and what parent
18:10
he gave to us with a software product
18:12
called foundry
18:13
is the ability to maintain that speed
18:16
but actually to actually commercially
18:17
then then be quicker to new to new
18:19
markets such as i mentioned you before
18:22
insurance risk uh such as automotive vrp
18:26
beyond that we're we're working various
18:28
new use cases that support our multiple
18:30
market strategies so
18:32
for us being in line with a partner like
18:33
that where they they've done a lot of
18:35
heavy lifting already in terms of
18:36
distribution but we control and have
18:38
exclusivity to the data asset from the
18:40
oem and we still do our own our own sort
18:42
of processing of data we standardize the
18:43
data before leveraging the foundry asset
18:46
in terms of our data insights was was a
18:47
great was a great combination and and
18:49
it's so pale to have been an incredible
18:51
investor you know a great partnership
18:52
where both of us get huge benefits from
18:54
leaving each other's expertise and
18:56
relationships in different industries
18:59
that's awesome can you can i actually
19:00
pull on that thread a little bit more so
19:02
it seems like it's a relationship that
19:04
benefits both of you we've talked we've
19:06
talked a little bit about how you
19:08
benefit from palantir can you talk a
19:10
little bit more about how palantir
19:11
benefits from you
19:13
yeah so we so we've got amazing oem
19:15
relationships um but we can't do
19:17
everything you know we are you know we
19:18
are we're a scaling team
19:20
and you know we're now being asked by
19:22
oems about how we can how we can
19:25
introduce and be and work with parents
19:27
here as sort of a try a triumph so to
19:29
speak of the oem palanchi and we jo
19:31
together uh i can't say more than that
19:34
but it's an interesting work stream
19:35
we're working on now
19:37
sure i definitely understand that so
19:40
palantir it's is it just about new
19:42
clients or does palutear also use some
19:44
of the information and insights that you
19:46
gain to develop these general out of the
19:48
box solutions and use cases for other
19:51
big data or other transportation
19:52
companies do they take the things you do
19:54
specifically and generalize them i guess
19:56
is my the heart of my question
19:58
uh so we um a couple of weeks ago we had
20:00
a hackathon with palantir uh and
20:03
the guys flew over from the u.s uh so it
20:06
was a 48-hour hackathon in our uk
20:08
offices it's on our youtube channel if
20:11
any of you any of your listeners would
20:12
like to see
20:13
and you know this is about leveraging
20:14
regions data asset of over 12 trillion
20:16
data points
20:18
we had six teams working through and
20:20
designing products in that in that 48
20:22
hour sprint and the outcomes were
20:24
incredible
20:25
uh the outcomes that are helping parents
20:27
utilities customers know where to or
20:29
optimize where to install
20:31
charging points for evs and that's just
20:33
one great example of how
20:35
the data set is wejos
20:37
the relationship we have with with the
20:39
oems is protected
20:42
the the idea of blending multiple oems
20:44
data assets and remember we have 17 area
20:47
and tier 1 relationships in place the
20:49
idea of having a blended outcome where
20:51
we don't sort of um
20:53
potentially overstep the line of sharing
20:55
pii data plus information data we don't
20:57
do that we don't we then control that
20:59
only trend edge is used for actually
21:00
still but still maximizes the outcome of
21:02
utilities companies know where to best
21:04
install charging points and that's just
21:05
one example of of our partnership with
21:08
parenting does palantir help you with
21:10
the end-to-end value chain or are there
21:12
certain parts where palantir is really
21:14
instrumental in shortening the time to
21:17
value for a specific step that we joe
21:19
has to get through the data
21:21
yeah you answer the question really uh
21:23
they help us in key marketplaces where
21:25
they've supercharged distribution
21:27
uh we we are we are agnostic to all our
21:30
partners where you know we've we've
21:32
built our own proprietary ip we're
21:34
adding two new patents per month
21:35
typically
21:37
in terms of protecting our own ip we
21:38
will work with great partners like
21:40
palantir but also great partners like
21:42
microsoft chief to provide the ultimate
21:44
perfect action a democratized data
