May 6, 2026 • 46 mins
Divya Thakur, founder of Yardbot, joins host Jim Belosic to discuss how he’s building a next-gen landscaping company powered by robotics. Instead of chasing full automation, Yardbot combines human crews with custom-built mowing robots to deliver better results at a lower cost.
Divya shares his journey from software engineering and finance to self-driving cars at Cruise, and how a failed Bitcoin startup taught him one of his biggest lessons: build for customers, not just cool tech. We get into the gritty reality of developing hardware, five generations of robot iterations, and what it takes to run a tech-driven business in a traditional industry.
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SPEAKER_01 (00:00):
And to be frank with you, everything just broke.
He's like, dude, what are youtalking about?
SPEAKER_00 (00:03):
You're not gonna be able to buy a house for a
million bucks.
If this whole Senkut Sen thingdoesn't work out, we're gonna go
back to mowing lawns.
That's how I made money for RCcars, which was like my my drug
addiction back in the early 90s.
SPEAKER_01 (00:14):
I was running a team of 120 engineers.
And how hard can it be to run asmall team of blue-collar l
workers?
SPEAKER_00 (00:20):
You can have a thesis, which is like my
freaking least favorite word.
Everyone has a thesis now.
Hey guys, welcome to the JustGonna Send It podcast.
I'm your host, Jim Belosik, andtoday I'm joined by Diviator of
(00:42):
Yardbot.
So Divya, thanks thanks forbeing on the pod.
Thank you.
SPEAKER_01 (00:46):
Happy to be here.
SPEAKER_00 (00:48):
Cool.
We're gonna dive into uh kind ofDivya's background and how he
finally got to Yardbot, uh,which is awesome if you haven't
seen it.
And so it's yard.bot, right?
That's the web address.
That's correct.
Yes.
Okay.
Logan Logan will put it righthere.
Um tell us a little bit aboutYardbot before we go into your
(01:08):
background and everything.
Like what's what's Yardbot allabout?
SPEAKER_01 (01:12):
Yeah, so uh Yardbot, or we sometimes call it Yard
Robotics, is a hybridlandscaping and a robotics
company.
So on the front end of thebusiness, we do what's
traditionally landscaping.
So mowing mowing lawns, weedeating, edging, blowing, and
also other activities likemulching and bush trimming for
residential and commercialcustomers.
So at the front end of thebusiness, we present as a as a
(01:33):
lawn care company.
And on the back end of thebusiness, uh we build robots, we
design and build robots that weultimately use for this uh
activity, primarily for themowing side.
Um so yeah, it's kind of acompany of these two sides,
essentially.
SPEAKER_00 (01:46):
Okay.
Um yeah, I I definitely thoughtit was more just like robots
that you could put in your yard,but it's it sounds like it's
yeah, a hybrid of an actual liketraditional landscape
maintenance company.
So are your guys are likehauling these things around on
trucks and then they're weedeating while these things are
mowing?
SPEAKER_01 (02:03):
That's correct.
So yeah, we we have a truck withthree robots and weed eaters,
Azures blowers.
We train our guys to use theserobots in remote control and in
autonomous mode.
So remote control when they'redriving it with a remote
controller or autonomous mode,let's say you see an open yard,
you can put the robot there andit can mow autonomously.
And at the on the other side ofthis, we're we're presenting as
(02:24):
a company that provides areplacement for a traditional
landscaping company.
So for the from a customer'sperspective, we are better, our
lines are straighter, our priceis lower, and on our side, we're
we're kind of doing this becausewe have these robots that we
built.
SPEAKER_00 (02:37):
Okay.
That's yeah, that's wild.
Um I've I feel like, you know,what, uh 10 years ago or
something, I heard about allthese companies that were trying
to do, you know, a Roomba foryour lawn.
And you know, I know a coupleguys who bought them and uh and
they sucked.
You know, they would like driveacross the street or or they'd
get stuck in a ditch orwhatever.
So it sounds like you guysdidn't try to um over-automate,
(03:00):
you know, it's a a mix of a mixof dudes and robots is is maybe
the magic.
SPEAKER_01 (03:05):
Yeah.
Yeah.
So so you know, when you thinkabout lawn care, Jim, the end
product is a good-looking lawn,right?
So the solution path can be arobot or can be a human being.
You know, the the insight thatsort of we built is that the end
customers, you know, these arelike moms, you know, they're not
they're they want a good lookinglawn.
They're not interested inlearning how to operate a robot
(03:27):
and make it do it correctly.
And even if even if it does aperfect job, there's human
activity, weed eating, edging,blowing the front drive, you
know, front of the house.
The robot's not gonna do that.
So, you know, ultimately thetrue solution, what we realized
is we people just want a betterquality lawn care service.
And it's a different mindset tothink about this from this
perspective versus let's we havea great robot and we want to
(03:49):
give it to people.
So that's that's sort of adifferent way.
unknown (03:51):
Yeah.
SPEAKER_00 (03:52):
Yeah, you're you're kind of speaking my language
because uh every day I see allthese companies that are trying
to just over-automate something.
