Today, we are joined by HiveMapper CEO, Ariel
Seidman. Ariel, thanks for coming on. Thank you
for having me, Brad. For sure. It has been a
little while since we last spoke, but there's
been a lot of developments, a lot of new changes,
very exciting things happening. We're going to
touch on all that. Of course, I think most people
are going to want to know about the B. We will
talk about all the nitty gritty details there,
but definitely want to talk about the demand
side because I would say HiveMapper is one of
the few projects in the deep end space right
now that's... truly seeing revenue and token
burns and some exciting new contracts. But before
we get into all that, for people that are watching
for the first time, I'd love for you to give
us a little bit about your background and what
you're doing leading up to HiveMapper. Sure.
Yeah. So my name is Ariel, CEO and co -founder
of HiveMapper. I've been in and around the mapping
world basically for 20 years now. So really my
first job out of school. was at Yahoo when it
was still fun to be at Yahoo. So I sat in the
Yahoo search group. We had about 30 % market
share. Google had roughly like 32, 35 % market
share. So we were still very much neck and neck.
And a massive percentage of the daily queries
on Yahoo search were related to local, right?
uh this could be a local pizza shop this could
be like how do i get from here to here this could
be i want to understand more about this you know
country or region of the world or whatever it
may be and so then i took ownership of all those
type of queries and then ultimately ran yahoo
maps which at its peak had about 90 percent market
share um and then google maps came around and
they started to really like just plow you know
just crazy amounts of money into building their
map And quite frankly, just took a lot of market
share from us. And I had a front row seat to
that. So I was able to kind of see and understand
how they built the map. And while they did a
good job, it was definitely over -engineered
and it was a very, very expensive way to build
the map. And I think there's a, especially now
in this era with all the new technologies, tools,
distribution, ubiquitous computing, all that.
there's a new way to go build a map from the
ground up and i think we even google i think
if they were doing it today they would do it
quite differently and so that's the opportunity
with hive mappers like how do you build a new
fresher more cost effective way to build a map
from the ground up that all makes a lot of sense
especially that you have the background in mapping
I'm sure that has been really helpful in HiveMapper's
success, especially talking about the revenue.
And that's what I want to talk about next. So,
yeah, if you could touch on the different categories,
the opportunities when it comes to revenue. So,
yeah, so there's there's three major categories.
I mean, there's two that are active right now.
So there's data solutions. Right. So we have
a host of various API data products. uh and you
know we set we go out to other businesses that
want access to that data and some of those apis
you know this can be another mapping company
this can be a logistic company this can be a
transportation company commercial real estate
autonomous vehicle company etc so that's data
solutions there's a new product called beekeeper
which is going after you know quite frankly just
a much larger market in commercial fleets right
and so there's 250 million to 300 million commercial
vehicles in the world and all of those commercial
fleets obviously they're doing routes throughout
the entire day they're going here they're going
there whatever they're dropping off stuff picking
up people etc uh that they want to protect that
asset and they want that asset to be incredibly
efficient And so we now have the opportunity
to kind of rebuild from the ground up, given
that we understand and see the world, you know,
something that every other provider in this space
just simply doesn't do because they don't have
the mapping background. So we're very excited
by that part. That's a huge market just getting
started now. And then the third one, which is
fairly nascent that you're going to see a lot
more about next year is platforms, right? So
how do other people utilize our software? They
called map AI that actually builds the map integrated
into many more vehicles, both in terms of contributing
and consumption. And there's a lot more there
to talk about. I'm not going to talk about it
all today, but that's obviously a big focus of
ours on the platform side. You just announced,
I guess it was a couple of weeks ago that you
have three new contracts and six figure contracts,
right? Yeah. You know, most of these contracts
that we're seeing today are in the data solutions
category. Right. And so these are other. businesses
integrating various data products into their
systems. Okay. Could be other mapping companies.
I think we've announced one of them, which is
called HERE Technologies, which is owned by many
of the large German car manufacturers like Audi
and so forth. So that's one type of customer.
The other is automotive companies. So this is
the Lucids of the world, the Rivians of the world.
You know, so you have kind of this new generation
of automotive companies as well as some of the
legacy automotive companies as well. And then
you have, you know, what I would refer to as
a smaller segment for sure, but it's like commercial
real estate. We have a lot of like just real
estate in general, like local government, those
customers as well. That is definitely a smaller
segment of the overall data solution. We're also
seeing now traction in autonomous vehicles. know
obviously tesla's a big player there obviously
waymo's a big player there but what people don't
realize is there's automotive there's av companies
autonomous vehicle companies just focus on like
trucking as an example right um but you have
a lot of other players as well right you have
nero you have zooks um you have mobileye to a
certain extent as well i don't think that it's
going to be a waymo tesla only game obviously
they'll have a significant percentage of market
share. Ultimately, if you kind of look out three
to five years, but there will be all these other
players as well. We can talk a little bit more
about that. So that's where most of the revenues
coming from is those kinds of categories and
those types of customers today. I'm curious,
you know, how those conversations have shifted
since you first started with this, because I
imagined at the beginning. and not to say you
don't still do proof of concepts but i imagine
at the beginning it was kind of pitching this
idea now you kind of have case studies and a
proven product yeah so i think those conversations
have gotten easier over time yeah 100 right and
i think like people look at the scale that we're
operating at right now and i i will remember
i mean this is kind of the funny part of discord
is like uh people were like all those like data
evals this is not this past summer i think it
was like the previous summer like people were
like going off on me and and hive mapper for
that matter basically being like all those data
evals you guys did like nothing ever came of
them they were all you guys failed you know and
i was looking at it and being like actually there's
like contracts like there's yeah happening and
yeah um so it was kind of like funny i was like
okay like do it yeah two different worlds at
the same time you have to remember most people
haven't worked in like the startup space or worked
in the business world to the extent of like having
to build a product you start off you know with
a proof of concept in many cases get in the door
with these businesses and customers and then
you know build bigger contracts over time bigger
opportunities over time uh so you know in the
crypto world everybody wants everything yesterday
right so it's it's hard to kind of get people
to wrap their head around that whole concept
of actually building something i'll give you
one i'll give you one example of a customer they
started you know well i can't share the specific
name but hopefully we will because we're been
very helpful and valuable to them um which is
which is great news so they started off with
a purchase order i think in the tens of thousands
of dollars and they thought that it was going
to basically last them for you know whatever
like nine months 12 months they basically used
all of their credits within about two to three
months um and and so they're they're happy right
because they're getting tremendous value out
of it and so now they're like oh geez we gotta
like work on a much bigger contract for 2025.