21:47
ecosystem for all oems and speaking of
21:50
palantir and microsoft do they help you
21:52
in sort of the same ways in different
21:54
use cases in completely different ways
21:56
can you help us compare and contrast
21:58
your relationship with palantir versus
22:00
your relationship with microsoft at a
22:01
high level
22:03
so
22:03
the in terms of this there's there's
22:05
layers of architecture so counter is a
22:08
substantial middleware player
22:10
and as microsoft is uh is is both an
22:13
infrastructure provider or a cloud
22:14
provider to ejo as well as also being a
22:17
significant buyer of data as well so so
22:19
an outcome of the data so there's
22:21
there's no sort of conflict between the
22:22
two uh and we've we've navigated that
22:26
through you know we we had we had a
22:27
number of conversations open
22:28
conversations between all three vendors
22:30
about how we're all going to work
22:31
together and the outcome's been
22:32
incredible
22:33
you know it's been it's been a it's been
22:35
a great time for it
22:36
yeah that's actually really interesting
22:38
to hear
22:39
you know most of my investment thesis
22:41
and my thoughts on palantir
22:43
always have them competing against
22:44
microsoft but it's really interesting to
22:46
see that in reality they serve two
22:48
different purposes at two different
22:50
layers of the stack and you can use both
22:52
of them in tandem and they even work
22:53
together
22:54
not just compete with each other
22:56
absolutely absolutely and say we and we
22:58
we're we're a great great great use case
23:00
you know we
23:01
you you'll have seen a consistent theme
23:03
with our news flare over the last few
23:04
months
23:05
where we've we've done useful palantir
23:07
we've done useful with microsoft there's
23:09
been a very clear
23:10
um differing position of our partnership
23:13
but ultimately the oems are looking for
23:15
us to actually manage those
23:16
relationships and to provide this this
23:19
data ecosystem democratizes data for all
23:22
and beyond that uh we've we've issued a
23:23
further press release from microsoft
23:25
this morning microsoft um aligns with
23:27
the open data standard microsoft is the
23:29
only cloud provider who's been very
23:32
clear about about how they position
23:33
themselves in terms of data
23:35
so again microsoft and ouija is a great
23:37
it's a great partnership i mean
23:38
microsoft introduces to oem clients
23:40
um you know so it's been a deliberate
23:42
strategy of ours to have an incredible
23:44
set of pipe investors microsoft palantir
23:46
and then obviously our original investor
23:48
general motors from three years ago
23:49
who've also been an investor in the pipe
23:51
you know and gm most people don't know
23:53
the gm
23:54
was was was he was was the was was that
23:56
the forefront of connected vehicle data
23:58
back 20 years ago
24:00
so
24:01
it's been incredible to build these
24:03
stronger relationships but that hasn't
24:05
stopped us working with ford with fiat
24:06
chrysler fca either so we've we've we've
24:09
navigated through to be the trusted
24:11
custodian of data and to be aligned with
24:14
with best partners that's awesome
24:16
um another question i have related to
24:18
palantir is you mentioned earlier that
24:20
you have many data analysts you know you
24:22
have people working in-house on machine
24:24
learning and ai algorithms but palutear
24:26
also provides some of those solutions
24:28
can you walk us through where you decide
24:30
to use palantirs out of the box
24:32
solutions versus where you feel you have
24:34
to invent a solution yourself from
24:36
scratch
24:37
yeah i mean i'm i'm not i'm not in for
24:39
reinventing the wheel um you know
24:41
there's there's enough to do out there
24:43
there's there's
24:44
we will protect our core infrastructure
24:46
our core ip
24:47
um but if there's machine learning
24:49
algorithms out there if there's ai out
24:51
there if we can be aligned with the
24:52
partner if we can find the right
24:54
commercial relationship we'll work with
24:55
that third party to ultimately
24:58
speed up our deployments and new
24:59
marketplaces in new territories um so
25:02
yeah we work and we leave some of some
25:04
of foundry's machine learning already
25:06
that's really cool to hear so it's
25:07
really a mix of in-house solutions and
25:10
where palantir has a solution already
25:12