They're like, oh man, we'regonna get rid of all of our
staff and just have robots.
Um, you know, or we're we'regonna have we're gonna automate
our forklifts, we're gonnaautomate, you know, all of our
lines, we're gonna havehumanoids, we're gonna do all
(04:13):
this stuff, and we don't needpeople anymore.
And we're just not there yet.
You know, and honestly, I don'tknow if we'll ever be there.
And, you know, one of the thingsI'm proud of at Send Cut Send is
we use dudes where it makessense to have a dude, and we use
robots where it makes sense tohave a robot.
So if it's repetitive or youknow, uh a very discrete problem
that's that's uh solvable in thesame way over and over and over
(04:36):
again, it's great to use arobot.
But uh I don't know, not a lotof companies are thinking about
you know uh what the mix islike.
You know, is did you did youknow that was gonna be your your
take from day one or you know,like the hybrid, uh, or or were
you trying to fully automate it,or how did you end up there?
SPEAKER_01 (04:56):
I mean, it was just lessons, right?
So I started off by thinking ifI design the perfect robot at a
great price, it's gonna solve myproblem.
And I built something.
And then I realized that it wasnot, it's not, it doesn't do the
full job.
So that you know, it was aseries of like lessons.
Then I, you know, the nextiteration of the business was
perhaps I can sell this totraditional landscaping
(05:17):
companies.
So then I started knocking ondoors there.
You know, the reality is thelandscaping industry is a you
know, it's a different world.
And the kind of talent thatworks in landscaping, I mean, I
came from software, hardwarebackground.
So it's a different type oftalent.
And they'd rather hop on a zeroturn and whip through 10 yards
than figure out how to sit infront of a computer and run a
fleet of 20 robots.
That's, you know, it's adifferent kind of person.
(05:39):
So it was a difficult time doingthat.
And then ultimately I realized,hey, if I can just be price
competitive and competitive froma quality perspective, I can
just start a landscaping companyand you know the robots can make
it happen.
So that's kind of the currentiteration of the business, and
this is the this is the onethat's been the most successful.
So to answer your question, it'sjust gone through a couple of
learnings here and ended upwhere we are today.
SPEAKER_00 (06:00):
Cool.
Yeah, I think that's that's ourour favorite motto here is uh
learn by doing.
Uh, you can have you can have athesis, which is like my
freaking least favorite word.
Everyone has a thesis now.
Like, no, just hey, I got arandom idea.
I have a gut feeling, I'm gonnago test it, I'm gonna go get
beat up.
And you you just have to knowthat where you're starting and
(06:20):
where you end are gonna be twocompletely different places, and
the most successful people arethe ones who can pivot and
adapt.
So uh it's it's funny.
My CTO and myself, um, we bothhave a background in
landscaping.
His he worked for like aprofessional big landscaping
company during college.
Okay, and Jacob Graham.
Um, and then I had a lawn mowingroute since I was like 11 or 12
(06:46):
or something.
Um so that's that's how I mademoney for RC cars, which was
like my my drug addiction backin the early 90s.
Um so he and I have always jokedthat you know, if this whole
Senkut Sen thing doesn't workout, we're gonna go back to
mowing lawns and uh and kind ofmake a new business model for
it.
So it sounds like you're alreadydoing it for us.
(07:07):
So hopefully I don't have to uhyou know run a blower all day uh
ever again.
Uh that that gets a little alittle crazy sometimes.
Um so what's what's next forYardbot?
Like I I looked at your robots,they're rad.
Um I see a lot of send cut sendparts on there.
Uh what generation are you on?
Like what how many how many uhiterations is this?
SPEAKER_01 (07:29):
So we're on our fifth iteration right now.
Um and it's um you know it's a36-inch deck, which is uh
perfect.
Most of our customers are sortof subdivision homes, like
residential homes.
We do about 600 locations in theHuntsville area, Huntsville,
Alabama area.
Um so we have a fleet of 20robots today.
We're building 10 more very,very soon.
(07:51):
We just actually got the orderin.
Thank you, Sankat San,yesterday.
Um, so uh these robots um uh areyeah, so they're 36 inch, 250
pounds, they have a fivekilowatt hour battery, uh triple
blade, um, and it's got anautonomous system that's lighter
and camera based.
And let's see, and yeah, it canrun all day.
(08:11):
It can do one robot can do 10yards, uh, and we put three, so
the guys are you know a littlebit, yeah, they they're a
little, they have too many, butum and yeah, that's kind of the
high level, the little specs ofthe system.
SPEAKER_00 (08:23):
Dude, that's that's amazing.
Um 36-inch deck is no joke.
Like, usually that's uh youknow, many horsepower gas engine
to to cut all that.
And then you're in Alabama wherethat that's like that's like
real grass.
That's not this dry ass, youknow, Nevada fake grass that we
have.
So uh like what are what aresome of the challenges you've
(08:45):
had?
You know, you're on fifthgeneration.
Like tell me about the failuresof one through four.
SPEAKER_01 (08:50):
Yeah, so you know, um a critical person in the
company is Nathaniel Chong.
He's my uh you know, he's headof engineering hardware.
