The AI That Killed the Cameraman: How Pixellot’s AI-as-a-Service Revolutionized the $0 Upfront Cost Sports Broadcast Economy
We provide a comprehensive expert analysis of Pixellot, a global leader in AI-automated sports production and analytics, which focuses on the underserved "long-tail" market of youth and amateur sports. The core of their business is an end-to-end solution that combines patented AI technology for autonomous video capture with an integrated Over-the-Top (OTT) streaming platform and the VidSwap analytics software. A critical component of their success is the innovative AI-as-a-Service (AIaaS) business model, which offers free hardware installation in exchange for a revenue share, effectively removing the significant capital expenditure barrier for clients. We conclude that Pixellot holds a highly defensible market position against competitors like Veo and Spiideo due to its scale, integrated offerings, and unique financial model, positioning it for substantial future growth. Case studies, such as the SuperSport Schools initiative in South Africa, demonstrate the quantifiable success in cost reduction and revenue generation across global markets.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to the Deep dive. Today we are looking at
something huge. I mean a really foundational
shift happening in sports media all around the world, and it's
not really in the big pro stadiums you might think of.
No, it's happening right now in local parks.
You know, high school gyms, amateur leagues everywhere.
We're talking about the tech, the actual infrastructure that's
(00:20):
kind of making camera crews, well, almost obsolete for a huge
chunk of sports. It's making every single game
visible, finally, from kids soccer games right up to semi
pro leagues, games that were just too expensive to film
before. They're now watchable, they're
analyzable, and, crucially, theycan actually make money.
And the company right at the heart of this is Pixel Lot.
(00:42):
So that's our focus today. We really want to understand how
they pulled this off, how they went from just, you know,
selling hardware to basically becoming this decentralized
nervous system for this massive,mostly ignored part of the
sports world, the long tail. Right, that huge number of games
below the top professional tier.Exactly.
And this isn't just clever automation.
It's about smart business strategy.
(01:04):
It's about using AI, artificial intelligence to smash through
these huge financial walls that were blocking access before and
that. Specific strategic move.
That's really what we want to dig into.
They've managed to take all thisincredible knowledge, the
wisdom, the experience that likeonly top broadcast engineers
used to have, right? And they've embedded it into an
AI system that runs itself. They didn't just build a better
(01:26):
camera. They built well and an economic
engine, an engine that cruciallyremoves that big upfront cost,
the capital expenditure problem for thousands and thousands of
organizations. So we're diving deep into how
they made that jump to what we're calling AI as a service or
AIS, and what that really means for sports, you know, at the
grassroots level globally. OK, let's get into it.
(01:49):
Where do we start? I guess with the tech itself?
How does this actually work? Yeah, we have to start there,
the engineering foundation, because replacing a whole human
production team with essential actually a box on a pole, that's
a well, it's incredibly complex.OK, so when I picture
traditional sports broadcasting,it's like multiple cameras, big
trucks, outside operators, zooming and panning pixel odd
(02:10):
gets rid of all that. So what's the basic hardware
setup? What are the components?
It all starts with their hardware, specifically the
custom designed multi camera head unit.
They call it the Chu, and that usually works together with a
dedicated video processing unit nearby.
Multi camera so it's not just one lens moving around.
No, not at all. That's key.
It's actually a whole set of cameras, fixed cameras, all
(02:33):
working together. OK.
So what's the advantage of having multiple cameras in one
unit like that? They capture this incredibly
wide panoramic view. Think ultra wide.
It sees the entire playing area all at once.
Whether that's a massive soccer pitch or, you know, an indoor
basketball court, The Chu sees everything corner to corner.
(02:53):
I see, so no action is ever truly off camera.
Exactly. Those separate camera feeds are
then digitally stitched togetherby that processing unit.
It creates a single massive, super high resolution image of
the whole field. That complete view is sort of
the raw material for everything else.
OK, that makes a lot of sense. If you have the whole field
(03:13):
covered, you literally can't miss a surprise breakaway or
something happening on the far side.
But like you said, that's just raw data, right?
A giant static image. The magic must be in the
patented AI automation engine that turns that into something
watchable. That's precisely it.
The patented AI engine. That's the brain.
It uses deep learning, computer vision, really advanced stuff to
(03:36):
analyze that huge visual stream constantly in real time.
And what's it doing? What's its main job?
Its primary job is basically to act like a really experienced
human camera operator and a video director all at the same
time, but completely automatically.
It decides intelligently where to electronically zoom, where to
pan across the stitched image, what part of the action the
viewer should actually be focused on from moment to
(03:58):
moment. Right.
Creating that virtual camera movement within the larger
static frame. Now I saw a technical detail
that really jumed out at me. This system has to process every
single frame in less than 10 milliseconds.
Yeah, 10 milliseconds. As someone with an engineering
background, my first thought is wow that's incredibly fast.
(04:18):
How do they achieve that kind oflow latency, especially for
something installed outdoors? Maybe dealing with changing
light or weather. That is where the real
engineering prowess comes in. Yeah, hitting that 10
millisecond mark outdoors reliably, it means the system
has to be unbelievably optimized.
The processing unit isn't just stitching images, it's running
(04:39):
these really complex deep learning models constantly.
Models that are tracking playersthe ball.
Right trajectory, predicting movement, all while filtering
out noise. Things like, you know, shadows
moving, trees swaying, stuff like that.
If the latency was higher, say even 50 milliseconds, you'd
notice the broadcast would feel laggy, jerky.
It wouldn't look professional. So it needs custom hardware.
Definitely custom purpose built hardware and extremely efficient
(05:03):
computing right there at the venue, what we call edge
computing, often using dedicatedGPU's graphics processing units
to handle that intense AI workload right on site.
And the AI isn't just blindly following the ball, is it?
It needs to understand what it'sseeing.
Absolutely. It needs a high level of
semantic understanding. It has to know the difference
(05:24):
between the important players involved in the active play
versus, say, the referee or the coaches on the sideline, or even
just a spectator walking too close to the field.
That sophistication in tracking is essential to make the final
output looks smooth and professional.
OK, that's impressive tech, but what makes it so hard for
someone else to just copy this? Yeah, well that leads us to the
adaptive and learning advantage.This is what they sometimes call
(05:47):
the data flywheel, and it's huge.
This AI isn't static. It wasn't just programmed once.
It's constantly learning. It's trained on this absolutely
massive data set from over 5 million games that pixel lot
systems have produced all over the world. 5 million games,
that's yeah, that's an almost unimaginable amount of training
data. How does that create a
competitive advantage or a Moat as they say?
(06:09):
Well, think about it. Every single one of those
millions of games provides more data, more examples, more edge
cases for the AI to learn from. Maybe it's a really poorly lit
evening game in Northern Europe,or tracking a tiny fast moving
hockey puck against white ice, or figuring out who has the ball
in a confusing rugby scrum. So it gets better with every
(06:30):
game recorded anywhere in the world.
Exponentially better the more varied situations it sees, The
more refined its predictions become, the faster it learns to
handle new scenarios. This constant improvement loop
is why they can claim such a high accuracy rate, something
like 98% accuracy and reliability for a new competitor
starting out. Even with good tech, they simply
don't have access to that scale of real world data to train
(06:53):
their AI. They can't catch up easily.
It's like the AI gets smarter the bigger the network gets.
Exactly, the scale itself becomes the advantage.
And it's not just one generic AIeither.
They specifically calibrate and train the AI models for each of
the 19 different sports they currently support.
That makes sense. Tracking basketball, which is
fast-paced in a confined space, must be totally different from
(07:16):
tracking, say, soccer on a huge field.
Totally different logic, different player formations,
different ball speeds. The AI needs to be tailored, and
that customization even extends to things like built in light
recognition tech so it can adaptquickly if clouds suddenly cover
the sun or if the stadium lightskick on halfway through a match.
So the core technology is specialized, it's tough, and
(07:38):
it's always learning. It essentially turns making
sports video from this big laborintensive hassle into an
intelligent automated service. Precisely and having that super
reliable core tech is what allows them to offer a whole
range of different products because obviously one size
doesn't fit all venues or sports.
Hashtag Hashtag 2 The Pixel Lot product lineup and hardware
components. Right.
(07:58):
So they need different solutionsfor different parts of that long
tail market. How do they breakdown their
product line? You can think about it in two
main categories. First, you've got the fixed
installation solutions for places where the camera stays
put, like permit stadiums or gyms, And second, there are
portable and mobile solutions for when you need flexibility.
OK, let's start at the top end. What about professional leagues
(08:22):
or premium venues that need the absolute best broadcast quality?
For them, Pixel Lot offers the Pixel Lot Prime.
This is their high end system. It's designed specifically for
broadcast level quality. It captures video and ultra high
resolution and crucially at a very smooth 50 to 60 frames per
second. Which is vital for things like
(08:43):
slow motion replays, right? Exactly.
Essential for professional broadcasts and detail analysis.
The prime systems are often usedin bigger venues, sometimes even
alongside traditional manned cameras.
But the prime provides that reliable, automated, always on
capture and can even support multi angle productions.
OK, Now moving down to what sounds like their core market,
(09:05):
the high schoolers, the amateur leagues, the semi pro teams,
where maybe budget and simplicity are bigger factors.
Right. And that's where the Pixel Lot
Show comes in. This is really their workhorse
product. It's widely seen as the industry
leader for this segment. The whole idea behind the show
is set and forget. Minimal fuss.