that fits you guys use that let's say we
25:15
just only a 250 person organization you
25:17
know we're forecasting to be 500 people
25:19
next year you know we are a relatively
25:22
small organization
25:23
compared to the demands we've got from
25:25
oems already which is exactly why we've
25:28
while we've aligned ourselves with
25:29
partners like microsoft and parents here
25:30
where that where they can help us with a
25:32
heavy lift i'm i'm curious in your
25:34
perspective as as the insider looking
25:36
out in my perspective as the outsider
25:38
looking in if you didn't have palantir
25:40
and microsoft how many people do you
25:42
think you would need to do this exact
25:44
same thing
25:47
total ballpark it's not like uh you know
25:49
yeah i mean if you think about uh i mean
25:51
there's an oem i can think of they have
25:53
over 10 000 developers
25:55
um
25:56
in in in their sort of i.t division
25:59
uh and yet they called on wijo to be
26:01
their data ecosystem
26:03
um so
26:05
you know so for us to be able to
26:06
replicate everything all our parts are
26:08
doing we need thousands of people sure
26:10
yeah so really what's happening is your
26:12
partners like microsoft like palantir
26:14
are allowing you to scale much faster
26:17
and at a much better rate sort of per
26:19
person you need to hire you can go
26:21
further faster
26:22
cheaper right like
26:24
but meanwhile we've developed and we've
26:26
we've developed some incredible
26:27
subverting relationships that you know
26:29
the um the ceo of palantir was quoted as
26:32
saying you know look you know we only
26:33
managed to get two oem relationships we
26:35
just got 17.
26:36
that's you know and that's because we've
26:38
had this clear focus to build the
26:40
trusted platform to be the trusted data
26:42
ecosystem for industry that's right yeah
26:44
you're you're focusing on the entire
26:46
vertical in one
26:48
i know it's many markets but in vehicle
26:51
and transport right right not focused on
26:53
developing generalized models you're not
26:55
focused on you know all these other
26:56
solutions it's all about connected
26:58
vehicles
27:00
all about mobility enablement yeah
27:02
mobility enablement i love that yeah
27:04
yeah so you have this big proprietary
27:07
data asset that you're using in a wide
27:09
variety of ways connecting back to
27:10
palantir connecting back to other people
27:13
and businesses who are extracting value
27:14
from it so i understand that you're
27:16
building a software as a service
27:18
platform and a marketplace both can you
27:21
explain your overall monetization
27:22
strategy for the data and how both of
27:24
those pieces sort of fit into the bigger
27:26
picture together for example why both
27:29
what do you plan doing with each and so
27:30
on
27:32
yeah so we've just been going for seven
27:33
years and and and i had a very clear
27:35
strategy from day one about how we were
27:37
going to be relevant as a startup seven
27:40
years ago in in this global arena of
27:42
oems
27:43
and you know the first three years
27:45
trying to convince the oems to to to
27:47
trust
27:48
a relative startup with their data asset
27:50
was tough but we got there
27:52
and eventually we we've now got 17
27:54
million relationships in place where
27:56
we're trusted to build monetization use
27:58
cases
27:59
commercially we typically share 65 of
28:01
gross receivables back to the oem and
28:04
that's our model there's no setup fees
28:06
the oem
28:07
provides access to vehicles or provides
28:09
access to cloud and we take a live feed
28:11
there's no obligation to oem to do
28:12
anything to the data asset we take the
28:14
raw data our job is to actually do the
28:16
cleansing the creating of the data to
28:18
build this common data model
28:20
but beyond that and arguably marketplace
28:22
and monetization is is just one of many
28:25
sas products we're now building software
28:27
service products
28:28
so we started a marketplace and that was
28:30
our flying wedge so to speak to to get
28:32
into the oem industry and now we're
28:34
showing to all oems that we're now
28:35
democratizing this idea of having access
28:37
to data but beyond that we're now
28:39
democratizing the whole data ecosystem
28:41
so and what we've found is that oem is
28:43
now asking us in fact we're being
28:45
introduced by microsoft to where we
28:46
empty who want this demand we've been
28:47
introduced by palantir from oems who
28:49