So I first started designingthem, Jim, and you know, I just
3D printed stuff because I camefrom a software background.
That was my first step intobuilding something.
And to be frank with you,everything just broke pretty
much right away.
So that's when I discovered SenConsent, to be frank, and I
(09:11):
realized I could just actuallyget metal parts and started
building the first couple ofgenerations.
The two areas I would say mostiteration we've done is the
drivetrain and the deck itself.
So the drivetrain, uh, westarted off with skateboard
motors, so e-skate motors, um,which were direct drive.
Over time, then we added areduction with a belt, and uh we
(09:31):
realized the grass gets into thebelt and tears the belt apart.
Next we moved to the chaindrive.
Well, uh, we moved to gears,actually, and uh we used
planetary gearboxes uh for thenext iteration.
Um we were using five to one,that was not enough, so we went
to 20 to 1, but the planetarygearboxes, the pinions were too
small, so they would they wouldshear off.
So then we finally moved to achain design, and that's a
(09:52):
current iteration, and it'samazing.
It's actually really, reallygood.
So it's uh it's a five to onereduction on a gearbox and then
a three to one on a chain, andwe do a it's a differential
drive.
So every every wheel is powered,and uh, we can basically climb
any hill or do anything we wantwith the robot at this point.
So it's it's really solid.
SPEAKER_00 (10:10):
Um what's that make sense?
Yeah, no, that's that'sincredible.
It goes back to like learn bydoing.
Yeah.
Um, you know, you can you canspend six months going through a
bunch of iterations in CAD orwhatever, or you spend six
months you know building fivedifferent versions, you'll have
a way better product if youactually get your hands dirty
(10:31):
and go see what's what breaks.
Um I've seen so many people thatare like, oh yeah, this is this
is gonna be super, super strong.
And then yeah, they there's theunknowns.
Um oh, I didn't realize grasswas gonna get stuck in there, or
I didn't realize, you know, thatin muddy conditions the damn
thing's gonna tip over.
So I gotta readjust my center ofgravity or whatever.
So just yeah, learn by doing.
(10:52):
Your your modeling in CAD iscute, but go out and actually
like get shit done too.
Um what's okay, so tell me tellme what is what is generation 10
gonna look like?
Like what's your dream?
SPEAKER_01 (11:04):
So I think okay, that's an interesting question,
Jim.
So um I think there's obviouslylike mean time to failure, which
we need to bring down across thefleet.
And we're already pretty good,but every year it surprises us.
We find new failure modes thatwe have to have to drive down.
Um I think the platform, Jim, Ithink about it more than just uh
(11:26):
mowing platform.
So uh the way it looks, you cankind of tell it doesn't look
like a traditional zero turn.
It's designed to hold a deck,but perhaps hold a spray system,
or perhaps hold a something topick up garbage.
I think about it as like a unitof automation that you can
deploy in your community andthen you know achieve like a
(11:46):
better quality of life.
That's kind of how I think aboutit mentally.
So um I haven't thought throughgeneration 10 yet.
I'm a little bit in theiteration mode right now, but a
platform that's super reliable,can run autonomously, and can do
multiple tasks across a largearea is kind of what what I'm
looking to do here.
SPEAKER_00 (12:04):
I'll I'll tell you that's that's a fun exercise
that I do all the time is whereare we at now, but then where
are we at in 10 years?
Or, you know, what doesgeneration 10 look like?
I've like throughout my career,I've always thought very small.
Um and sometimes that's bit mein the ass a little bit.
You know, when I was uh my myprevious life, I was a graphic
(12:25):
artist and you know had a littletiny advertising agency.
And I was like, oh man, I wantto be the best agency in Reno,
Nevada.
And then I was like, oh, maybeon the West Coast, and then the
nation, and then you know,global or whatever.
And but I always thought inthese very, very small steps.
And I think it's a good, it's agood way to think, okay, how
what do I look like when I'mdominating the globe and then
(12:49):
work backwards and then go backand focus on your little small
town.
So if you can think about, youknow, what does this thing look
like uh generation 50 and thengo back to like generation six
or something like that, it'llit'll help your roadmap a little
bit.
It exercises your mind a littlebit, and then and then you'll be
discouraged because you'llyou'll never make it to that
(13:11):
that crazy um you know hundredthgeneration or whatever.
But uh our our uh VP ofoperations, Brian Wolf, he
always says, he says, aim forperfection and you'll achieve
excellence, or something likethat.
It's like aim for the stars andyou'll still get the moon or
whatever.
Like you'll still be prettysatisfied.
So um okay, so you guys are inyou're in Huntsville, Alabama
(13:32):
Alabama.
Lots of grass to cut.
Um is that where you're from, orhow did how did you end up
there?
SPEAKER_01 (13:39):
No, I've I've I've I'm from a lot of places, but
the last place I was in was uhSan Francisco.
But sorry, uh but just Jim, justto touch on that, yes, the the
vision for us is can we takethis and replicate it across the
country, right?
But that's still not V10.
Okay, just so I'm thinking aboutyou, you've encouraged me to
think broader, but I'm stillthinking about it.