Minimal fuss. It delivers fully automated HD
(09:27):
capture and streaming. The design focuses on ease of
use, maximizing fan engagement, and enabling monetization with
the least amount of human intervention needed.
A league installs it, sets up their preferences on the
platform, and then the system just handles the production for
every game. You invest the time upfront and
it saves countless hours down the line.
That's the idea. Now, of course, not every game
(09:48):
happens at the home stadium. Teams play away games.
They practice in different locations.
Yeah, how would they handle thatneed for mobility?
Capturing stuff that isn't in a fixed venue.
That's where the portable solutions come in.
The main one here is the Pixel Lot Air NXT.
Think lightweight battery operated camera.
Easy to carry around. Exactly design for simplicity
for on the You record the footage and it still gets
(10:17):
automatically tracked by the AI,recorded and uploaded for
analysis later. So it extends that capture
capability beyond just the main venue.
It helps film basically everything that's relevant.
Right, democratizing the abilityto film all the important
events. And then there's another really
interesting angle for accessibility, which is letting
people use their own cameras. This is the Pixel Lot UU
(10:40):
solution. It's more consumer facing.
It allows basically anyone, a parent, a coach, A-Team manager
to use their own wide angle action camera, I think like a
GoPro or something similar. So I could just use my GoPro?
Yeah, you record the game with your own camera, capture that
wide view, then you upload that raw footage to the Pixel Up
platform, and their AI then processes your footage, applying
(11:02):
the automated production, the player tracking, generating
highlights, the whole deal, but from your consumer camera
footage. That's a really smart way to
lower the barrier to entry. People use gear they already
have. It's a very low friction way for
people to get started within their ecosystem.
Now, we talked about these beingfixed installations.
Often they're out there in the elements.
How tough is this hardware? Can it really handle being
(11:24):
installed permanently outdoors? Oh yeah, robustness is
absolutely critical for these unattended systems.
The fixed models like their S1 and S2 cameras, they boast an
IP66 rating. What does IP66 mean in practical
terms? It means they are completely
sealed against dust and grit andthey can withstand powerful
water jets, so heavy rain, snow,sleet, it's not an issue.
(11:47):
They're properly weatherproof, and the temperature tolerance is
pretty staggering too. Some models have internal
heaters and can operate reliablyin temperatures ranging from a
freezing manina 50°C up to a scorching 50°C.
Wow -50 so from like Siberia to the Sahara basically.
Pretty much they're engineered to be installed and then largely
(12:08):
ignored, just reliably doing their job day in day out
regardless of the conditions. OK.
So we've got this really robust specialized hardware built for
the elements and this incrediblysophisticated AI brain running
the automated production. But you mentioned earlier the
real value isn't just the camera, it's the whole package,
right? Everything that happens after
(12:28):
the video is captured. That is absolutely the core
insight here. Pixel Lot isn't really in the
business of just selling cameras.
They're selling sports organizations big and small,
basically an entire media operation in a box.
The value proposition goes way beyond just capture.
It includes distribution, monetization and deep analytics.
It's an A-Z solution as they say.
(12:49):
Hashtag tag tag 3. The end to end solution
ecosystem. OK, let's break that down.
Start with distribution. How does say, a local high
school Football League, which has probably 0 broadcasting
experience, actually get its games out to its audience?
They do that using Pixel Lot's integrated OTT platform.
OTT just means over the top delivering video directly via
the Internet, bypassing cable orbroadcast channels.
(13:12):
It's like a streaming service. Exactly.
But it's a white label service. That means the sports
organization gets to put its ownbrand on it.
It could be the Maplewood High athletics channel or the City
Youth Soccer network. It becomes their own dedicated
branded channel. Their own little TV network,
essentially. Pretty much for delivering both
live games and on demand contentlike replays or highlights
(13:34):
straight to their fans wherever they are.
No need for complicated deals with big broadcasters.
And this is critical, especiallyfor that AIS model we talked
about. This platform has to enable
monetization, right? It needs to potentially pay for
itself. How does it do that?
Yeah, the platform is built fromthe ground up with monetization
in mind. It allows leagues to set up
(13:55):
multiple ways to generate revenue.
They can offer subscription models, maybe an all Access
season pass or pay-per-view for a specific championship game.
OK, subscriptions make sense. What else?
They can also embed advertising.Think pre roll ads that play
before the stream starts, mid roll ads like commercial breaks
during halftime, or even banner ads displayed on the screen.
(14:17):
And finally, they can sell dedicated corporate sponsorships
for their channel or specific broadcasts.
So it gives these organizations,which maybe you're only spending
money on video before, if they could even afford it, the tools
to actually turn video into a revenue stream.
That's the goal. Turn it from a cost center into
a potential profit center. OK.
Distribution and monetization covered.
(14:39):
Now let's pivot to the coaching side, performance improvement.
You mentioned Vidswap Analytics earlier.
Why was acquiring an analytics company like Vidswap so
important for Pixlot? They recognize that just
creating the video wasn't enough, especially for this long
tail market focused on development.
Analysis is absolutely crucial for coaches and players to
actually improve. NID Swap, which they bought back
(15:01):
in 2019, is now fully baked intothe Pixelot platform.
And what does vid swap do? How does it help a coach?
Its main job is to automate whatused to be an incredibly
tedious, time consuming manual process, breaking down game
film. You mean like tagging every
single play, every shot, every turnover?
Exactly. Traditionally a coach or maybe
(15:21):
an assistant or even a student manager would spend hours and
hours manually watching the footage and tagging all those
key moments. Vidswap uses AI to do that
automatically. How does the AI know what to
tag? It analyzes the game footage,
identifies players, tracks the ball, and understands the flow
of the game based on the specific sports rules and
(15:42):
commonplace. It then automatically tags key
events, shots, passes, turnovers, defensive formations,
whatever is relevant for that sport.
And it presents that informationto the coach.
Yes, it provides coaches with really actionable data-driven
insights. Things like in depth team and
player statistics, breakdowns ofthe game segmented by play type
like show me all our third down conversions, and visual tools
(16:04):
like shot charts showing where shots were taken from or heat
maps. A heat map could instantly show
a basketball coach where their team is most effective
offensively, or where the opponent is scoring most easily.
That's powerful stuff. And how quickly does this
analysis become available after a game?
That's another key benefit speed.
(16:24):
Typically, the full breakdown and analytics are available
within 4 to 8 hours after the game finishes. 48 hours.
That's incredibly fast compared to manual methods.
It's transformative. It means a coach can review the
game, identify issues, and plan the very next practice session
based on objective data almost immediately.
It maximizes the impact of the analysis.
(16:44):
It also makes it easy to exchange video with opponents
for scouting and even supports live tagging during the game if
needed. OK, so Vidswap tackles the
tactical side. Then there's the automated
highlight generation. This seems more geared toward
visibility, engagement, sharing.How does the AI create highlight
reels that actually look good? It uses different AI models,
these ones trained to recognize moments of high excitement or
(17:05):
importance in the game. Think goals, amazing saves, long
touchdown runs, I mean a buzzer beater shot.
It automatically identifies these key moments.
And clips them together. Right.
It curates them and compiles them into ready to watch
highlight packages or condensed game recaps.
This saves teams a massive amount of time and money
(17:26):
compared to having someone manually sift through hours of
footage to find and edit the best bits.
The time saving alone must be huge.
It really is, but the really clever part strategically is how
it optimizes these highlights for how people actually consume
media today. You mean for social media?
Exactly. The system doesn't just create a
standard landscape video clip, It automatically reformats and
(17:47):
resizes highlights specifically for platforms like Instagram
Reels, TikTok, YouTube Shorts, meaning it generates clips in
vertical portrait mode. The tall, skinny video format.
Yes, Ready made for mobile viewing and sharing on those
platforms where, frankly, the younger audience and players
actually spend their time. It makes sharing those Wow
moments incredibly easy, boosting player exposure and fan
(18:11):
engagement instantly. That's actually brilliant.
It closes the loop, takes the raw capture, puts it on a
branded channel, analyzes it deeply for coaches, and packages
the best bit perfectly for social media buzz.
That really is an A-Z ecosystem.It covers the whole life cycle
of the content, hashtag, hashtag4, the market and core value
proposition. And this entire comprehensive
(18:31):
ecosystem, it was clearly built with a very specific market in
mind, that long tail you mentioned earlier.
Absolutely. The whole strategy revolves
around addressing the economics of the market that traditional
broadcasters basically ignored. So let's be crystal clear on the
strategic thinking here. When we say long tail, we're
talking about everything below the absolute top tier of pro
sports, right? Yes, it's the literally millions
(18:55):
of games played every year in youth leagues, amateur clubs,
high schools, colleges, semi professional setups, games where
the cost of sending a traditional broadcast crew was
just completely prohibitive. Pixlot CEO Duron Gerstel he
doesn't see this as small fry. He calls youth sports a billion
dollar investment opportunity. A billion dollars?
(19:18):
Wow. Do we have a sense of the scale?
Like how many people are involved?
The numbers are huge. Just in the United States alone,
estimates suggest over 27,000,000 young people
participate in organized youth sports.
Think about that. 27,000,000 athletes, plus their parents,
grandparents, coaches, friends. It's a massive, highly engaged,
but very fragmented audience. An audience that was largely
(19:39):
invisible from a media perspective before.