want this demand
28:50
is that we've built this incredible
28:51
capability of processing huge volumes of
28:53
data and to identify the needles in a
28:55
haystack the high value in this huge
28:57
one-to-date process you know we process
29:00
450 000 data points a second at peak
29:02
oems now licensing from wejo that
29:05
capability that not just to monetize the
29:07
data but actually in some instances the
29:08
oems just wanted us to do the heavy
29:10
lifting data to present insights back to
29:12
their own their their own business units
29:14
and that's how we're now broader more
29:16
broadly
29:18
as a business
29:19
weejo when it starts issuing its 10ks
29:21
and its 10 qs will be very transparent
29:24
about live vehicles on platform the uni
29:27
economics in the in the marketplace but
29:28
also number sas licenses as well to to
29:31
oems that have been sold so we complete
29:33
so we're showing to all investors about
29:35
how we're becoming the go-to source of
29:39
data ecosystem licensing to oems to go
29:41
to source of software of software
29:43
products in the whole space of connected
29:45
vehicles and ultimately autonomous
29:46
vehicles that's super exciting and just
29:49
just for clarity can you give us a
29:50
couple examples of those insights that
29:52
they're asking you to generate and how
29:54
you would go about generating them is it
29:56
all machine learning is there any human
29:58
analysts in the loop like just kind of
30:00
describe one of those use cases for us
30:02
yeah i mean um machine learning is an
30:04
interesting one because ultimately
30:06
machine learning is is is uh to some
30:08
extent is as good as the uh the human at
30:10
the start who did the programming of the
30:11
machine learning so uh we we have a huge
30:14
team of data scientists
30:16
so we we get steering yaw or steering
30:19
angle from from steering wheels
30:21
we know elevation changes
30:23
so we machine learned how to identify
30:25
and these flows of data
30:26
um when a vehicle was doing a hard turn
30:29
and the vehicle was doing an elevation
30:30
change
30:31
to work through about
30:33
where locking in was to a parking lot
30:35
and also scanning about whether it was
30:37
free parking availability
30:39
we then built a visualization platform
30:41
that is in that is in our investor
30:43
demonstration
30:44
um where you can see the outcome in a
30:46
visualized format but actually the use
30:48
case
30:49
or the commercial outcome is the
30:50
insights that are then going to be used
30:52
to actually share with other cars in the
30:53
area other vehicles in the area free
30:55
parking availability
30:57
in an autonomous vehicle
30:59
your you you instruct your car to drop
31:01
you off at the curb side and you tell
31:03
your car to then drive off the park
31:04
somewhere another car in the area will
31:07
it will have informed your car whether
31:09
it's free parking availability
31:11
we've powered that
31:12
and that's the power of the data we're
31:14
doing and that's all that's that's one
31:15
of our outcomes of the machine learning
31:17
we built in-house
31:18
that's incredible and so i have a bunch
31:21
of maybe silly questions about that but
31:23
are you using location data of where
31:25
vehicles are not are you using camera
31:28
data from different vehicles that are
31:29
connected how do you go about telling a
31:31
different car hey there's parking
31:33
availability here can you shed a little
31:35
more light on how how you're kind of
31:36
doing that at a high level
31:38
yes it depends on every make and model
31:40
of vehicle as to what data we have
31:41
access to so we have access to instead
31:43
instances ultrasonic sensors which are
31:45
the parking sensors around the around
31:47
the bumper of the vehicle that they're
31:48
low resolution um that they can be high
31:51
frequency but that's actually good
31:52
because in terms of terms of that helps
31:55
the scan parking availability
31:57
so a combination of having ultrasonic
31:59
sensors having rough location knowing
32:01
this knowing the the the um the speed
32:03
you're the the tyres spinning at
32:06
applying all these things together even
32:08
if we have poor location we can still
32:10
work out as well as parking availability
32:13
so if you think in a parking lot
32:14
environment you may want to we may or
32:15
not get an accurate gps reading but
32:17
we've got enough other data to actually
32:19
recreate exactly what is parking