You know, all right, let's go like figure out
how to scale up to your point about, yeah, you
get in the door, you show the customer that you're
really valuable and you're really useful. And
I think what we've also learned in terms of the
data solutions product is in addition to just
providing like, hey, you can hit our API for
all the imagery that you want or all the math
features that you want. Customers are looking
for changes. Right. And so they're looking for,
hey. you know, tell me about whether or not that
speed limit has changed. Tell me about whether
or not there's, you know, road construction here.
And if so, what's the impact, right? Tell me
about whether or not that road construction is
now gone, right? And so just think about all
the changes that are happening. That is, you
know, just honestly like 10 times more valuable
than just like a data feed. of just massive amounts
of data where they then have to kind of sift
through and figure out what's interesting within
that data feed yeah that that's one you know
really interesting thing that we've learned from
our customers the second thing is there's a lot
of customers that are looking for you know very
abnormal situations okay so this is a good one
um you have a school bus right with like that
little stop sign that comes up dynamically that's
not a that's not a persistent stop sign right
like to a normal you know av system that will
oftentimes just like look like another stop sign
right but it's not it's dynamic it's it's not
it's not associated it's not fixed and so like
really being able to understand these nuanced
differences in the world is really valuable um
or like hey somebody got into an accident in
this scenario That type of data is incredibly
valuable to a lot of the autonomous vehicle companies
in terms of training their models. All that makes
a lot of sense. And also, and we'll talk more
about the B here in a little bit later in this,
but having the edge processing, having that data
more quickly and more real time, I think, well,
I imagine will even make that data product better,
right? And open up more doors for you guys on
that end. Oh, 100%. The incremental revenue that
is tied to the B is obviously like massive. And
I don't think we quite fully appreciate how big
it is. Because to your point is, it doesn't just
lower costs, which was actually like one of the
primary reasons we did the B was we're like,
geez, like processing all of this data on the
servers on AWS is crazy expensive. um so that
was like the very very original motivation for
it obviously we also realized we want this real
-time data we want all of the processing at the
edge so we can really much more dynamically understand
what's going on in the world which talk more
about what I mean by that because it does introduce
a lot of new revenue opportunities and we're
seeing that play out right now and so like you
know I mean I think a lot of people understandably
so and incredibly patient for the B we want to
get this out there not only because we want to
satisfy all the people who did the pre -orders
uh and thank them for their patience but also
because from a revenue perspective there's a
lot of incremental revenue tied to the B And
by the way, I do want to pull this up because
I'm excited about the revenue that's going to
be coming. But we've been seeing, I mean, you
look at this growth in the honey burns right
now. I have a couple other visuals here. This
is a good one, too, that shows the growth over
time. I really like this one because it shows
it versus some of the other projects out there
from a percentage growth standpoint. But, you
know, you all are seeing. a lot of revenue growth
and that's exactly what you want to see i mean
to your point though too and i've seen some people
that you know they're like well how come the
burns are going up but the revenue is you know
kind of up and down it's like well uh just because
you're seeing the tokens being burned that's
the data credits being used you know there are
going to be companies that buy a lot of data
credits at once and then use them over time so
you might see some spikes in revenue right yeah
i i think it's like you have customers that come
in like you you definitely have some big customers
that come in and you know do like fairly sizable
orders and that can spike you know you know and
then it kind of goes back to normal and then
you have some some new new customers that come
in and then boom they accelerate right and then
again you see a spike so it will smooth out right
like you know once you have like 50 to 100 big
customers, then it will definitely smooth out
over time. So I'm not too worried about that.
It's just like the normal course of any sort
of growth business. The other thing that will
smooth this out is Beekeeper. Beekeeper is like
a different type of, it's a subscription revenue
product and it has different financial implications
in terms of all that stuff that we're looking
at right now. And that is actually the next category.
Yeah. Let's talk about Beekeeper. Okay. So I
have it pulled up here. Obviously this is for
fleet management tools, but as opposed to a lot
of fleet management tools out there, which by
the way, I know a lot of business owners, they
pay a lot of money for fleet management tools,
but they don't generally have visual aspects
of that. So with this, obviously you have a whole
new type of data set, new insights you can provide.
So yeah, I would love for you to talk about kind
of your differentiators versus other. fleet management
tools and just how big of an opportunity you
think this is? Yeah, no, this is really big.
I mean, so let's first define who the customer
is and define what their problems are, right?