Pretty much. And Pixel Lot's focus on this
underserved market has led to really impressive global reach.
What kind of numbers are we talking about for their
footprint? They've got over 35,000 of their
camera systems installed across something like 77 countries now.
Their penetration in the US highschool market is particularly
strong. They're in over 8000 high
schoolers. 8000 high schoolers. How did they manage that kind of
(20:03):
scale in the USA? Lot of that comes through
strategic partnerships. A key one is with the NFHS
network. That's the National Federation
of State High School Associations.
The NFHS network uses Pixelot technology extensively to stream
a mind boggling number of games,Over 70,000 high school games
per month. 70,000 a month. OK, the scale is undeniable.
(20:25):
Let's talk about the value proposition then.
Who benefits most? Starting with the athletes
themselves, how does this changethings for young player?
For the individual athlete, it fundamentally comes down to
exposure. Suddenly every game they play
can be professionally captured, and those automated highlights
we talked about, they get generated automatically.
Creating a ready made recruitment reel.
(20:46):
Precisely that professional looking video content is
absolutely essential for collegerecruitment these days, or for
getting noticed by scouts at higher levels.
It fulfills that promise that theoretically every athlete
should have the chance to be seen, celebrated and scouted, no
matter where they live or how small they're club is.
It removes geography as a barrier to visibility.
OK, huge value for the players. What about the coaches?
(21:09):
We touched on Vidswap, but what's the overall competitive
edge? Beyond the deep analysis from
Vidswap, it brings consistency and objectivity to performance
review. Coaches get reliable footage and
data from every single game, notjust the ones they managed to
film poorly with a phone. This allows for much better
strategic planning, tracking player development over a whole
(21:30):
season, and scouting opponents with a level of detail that used
to be reserved for, you know, top college or pro teams.
Yeah, makes sense. And finally, for the leagues,
the schools, the clubs themselves and their fans,
what's the core value there? For the organizations, it really
boils down to cost. Democratization, by automating
the production pixel law, dramatically lowers the cost of
(21:51):
creating high quality video cones.
We're talking potentially eliminating the need for crews
that cost hundreds or even thousands of dollars per game.
Removing that massive financial hurdle.
Exactly. It takes away the single biggest
reason why these leagues couldn't broadcast their games
before. And for the fans, the parents,
the alumni, the local community,the value is engagement.
(22:12):
They can suddenly watch games live or on demand from anywhere
in the world. Grandma living across the
country can watch her grandson score a goal.
It strengthens those community ties and allows people to
support their teams even if theycan't physically be there.
But all of this hinges on the organization's actually being
able to afford and adopt the technology in the 1st place,
which brings us back to that really crucial business model
(22:35):
innovation. The AIS model?
Yes, that's the linchpin that makes the whole thing work at
scale. Hashtag tag tag 5.
The AI as a service. AIS, business model innovation.
OK, this feels like the absolutecore of their success.
You said they pivoted from a standard model to AI as its
service AISS. Why was the old way, maybe like
a traditional software as a service ISS model, not quite
(22:56):
right for this market? The big problem was the hardware
cost. Even with a subscription for the
software or platform, the organization still had to buy
the physical camera system itself plus pay for
installation. That's a significant upfront
capital expenditure or CapEx. Right, that initial lump sum
payment, how much we talking potentially?
(23:17):
Oh, it could easily be thousands, maybe even 10s of
thousands of dollars for a good fixed installation, depending on
the venue complexity. For a typical high school
athletic department or a small town amateur league, finding
that kind of cash upfront is, well, it's often impossible.
Their budgets are already stretched thin.
So that CapEx requirement was acting as a major bottleneck
(23:37):
slowing down adoption. A huge bottleneck.
It meant long budget approval processes, grant applications,
fundraising. It just made everything slow and
difficult. OK.
So how does the AISS structure directly attack that CapEx
problem? It obliterates it under the AIS
model. PIX a lot provides the hardware
completely free of charge to qualified facilities, and they
(23:57):
often cover the installation costs as well.
Wait, free hardware and free installation?
That sounds like a massive risk for Pixel lot to take on.
What's the catch? What do they get in return?
There's no catch really. It's a different kind of deal.
It's a revenue sharing agreement.
Pixel Lot takes on the upfront cost and risk, and in return,
the client organization agrees to share the money they make
(24:19):
from the platform. Sharing the revenue from
subscriptions, ads, etcetera. Exactly pixel lot that typically
takes a significant percentage of that revenue.
The numbers vary, but it can be up to 50% of the income
generated from subscriptions, pay-per-view sponsorships and AD
renewals facilitated by their OTT platform.
Wow, up to 50%. That fundamentally changes the
(24:41):
whole relationship, doesn't it? It's not just selling them a
product anymore. It's not a vendor client
transaction. It becomes a true partnership.
Pixel Lot is now financially invested in the success of that
league's streaming and monetization efforts.
If the league does well and makes money, Pixel Lot does well
too. Their incentives are completely
aligned. Perfectly aligned.
If the client struggles to monetize, Pixel Lot doesn't make
(25:03):
much money from that specific install, but they still have
their hardware deployed, gathering valuable game data to
train their AI and expanding their strategic footprint.
But the key is this model instantly removes that upfront
cost barrier. Making adoption almost
frictionless for the client. Right, the school or league gets
a state-of-the-art production system for essentially $0.00
(25:24):
upfront cost and they immediately have the tools to
start generating revenue. It bypasses the biggest hurdle
that was killing adoption in this market.
Let's really drive this home. Can you contrast the financial
risk and potential return on investment ROI between the old
way and this AIS model? OK, traditional production,
super high risk. You've got significant upfront
(25:46):
cost for equipment, let's say 2000 to $5000 or more just for
basic gear. Then you have high operational
cost for every single game. Hiring a camera crew, maybe an
editor, that could be 500 to $1500 per game.
The organization pays all that before they sell a single stream
or ad. A big gamble, especially for
smaller organizations. Huge gamble.
Now compare that to Aias $0.00 upfront cost.
(26:10):
The operational cost is basically just the revenue
share. Video production instantly flips
from being a guaranteed high risk expense into being a low
risk potential profit center. That strategic shift in the
business model is arguably just as innovative as the AI
technology itself. It's what unlocked the massive
scale and. That scale, powered by this
(26:31):
clever business model, must givethem a serious edge when
competing against other companies trying to crack this
market. Absolutely.
It positions them very differently.
Hashtag hashtag 6. The competitive landscape and
market differentiation. OK, so Pixel Lot isn't alone and
seeing the potential here, this whole automated sports video
market seems to be heating up. Oh, it's booming and it's driven
(26:51):
by those two big forces we've discussed.
Firstly, the massive cost savings.
Organizations report saving up to 67% compared to traditional
production methods. And secondly, just the sheer
demand from fans and families for access to this content.
So who are the main competitors Pixel Lot is up against in this
space, and how does Pixel Lot stand out?
There are a few key players. The names you hear most often
(27:13):
are probably Vio, Hurdle and Spedio.
Let's take Vio first. OK, Vio, what's their angle?
Vio is really well regarded for its portability.
There Vio Cam, especially the newer models like the Vio Cam 3
is known for being lightweight, easy to set up, very user
friendly, so they're quite attractive for individual teams
or smaller clubs who maybe just want simple capture for training
(27:34):
or away games. But the business model is
different from pixel lots AIS, right?
Very different. VO operates on a more
traditional model. You buy the camera hardware
upfront, that's your initial capX, and then on top of that you
have to pay a mandatory subscription fee, which is
tiered based on features and canrange from say $67 up to maybe
$250 a month, often billed annually.
(27:55):
So the client still has that upfront cost barrier and a fixed
recurring cost regardless of whether they monetize.
Exactly. Veo makes money on the hardware
sale and the subscription, whilethey excel at portability.
If you compare the actual video and audio quality or the
sophistication of the end to endplatform, including the OTT
streaming and monetization tools, Pixelot often offers a
(28:18):
more robust integrated solution,especially with the AIS model
removing that initial financial hurdle.
OK, got it. Next up, Hurdle.
Hurdle's a huge name, particularly in American high
school and college sports, right?
Mostly known for analysis. Dominant in analysis, Hurdle's
historical strength and where they built their reputation is
definitely in their analysis platform, video exchange tools
(28:40):
and scouting features. Their whole ecosystem for
reviewing game film is incredibly popular.
They do have their own automatedcamera, the Huddle Focus Flex.
But it's less of an end to end broadcast solution.
Generally, yes. It feels more geared towards
capturing footage for their analysis platform rather than
being a set and forget broadcastand monetization engine like
(29:00):
Pixel Lot Show seems to be, and Pixel Lot recognized this.
By acquiring Vidswap and integrating that powerful
analysis capability, they effectively challenged Huddle's
main advantage. Now Pixlot can offer both the
automated content creation and top tier analysis within one
ecosystem. Neutralizing huddles, core
strength. OK.
And the third one you mentioned was Speedo.
(29:21):
How do they compare? They sound perhaps the most
similar technologically. They are probably the most
direct competitor in terms of offering a truly similar end to
end platform. Spideio has their Speedo perform
for analysis and spideo play forautomated production and
broadcasting. They use similar principles,
panoramic capture, AI tracking, they call it auto follow.