32:21
availability
32:22
to to to use an example you know when
32:24
you drive your vehicle in a tunnel you
32:26
may will use gps location but your
32:27
vehicle still knows where you're on
32:29
tunnel we're using this we're applying
32:30
the same sort of methodologies but with
32:32
additional sensors we have access to in
32:34
vehicle to to actually pinpoint exactly
32:36
what is parking availability in a
32:37
parking lot you mentioned that you
32:39
yourself won't be developing apps on top
32:42
of this data for example for people do
32:44
you see other companies being able to
32:46
develop apps on top of your data will
32:48
there be apps on cell phones that use
32:50
wijo data kind of like for live mapping
32:52
you know as an alternative to google
32:53
maps or other types of apps yeah so we
32:56
so we so we so we are now a data asset
32:58
that's powering um as your maps you know
33:00
and as soon as developers using as your
33:01
apps in in their in their own
33:03
applications
33:04
uh we have other clients who are taking
33:06
live insights from our data
33:08
so you know we we receive 17 billion
33:10
data points in we may we'll be only
33:12
presenting out a few thousand data
33:14
points more broadly we do it has also
33:17
built its own visualization platform
33:19
called call we jo studio where we have
33:21
when we have customers who sign up to
33:23
that service where they get
33:25
visualizations
33:26
uh dependent on the marketplace they
33:28
want to go into whether it's traffic
33:29
management whether it's or whether it's
33:31
any other marketplace we operate in
33:33
that's awesome yeah i'm excited to see
33:35
the wide variety of different apps that
33:37
could come out of this data can you walk
33:39
us through your exact revenue model walk
33:42
us through the business
33:43
so
33:44
we receive this huge volume of data from
33:46
oems on an exclusive basis in defined
33:48
markets unless the oem has an existing
33:50
commercial relationship in place
33:53
so
33:53
with that with that long-term agreement
33:55
and typically
33:56
those agree five and seven years
33:58
we receive the data into our platform
34:01
and it's our job to build marketplaces
34:03
um where we have these exclusivities
34:06
if we successfully sell data to a
34:08
defined market
34:09
where we go through a tight information
34:11
security process which is 300 pages
34:13
thick so we don't so to protect the data
34:16
asset from being sold about actors then
34:18
we typically rev share 65 of gross
34:21
receivables back to the oem
34:23
that's that's our core model as a
34:25
marketplace
34:27
in terms of broader broader software as
34:29
a service offering we charge um setup
34:32
fees we charge subscription fees we
34:34
charge transaction fees so we charge the
34:36
setup uh for building a platform we then
34:39
charge a monthly subscription fee
34:42
where and then and then subject to the
34:44
oem delivering a certain number of
34:45
vehicles or data points over that will
34:47
then charge transaction fees on top
34:49
that's awesome yeah i so i have two
34:51
follow-up questions directly to that the
34:53
first is you know you have a lot of
34:55
opportunities in front of you
34:57
what are the what are some of the most
34:58
exciting near-term opportunities that
35:00
you guys are addressing and tackling
35:02
next for your data product and your data
35:04
marketplace we have now a clear target
35:06
update tomorrow about how we're going to
35:07
be rolling out on a territory basis to
35:10
deliver the demands from oems he wants
35:11
us to be live in latam wants us to be
35:13
live in india wants us to be live more
35:14
broadly in apac then on the flip side we
35:17
also have a clear plan about the
35:18
marketplaces we're going to roll out so
35:20
as i mentioned to you before we're
35:21
already building products for sompo for
35:23
an end-to-end insurance product which
35:25
should be globally unique
35:27
we already are building products to
35:28
heller for remote diagnostics so we're
35:30
leveraging our key strategic partners
35:32
and investors in ouijo to deliver unique
35:35
commercialized products in multiple
35:37
markets which is all clearly defined in
35:39
our plan uniquely for ouija in terms of
35:41
our forecasting we did a bottom approach
35:44
to our modelling we interviewed over 200
35:46
prospects who showed demand for
35:47
connected vehicle data in more in broad
35:49
territories
35:50