And what they're trying to accomplish. So the
customer is anybody, any business with call it
10 vehicles all the way up to many, many thousands
of vehicles, right? You know, the companies that
have many, many thousands of vehicles that we
all know, right? So that would be like the FedEx's
of the world, the UPS's, even Pepsi. uh i think
pepsi has something like 45 000 different vehicles
right this would be like you know bigger trucks
smaller delivery trucks etc um and so the number
of vehicles that are attached to a commercial
fleet is in the range of 250 million to 350 million
globally so it's a lot and so what the the customer
is trying to really solve is a couple of issues
one is uh hey that's an expensive asset that
i have right that vehicle itself if it gets into
an accident you have obviously the vehicle itself
but you also have the driver you know that could
be a catastrophic situation right so they're
trying it that's about safety obviously and so
how do you keep the driver in safe situations
as much as possible the second thing they're
looking to do is optimize the cost efficiency
of it right so if you send your driver down a
road Which they're going to have to then circle
back in because the road is closed or, you know,
that type of vehicle, because it's too high or
whatever the case may be, can actually access
the delivery point or wherever they need to go.
That is an inefficient use of that asset. Right.
Or even more basic things. Hey, that road over
there sucks and it's going to tear apart your
car. Right. Because of all the road construction
going on here. Here's how you can avoid it. Right.
And then there's like, hey. If this vehicle is
operating in places that it shouldn't be, right,
either the driver is using it for personal reasons
or the driver is taken into like neighborhoods
or places that they shouldn't be going into,
you know, I want to know about that, right? And
so most of the existing systems today are what
I would call nanny cams. So they're just basically
there to annoy the driver, but they don't actually
provide the driver with intelligence, with an
understanding of what's happening. So for example,
if you're a truck driver and you got to pull
into a way station um that inspector can ruin
your day and ruin your week right not because
you're doing anything wrong but just like they
got a thing they want to cause problems for you
blah blah blah they got you know some quota to
meet and so that's issue number one is like look
if there's a way station they want a way around
it right they want to know it's open a and if
so they want the most efficient way around that
Number two is they do have to stop, right, for
whatever reason. They want to know, okay, how
backed up is it, right? Are there two trucks
there? Are there 20 trucks there? That's a meaningful
difference in terms of how much time is this
going to eat out. So let's talk about this in
the context of the B. So the B, you know, it
can do all the basic fleet monitoring stuff.
That's not that hard, quite frankly. But the
really cool part about it is that it can see
and understand the world, right? So it can see,
okay, that's a truck weigh station. okay that's
open okay there's 18 trucks there right and so
it really has that spatial intelligence of what's
happening at a very specific location that can
then be fed back into that driver that gives
them valuable intelligence so it's not just a
nanny cam anymore it's actually like something
they want to use because it's providing them
with real utility and so i think just because
of where we started, which was like, we're fundamentally
a mapping company. We have built this technology
that enables us to see and understand the world
and then feed that data back into the driver
and back into the fleet manager. Okay. And that's,
that's something I do want to dig in more on
because, you know, it has been years now of you
all building out these tools, doing the AI training,
all of that. So how does, how does that work?
That foundation you touched on a little bit,
but digging a little bit more how that foundation
helps. with this new product that you're launching?
Yeah, it's a good question. Okay. So the AI training
really serves two purposes. One is they're training
all the data, right? So upfront, like in my example
of the way station, you know, we need to train,
we need to see many, many, many, many different
examples of what a way station looks like, what
a way station looks like when it's open and so
forth. And so all of that is fed into these training,
into these training models. and the data has
been labeled by all these ai trainers that exist
on our platform today and so our cost to actually
produce the data and then the speed at which
we can do the data as well has rapidly increased
right so yeah we launched the ai trainer platform
about two years ago and so now it's just it's
a much more mature platform and you can just
do a lot more labeling across many more objects
and things in the world so that's step one once
those models are trained up you know utilizing
all this label data then they get deployed to
the b and that will happen over the air right
so you can start to make changes very quickly
introduce new capabilities very quickly okay
so what happens on the b the b does three things
so first thing it does is obviously detect objects
so it says okay i've been trained to look for
speed limit signs or truck way stations or billboards
or whatever it may be right and boom i got it
that's that's a speed limit sign now we need
to then position it right so we need to understand
where it is in the three -dimensional world with
the stereo depth cameras um that's that's the
role of the stereo depth cameras are one of the
roles the key roles is actually to then position
all the objects and understand its relationship
relative to everything else so like a turn restriction
sign if you just know like oh there's a turn
restriction sign That's not very helpful. You
need to know where it is because you need to
know which drive path it's actually associated
with. The third piece about it is spatial intelligence.
Maybe we'll come up with a better name for it.
But the basic idea is, OK, it's yes, it's nice
to know there's a truck way station over here.
Here is where it's located. But now I want to
know whether or not it's open. Right. I want
to know how many trucks there are. Right. And
so that's the spatial intelligence piece, which
is incredibly valuable. and that's where we're
utilizing a lot of the the new ai models that
have come to bear is not just you know being
able to detect objects position objects but also
build a scene description and understanding of
what's actually happening in that scene so all
that data can be informed and sent back to these
drivers and ultimately the fleets the fleet managers
themselves Okay, that's really good context.
And this is, again, this is a massive industry.
I think it's close to $29 billion industry with
fleet management somewhere around there. And
I'd expect it's only going to grow. I'll say
you're kind of opening the door for a new type
of fleet management, I think. And I think with
AI, especially having the ability to make this
visual data really useful. Yeah. And we want
to work with, you know, for example, there's
a company in the logistics space and they said
to us like, hey. you know what's really you know
we'd love to deploy these right on our vehicles
that's a so and then they start asking questions
right it's pretty cool to see like hey uh you
know what's a big problem for us um we're looking
for very specific containers so they do like
a a lot of pickups from ports all across the
united states so like especially like in southern
california as an example and then they drive
them to like various places throughout southern
california whether it be a train station or other
delivery points etc okay and so they're like
you know sometimes we lose track of a very specific
container okay and so it's somewhere right like
where we often and so the so the ability to then
train these models to look for very specific
things so they can find them is really cool right
and so like you almost can imagine like these
like specialized ai agents kind of traveling
through these bees looking for very specific
things that the customer needs in order to solve
an underlying business problem that they have.