Technologically, they are very capable and offer a compelling
(29:44):
alternative. So if Spideio offers a very
similar tech stack, an end to end solution, what gives Pixel
Lot the edge? What's the key differentiator
there? It really comes down to two main
things, both stemming from PixelLot's execution and strategy.
First scale Pixel Lot simply hasa much larger global installed
base, those 35,000 plus systems we mentioned.
(30:06):
This means their AI data flywheel is spinning much much
faster. More games, more data, faster
learning. Exactly.
Their AI gets exposed to a vastly wider range of sports,
lighting, conditions, play styles, you name it, across 77
countries. This accelerates the refinement
and reliability of their AI at apace that's hard for competitors
(30:26):
with smaller footprints to match.
And the second differentiator? Arguably the most powerful one.
Let me guess the AIS model again.
You got it. The AIS business model.
While VO, Hurdle and Speedo generally still require the
client to put up significant capital upfront for hardware or
commit to fixed monthly annual subscription fees from day one,
Pixel Lot's $0.00 upfront cost option fundamentally changes the
(30:49):
risk calculation for the customer.
It makes adopting their platformthe path of least financial
resistance for thousands of schools and leagues worldwide.
So it's not just about having great tech, it's about having
the business model that makes that tech accessible and
scalable for the target market. That's the winning combination.
The business strategy unlocks the full potential of the
technological advantage. OK, this all sounds incredibly
(31:12):
powerful in theory. Let's bring it down to earth.
How has this actually played outin the real world?
Can we look at some specific examples of the impact?
Absolutely, because the real test is whether this model can
overcome real world challenges in diverse markets and actually
deliver results. Hashtag tag tag 7.
Real world impact and global case studies.
(31:32):
Where's a good place to start? I know you mentioned the Super
Sports Schools initiative in South Africa.
That sounds like a major undertaking.
It's a fantastic case study. It was a partnership between
Pixel Lot and Super Sport, whichis the leading broadcaster
across Africa. The big challenge there was
twofold the extremely high cost of traditional sports
production, which meant very fewschool sports ever got covered,
(31:54):
combined with some unique infrastructure hurdles.
But there's a huge youth population passionate about
sports. Massive and very digitally
connected, but traditional broadcasting just couldn't scale
down cost effectively to cover the sheer volume of school
sports. Pixel Lots model was basically
tailor made to tackle that exactproblem, rapidly increasing
coverage across a huge underserved market.
(32:16):
And did it work? What were the tangible results?
The results were pretty dramatic.
The sheer volume of content justexploded.
For schools participating in theprogram, the average number of
live events they broadcasted perseason jumped from maybe 20 up
to over 100. Wow, A5 fold increase just like
that. A5 fold increase generated
autonomously and critically. It expanded the reach.
(32:39):
They went from being able to cover maybe 20% of schools with
traditional methods to having a pathway to reach 100% of schools
with the automated system and the audience responding.
The Super Sports Schools app sawover 160,000 downloads, proving
that the demand was absolutely there.
That's incredible validation. Now you mentioned infrastructure
challenges, things like unreliable electricity, maybe
(33:01):
spotty Internet. How did the technology handle
that? Yeah, that's where the hardware
robustness we talked about becomes crucial.
The Pixel lot systems deployed there included features like
strong battery backups, so if there were power outages or load
shedding, which can be common, the cameras could keep recording
uninterrupted. Smart and Internet.
The streaming tech is also optimized for challenging
(33:24):
conditions. It has a surprisingly low
minimum bandwidth requirement, needing only about 5 megabits
per second upload speed, and it can run effectively over
standard 3G or even 4G mobile networks, which is vital in
areas where fixed line broadbandmight be unreliable or
unavailable. So the tech itself was designed
to overcome those specific emerging market challenges.
(33:46):
Did it also help with funding attracting sponsors?
That's often a huge issue for smaller leagues.
Yes, that was another major benefit.
Historically, it was really hardfor school or amateur leagues to
attract big corporate sponsors because they just couldn't
provide reliable data on who waswatching or how many people were
watching. No verifiable audience metrics.
Exactly. But Pixel lot centralized OTT
(34:07):
platform changes that. It generates precise, measurable
data. Viewership numbers, audience
demographics, engagement times. Suddenly the league can go to
potential sponsors with hard data proving their reach and
impact. This verifiable data is gold for
attracting major sponsors who previously had no way to justify
investing in grassroots sports. It makes the invisible audience
(34:29):
visible and therefore bankable. Turning viewership into a
quantifiable asset that's powerful now.
The pandemic must have been a major Test case too.
Suddenly no fans in stadiums. The pandemic, ironically, became
a massive accelerator for adoption.
With stadiums empty, streaming suddenly became the only way for
many clubs to connect with fans and, crucially, generate
(34:51):
revenue. We saw this clearly with smaller
clubs, for instance in the Scottish Football League.
What happened there? Well, you had clubs like, say,
Albion Rovers, smaller communityclubs whose entire financial
model relied heavily on ticket sales, match day revenue.
When that disappeared overnight due to lockdowns, they were
facing genuine existential threats.
They could have gone out of business.
Easily, But by quickly adopting the Pixel Lot streaming model,
(35:13):
offering season passes or pay-per-view access to their
games online, they were able to create a vital new revenue
stream almost instantly. Reports indicated that
collectively, the smaller Scottish clubs generated
significant sums. Figures up to 200,000 lbs were
mentioned in what was essentially game saving revenues
directly from streaming during that period. 200,000 lbs for
(35:36):
small clubs, that's absolutely massive.
That's the difference between surviving and folding.
Now just to clarify, was that figure the total money brought
in the gross revenue or was thatafter pixel lot took their
revenue share? That figure typically represents
the gross revenue generated through the platform from
subscriptions and PPV sales. So yes, Pixel Lot would take
(35:57):
their agreed upon revenue share from that maybe up to 50%.
But even netting, say, £100,000 or more would still be an
absolute lifeline, a completely new and substantial income
stream that these clubs simply didn't have before.
It proved the model wasn't just a nice to have, but potentially
a critical component of financial stability, especially
for organizations operating on tight margins.
Exactly. It established streaming as a
(36:20):
scalable long term commercial Ave. for these non elite clubs.
Pandemic or not, it showed the AIS model could deliver tangible
financial results globally. Providing stability, driving
growth, improving access. It really does seem like the
business model innovation is just as disruptive, if not more
so, than the AI tech itself. They work hand in hand, one
(36:43):
enables the other at scale. Hashtag hashtag 8, The future
outlook and final thought. So looking ahead, where does
this technology go next? Pixel Lot and its competitors
have cracked automated production.
What's the next evolution? The trajectory seems to be
moving towards even more intelligent, more autonomous
systems. We're starting to hear the term
agentic systems being used. Agentic systems.
(37:04):
OK, unpack that for us. What does that mean in this
context? It sounds a bit sci-fi.
It's not quite robots taking over, but it means the AI
evolves from just reacting like following the ball to
proactively acting on behalf of the user to achieve more complex
goals with less and less human input needed.
So moving beyond just filming the game.
Right. Imagine an AI that doesn't just
(37:24):
produce the broadcast feed, but can also, for example,
automatically generate multiple different highlight packages
tailored for specific social media platforms.
Tag specific tactical plays in real time based on the coach
predefined criteria. Perhaps even automatically
create personalized video summaries for each player on the
team after the game, and distribute all of this content
(37:46):
intelligently according to the league's communication strategy.
So the AI becomes less of a tooland more of a like an automated
production assistant or even a content strategist.
Exactly. It takes on more of the complex
workflow, freeing up humans to focus on higher level tasks.
And for the viewers, the fans, the next big frontier is likely
personalization. How so?
(38:07):
What could that look like? Remember, the system initially
captures that ultra wide panoramic view of the entire
field. That raw footage contains way
more information than what's shown in the standard broadcast
feed that follows the main action.
So you could potentially access that other information.
That's the idea. Future systems could allow fans
to actually choose their own viewing experience.
(38:28):
Maybe you want to switch to a view from behind the goal for a
replay, or perhaps you want to follow your favorite player
exclusively. Just tell the system and the AI
dynamically generates a feed that keeps that specific player
centered even when they're off the ball.
That's like giving every single viewer their own personalized
director's cut of the game, all derived from that single initial
(38:49):
automated capture. Precisely Highly efficient,
highly personalized. And this all ties back into what
seems to be Pixel Lot's ultimateambition.
They're not just trying to sell cameras or software, they appear
to be aiming to become the essential content and data
infrastructure underpinning the entire global long tail sports
economy. The operating system for
grassroots sports. Almost.
(39:10):
You could think of it that way. By making video capture
accessible, providing deep analytics, and enabling viable
monetization, they're a poweringpotentially millions of athletes
and thousands of leagues at the grassroots level to develop
talent, gain exposure, secure funding, and just generally
reach their full potential. It really is that powerful
combination we've discussed the sophisticated AI capture, the
(39:34):
integrated OTT platform for distribution and monetization,
the vidswap analytics providing the coaching edge and all of it
unlocked and made accessible at scale by that clever risk
reducing AIS business model. It's a complete ecosystem and it
has without doubt fundamentally changed the economics of how
sports content outside of the very top tier is produced and
(39:56):
consumed. Absolutely.
Which brings us to our final thought, the big question.
We want to leave you, the listener pondering long after
this deep dive finishes. We've seen the technologies
here. It's affordable, it's scalable.