because ultimately our biggest risk
35:53
is is operational risk
35:55
um you know building insurance products
35:57
in japan is fundamentally different to
35:58
building insurance products in in u.s or
36:00
europe we've got a discreet team of
36:02
people now working for wejo in japan to
36:04
make sure we build a unique product
36:06
that's gonna be loved by drivers being
36:07
loved by industry
36:08
but there's risk of that execution
36:11
and we're actually aware of that we and
36:12
that and that's actually why we're going
36:14
through this back process to make sure
36:15
we're satisfactory capitalized to to to
36:17
meet all the demands we've got from oems
36:18
who want us to be in all territories in
36:21
all marketplaces as soon as possible
36:23
that's huge yeah i love hearing that i
36:25
love hearing that you're expanding to
36:26
new markets but also new verticals
36:28
within those markets and you're thinking
36:30
about how to localize for each market
36:32
that's one thing i really preach on my
36:34
channel is it's not enough to just enter
36:36
a new market and you hit the nail on the
36:38
head right people in japan and people in
36:40
america might use the same product and
36:42
service completely differently just
36:44
because they have a separate culture
36:45
around it and separate interactions with
36:47
it so it's great that you're thinking
36:49
about that from the get-go in terms of
36:52
competition do you have a global
36:54
competitor will you have local
36:55
competitors in each market can you
36:57
describe a little bit of the competitive
36:59
landscape and your position in it yeah i
37:01
mean our competition is is is the is the
37:04
significant cloud vendors out there you
37:06
know there's there's there's a there's
37:08
there's a one-off opportunity
37:10
you know in this fundamental change in
37:12
the automotive industry and it's and
37:14
it's it's the biggest change in 50 years
37:16
there's huge cloud vendors out there
37:18
who'd all like to be lined with oems
37:20
so they're our key competitors
37:22
marketplaces as a subset of sas uh you
37:25
know there are plenty of marketplace
37:27
businesses out there that haven't got
37:29
anywhere near the revenues we've got
37:30
haven't got anywhere near the the the um
37:33
the exclusive arrangements of oems
37:35
haven't got the data points on platform
37:37
and every breakering data in
37:39
but we take them seriously we take every
37:41
one of our potential competitors
37:42
seriously but ultimately only one
37:44
business
37:45
has made it very clearly that we want to
37:47
be a data ecosystem that democratizes
37:49
all data access for all oems and will
37:51
eventually become the com stack for the
37:53
next generation of vehicles that will be
37:54
on the road by 2025 and beyond
37:56
yeah that's great to hear as an investor
37:58
i love hearing that speaking of
38:00
investing and risk one of the things
38:02
that i know my audience cares a lot
38:04
about is understanding your path to
38:06
profitability can you explain and
38:08
convince our investors
38:10
what is your path to profitability and
38:11
why we should believe you'll become
38:13
profitable doing this
38:15
so we so we are very transparent with
38:17
with
38:18
our with our key kpis uh we're
38:20
transparent about the vehicles on
38:22
platform we're transparent about our
38:24
unit economics per marketplace so for
38:26
example uh in the marketplace of traffic
38:28
management which includes mapping
38:30
we reported last year the uh revenue per
38:34
vehicle per year
38:35
uh was 40 cents
38:37
this
38:38
year it will go to 70 cents so a near 80
38:41
improvement in unit economics
38:43
as well as that we showed vehicle
38:44
onboarding uh increasing by 40 as well
38:48
so by our revenues sorry by our unique
38:50
economics increasingly by set by 80
38:53
by vehicles on platform increasing by 40
38:56
those two metrics meant that our our
38:58
revenues would scaled by more than 150
39:01
percent
39:02
uh which is
39:03
so that's that's this year
39:05
next year we're forecasting vehicle
39:07
growth from 14 million vehicles to over
39:09
30 million vehicles so more than 110 110
39:13
growth in vehicles
39:14
on platform we're also showing that that
39:17
uh that our uni economics overall will
39:20
improve by 200 percent next year
39:22
so by vehicle volume's improvement by
39:24
100
39:25
by uni economics improvement by 200 over
39:28
the next year that will actually more