And also, I mean, once you work with a business
on a niche use case like this, this is something
you can take to market with other companies too,
right? So that's also pretty cool. And maybe
look for like drones and nuclear waste in New
Jersey or something. But so, yeah, so obviously
this fleet manager, the beekeeper, that's a massive
opportunity, man. Can I, do you want to, because
I know a lot of your listeners are, they're they're
just kind of individual contributors and i think
there's this like tension right which is like
hey they think they're gonna like compete i am
not concerned by that and i say that with 100
sincerity for a lot of reasons one is i mean
just take a place like la right to do all of
the layers of the map that we want to do like
all the way up to like traffic and stuff like
that we just need a lot more devices quite frankly
and so you know we are nowhere near kind of reaching
that saturation point and even with fleets you
know and going aggressive with fleets i don't
see that happening where we're going to reach
that saturation point that that's number one
number two is i i don't think it's healthy right
to have a network where it's just a hundred percent
commercial fleets you definitely want those individual
contributors They just come with like a different
set of passions and motivations that I think
is really important in terms of just building
up a community. So I don't view it as like competitive
the way that some other people do. I think that
they can both operate and be successful and they
bring different things to the table. For sure.
And, you know, having a little bit of diversification
is not a bad thing. Plus, individual drivers
are more likely to cover certain regions, certain
roads that you might not get with commercial
fleets. And the thing I'll add too is for people
that are building a fleet, this is, we'll talk
more about that when we talk about the B, but
this is a huge opportunity, right? You can kind
of tackle this from a couple of different ways,
but A, it's going to be a lot easier to deploy
into fleets, but also businesses, because in
the past, there's been this kind of layer that
you've had to work through with a business owner
and then to the individual employees. Well, with
the B, it's going to be a lot more seamless.
They're just going to have to put it up there.
You don't have to train them on the uploading
of data, et cetera. But also you can use this
tool, this beekeeper tool as maybe a sales tool
to get deployed with some of these fleets. So
I think for entrepreneurs that are looking to
build out fleets, this is a game changer as well,
in my opinion. Yeah, 100%. Like, I think it's
just be a lot. I mean, there's, there's a lot
of people that you would, I mean, you, you've
said this, you know, to me many times, it's like,
look, there's certain types of drivers, I just
can't give the height map or dash cam to because
like, they just, you know, it just won't work.
Yeah. So like, all of a sudden, yes, the BLT
definitely opens up that options where the number
of people that you can, you know, onboard into
your fleet is just, it's just much bigger. Absolutely.
And I think that's good that you addressed that
because I do feel like there was a sentiment
there for a little bit that the focus was going
completely to commercial fleets and completely
away from the individual contributors. So that's
certainly not the case. And I do think it's good
to have a healthy mix of both moving forward.
And I think that's what we'll see. So there's
one more category to touch on as far as potential
revenue looking further out. The third category
is platform. Right. And so the idea there basically
is there's some very early conversations, you
know, with automotive companies about integrating
the B software map AI into their vehicles. Right.
So that their vehicles can both become contributors
as well as consumers. That will take a while
to kind of play out, obviously, because anybody
that we're talking to now, we're literally talking
to them about their 2029 models. Yeah. Right.
And so. There's other types of companies that
we're starting conversations with that are upfitters,
right? So these are people like if you go and
buy whatever, a thousand Ford commercial delivery
vans from Ford, you then take it to a different
company that basically outfits it with all of
your technology, with your branding, with all
of your little nuance configurations. So those
type of companies are interesting because it's
a faster timeline, right? Rather than it being
2029. you know maybe we can do something with
those guys in 2025 or 26 right where they're
taking the underlying technology that exists
within the b and kind of retrofitting it and
integrating it much more deeply into the vehicle
and so that's all about platform stuff so that's
that's one aspect of the platform there's some
other stuff on the platform stuff that i'm gonna
hold back mentioning because it's a it's a big
uh announcement that we're making with another
crypto project in uh in the january time frame
but i think it'll become clear as to like how
that also is another aspect of what we're doing
on the platform side okay cool yeah the automotive
industry moves pretty pretty slowly so i i can
you know 2029 That would make sense with the
five years out. So but that could be a massive
opportunity down the road. So it's good to start
having those conversations now. Right. But the
data solutions, fleet management tools, I mean,
those are massive opportunities that you can
start tackling today. And we're seeing it. That
does bring me to one thing I want to bring up
real quick. This new. MIP, don't need to talk
about this too long, but you guys, that's a 50
% increase in the cost of data credits. So that
should lead to certainly more revenue, more token
burns. Yeah, that's true. I mean, the reason
that we did it is pretty straightforward, which
is like the MapImage API in terms of the amount
of value that BMAPs Inc. or was previously HiveMapper
Inc. is providing on top of the HiveMapper network.
is is more limited right with something like
beekeeper we're providing obviously and building
a tremendous amount of value on top of the data
that we're paying the high mapper Network for
so there you know I think the the it'll be a
little bit different but you know like let's
say we're generating ten dollars sorry you know
10 15 20 dollars of revenue per device from beekeeper
you know, I think the split is going to be a
little bit different, right? But in terms of
the map image API, you know, we're obviously
adding value on top of it. And then there's a
sales cost, the support cost with the customer,
all that type of stuff. But in terms of that
product itself, you know, we thought it was just
appropriate to like, you know, drive more value
into the network. Okay. I mean, I like it, man.