The real question now becomes. What happens to the future of
sports globally when the opportunity for every single
athlete, no matter how small their club, how remote their
town, or how limited their team's budget to actually be
(40:19):
seen to be celebrated and potentially to be scouted,
transforms from an expensive geographical lottery into a near
universal technological guarantee?
What does that truly global meritocracy of talent discovery
look like? That's a powerful question to
consider. A very powerful thought indeed.
We've taken the deep dive. Thanks for joining us, and we'll
catch you on the next one.
Welcome to the Deep dive. Today we are looking at
something huge. I mean a really foundational
shift happening in sports media all around the world, and it's
not really in the big pro stadiums you might think of.
No, it's happening right now in local parks.
You know, high school gyms, amateur leagues everywhere.
We're talking about the tech, the actual infrastructure that's
(00:20):
kind of making camera crews, well, almost obsolete for a huge
chunk of sports. It's making every single game
visible, finally, from kids soccer games right up to semi
pro leagues, games that were just too expensive to film
before. They're now watchable, they're
analyzable, and, crucially, theycan actually make money.
And the company right at the heart of this is Pixel Lot.
(00:42):
So that's our focus today. We really want to understand how
they pulled this off, how they went from just, you know,
selling hardware to basically becoming this decentralized
nervous system for this massive,mostly ignored part of the
sports world, the long tail. Right, that huge number of games
below the top professional tier.Exactly.
And this isn't just clever automation.
It's about smart business strategy.
(01:04):
It's about using AI, artificial intelligence to smash through
these huge financial walls that were blocking access before and
that. Specific strategic move.
That's really what we want to dig into.
They've managed to take all thisincredible knowledge, the
wisdom, the experience that likeonly top broadcast engineers
used to have, right? And they've embedded it into an
AI system that runs itself. They didn't just build a better
(01:26):
camera. They built well and an economic
engine, an engine that cruciallyremoves that big upfront cost,
the capital expenditure problem for thousands and thousands of
organizations. So we're diving deep into how
they made that jump to what we're calling AI as a service or
AIS, and what that really means for sports, you know, at the
grassroots level globally. OK, let's get into it.
(01:49):
Where do we start? I guess with the tech itself?
How does this actually work? Yeah, we have to start there,
the engineering foundation, because replacing a whole human
production team with essential actually a box on a pole, that's
a well, it's incredibly complex.OK, so when I picture
traditional sports broadcasting,it's like multiple cameras, big
trucks, outside operators, zooming and panning pixel odd
(02:10):
gets rid of all that. So what's the basic hardware
setup? What are the components?
It all starts with their hardware, specifically the
custom designed multi camera head unit.
They call it the Chu, and that usually works together with a
dedicated video processing unit nearby.
Multi camera so it's not just one lens moving around.
No, not at all. That's key.
It's actually a whole set of cameras, fixed cameras, all
(02:33):
working together. OK.
So what's the advantage of having multiple cameras in one
unit like that? They capture this incredibly
wide panoramic view. Think ultra wide.
It sees the entire playing area all at once.
Whether that's a massive soccer pitch or, you know, an indoor
basketball court, The Chu sees everything corner to corner.
(02:53):
I see, so no action is ever truly off camera.
Exactly. Those separate camera feeds are
then digitally stitched togetherby that processing unit.
It creates a single massive, super high resolution image of
the whole field. That complete view is sort of
the raw material for everything else.
OK, that makes a lot of sense. If you have the whole field
(03:13):
covered, you literally can't miss a surprise breakaway or
something happening on the far side.
But like you said, that's just raw data, right?
A giant static image. The magic must be in the
patented AI automation engine that turns that into something
watchable. That's precisely it.
The patented AI engine. That's the brain.
It uses deep learning, computer vision, really advanced stuff to
(03:36):
analyze that huge visual stream constantly in real time.
And what's it doing? What's its main job?
Its primary job is basically to act like a really experienced
human camera operator and a video director all at the same
time, but completely automatically.
It decides intelligently where to electronically zoom, where to
pan across the stitched image, what part of the action the
viewer should actually be focused on from moment to
(03:58):
moment. Right.
Creating that virtual camera movement within the larger
static frame. Now I saw a technical detail
that really jumed out at me. This system has to process every
single frame in less than 10 milliseconds.
Yeah, 10 milliseconds. As someone with an engineering
background, my first thought is wow that's incredibly fast.
(04:18):
How do they achieve that kind oflow latency, especially for
something installed outdoors? Maybe dealing with changing
light or weather. That is where the real
engineering prowess comes in. Yeah, hitting that 10
millisecond mark outdoors reliably, it means the system
has to be unbelievably optimized.
The processing unit isn't just stitching images, it's running
(04:39):
these really complex deep learning models constantly.
Models that are tracking playersthe ball.
Right trajectory, predicting movement, all while filtering
out noise. Things like, you know, shadows
moving, trees swaying, stuff like that.
If the latency was higher, say even 50 milliseconds, you'd
notice the broadcast would feel laggy, jerky.
It wouldn't look professional. So it needs custom hardware.
Definitely custom purpose built hardware and extremely efficient
(05:03):
computing right there at the venue, what we call edge
computing, often using dedicatedGPU's graphics processing units
to handle that intense AI workload right on site.
And the AI isn't just blindly following the ball, is it?
It needs to understand what it'sseeing.
Absolutely. It needs a high level of
semantic understanding. It has to know the difference
(05:24):
between the important players involved in the active play
versus, say, the referee or the coaches on the sideline, or even
just a spectator walking too close to the field.
That sophistication in tracking is essential to make the final
output looks smooth and professional.
OK, that's impressive tech, but what makes it so hard for
someone else to just copy this? Yeah, well that leads us to the
adaptive and learning advantage.This is what they sometimes call
(05:47):
the data flywheel, and it's huge.
This AI isn't static. It wasn't just programmed once.
It's constantly learning. It's trained on this absolutely
massive data set from over 5 million games that pixel lot
systems have produced all over the world. 5 million games,
that's yeah, that's an almost unimaginable amount of training
data. How does that create a
competitive advantage or a Moat as they say?
(06:09):
Well, think about it. Every single one of those
millions of games provides more data, more examples, more edge
cases for the AI to learn from. Maybe it's a really poorly lit
evening game in Northern Europe,or tracking a tiny fast moving
hockey puck against white ice, or figuring out who has the ball
in a confusing rugby scrum. So it gets better with every
(06:30):
game recorded anywhere in the world.
Exponentially better the more varied situations it sees, The
more refined its predictions become, the faster it learns to
handle new scenarios. This constant improvement loop
is why they can claim such a high accuracy rate, something
like 98% accuracy and reliability for a new competitor
starting out. Even with good tech, they simply
don't have access to that scale of real world data to train
(06:53):
their AI. They can't catch up easily.
It's like the AI gets smarter the bigger the network gets.
Exactly, the scale itself becomes the advantage.
And it's not just one generic AIeither.
They specifically calibrate and train the AI models for each of
the 19 different sports they currently support.
That makes sense. Tracking basketball, which is
fast-paced in a confined space, must be totally different from
(07:16):
tracking, say, soccer on a huge field.
Totally different logic, different player formations,
different ball speeds. The AI needs to be tailored, and
that customization even extends to things like built in light
recognition tech so it can adaptquickly if clouds suddenly cover
the sun or if the stadium lightskick on halfway through a match.
So the core technology is specialized, it's tough, and
(07:38):
it's always learning. It essentially turns making
sports video from this big laborintensive hassle into an
intelligent automated service. Precisely and having that super
reliable core tech is what allows them to offer a whole
range of different products because obviously one size
doesn't fit all venues or sports.
Hashtag Hashtag 2 The Pixel Lot product lineup and hardware
components. Right.
(07:58):
So they need different solutionsfor different parts of that long
tail market. How do they breakdown their
product line? You can think about it in two
main categories. First, you've got the fixed
installation solutions for places where the camera stays
put, like permit stadiums or gyms, And second, there are
portable and mobile solutions for when you need flexibility.
OK, let's start at the top end. What about professional leagues
(08:22):
or premium venues that need the absolute best broadcast quality?
For them, Pixel Lot offers the Pixel Lot Prime.
This is their high end system. It's designed specifically for
broadcast level quality. It captures video and ultra high
resolution and crucially at a very smooth 50 to 60 frames per
second. Which is vital for things like
(08:43):
slow motion replays, right? Exactly.
Essential for professional broadcasts and detail analysis.
The prime systems are often usedin bigger venues, sometimes even
alongside traditional manned cameras.
But the prime provides that reliable, automated, always on
capture and can even support multi angle productions.
OK, Now moving down to what sounds like their core market,
(09:05):
the high schoolers, the amateur leagues, the semi pro teams,
where maybe budget and simplicity are bigger factors.
Right. And that's where the Pixel Lot
Show comes in. This is really their workhorse
product. It's widely seen as the industry
leader for this segment. The whole idea behind the show
is set and forget. Minimal fuss.
Minimal fuss. It delivers fully automated HD
(09:27):
capture and streaming. The design focuses on ease of
use, maximizing fan engagement, and enabling monetization with
the least amount of human intervention needed.
A league installs it, sets up their preferences on the
platform, and then the system just handles the production for
every game. You invest the time upfront and
it saves countless hours down the line.
That's the idea. Now, of course, not every game
(09:48):
happens at the home stadium. Teams play away games.