39:30
that that that will more than triple our
39:32
our revenues for the year
39:33
and that's the benefit of of of
39:35
measuring economics per marketplace it's
39:37
the benefit of reporting on the licenses
39:39
sold for sas it's the benefit for
39:41
reporting on live vehicles on platform
39:43
you can you and all your investors can
39:44
form your own view about how we're
39:46
scaling beyond 2025 we're forecasting
39:49
125 million live vehicles on platform um
39:52
we have visibility of more than 60 of
39:54
where that's coming from already so
39:56
even if our unit economics stayed flat
39:58
but our vehicle escaped but our vehicles
40:00
could need to scale as they are doing
40:01
then that would still show substantial
40:03
scales of revenues but actually our
40:04
revenues won't stay flat the fact that
40:06
we're now building insurance products
40:07
with sompo the fact we're building
40:09
remote diagnostics products with heller
40:10
all those things will be a creative to
40:12
our toby uni economics so all these
40:14
things and help us support our 1.39
40:17
billion dollar gross sales forecast for
40:20
2025 which by the way is is a very small
40:23
part of the total tam
40:25
the total tam is over 61 billion dollars
40:27
yeah and i'm sure there's a ton of other
40:29
use cases and products and services that
40:31
will get weared in there over time that
40:33
we may not not even be aware of today so
40:35
one of the things that a lot of people
40:37
are concerned with when it comes to the
40:38
auto industry is obviously the pandemic
40:41
as people have been driving less over
40:43
the last couple years now how does that
40:45
impact weijo's road map and future
40:47
timelines
40:49
do you know people drove less for a
40:52
while
40:53
um and now they drive differently
40:56
okay so
40:57
so before coverage you'd see people
41:00
driving to work at seven a.m
41:03
seven to eight a.m in the morning and
41:05
then they drive home at 5 00 pm
41:06
typically
41:08
now you see people driving to work at 10
41:09
a.m in the morning and going home at 5
41:11
00 pm in the in the afternoon so we're
41:13
seeing people shortening their days in
41:15
the office when they are when they when
41:17
when they're working in the office
41:19
then other days when it's hybrid we're
41:21
seeing people drive longer journeys
41:23
is that we're seeing people who may
41:24
historically have parked at a a at
41:27
an airport to maybe get a domestic fight
41:30
we're now seeing more longer journeys
41:32
where perhaps now people are actually
41:34
deciding to to drive a longer journey
41:36
and then they're going to shorter a
41:38
short domestic flight instead
41:40
so in terms of we joe at the start of
41:42
covered our data points dropped 58
41:45
we pulled back that within two quarters
41:48
and what we've seen this year is we've
41:49
seen a scale of revenue points so sorry
41:51
a scale of data points over the year
41:54
so
41:55
when we issued our 8k in um q2 this year
41:58
we were we were receiving about 14
42:00
billion data points a day
42:02
uh we now in in our latest s4 show that
42:05
we're we're processing over 17 billion
42:07
data points a day
42:09
so about a 20 increase in date in data
42:11
points
42:12
in terms of live live cars on platform
42:15
live vehicles and platform we were
42:16
reporting in our 8k
42:18
uh 10 million vehicles live on platform
42:21
we're now showing 11.9 million vehicles
42:23
on platform again about 20 percent
42:24
growth and we're forecasting by then the
42:27
q4 that will be at 14 million live
42:29
vehicles on platform
42:30
so what we're showing is that is that
42:32
we're showing vehicles being onboarded
42:34
on platform by oems
42:36
we're showing that people are actually
42:37
driving
42:38
and we're sharing it interestingly we've
42:40
got a unique data set where we
42:42
understand the before during and after
42:43
of covid pandemic and that helps dots
42:46
that's hugely valuable for multiple
42:47
industries to have that that in-depth
42:49
understanding
42:50
that's incredible that sounds like such
42:52
a unique perspective on the whole
42:53
pandemic that's actually very counter to
42:56
what i thought right i thought there
42:57
would be just less cars on the road
42:59
people driving less often just because
43:00
that's my unique experience but sort of
43:02
what you're telling me is or not sort of
43:04
what you're telling me is not only did
43:06