Anything that's going to be more revenue, more
token burn is a good thing. All right. Let's
pivot to the B. This is the topic I think most
people are going to be interested in. We've got
it. Here's the website there. But I also have
some pictures here of some manufacturing that
you posted. So progress is being made there.
It looks like the finished product there. So
what can you tell us about the B, the manufacturing?
How are things looking right now? Things are
looking good. I mean, a lot better than they
were back in June timeframe for sure. In the
June timeframe, I'll just like share everything
so everyone has the context on this. Yeah. In
the like May, June timeframe, things were quite
frankly just broken. They weren't working. And
throughout the summer, we kind of stuck with
it, stuck with it, stuck with it. and then in
september the device we got more and more devices
out there and the data that it was producing
was excellent right and so what i described there
in terms of being able to detect an object you
know properly position the object understand
kind of what dry path is associated with and
then add that um spatial intelligence around
it um starts to work and like you know, after
a while, just kind of looking at the data kind
of got boring. Cause like, obviously like you're
looking for problems in the early days and it
was boring. Cause like there was no problems.
I don't want to say no problems, but there was
like very few problems. Right. And so we're like,
all right, this is pretty, this is pretty awesome.
um so then now the focus has shifted on like
okay how do we produce these and how do we produce
them reliably how do we produce them cost effectively
you know how do we make sure that everyone every
single one that leaves the the manufacturing
line has complete um and very thorough end -of
-line testing for all the different things that
we care about whether it be gps or gnss whether
it be you know depth sensing whether it be compute
whether it be you know thermal so there's just
a tremendous amount of work happening you know
this this past month to kind of gear up that
production line so now i want to caution that
the production line like any production line
when you when you bring it up there's always
going to be little issues right teething issues
okay that needs to be fixed or that needs to
be fixed etc so we are definitely not going to
hit our full production daily output in december
Okay. Probably not even in January. February
is when I think we'll start to hit like our full
production, you know, daily output. So the idea
is that there are some shipping this month. There
will be more shipping in January. Right. And
then February is when you'll start to really
see them kind of flying off the shelves in a
much, much, much faster pace, quite frankly.
How does the manufacturing capacity for the B?
compared to the hdc xi and we talked about this
a little bit before the manufacturing process
itself some of the automation you've added um
you know this time around is quite different
and obviously it took some capital investment
to get there but i'm curious you know what what
that manufacturing process looks like and how
it differs and then how that can lead to more
production. So I think we hit a peak with the
HTCs. We hit a peak of about 200 per day, maybe
two 25 per day. We haven't proven it yet, but
we are cautiously optimistic that we can get
it to at least 375, 400, right? Maybe 500. And
so. that's kind of where we're at in terms of
just, and the question is why. And so there's
fewer actual overall parts in the actual B than
there were in the HTC, which is kind of crazy,
but it's true. And so we've just been a lot more
thoughtful in terms of when we're designing this
thing, how do we design it for assembly? It's
not just like we're going to design it. Quite
frankly, with HTC, we didn't know exactly how
many orders there were going to be. And so it's
kind of crazy to invest a ton of money in terms
of automating everything if there's only going
to be like 3 ,000 or 4 ,000 or 5 ,000. With this,
it was a lot more clear that there was going
to be many, many, many tens of thousands of devices,
hopefully hundreds of thousands of devices. So
it made a lot more sense to actually spend it,
spend the time to do it. There was also, let
me just kind of get into a little bit more details
here. Like in HTC, there was like this glue and
it was very tedious to apply. And then like once
you apply to you, you have to kind of wait for
it to all cure and stuff like that. And so that
chewed up a lot of additional time. Then we had
issues in terms of, you know, the actual boxing
itself, like the boxes that we had for the HTC
were really challenging for our robot to actually
kind of fold properly. So now we have a box that's
much easier for a robot to do. um and so literally
the robot at the very end of that of the process
can take all the different parts that are going
to go into the box so it's not just about the
device itself but there's you know the core there's
the adapter you know the the pamphlet all that
type of stuff and they the robot puts it into
the box and then closes the box right and so
a human and i think they also put like the label
sticker on top of it as well that stripping label
thing yeah before you literally had one person
or in some cases, a couple of people doing all
of that. And now it's all automated and you can
scale it up to infinity or that specific step.
So that's one little detailed example of kind
of how we've just been a lot more thoughtful
about how do you actually build this so that
we can scale it. Yeah, that's huge. And that's
exciting that you are able to scale. And I think
it's fascinating to understand kind of the manufacturing
process because, you know, there's a lot of people
that are out there and be like, oh, yeah, sure.
They're going to scale production. It's like,
OK, well, this is how they're doing it. So I
think it's important to go through that. I do
want to talk about and we've touched on this
a little bit already, but the features. OK, the
difference between the HTC and the B and how
that's going to improve the quality of data.
also with the edge processing then talk about
you know the lte side of things and the value
and getting that data uploaded faster yeah there's
a lot there so um uh let's start with with the
positioning right and so you know at the end
of the day you have to have excellent positioning
of the device because if the device isn't properly
localized in the real physical world then even
if you have amazing object detection even if
you have you know amazing spatial intelligence
everything is going to be off right There's some
things that you can do to correct that, obviously,
but getting that positioning, you know, what
we consider high quality is lane level positioning,
okay? Which is anywhere in the range of, I mean,
people have different definitions of this. You
know, some people think that's five centimeters.