But somewhere in there isreplicate this across the entire
(14:02):
country.
But yeah, I don't know if that'sV10.
SPEAKER_00 (14:04):
But okay, so but yeah, a little bit excited about
the sprayer too, because uh frommy my you know 12 to 13 year
old, 14-year-old self, um, Iwhen I realized that I could use
the spreader and put outfertilizer and then upsell that
fertilizer, like it doubled myrevenue.
Um and that was it, that was thefirst experience I ever had to
(14:27):
learn about like margin increaseor whatever.
I was like, oh man, if I, youknow, I'm already there, I'm
gonna do it for you know 15bucks, but I could actually make
30 bucks if I did thisfertilizer service and this
fertilizer only cost me five orwhatever.
Um all of a sudden I was like,oh man, I'm making like double
per hour or whatever.
That was really cool.
So yeah, add-ons like that on aon an existing platform, that
(14:49):
would be that would be prettyamazing.
SPEAKER_01 (14:51):
We just started doing it.
So we started offering sprayingservices.
So weed control, fertilization,you know, weed control is
liquid, so we're still trying tofigure out how to get that on
the bot.
Um, but yeah, I'm I'm I'maligned to you thinking your
13-year-old landscaper is livingin my 40-year-old body, I guess.
But yeah.
Do you guys have to aerate in inAlabama?
Yes.
Okay.
Yes.
So we we have a toe-behindaerator for the robot.
(15:14):
So we we kind of hitch it on andwe can run the bot.
Um it's not as big a service asI I would think, but uh when
customers ask for it, we do it.
SPEAKER_00 (15:24):
Okay.
Yeah, and usually it's a goodway to destroy sprinkler heads.
That's that's the best use ofaeration, I guess.
Or to locate sprinkler headsthat you forgot about because
you'll find it.
Yeah.
Um yeah.
Yeah.
Well thanks for those ideas.
SPEAKER_01 (15:37):
Yeah.
Yeah.
You were in you were in SanFrancisco.
Yes.
So just going backwards here.
So um let's see.
Maybe I'll start.
I I moved to the uh let's see.
Okay, let's start with SanFrancisco.
So I was in San Francisco forabout seven, eight years.
I worked at a company calledCruz, which is a self which is
was a self-driving car company.
It was bought by General Motors.
Um I started there as you know,when it was pretty small, about
(16:00):
60, 70 people.
I started as a front-endengineer.
Okay, that was my previous,previous life as a software
engineer.
And then uh the company grewreally fast.
It grew to about 2,000 people bythe time I was done with them.
And it was um I was uh you know,I was running a team of about
100 engineers and I ran um uhproduct engineering.
So that was all websites, apps,mapping systems, some machine
(16:22):
learning systems, some you know,uh collect fleet management, a
collection of sort of uhsoftware, software pieces.
So I did this in San Francisco,and then during COVID, I
decided, okay, you know, I wantto I want to live somewhere new.
Um and uh I just kind of youknow built a spreadsheet of
fastest growing cities in the USwith great quality of life and
low cost of living.
(16:43):
And I convinced my wife at thattime, like my wife, like, hey,
listen, let's move toHuntsville, Alabama.
And we've never been, we'venever been to Alabama before.
But you know, yeah, yeah.
Yeah, but remember, COVID was atime of like, you know,
rethinking everything, right?
So so we flew out out toHuntsville, we liked it, we saw
a couple of houses, we're like,all right, we you know, let's
get this house.
(17:03):
And we got a house, which wecould, you know, we try to buy a
house in San Francisco, it wasimpossible for us.
SPEAKER_00 (17:08):
Yeah, can you can you talk to me a little bit
about that?
Like tell me, yeah.
Like you know, the price of athree-bedroom, two-bath house in
San Francisco versus Huntsville,like what does that look like?
SPEAKER_01 (17:20):
Yeah, so today, uh so I live in a three-bedroom,
two-bath or three-bath house.
And, you know, I paid, I think,$350 for it uh when I moved here
and during COVID.
Now Huntsville has seen a lot ofgrowth, so it's probably closer
to$500 today.
Um I was living in SanFrancisco, I was paying about
$4,000 in rent for a one-bedroomloft, uh, I guess, you know, so
(17:45):
really just one room.
Um and I tried to buy a house,you know, because I was getting
older.
I was like, I told my wife,let's get a house.
So it was tough, man.
It's really hard to buy a place.
I remember the funniest thing,interaction, was I went, I was
taking a cab in San Francisco tosome place, and the cab driver I
was asking, Do you like SanFrancisco?
I love San Francisco.
(18:05):
He's like, Well, I'm look- Itold him I'm looking to buy a
house, and um I I can spend upto a million dollars.
And for me, that was a stretch,okay, because that's a lot of
money.
Okay.
So I I told him and he laughedat me.
He's like, dude, what are youtalking about?
You're not gonna be able to buya house for a million bucks.
And that I kind of realized likesomething was wrong.
Uh now this was during COVID,things might have changed.
Maybe San Francisco's moreaffordable.
I I don't think so, but maybe itis.