They practice in different locations.
Yeah, how would they handle thatneed for mobility?
Capturing stuff that isn't in a fixed venue.
That's where the portable solutions come in.
The main one here is the Pixel Lot Air NXT.
Think lightweight battery operated camera.
Easy to carry around. Exactly design for simplicity
for on the You record the footage and it still gets
(10:17):
automatically tracked by the AI,recorded and uploaded for
analysis later. So it extends that capture
capability beyond just the main venue.
It helps film basically everything that's relevant.
Right, democratizing the abilityto film all the important
events. And then there's another really
interesting angle for accessibility, which is letting
people use their own cameras. This is the Pixel Lot UU
(10:40):
solution. It's more consumer facing.
It allows basically anyone, a parent, a coach, A-Team manager
to use their own wide angle action camera, I think like a
GoPro or something similar. So I could just use my GoPro?
Yeah, you record the game with your own camera, capture that
wide view, then you upload that raw footage to the Pixel Up
platform, and their AI then processes your footage, applying
(11:02):
the automated production, the player tracking, generating
highlights, the whole deal, but from your consumer camera
footage. That's a really smart way to
lower the barrier to entry. People use gear they already
have. It's a very low friction way for
people to get started within their ecosystem.
Now, we talked about these beingfixed installations.
Often they're out there in the elements.
How tough is this hardware? Can it really handle being
(11:24):
installed permanently outdoors? Oh yeah, robustness is
absolutely critical for these unattended systems.
The fixed models like their S1 and S2 cameras, they boast an
IP66 rating. What does IP66 mean in practical
terms? It means they are completely
sealed against dust and grit andthey can withstand powerful
water jets, so heavy rain, snow,sleet, it's not an issue.
(11:47):
They're properly weatherproof, and the temperature tolerance is
pretty staggering too. Some models have internal
heaters and can operate reliablyin temperatures ranging from a
freezing manina 50°C up to a scorching 50°C.
Wow -50 so from like Siberia to the Sahara basically.
Pretty much they're engineered to be installed and then largely
(12:08):
ignored, just reliably doing their job day in day out
regardless of the conditions. OK.
So we've got this really robust specialized hardware built for
the elements and this incrediblysophisticated AI brain running
the automated production. But you mentioned earlier the
real value isn't just the camera, it's the whole package,
right? Everything that happens after
(12:28):
the video is captured. That is absolutely the core
insight here. Pixel Lot isn't really in the
business of just selling cameras.
They're selling sports organizations big and small,
basically an entire media operation in a box.
The value proposition goes way beyond just capture.
It includes distribution, monetization and deep analytics.
It's an A-Z solution as they say.
(12:49):
Hashtag tag tag 3. The end to end solution
ecosystem. OK, let's break that down.
Start with distribution. How does say, a local high
school Football League, which has probably 0 broadcasting
experience, actually get its games out to its audience?
They do that using Pixel Lot's integrated OTT platform.
OTT just means over the top delivering video directly via
the Internet, bypassing cable orbroadcast channels.
(13:12):
It's like a streaming service. Exactly.
But it's a white label service. That means the sports
organization gets to put its ownbrand on it.
It could be the Maplewood High athletics channel or the City
Youth Soccer network. It becomes their own dedicated
branded channel. Their own little TV network,
essentially. Pretty much for delivering both
live games and on demand contentlike replays or highlights
(13:34):
straight to their fans wherever they are.
No need for complicated deals with big broadcasters.
And this is critical, especiallyfor that AIS model we talked
about. This platform has to enable
monetization, right? It needs to potentially pay for
itself. How does it do that?
Yeah, the platform is built fromthe ground up with monetization
in mind. It allows leagues to set up
(13:55):
multiple ways to generate revenue.
They can offer subscription models, maybe an all Access
season pass or pay-per-view for a specific championship game.
OK, subscriptions make sense. What else?
They can also embed advertising.Think pre roll ads that play
before the stream starts, mid roll ads like commercial breaks
during halftime, or even banner ads displayed on the screen.
(14:17):
And finally, they can sell dedicated corporate sponsorships
for their channel or specific broadcasts.
So it gives these organizations,which maybe you're only spending
money on video before, if they could even afford it, the tools
to actually turn video into a revenue stream.
That's the goal. Turn it from a cost center into
a potential profit center. OK.
Distribution and monetization covered.
(14:39):
Now let's pivot to the coaching side, performance improvement.
You mentioned Vidswap Analytics earlier.
Why was acquiring an analytics company like Vidswap so
important for Pixlot? They recognize that just
creating the video wasn't enough, especially for this long
tail market focused on development.
Analysis is absolutely crucial for coaches and players to
actually improve. NID Swap, which they bought back
(15:01):
in 2019, is now fully baked intothe Pixelot platform.
And what does vid swap do? How does it help a coach?
Its main job is to automate whatused to be an incredibly
tedious, time consuming manual process, breaking down game
film. You mean like tagging every
single play, every shot, every turnover?
Exactly. Traditionally a coach or maybe
(15:21):
an assistant or even a student manager would spend hours and
hours manually watching the footage and tagging all those
key moments. Vidswap uses AI to do that
automatically. How does the AI know what to
tag? It analyzes the game footage,
identifies players, tracks the ball, and understands the flow
of the game based on the specific sports rules and
(15:42):
commonplace. It then automatically tags key
events, shots, passes, turnovers, defensive formations,
whatever is relevant for that sport.
And it presents that informationto the coach.
Yes, it provides coaches with really actionable data-driven
insights. Things like in depth team and
player statistics, breakdowns ofthe game segmented by play type
like show me all our third down conversions, and visual tools
(16:04):
like shot charts showing where shots were taken from or heat
maps. A heat map could instantly show
a basketball coach where their team is most effective
offensively, or where the opponent is scoring most easily.
That's powerful stuff. And how quickly does this
analysis become available after a game?
That's another key benefit speed.
(16:24):
Typically, the full breakdown and analytics are available
within 4 to 8 hours after the game finishes. 48 hours.
That's incredibly fast compared to manual methods.
It's transformative. It means a coach can review the
game, identify issues, and plan the very next practice session
based on objective data almost immediately.
It maximizes the impact of the analysis.
(16:44):
It also makes it easy to exchange video with opponents
for scouting and even supports live tagging during the game if
needed. OK, so Vidswap tackles the
tactical side. Then there's the automated
highlight generation. This seems more geared toward
visibility, engagement, sharing.How does the AI create highlight
reels that actually look good? It uses different AI models,
these ones trained to recognize moments of high excitement or
(17:05):
importance in the game. Think goals, amazing saves, long
touchdown runs, I mean a buzzer beater shot.
It automatically identifies these key moments.
And clips them together. Right.
It curates them and compiles them into ready to watch
highlight packages or condensed game recaps.
This saves teams a massive amount of time and money
(17:26):
compared to having someone manually sift through hours of
footage to find and edit the best bits.
The time saving alone must be huge.
It really is, but the really clever part strategically is how
it optimizes these highlights for how people actually consume
media today. You mean for social media?
Exactly. The system doesn't just create a
standard landscape video clip, It automatically reformats and
(17:47):
resizes highlights specifically for platforms like Instagram
Reels, TikTok, YouTube Shorts, meaning it generates clips in
vertical portrait mode. The tall, skinny video format.
Yes, Ready made for mobile viewing and sharing on those
platforms where, frankly, the younger audience and players
actually spend their time. It makes sharing those Wow
moments incredibly easy, boosting player exposure and fan
(18:11):
engagement instantly. That's actually brilliant.
It closes the loop, takes the raw capture, puts it on a
branded channel, analyzes it deeply for coaches, and packages
the best bit perfectly for social media buzz.
That really is an A-Z ecosystem.It covers the whole life cycle
of the content, hashtag, hashtag4, the market and core value
proposition. And this entire comprehensive
(18:31):
ecosystem, it was clearly built with a very specific market in
mind, that long tail you mentioned earlier.
Absolutely. The whole strategy revolves
around addressing the economics of the market that traditional
broadcasters basically ignored. So let's be crystal clear on the
strategic thinking here. When we say long tail, we're
talking about everything below the absolute top tier of pro
sports, right? Yes, it's the literally millions
(18:55):
of games played every year in youth leagues, amateur clubs,
high schools, colleges, semi professional setups, games where
the cost of sending a traditional broadcast crew was
just completely prohibitive. Pixlot CEO Duron Gerstel he
doesn't see this as small fry. He calls youth sports a billion
dollar investment opportunity. A billion dollars?
(19:18):
Wow. Do we have a sense of the scale?
Like how many people are involved?
The numbers are huge. Just in the United States alone,
estimates suggest over 27,000,000 young people
participate in organized youth sports.
Think about that. 27,000,000 athletes, plus their parents,
grandparents, coaches, friends. It's a massive, highly engaged,
but very fragmented audience. An audience that was largely
(19:39):
invisible from a media perspective before.
Pretty much. And Pixel Lot's focus on this
underserved market has led to really impressive global reach.
What kind of numbers are we talking about for their
footprint? They've got over 35,000 of their
camera systems installed across something like 77 countries now.
Their penetration in the US highschool market is particularly
strong. They're in over 8000 high
schoolers. 8000 high schoolers. How did they manage that kind of
(20:03):
scale in the USA? Lot of that comes through
strategic partnerships. A key one is with the NFHS
network. That's the National Federation
of State High School Associations.