I don't think you, in our use case, at least,
we don't need five centimeters. You know, maybe,
you know, down the road we will, but definitely
not right now. But we feel very comfortable if
we're at air 50 centimeters one way or another,
right? And we're seeing that. And we're seeing
that pretty reliably across most of the devices
in most of the different regions. There's definitely
still going to be issues where like, you know,
you're downtown San Francisco or some other area
that you're not going to get 50 centimeter lane
level positioning, right? um because of the multi
-path issue but anyways that positioning on this
device is just far superior to anything that
we've seen um you know quite frankly like i would
like to have this in my phone because it's just
so much better that's number one number two is
that the combination of the main imager you know
with the compute to be able to do just a tremendous
number of object detections um and be able to
utilize many many more frames than we previously
were with the hdc so the hdc have maxed out at
roughly like eight to nine frames per second
some case cases 10 or 11. here we're dealing
with 30 frames per second and so that means is
that we just have many many more more opportunities
to actually detect objects across many more different
frames right and so especially when you're starting
to travel at high speeds that can become an issue
when you're only dealing with like eight or nine
ten frames per second and then obviously the
stereo depth cameras so we did have stereo depth
cameras on the s model and we learned a lot from
them um the biggest issue with the previous generation
was the baseline which is basically the width
between them was not wide enough um and then
the second issue is the the quality of the of
the imagers themselves or the stereo images themselves
so we increase the resolution on those two in
the in the b to i think 800p um and then it's
wider i think it's 13 centimeters so that's why
the device is wide so basically the device is
thin but wide so the reason for the width has
everything to do with the stereo depth cameras
And what we did was we tried to shrink it down
in terms of height so that just in terms of installing
it, like we felt, and we'll see what the community
feels, we felt that it was better to be thin
so that it didn't obstruct your view, right?
The big issue with the height map or dash cam
was like, it was just like a block, right? And
it obstructed your view. And so we feel like,
yes, we've increased the width. but we've made
the thing. It's actually 20 % thinner than the
S, which was already pretty thin as far as dash
cams come. Yeah, no, I think it's good to understand,
A, the learnings from some of the prior manufacturing,
the new features, how the quality of data is
going to get better. I do want to talk about
the LTE. because i mean there are still some
questions about that i'm seeing out there about
you know what the cost of lte is i knew for one
thing though with the edge processing you know
the goal is to select the data that's valuable
and upload that data instead of having to upload
everything right uh is that that's still yes
the plan okay that should help as far as how
much data needs to be uploaded but um yeah i
mean that opened the door for you to talk about
the lte versus the wi -fi is the wi -fi still
in part know part of the plan moving forward
or not and then uh you know cost of the lte the
lte uh model um both models will do this but
definitely the lt model will be more aggressive
in terms of selecting only the data that it absolutely
wants for the purposes of building the map right
so let's let's break this into categories so
obviously the heaviest data is basically like
all imagery right um and so that's obviously
what the high member dash cam did was just like
all the imagery got uploaded so that that's a
lot of data to go through the lte then there
is uh map features so in other words speed limit
sign stop sign turn restriction sign everything
you see around it that's what we refer to as
just like the map feature metadata and then the
map feature metadata may also be combined with
what's referred to as an image chip right which
is just imagine okay that speed limit sign you
know with a little bit of context around it right
so not the entire image itself but just like
that object with a little bit of context around
it so you can understand where it is and all
that type of stuff and so that that's a little
bit heavier obviously because now you're dealing
with pixels which are obviously heavier um but
still a tiny tiny fraction of uploading all imagery
And then the third thing is what's referred to
as AI event detections. So this is the vehicle
got into some sort of weird situation. Like imagine
a kid running into the street, chasing a ball,
you slam on the brakes, the car starts to aggressively
shake and all that type of crazy stuff. That
would be considered an AI event detection, harsh
braking. And then it would upload. a video segment
of anywhere between 10 seconds all the way up
to about 90 seconds. So it depends on what the
issue is. So that's the third category. Obviously,
that can be heavy, but there's not that many
of them. That doesn't happen that frequently.
So those are the three categories. And so what's
really happening is that the LTE model specifically
is managing how much data can't just upload.