(18:26):
But um, I think cost of livingmatters, you know, just to be
frank.
SPEAKER_00 (18:32):
Yeah, if if you can lower your your monthly nut, you
know, down to, you know, uh asas low as you possibly can, like
your health improves, you know.
Otherwise, you're making amortgage on a million dollars or
whatever.
Plus, you know, in SanFrancisco, it's like seven bucks
for a black coffee and eighteenbucks for a burrito and all this
(18:53):
other stuff.
And then you go somewhere likeAlabama or uh, I'm a little more
familiar with Kentucky becausewe have we have a facility
there.
Uh we any anytime I travel, Iopen up Zillow and I try and
figure out like, okay, what'swhat's my even trade, you know,
for my home in Reno versus whatcan I get here?
And I think in uh, you know, wewe have a uh three-bedroom house
(19:15):
here in Reno, and I think thestraight trade in Paris,
Kentucky was like 80 somethingacres of like horse farm with
like a barn and all this otherstuff.
And I was like, oh my God, youknow, and that's that's a Reno,
which is a step down from youknow a big coastal city like
like SF or or uh Los Angeles orNew York or something like that.
(19:35):
So yeah, anyone listening outthere, your quality of life will
be better, your health will bebetter, your stress will be gone
if you can just yeah, lower,lower your nut and uh uh I don't
know, buy a buy a house in agood community.
That's small.
SPEAKER_01 (19:51):
But Jim, can we but Jim, can we say something nice
about San Francisco?
It has some magic, right?
It has magic.
It has a it is a magnet fortalent, you know, quality of
people is incredible.
So, you know, it there are someupsides, so it's not a straight
decision for everybody, you knowwhat I mean?
But no, I agree.
SPEAKER_00 (20:10):
You know, if you're when you're living in a hub,
there's just totally differentopportunities.
Right.
You'll meet people that youwould have never met otherwise.
Um, you'll have opportunitiesthat you've never had otherwise.
Um, you know, your neighbor willbe like, hey, you know, I got a
job with this new company.
Maybe I can get you a job.
Like that happens in those bigcities.
Uh just running into people onthe subway or something, it's
(20:33):
it's pretty cool.
Um, yeah, it's it definitelyhappens less, especially, you
know, if you have a little bitof land and then you don't
actually see anyone.
Uh which is that's kind of mygoal uh someday.
I I'm I'm a bit of a hermit uhdown deep.
But right anyway, so so you'rein San Francisco working at
Cruz.
Okay, so so yeah, I'm familiarwith Cruz.
(20:54):
That was that was a super coolcompany, um which makes sense
the jump into Yardbot.
Obviously, there's there's a lotof connections there, like the
LIDAR and and everything.
Um, but before Cruise, like youwere doing you were doing stuff
in like banking or something,right?
You were at Goldman?
SPEAKER_01 (21:10):
Yes, yes, yes.
So um so after I graduated fromcollege, I went to Toronto, so
University of Toronto in Canada.
Um I moved to the U.S.
So my first stop was Seattle,Washington.
So I moved to I worked atMicrosoft.
Um that was a short stint, butvery educational for me, but it
wasn't for me.
So I moved out of there.
(21:31):
I went back to Toronto and Itold my parents at that time,
like, hey, I want to I want totry a new industry.
I I I for like very quickly atan early age, no, I don't want
to be in the corporate, like bigcompany situation.
So I want to try a completelynew industry.
So I decided to focus onfinancial markets.
I try to study them, figure outwhat to do.
Ultimately I ended up in a in afirm called Ready, which was
(21:54):
part of Goldman, uh, which diduh electronic trading, so uh you
know, options for.
And I built systems for like youknow routing trades, interfaces
for placing orders, differenttypes of you know, order types,
like pair trades and differentoption types and different
option strategies.
So we we own that interface andthat system and our team.
(22:17):
And yeah, I joined that companyand that that was awesome.
I living in New York, you know,it was uh you know, seven, eight
years there.
And after that, um, I decidedthis was the first time I kind
of realized that, hey, I movedto America, I can do something
entrepreneurial.
So I decided started a companyin the Bitcoin space.
And the Bitcoin is 20, 20, 20,no, 2012, 2013.
(22:38):
Yeah, it's just coming up.
It's just coming up at thattime.
And for me, um, you know, theinteresting thing was that it
was kind of like a financialproduct, but not exactly at that
time.
It was a technical product, liketechnology product, but also a
financial product.
So I came at it from aperspective of like, hey, uh,
I'm working in Goldman's readysystem, and it's it it routes
(23:00):
all these trades from differentexchanges and buys finds the
best price.
And perhaps I should build asystem for um, you know, Bitcoin
across Mt.
Gox and BTCE.
There's like all these exchangesback there.
SPEAKER_00 (23:11):
I don't know how familiar I am with Bitcoin gym,
but you know, there's like I Iremember uploading my driver's
license to Mt.
Gox to get an account created,and it was so freaking sketchy.
I think I had sent fax at onepoint, which is like really
dating me.
I'm I'm 45 or 46.
I think 46.