driving not slow down but from wijo's
43:08
perspective driving is different but
43:11
more and more cars are on the road more
43:13
and more they're just behaving
43:14
differently and you guys have that
43:15
insight based on millions and millions
43:17
of data points yeah people drove less
43:20
during lockdown now out of lockdown they
43:22
bounce back the journey's different
43:24
there's now a clear hybrid model around
43:26
people working but vehicles are being
43:28
used
43:29
so this has been a huge morning for
43:31
ouijo one of the biggest investment
43:32
firms in the world apollo has just
43:34
announced an investment in wijo one of
43:37
the biggest software companies in the
43:38
world microsoft has just extended their
43:40
partnership with wijo and one of the
43:41
biggest car companies in the world gm is
43:44
constantly working with wijo tell us a
43:46
little bit about the breaking news
43:48
that's happening to ouijo right now as
43:50
we conduct this interview
43:52
yeah i mean gm is a great is a great a
43:54
great point to start with they've been a
43:56
consistent investor in wijo for the last
43:58
three years and they also are part of
43:59
the pipe but then more broadly microsoft
44:02
who don't do pipe investments and now
44:04
it's four months ago there they were a
44:05
pipe industrial ouija and now this
44:07
morning they've now gone they've now
44:09
doubled down and said you know we are
44:10
their preferred partner in data
44:12
ecosystems they are introducing us to
44:14
multiple motor manufacturers
44:16
area where where we are licensing our
44:18
technology to those oems which will be
44:20
then running on the azure platform
44:22
and beyond that a couple weeks ago
44:24
microsoft also showed their commercial
44:25
intent by by signing a multi-million
44:27
dollar contract to license weejo's data
44:29
asset
44:30
then finally apollo won the world's
44:32
biggest funds with over 500 billion
44:34
dollars under management they've signed
44:35
a unique forward purchase agreement
44:37
which i don't believe has been seen in
44:38
the uh
44:39
and in the spat community before where
44:41
they've committed to buying up to 75
44:43
million dollars of redeeming stock over
44:45
the coming days so a real endorsement
44:47
from as you say the the leaders in their
44:49
respective industries
44:51
that's super exciting i'm super excited
44:53
to see how you guys continue to scale in
44:55
all these different markets all across
44:57
the globe i'm excited to see your
44:59
relationship with both palantir and
45:01
microsoft continue to grow and i'm
45:03
excited to see the development of both
45:04
your marketplace and your platform
45:06
itself but thank you for your time that
45:08
was uh it was really enjoyed that really
45:09
enjoyed it so while other automotive
45:12
focused spat companies might make
45:14
uncertain promises about the future we
45:16
joe is already getting data from over a
45:18
dozen major car companies today from
45:20
ford and honda and toyota to companies
45:22
like bentley and even lucid motors ouijo
45:25
has developed strong technology
45:27
partnerships with companies like
45:28
palantir and microsoft and received
45:30
large investments from companies like gm
45:32
and apollo which is one of the biggest
45:34
investment firms in the world with over
45:36
450 billion dollars in assets under
45:39
management that's around 10 times the
45:41
size of arc invest in my opinion their
45:44
path to growth is clear as vehicles get
45:46
smarter and more advanced each one will
45:48
have more data to provide that compounds
45:50
with more and more vehicles getting
45:52
connected to wijo each and every day all
45:55
of those cars with all that data will
45:57
continue to unlock new use cases and
45:59
ultimately new markets and geographies
46:01
for wijo to enter and use their massive
46:03
amount of proprietary automotive data
46:05
for good that's a future i'm really
46:08
excited about so on behalf of the ticker
46:10
symbol you community a big thank you to
46:12
wejo's founder and ceo richard barlow
46:15
for doing this exclusive interview as
46:17
well as the rest of the wijo team for
46:19
making this happen during such an
46:20
exciting time for the company and most
46:22
of all thank you for watching this
46:25
extended episode and exclusive interview
46:27
until next time this is ticker symbol
46:29
you my name is alex reminding you that
46:32
the best investment you can make is in
46:34
you