know tens upon tens of gigs every single month
that's crazy and so it's trying to manage all
those three and say okay i need to make sure
that i have enough for ai event sections right
so like i don't want to use too much of my quota
you know of my quota uh you know metadata for
map features it's like you can almost send unlimited
right because it's such a limited amount of data
that you know like every single one boom you
can you can upload you know metadata plus image
chip okay you need to be a little bit more cautious
but not crazy cautious and then the thing that
you need to be the most cautious about is imagery
right and so that's how we kind of think about
it and you know just quite frankly is i think
it'll be a con i don't know constant but i think
at least for the first four to six months of
this process, I think that we'll be constantly
tuning this, right? To make sure that we're being
very, very efficient with that LTE pipe, because
we know it's valuable and we know it's costly,
right? We're not, you know, we don't want to
be cavalier about this. Yeah, I imagine that
there's, you know, a fine line or kind of this
dance, right? You don't want to... pull too much
data but you also don't want to miss out on the
valuable data at the same time right so it will
take some fine tuning as far as the the lte plans
go you know is this something that is going to
be open for you to use like helium if you want
or you know 18t i mean are people going to have
options do you have any idea what the cost is
yeah so in the us for sure like you can use helium
um you can use t -mobile for sure and you know
AT &T I think will probably take AT &T and Verizon
probably take a little bit longer because they
have like a long process in terms of like you
know going through their crazy it's not crazy
but it's they need to validate the device won't
cause them any problems fundamentally Europe
is actually a lot easier Europe is uh I think
the big player there is Vodafone so like any
any network that is on vodafone should work you
know immediately no issues they don't have like
a complicated validation process at all um you
know some of the other carriers i'd have to kind
of look at it one by one um but australia was
the other one they were were prioritizing i forgot
which one we chose i think it's on the website
actually um under tech specs you'll see like
by country or region you know which are the supported
ones the short version is asia uh and europe
are easier in terms of like us validating um
north you know canada mexico us and australia
are harder that's like the short version of it
uh but we want people to obviously have as much
choice as possible as far as like the cost of
monthly plan do you have any idea you know what
that looks like as far as how much data will
be needed yeah it's a good question mean we've
seen i've shared some data about this in discord
so i think there is like some data floating around
there right and then people can then say i think
it was like a couple of gigs per month right
like two two gigs per month or three gigs per
month but it's all there in the discord channel
under b device um that was some relatively preliminary
but it's actually it seems to be holding you
know look i i just think realistically there's
no world in which like we can get people nor
does it make sense to say like oh you need like
a 10 gig plan that's just you know kind of off
the table for us like we wouldn't um and i don't
think that's necessary either i think we're looking
to try to keep it within like one to four gigs
per month that will obviously vary a lot by like
where you're driving and a whole bunch of other
factors how much you're driving etc but that's
kind of the sweet spot that we're looking at
so that people can say okay you know this data
plan that i have is you know depending upon which
part of the world it's you know 10 bucks a month
it's 15 bucks a month it's 20 bucks a month that's
kind of like where we would like it to be and
then as far as The Wi -Fi version. Originally,
it seems like you're pushing more for the LTE.
Is the Wi -Fi version still in the plans moving
forward? Are you guys potentially scrapping that?
What does that look like moving forward, the
LTE versus the Wi -Fi version? Yeah. I mean,
okay, here's my perspective. Are there scenarios
where the Wi -Fi version makes sense? Yes. I
think a couple of people in the Discord community
have been pretty adamant. Like, look. you know
i can do a hot spot i already have other um yeah
i'm a professional truck driver or whatever it
may be and i already have existing uh lt connections
in my car or my vehicle i can just do a quick
hot spot from my wi -fi from the you know from
the b wi -fi version to this why do you know
why are you forcing me to pay for another lt
plan when i don't really need it right i think
that's reasonable okay i think that's not the
vast majority of people also and so my take on
this is yes the wi -fi has a place in the world
or there are some places in the world where lte
connections still suck unfortunately or they're
just outrageously expensive right again there
are there is a role for the wi -fi but i would
like to see the wi -fi version be a minority
percentage of the overall network and explain
to you why I'm going to put the perspective of
the map, right? So if you look at it in the perspective
of the map, obviously it wants real -time data,
right? And it wants to know like every hour and
every day that assuming there's X number of drivers
on the road, that those drivers are all uploading
real -time. With LTE, that's a pretty good assumption,
right? Like if you have, you know, let's just
take LA, if there are 5 ,000 devices that are
on the road every single day, in the LA region,
each driving an average of three hours per day,
whatever it is, you can rest assured that if
the vast majority of those are LTE, then you
can be rest assured that you're going to be getting
real -time data from all of those. Now let's
invert that. So now let's say that 90 % of those
devices in LA, of those 5 ,000 active devices
in LA, are all Wi -Fi. OK, some of them may be
uploading real time. Some of them may not. Right.
But you just don't know. Right. Some days people
are doing the hotspot thing and sure you're getting
the real time data, which is great. But other
times they're not for whatever reason. Or some
of them don't do the hotspot at all. Right. And
then they're just getting they're uploading their
data an hour, two hour, three hour, four hours
later, whatever it may be. That fundamentally
breaks the system when it comes to, you know,
doing all layers of the map. So that's my perspective.
A lot of people will try to argue with that in
terms of like, if my device is providing real
-time data from a Wi -Fi, why does it matter
to you? That's correct for that specific device,
but you have to look at it as a network as a
whole, which if you have a Wi -Fi device, I am
very confident. And I've seen this in the data
because we have the data today that the vast
majority of them are not uploading real time.
There is going to be a difference in rewards,
though, between the LTE and both the HDC and
the Wi -Fi. Do you have any idea right now or
is that still kind of being figured out what
the difference will be roughly in the reward
allocation? So the short version is that I think
the best approach that I've seen. is you know
one that i think mishmir and then josh also kind
of chimed in as well it's kind of a combination
of a couple different ideas but basically if
you're driving with a wi -fi version it will
basically say okay if that wi -fi version was
uploading at least let's call it like 95 of the
time for that period for that weekly period uh
it was uploading um in real time then that will
be treated like almost like an lte version right
now with the caveat that because the lt version
is a little bit more expensive right both in
terms of the cost of the device as well as the
lt plan i think that one should still earn a
little bit more but the wi -fi version will get
pretty damn close to the lt version assuming
that it is uploading you know at least 95 of
its data in real time throughout the entire week
and so what that does is it addresses the problem
of okay this device we don't want devices that
are kind of you know one day they're uploading
real time the next day they're not like that
inconsistency is what we're trying to head off
and so this incentivizes people to basically
be consistent and so you know maybe the the hot
spot you know like hey you already have a fixed
lte thing in there boom it's hot spotted into
that boom every time you get into the vehicle
it all works and it's uploading okay Again, I
think that's a minority of users, but if that's
what's working for you, then yes, you'll earn
pretty close to an LTE, but not 100 % of the
LTE. That's good. I'm sure people were wondering
about that. The last thing before we move on
from the B, there's been talk off and on about
the potential for mapping at night. Is that something
you're still looking at? Yeah. oh definitely
i mean it won't upload imagery i mean it won't
it won't use the imagery at night but in terms
of being able to detect objects okay so here's
what we're seeing in the test is that uh if you're
in a city right with just a lot of light from
you know the street lights and all the buildings
and all that type of stuff then you can actually
get a fair amount of the objects etc right uh
if you're driving in a rural area with no you
know city lights street lights etc then you generally
just get what's directly in front of you. In
other words, you know, the light from the vehicles
lighting up the signs and all the other type
of stuff, you know, that is when you can, you
know, you'll start to be able to detect those
types of stuff. So there will be differences
based upon where you're driving in terms of probably
your total rewards. But yeah, like we definitely
want people driving a night with this thing.