The NFHS network uses Pixelot technology extensively to stream
a mind boggling number of games,Over 70,000 high school games
per month. 70,000 a month. OK, the scale is undeniable.
(20:25):
Let's talk about the value proposition then.
Who benefits most? Starting with the athletes
themselves, how does this changethings for young player?
For the individual athlete, it fundamentally comes down to
exposure. Suddenly every game they play
can be professionally captured, and those automated highlights
we talked about, they get generated automatically.
Creating a ready made recruitment reel.
(20:46):
Precisely that professional looking video content is
absolutely essential for collegerecruitment these days, or for
getting noticed by scouts at higher levels.
It fulfills that promise that theoretically every athlete
should have the chance to be seen, celebrated and scouted, no
matter where they live or how small they're club is.
It removes geography as a barrier to visibility.
OK, huge value for the players. What about the coaches?
(21:09):
We touched on Vidswap, but what's the overall competitive
edge? Beyond the deep analysis from
Vidswap, it brings consistency and objectivity to performance
review. Coaches get reliable footage and
data from every single game, notjust the ones they managed to
film poorly with a phone. This allows for much better
strategic planning, tracking player development over a whole
(21:30):
season, and scouting opponents with a level of detail that used
to be reserved for, you know, top college or pro teams.
Yeah, makes sense. And finally, for the leagues,
the schools, the clubs themselves and their fans,
what's the core value there? For the organizations, it really
boils down to cost. Democratization, by automating
the production pixel law, dramatically lowers the cost of
(21:51):
creating high quality video cones.
We're talking potentially eliminating the need for crews
that cost hundreds or even thousands of dollars per game.
Removing that massive financial hurdle.
Exactly. It takes away the single biggest
reason why these leagues couldn't broadcast their games
before. And for the fans, the parents,
the alumni, the local community,the value is engagement.
(22:12):
They can suddenly watch games live or on demand from anywhere
in the world. Grandma living across the
country can watch her grandson score a goal.
It strengthens those community ties and allows people to
support their teams even if theycan't physically be there.
But all of this hinges on the organization's actually being
able to afford and adopt the technology in the 1st place,
which brings us back to that really crucial business model
(22:35):
innovation. The AIS model?
Yes, that's the linchpin that makes the whole thing work at
scale. Hashtag tag tag 5.
The AI as a service. AIS, business model innovation.
OK, this feels like the absolutecore of their success.
You said they pivoted from a standard model to AI as its
service AISS. Why was the old way, maybe like
a traditional software as a service ISS model, not quite
(22:56):
right for this market? The big problem was the hardware
cost. Even with a subscription for the
software or platform, the organization still had to buy
the physical camera system itself plus pay for
installation. That's a significant upfront
capital expenditure or CapEx. Right, that initial lump sum
payment, how much we talking potentially?
(23:17):
Oh, it could easily be thousands, maybe even 10s of
thousands of dollars for a good fixed installation, depending on
the venue complexity. For a typical high school
athletic department or a small town amateur league, finding
that kind of cash upfront is, well, it's often impossible.
Their budgets are already stretched thin.
So that CapEx requirement was acting as a major bottleneck
(23:37):
slowing down adoption. A huge bottleneck.
It meant long budget approval processes, grant applications,
fundraising. It just made everything slow and
difficult. OK.
So how does the AISS structure directly attack that CapEx
problem? It obliterates it under the AIS
model. PIX a lot provides the hardware
completely free of charge to qualified facilities, and they
(23:57):
often cover the installation costs as well.
Wait, free hardware and free installation?
That sounds like a massive risk for Pixel lot to take on.
What's the catch? What do they get in return?
There's no catch really. It's a different kind of deal.
It's a revenue sharing agreement.
Pixel Lot takes on the upfront cost and risk, and in return,
the client organization agrees to share the money they make
(24:19):
from the platform. Sharing the revenue from
subscriptions, ads, etcetera. Exactly pixel lot that typically
takes a significant percentage of that revenue.
The numbers vary, but it can be up to 50% of the income
generated from subscriptions, pay-per-view sponsorships and AD
renewals facilitated by their OTT platform.
Wow, up to 50%. That fundamentally changes the
(24:41):
whole relationship, doesn't it? It's not just selling them a
product anymore. It's not a vendor client
transaction. It becomes a true partnership.
Pixel Lot is now financially invested in the success of that
league's streaming and monetization efforts.
If the league does well and makes money, Pixel Lot does well
too. Their incentives are completely
aligned. Perfectly aligned.
If the client struggles to monetize, Pixel Lot doesn't make
(25:03):
much money from that specific install, but they still have
their hardware deployed, gathering valuable game data to
train their AI and expanding their strategic footprint.
But the key is this model instantly removes that upfront
cost barrier. Making adoption almost
frictionless for the client. Right, the school or league gets
a state-of-the-art production system for essentially $0.00
(25:24):
upfront cost and they immediately have the tools to
start generating revenue. It bypasses the biggest hurdle
that was killing adoption in this market.
Let's really drive this home. Can you contrast the financial
risk and potential return on investment ROI between the old
way and this AIS model? OK, traditional production,
super high risk. You've got significant upfront
(25:46):
cost for equipment, let's say 2000 to $5000 or more just for
basic gear. Then you have high operational
cost for every single game. Hiring a camera crew, maybe an
editor, that could be 500 to $1500 per game.
The organization pays all that before they sell a single stream
or ad. A big gamble, especially for
smaller organizations. Huge gamble.
Now compare that to Aias $0.00 upfront cost.
(26:10):
The operational cost is basically just the revenue
share. Video production instantly flips
from being a guaranteed high risk expense into being a low
risk potential profit center. That strategic shift in the
business model is arguably just as innovative as the AI
technology itself. It's what unlocked the massive
scale and. That scale, powered by this
(26:31):
clever business model, must givethem a serious edge when
competing against other companies trying to crack this
market. Absolutely.
It positions them very differently.
Hashtag hashtag 6. The competitive landscape and
market differentiation. OK, so Pixel Lot isn't alone and
seeing the potential here, this whole automated sports video
market seems to be heating up. Oh, it's booming and it's driven
(26:51):
by those two big forces we've discussed.
Firstly, the massive cost savings.
Organizations report saving up to 67% compared to traditional
production methods. And secondly, just the sheer
demand from fans and families for access to this content.
So who are the main competitors Pixel Lot is up against in this
space, and how does Pixel Lot stand out?
There are a few key players. The names you hear most often
(27:13):
are probably Vio, Hurdle and Spedio.
Let's take Vio first. OK, Vio, what's their angle?
Vio is really well regarded for its portability.
There Vio Cam, especially the newer models like the Vio Cam 3
is known for being lightweight, easy to set up, very user
friendly, so they're quite attractive for individual teams
or smaller clubs who maybe just want simple capture for training
(27:34):
or away games. But the business model is
different from pixel lots AIS, right?
Very different. VO operates on a more
traditional model. You buy the camera hardware
upfront, that's your initial capX, and then on top of that you
have to pay a mandatory subscription fee, which is
tiered based on features and canrange from say $67 up to maybe
$250 a month, often billed annually.
(27:55):
So the client still has that upfront cost barrier and a fixed
recurring cost regardless of whether they monetize.
Exactly. Veo makes money on the hardware
sale and the subscription, whilethey excel at portability.
If you compare the actual video and audio quality or the
sophistication of the end to endplatform, including the OTT
streaming and monetization tools, Pixelot often offers a
(28:18):
more robust integrated solution,especially with the AIS model
removing that initial financial hurdle.
OK, got it. Next up, Hurdle.
Hurdle's a huge name, particularly in American high
school and college sports, right?
Mostly known for analysis. Dominant in analysis, Hurdle's
historical strength and where they built their reputation is
definitely in their analysis platform, video exchange tools
(28:40):
and scouting features. Their whole ecosystem for
reviewing game film is incredibly popular.
They do have their own automatedcamera, the Huddle Focus Flex.
But it's less of an end to end broadcast solution.
Generally, yes. It feels more geared towards
capturing footage for their analysis platform rather than
being a set and forget broadcastand monetization engine like
(29:00):
Pixel Lot Show seems to be, and Pixel Lot recognized this.
By acquiring Vidswap and integrating that powerful
analysis capability, they effectively challenged Huddle's
main advantage. Now Pixlot can offer both the
automated content creation and top tier analysis within one
ecosystem. Neutralizing huddles, core
strength. OK.
And the third one you mentioned was Speedo.
(29:21):
How do they compare? They sound perhaps the most
similar technologically. They are probably the most
direct competitor in terms of offering a truly similar end to
end platform. Spideio has their Speedo perform
for analysis and spideo play forautomated production and
broadcasting. They use similar principles,
panoramic capture, AI tracking, they call it auto follow.
Technologically, they are very capable and offer a compelling
(29:44):
alternative. So if Spideio offers a very
similar tech stack, an end to end solution, what gives Pixel
Lot the edge? What's the key differentiator
there? It really comes down to two main
things, both stemming from PixelLot's execution and strategy.
First scale Pixel Lot simply hasa much larger global installed
base, those 35,000 plus systems we mentioned.
(30:06):
This means their AI data flywheel is spinning much much
faster. More games, more data, faster
learning. Exactly.