Um 41, so yeah, about the same.
Yeah.
And yeah, we were miningBitcoin.
(23:32):
We had these little miners fromum uh Butterfly Labs, I
remember.
Yeah.
And they wouldn't send them tome because they were mining with
the miners before they soldthem.
And uh yeah, and then I Istarted losing sleep because I
think big Bitcoin went up tolike 200 bucks or something like
that.
And I was like, oh my god, thisis crazy.
(23:53):
I gotta get out.
And then uh yeah, sold, sold mystake.
So yeah, no more Bitcoin for me.
But my my health has beenbetter.
SPEAKER_01 (24:00):
Okay.
Okay, yeah, yeah.
Are you are you still do youstill have a stake?
I have a little bit of Bitcoin,but nothing, you know.
My company, my yard bot is worthway more for me.
So I'm not I've nothing nothingmeaningful.
I I mean I sold.
That's the that's the reality,you know.
You're supposed to hodle.
That's the that's the wholeidea.
SPEAKER_00 (24:19):
But uh I that's not for me, man.
I'm not a day trader.
I'm just not told for it.
Those guys are built totallydifferent.
Um I just couldn't do it.
I think yeah, you you hear aboutpeople spending, you know, uh 25
Bitcoin on a pizza or whatever.
And right, yes.
If I look back at how much Isold, it would be nice to have
today, but uh, you know, I don'thave a time machine.
SPEAKER_01 (24:40):
Hard to look, you know, I I ordered some commander
you remember Dogecoin, right?
It was uh it was a thing backthen.
So I ordered these uh theselittle gold Dogecoin things by
paying for them the Dogecoin,and obviously the amount that
the Dogecoin's worth versusthose little you know commander
com uh you know, just like coinsis is is way different.
(25:00):
So anyway, but just to just tokind of go there, you know, I
worked on the Bitcoin space.
I I but this was a big lessonfor me, Jim.
It was like I didn't I wanted todesign something that I wanted
to do, and I didn't care aboutthe customers, okay?
This is I know it's obvious,okay, but hard lesson is like I
wanted to design amulti-exchange Bitcoin clearing
system for the for thesophisticated trader trading
(25:21):
Bitcoin.
Okay, those things, none ofthose make sense.
Okay, they they in in 2024, 20,or sorry, 2012, 2020, whatever
time frame it was, none of thosethings were needed by anybody.
Okay, so I built a system, uh,nobody wanted it, and then yeah,
it was a struggle to try to makesense of it.
And it was a hard lesson.
And you know, uh at some point Italked to my wife, I said, Hey,
(25:43):
everything is uh, you know, bankaccounts are zero.
We you don't have anything goingon, so I think I need to get a
job at this point.
So, and that's where I ended upat Cruz uh and we decided to
change the scenery as well.
We went from from New York toSan Francisco.
SPEAKER_00 (25:56):
Okay, okay.
That's yeah, that's awesome.
I I'd love to hear aboutpeople's backgrounds and the
weird journey, because you know,uh sometimes like you know, a
new grad will come up to me andbe like, Jim, yeah, how did you
do this?
You know, you're yeah, you havethis this cool company, and you
know, man, you must have likeknown exactly what you were
gonna do.
I was like, hell no, man.
(26:16):
I was I was selling candy on thebus when I was eight, and I was
mowing lawns at you know, 12 orwhatever, and uh I was working
at um Office Depot.
Like I everyone has these weirdbackgrounds, um, but you you
learn a little bit, you know,everywhere.
So if you just had a straightline, like if I was always in
(26:36):
manufacturing or something likethat, um I think that that's
what people expect.
But really, I think it'ssampling all these different
experiences and then putting toputting them together in a a
different way um for a legacyindustry is really interesting.
So and that's that's exactlywhat you've done.
You've taken background from youknow Goldman and the the company
(26:57):
was celery, right?
But that was the Bitcoin.
That's right.
That's right, yes.
Okay, so and and then cruise andeverything, and you applied it
all into a very legacy uhindustry like landscape
maintenance, and and that's whyyou're disruptive.
So um I don't know, a hugeinspiration, man.
This is this is super, supercool.
Um however, I know it's noteasy.
(27:19):
Uh there's you we've talkedabout some of the the failures
along the way.
Can can you tell us any of thethe down and dirty behind the
scenes of like failures?
Um like we we talk about ourraccoons in the ceiling.
You know, we we bought a afacility, the ceiling was full
of raccoons, we had to figureout how to get them out, you
know, just crazy stuff that youdon't think about.
Um when you're like, I want tobe a business owner.
(27:42):
Uh what are some of the failuresthat you've had either with Yard
Bot or anything that that elsethat you can talk about?
SPEAKER_01 (27:49):
Yeah, I mean, I think um the first thing comes
to me, Jim, is like uh honestly,people, humans, you know, um the
kind of environments that Ispent time in at Cruz or in New
York and Goleman, it's a verydifferent type of environment
than working with landscapersand you know, and it's uh it's
(28:11):
just a different world.
Blue-collar work essentially isa different world.