There's a lot of value in terms of like, you
know. crazy shit happens at night, right? Like
road construction happens at night. A lot of
other things happen at night. You know, we want
to be able to see it, understand it, and then
boom, share it with them out. I know I've been
wondering about that. I'm sure people in the
community have been wondering about that. So
I want to move into the regulatory environment
because for the first time ever, really for crypto,
especially in the US, it seems that we have a
clear path. for innovation and crypto in the
us and i'll add i've i've read and i'll let you
uh state whether it's true or not the new cryptos
are if you will david sachs i was an early investor
in hive mapper is that true that is true okay
the thing i'm most curious about though is you
know how was this uh regulatory environment looking
forward going to change things potentially for
hive mapper okay i want to be very careful here
i'm not speaking for david sachs i have not had
any conversations with david recently about this
topic so what i'm sharing with you is my own
perspective and obviously i agree with a lot
of what david sachs is trying to do all right
let's start with like super high level There's
a lot of new technologies in the world, and I
think it is absolutely critical for the United
States to be the leader in those new technologies.
The only way you become the leader in those technologies
is if you allow entrepreneurs, communities, you
know, like the one that we have here with HiveMapper
that's so amazing, to experiment, right? Experimentation,
there's going to be a lot of ideas. Some of them
are going to work. Some of them aren't going
to work. But fundamentally, you have to run through
that experimentation process. And so the issue
with the prior administration was they were not
allowing that experimentation to happen. There
are a lot of frauds and other crap and scams
in the crypto. And I really wish that they would
have spent their time going after that stuff
rather than going after the real projects, the
real businesses, the real interesting technologies,
whether it be things like Ethereum, whether it
be things like Coinbase and many others. My perspective
now is under this new Trump administration is
that it finally gives us the opportunity to run
those experiments right and many of them like
every other technology in the world many of them
will not be successful but there will be some
that will be incredibly successful and those
will be the things that are the next googles
of the world the next apples of the world etc
uh even the next jp morgan's other world right
and so i think that is what's finally finally
happening here and so that that's part one part
two is i don't think many people appreciated
in the community at least in the high mapper
community that projects like hive mapper others
like helium maybe even render etc that they are
based in the united states right like the core
teams for hive mapper is based in the united
states the core team for helium is based in the
united states the same for render etc right and
that really really limited what we could do right
even just limited the words we could use etc
right um and we were effectively just fighting
with one hand tied behind our back and so we're
finally allowing this innovation engine and as
it relates to crypto finally unleashing it right
and finally allowing it to go and build new products
and build new services uh in the crypto world
and so that that's very very exciting and i think
it's great for america i think it's great for
the high mapper community And this is the most
important thing that's happened to HiveMapper,
quite frankly, in the last 12 months. That's
a big statement, man. I have to say, I agree.
Look, blockchain technology is going to be a
huge part of our lives moving forward. I think
that there's a revolution happening right now,
not just in the finance sector, but Deepin's
a great example. of real productivity, right?
And real use cases that leverage that blockchain
technology. So having a clear path, being able
to actually build, hopefully attracting more
people into the US to build here is very exciting.
And overall, I mean, look, everything you're
doing with HighMapper, very exciting. I know
we're coming up on time here, but the revenue
trends, the B, as stated earlier. February is
what we're targeting for full -on production,
but obviously the shipments are about to start
going out here, so very exciting. And I think
that the future is very bright, man. And the
other thing I'll say is you and your team, I
mean, obviously you've been mapping for a long
time, but you guys are working around the clock.
You're very, very committed. The same can't be
said of all projects out there, but you all do
a really good job of constantly grinding. uh
getting these tools built where we're at now
compared to two years ago i mean night and day
um it's been a really cool journey to be a part
of and i think i told you this you know i got
the inspiration for my youtube channel when i
was in san francisco we were sitting in in the
office and you're asking me like you know early
days of hi mapper you're like hey so how are
you building your fleet and i gave you my whole
you know pitch i was using with uber and lyft
drivers and um you know i was out there for a
wedding and i remember being at the wedding that
night thinking to myself like man that i need
to create a youtube channel and try to get the
word out there and help other people get in this
space. So I remember that moment very clearly.
Hard to believe it was a couple of years ago
now. But yeah, man, as always, I really appreciate
having you on. Curious if there's anything else
you want to add before we cut off here. No, I
think this has been an amazing conversation.
I want to thank you, Brad, for everything you've
done for the HiveMapper community. I always appreciate
you as a voice of thoughtfulness and reason.
you know communities especially like one like
ours you know you got a lot of you know high
passion high emotion one way or another and i
think like you've always kind of brought this
even keeled approach to it which uh is much appreciated
so appreciate everything you've done for the
community um and you know love to see that your
channel here is gaining traction which is fun
to see it is it is you're one of my first subscribers
so uh but it's yeah it's cool to see it's growing
hopefully continues on that path uh but man as
always ariel i appreciate the time thank you