Their AI gets exposed to a vastly wider range of sports,
lighting, conditions, play styles, you name it, across 77
countries. This accelerates the refinement
and reliability of their AI at apace that's hard for competitors
(30:26):
with smaller footprints to match.
And the second differentiator? Arguably the most powerful one.
Let me guess the AIS model again.
You got it. The AIS business model.
While VO, Hurdle and Speedo generally still require the
client to put up significant capital upfront for hardware or
commit to fixed monthly annual subscription fees from day one,
Pixel Lot's $0.00 upfront cost option fundamentally changes the
(30:49):
risk calculation for the customer.
It makes adopting their platformthe path of least financial
resistance for thousands of schools and leagues worldwide.
So it's not just about having great tech, it's about having
the business model that makes that tech accessible and
scalable for the target market. That's the winning combination.
The business strategy unlocks the full potential of the
technological advantage. OK, this all sounds incredibly
(31:12):
powerful in theory. Let's bring it down to earth.
How has this actually played outin the real world?
Can we look at some specific examples of the impact?
Absolutely, because the real test is whether this model can
overcome real world challenges in diverse markets and actually
deliver results. Hashtag tag tag 7.
Real world impact and global case studies.
(31:32):
Where's a good place to start? I know you mentioned the Super
Sports Schools initiative in South Africa.
That sounds like a major undertaking.
It's a fantastic case study. It was a partnership between
Pixel Lot and Super Sport, whichis the leading broadcaster
across Africa. The big challenge there was
twofold the extremely high cost of traditional sports
production, which meant very fewschool sports ever got covered,
(31:54):
combined with some unique infrastructure hurdles.
But there's a huge youth population passionate about
sports. Massive and very digitally
connected, but traditional broadcasting just couldn't scale
down cost effectively to cover the sheer volume of school
sports. Pixel Lots model was basically
tailor made to tackle that exactproblem, rapidly increasing
coverage across a huge underserved market.
(32:16):
And did it work? What were the tangible results?
The results were pretty dramatic.
The sheer volume of content justexploded.
For schools participating in theprogram, the average number of
live events they broadcasted perseason jumped from maybe 20 up
to over 100. Wow, A5 fold increase just like
that. A5 fold increase generated
autonomously and critically. It expanded the reach.
(32:39):
They went from being able to cover maybe 20% of schools with
traditional methods to having a pathway to reach 100% of schools
with the automated system and the audience responding.
The Super Sports Schools app sawover 160,000 downloads, proving
that the demand was absolutely there.
That's incredible validation. Now you mentioned infrastructure
challenges, things like unreliable electricity, maybe
(33:01):
spotty Internet. How did the technology handle
that? Yeah, that's where the hardware
robustness we talked about becomes crucial.
The Pixel lot systems deployed there included features like
strong battery backups, so if there were power outages or load
shedding, which can be common, the cameras could keep recording
uninterrupted. Smart and Internet.
The streaming tech is also optimized for challenging
(33:24):
conditions. It has a surprisingly low
minimum bandwidth requirement, needing only about 5 megabits
per second upload speed, and it can run effectively over
standard 3G or even 4G mobile networks, which is vital in
areas where fixed line broadbandmight be unreliable or
unavailable. So the tech itself was designed
to overcome those specific emerging market challenges.
(33:46):
Did it also help with funding attracting sponsors?
That's often a huge issue for smaller leagues.
Yes, that was another major benefit.
Historically, it was really hardfor school or amateur leagues to
attract big corporate sponsors because they just couldn't
provide reliable data on who waswatching or how many people were
watching. No verifiable audience metrics.
Exactly. But Pixel lot centralized OTT
(34:07):
platform changes that. It generates precise, measurable
data. Viewership numbers, audience
demographics, engagement times. Suddenly the league can go to
potential sponsors with hard data proving their reach and
impact. This verifiable data is gold for
attracting major sponsors who previously had no way to justify
investing in grassroots sports. It makes the invisible audience
(34:29):
visible and therefore bankable. Turning viewership into a
quantifiable asset that's powerful now.
The pandemic must have been a major Test case too.
Suddenly no fans in stadiums. The pandemic, ironically, became
a massive accelerator for adoption.
With stadiums empty, streaming suddenly became the only way for
many clubs to connect with fans and, crucially, generate
(34:51):
revenue. We saw this clearly with smaller
clubs, for instance in the Scottish Football League.
What happened there? Well, you had clubs like, say,
Albion Rovers, smaller communityclubs whose entire financial
model relied heavily on ticket sales, match day revenue.
When that disappeared overnight due to lockdowns, they were
facing genuine existential threats.
They could have gone out of business.
Easily, But by quickly adopting the Pixel Lot streaming model,
(35:13):
offering season passes or pay-per-view access to their
games online, they were able to create a vital new revenue
stream almost instantly. Reports indicated that
collectively, the smaller Scottish clubs generated
significant sums. Figures up to 200,000 lbs were
mentioned in what was essentially game saving revenues
directly from streaming during that period. 200,000 lbs for
(35:36):
small clubs, that's absolutely massive.
That's the difference between surviving and folding.
Now just to clarify, was that figure the total money brought
in the gross revenue or was thatafter pixel lot took their
revenue share? That figure typically represents
the gross revenue generated through the platform from
subscriptions and PPV sales. So yes, Pixel Lot would take
(35:57):
their agreed upon revenue share from that maybe up to 50%.
But even netting, say, £100,000 or more would still be an
absolute lifeline, a completely new and substantial income
stream that these clubs simply didn't have before.
It proved the model wasn't just a nice to have, but potentially
a critical component of financial stability, especially
for organizations operating on tight margins.
Exactly. It established streaming as a
(36:20):
scalable long term commercial Ave. for these non elite clubs.
Pandemic or not, it showed the AIS model could deliver tangible
financial results globally. Providing stability, driving
growth, improving access. It really does seem like the
business model innovation is just as disruptive, if not more
so, than the AI tech itself. They work hand in hand, one
(36:43):
enables the other at scale. Hashtag hashtag 8, The future
outlook and final thought. So looking ahead, where does
this technology go next? Pixel Lot and its competitors
have cracked automated production.
What's the next evolution? The trajectory seems to be
moving towards even more intelligent, more autonomous
systems. We're starting to hear the term
agentic systems being used. Agentic systems.
(37:04):
OK, unpack that for us. What does that mean in this
context? It sounds a bit sci-fi.
It's not quite robots taking over, but it means the AI
evolves from just reacting like following the ball to
proactively acting on behalf of the user to achieve more complex
goals with less and less human input needed.
So moving beyond just filming the game.
Right. Imagine an AI that doesn't just
(37:24):
produce the broadcast feed, but can also, for example,
automatically generate multiple different highlight packages
tailored for specific social media platforms.
Tag specific tactical plays in real time based on the coach
predefined criteria. Perhaps even automatically
create personalized video summaries for each player on the
team after the game, and distribute all of this content
(37:46):
intelligently according to the league's communication strategy.
So the AI becomes less of a tooland more of a like an automated
production assistant or even a content strategist.
Exactly. It takes on more of the complex
workflow, freeing up humans to focus on higher level tasks.
And for the viewers, the fans, the next big frontier is likely
personalization. How so?
(38:07):
What could that look like? Remember, the system initially
captures that ultra wide panoramic view of the entire
field. That raw footage contains way
more information than what's shown in the standard broadcast
feed that follows the main action.
So you could potentially access that other information.
That's the idea. Future systems could allow fans
to actually choose their own viewing experience.
(38:28):
Maybe you want to switch to a view from behind the goal for a
replay, or perhaps you want to follow your favorite player
exclusively. Just tell the system and the AI
dynamically generates a feed that keeps that specific player
centered even when they're off the ball.
That's like giving every single viewer their own personalized
director's cut of the game, all derived from that single initial
(38:49):
automated capture. Precisely Highly efficient,
highly personalized. And this all ties back into what
seems to be Pixel Lot's ultimateambition.
They're not just trying to sell cameras or software, they appear
to be aiming to become the essential content and data
infrastructure underpinning the entire global long tail sports
economy. The operating system for
grassroots sports. Almost.
(39:10):
You could think of it that way. By making video capture
accessible, providing deep analytics, and enabling viable
monetization, they're a poweringpotentially millions of athletes
and thousands of leagues at the grassroots level to develop
talent, gain exposure, secure funding, and just generally
reach their full potential. It really is that powerful
combination we've discussed the sophisticated AI capture, the
(39:34):
integrated OTT platform for distribution and monetization,
the vidswap analytics providing the coaching edge and all of it
unlocked and made accessible at scale by that clever risk
reducing AIS business model. It's a complete ecosystem and it
has without doubt fundamentally changed the economics of how
sports content outside of the very top tier is produced and
(39:56):
consumed. Absolutely.
Which brings us to our final thought, the big question.
We want to leave you, the listener pondering long after
this deep dive finishes. We've seen the technologies
here. It's affordable, it's scalable.
The real question now becomes. What happens to the future of
sports globally when the opportunity for every single
athlete, no matter how small their club, how remote their
town, or how limited their team's budget to actually be
(40:19):
seen to be celebrated and potentially to be scouted,
transforms from an expensive geographical lottery into a near
universal technological guarantee?
What does that truly global meritocracy of talent discovery
look like? That's a powerful question to
consider. A very powerful thought indeed.
We've taken the deep dive. Thanks for joining us, and we'll
catch you on the next one.
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