Mentioned in Video:
- 🚀 12 Stocks That Could 10X Thanks to Palantir (PLTR): https://www.youtube.com/watch?v=5r3fEl6_e_c
- 🏆 Tesla Stock | Tesla's Huge Data Lead Game Over for Car Companies: https://www.youtube.com/watch?v=VxHwgJXqZI4
- Unlocking the Power of Connected Vehicle Data | Wejo + Palantir: https://www.youtube.com/watch?v=Hj9TcH6-pP8
- WEJO (VOSO stock, to become WEJO stock) Investor Presentation & News: https://www.wejo.com/presentations
- ARK Invest Autonomous Technology & Robotics ETF (ARKQ) – https://ark-funds.com/arkq
- ARK Invest Next Generation Internet ETF (ARKW) – https://ark-funds.com/arkw
- Support the channel and get extra member-only benefits by joining us on Patreon: https://www.patreon.com/tickersymbolyou
🤯 #Palantir (#PLTR) is just one of many awesome companies partnering with #Wejo (VOSO stock, soon to be WEJO stock). But in a stock market full of newly listed automotive-focused companies like Lucid Motors (#LCID stock) and Rivian (#RIVN stock), what makes @wejo special? To answer that, I sat down with Wejo's CEO and Founder, Richard Barlow. We talked about connected vehicle data – Wejo has 20x more data than Tesla (TSLA stock) – as well as their relationship with @Palantir, business model, unit economics, and path to profitability. I think this would be a no-brainer for #CathieWood to hold inside #ARKQ, the #ARKInvest fund focused on the autonomous revolution, as well as #ARKW, @ARK Invest‘s fund themed around the next generation of internet applications. In fact, I'm starting to think Wejo stock could 10X from here and be one of the best stocks to buy now.
Video Transcript:
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
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