So because of the hybrid natureof yard robotics, yard yard bot,
it's you know, I have to have asizable force of people.
Like we have nine right now thatkind of go out and drive the
trucks and and take the robots.
And, you know, everything I kindof I thought I was I knew
everything about managingpeople.
(28:31):
I was running a team of 120engineers, and how hard can it
be to run a small team ofblue-collar workers?
And it's it's just completelydifferent.
Their incentive structures aredifferent and interactions are
different.
And you know, when you thinkabout a corporate environment,
you're like, oh, you know, thiswould be unacceptable or
acceptable.
All those things change.
So I had a lot of failures therelearning about you know what
(28:54):
motivates people and what keepsthem happy in, you know, if
they're not getting paid thesame way as as we as people get
paid in San Francisco.
So uh yeah, I'm sorry, I'm notvaguely being specific there,
but you know, it's just a hardlearning uh on on the on the
blue-collar side.
On the robot side, I mean, youknow, we've had robots end up in
(29:15):
a lake, um, you know, and had tohad to kind of pull them out.
We've had robots fall apart, youknow, the deck falls off, you
know, all those kind of things.
So we've just had a bunch offun, random things.
Thankfully, every everything isrecoverable, everything is
everybody's understanding.
The customers honestly love therobots and seeing them in their
yards.
So nothing super crazy.
(29:36):
But yeah, it was it was asurprise when the robot was in
the lake and they were trying topush it up and they called me
saying, Hey, I think we needanother to get this out of here.
SPEAKER_00 (29:44):
It's it's definitely a flex uh to have the robots
mowing your lawn.
Like I would, I would definitelyshow that off to my neighbors
and be like, oh yeah, no, I gotI got robots who come in here.
Yeah, I was gonna ask you alittle bit about um, you know,
how you go about recruiting forthese teams, because it does
seem like a hybrid.
Like you have to, you know,you're you're gonna run this
(30:08):
piece of high technology thathas LiDAR on it and stuff, um,
but then I also need to throw abackpack blower on you and you
know go go blow leaves out ofstuff.
Um can you can you get atraditional landscaper to, you
know, uh get on board with theserobots, or or do you need
(30:29):
someone who is like a littlemore interested in technology,
or what's the ideal candidatethere?
SPEAKER_01 (30:34):
I think it works on both sides, right?
So we we have traditionallandscapers and we have guys
who've basically never mowed alawn before this.
Um the way I think about thisis, and this this is something I
do carry from my pastexperiences when when we think
about individuals to hire, wethink about skills, values, and
abilities.
And I think I was taught this inNew York, I think it was maybe
(30:55):
um uh Ray Dalio, but I'm notsure who kind of came up with
the system.
But it's basically skills beingsomething that's super uh easy
to acquire, three to four weeks,abilities taking about a year,
and values are something you'reborn with, right?
So values are stuff like do youwork well with others?
How much how big is your ego?
And you know, things like that.
So, you know, in engineering,when I when I recruited people,
(31:15):
it was always about values only,because I mean there's skill
tests and you know and abilitiesare kind of like you kind of get
them over time, but it's aboutvalues.
Can you work in a teamenvironment, things like that?
So that kind of came came, youknow, in the blue-collar work as
well, except there is aconstraint.
The constraint is because thejust to be frank about it and
honest about it, because of theway the system works, they're
(31:36):
not paid as much.
So money is really critical.
And a lot of incentives getwrapped around compensation,
right?
Uh it's it does so inengineering as well, but in
blue-collar work, it really,really matters, right?
So, you know, so I still lookfor values, but you do need
skills because we don't, youknow, we're we're not gonna be
able to teach you how to weedeat in a in the time frame that
we're looking for you toperform.
(31:58):
And the the key other element ofthis gym is training, right?
So we have a rigorous trainingprogram.
So every mistake we've madeever, right, is in our system.
Every time we forgot a modebehind a fence, every time we
hit a sprinkler head, we have anote and photos.
So it's like a dream of trainingmaterial, right?
(32:19):
And thank you, LLMs.
You know, this is one placethey're really good.
So we can take all this data.
SPEAKER_00 (32:24):
It sounds like a perfect application for LLMs.
SPEAKER_01 (32:28):
It's perfect.
So we have a handbook that hasthe 50 different things
everybody needs to be uh needsto keep in mind, everything from
like soft stuff like yourpresence in a neighborhood,
yelling loudly or smoking or youknow, things like that, to
precise stuff like what isedging?
What does that mean?
You know, how do you how do youscrew it up?
How can you make mistakes?
You know, we have photos andexamples of that across every
(32:48):
single aspect of the business.
And, you know, thank you, LLMsagain.
You know, we can aggregate allthis data and present a solid
training program that our guyscan take.
And then once they do that, wepair them up so we'd send a deal
truck out.
Our typical, our sort ofstandard procedures to send one
person per truck, but we'll sendtwo out when they're training,
and then we send one out.
And so far, honestly, this isafter a couple of iterations,
(33:09):
this has been the best systemfor us so far.
So this is not the a preciseanswer to what you're asking,
but these are sort of all theconsiderations when it looks to
hiring hiring people for ourfirm.
SPEAKER_00 (33:17):
Yeah, um, I agree with you a million percent on
values.
Like I I can teach anyone to runa machine, you know, or drive a
forklift or you know, um typeinto a piece of software or
whatever, but I I can't teachyou how to be friendly, um, you
know, how to how to answer thedoor when a customer is knocking
(33:38):
or whatever.
Like your parents should havedone that.
So we we try and identify thosethings during the hiring
process.
Um one of my favorite things todo is like I'll grab a tote of
heavy parts in it, and I'll belike, hey, you know, follow me.
Let's go out on the shop floor,and I'll walk towards a door
that needs to be opened for me.
And I'll see if they grab thedoor.
You know, if they just arethinking one step ahead, like,
(33:59):
oh, he his hands are full.
How's he gonna open up thatdoor?
Let me let me grab it for him.
And then there's some peoplethat'll just sit there and like
watch me struggle.
And I'm like, oh, come on, man.
You know, like who raised you?
So it all goes back to theparents, but um so yeah, we were
we were talking about, you know,AI is that's actually a perfect
use of this.
Um you take all this data, likejust log it, just log it, log
(34:22):
it, log it, and then use LLMs togo through it and identify these
major pain points.
So um, how effective has thatbeen?
Like, can you identify, oh man,um we need sprinkler head
detection in the next revision,or uh, hey, we've identified we
need to make the mower deck 35inches so it can fit behind a
(34:43):
gate better or something likethat.
Like, what are what are some ofthe big things that AI has
revealed to you that that arekind of unexpected?
SPEAKER_01 (34:50):
Yeah, so the uh so Jim, AI, um the way I think
about it is first is you know,there's different aspects.
So computer vision and LLMs,right?
So on the computer vision side,this is where I kind of learned
some things at Cruise.
You know, how can we use ourcameras to detect things like
there's a hose on the ground oryou know, like uh there's
there's a dog toy in the city.
SPEAKER_00 (35:10):
I ran over a lot of hoses.
Yeah.
SPEAKER_01 (35:12):
Yeah, yeah.
And you know, they cost 60, 70bucks, so it takes takes the you
know price of the mow away.
So um those are sort of sort ofthe foundational things that we
have, and we're iterating on,we're collecting data on, we
record, you know, all camerasacross all our systems.
And you know, we're very small.
So I do all the software work inthe company, so you know, we're
(35:32):
iterating on it still.
So it's not fully perfect, butthat's kind of what the AI uses
on the computer vision side forour for our robots.
The place where it's actuallybeen incredible to use LLMs in
the in the sort of the in thatpart of the AI is obviously
programming.
So, you know, when it comes tofront-end software development,
even back end, likerun-of-the-mill crud stuff, you
(35:54):
know, database stuff, you know,the low-hanging fruit of are,
you know, you're you can be somuch more productive today than
you could just honestly lastyear.
So that's changed thingscompletely.
And then the last sort of areawhich you're you're touching on
is when you have, you know, Icall it like context shaping.
I don't know if this is theright term in the LM industry,
but like you have all thiscontext, you have all this
(36:15):
customer data.
Can you use it to do, forexample, a training manual?
Okay, that we can.
How about how about we do asentiment analysis on every um
every phone call text and emailcoming from every customer every
single day, so we know by theend of the day which customers
are the most happy or mostunhappy, and use that to build a
model to see which one's mostlikely to churn because we get
the same mistake two times in arow, or you know, the language
(36:38):
from uh their text or the thetranscription of their phone
call.
You know, it's just uh somethingthat's honestly never been
possible.
And it's that gets me veryexcited.
Uh, but obviously it comes withlimitations, but that is where I
think LLMs have just beenincredible for us that we can
with it with a super small team,we can just provide such a high
quality of service, is what'sbeing different for first.
SPEAKER_00 (37:01):
It it allows a super small team to you know uh have
the same performance as a as alarge team.
You know, the the way that wesee it is uh we don't see LLMs
here to eliminate jobs, we seeit as a way to make the existing
jobs like you know better andbetter.
So then we can actually hire youknow more people, but they're
(37:22):
every time we hire someone andpair them with an LLM or
something, they become asuperhuman.
So um yeah, it allows it allowsthe teams to be lean.
And I actually think in thefuture, you know, if we're doing
it the yard bot method whereyou're allowing a small team to
act like a big team, I thinklayoffs will be much more rare
in the future because companiesaren't gonna overstaff and have
(37:45):
you know 10,000 accountants orwhatever.
They're gonna start with twoaccountants and then layer on
this like superhuman ability andthen grow at the right pace and
and hopefully you know not havehave to suffer from these like
massive layoffs that you see atthese big corporations.
So I think we're going through achurn time right now.
Um, but in the future it'll be alittle more predictable, I hope.
(38:06):
Dom was saying there there's nosnow in uh in Alabama.
Because we were we're talkingabout like, could these things
what else could they do?
Could they could they blow snow?
Could they uh you put a dozerblade on the front of it?
Like have you ever thought aboutsomething like that or in
different markets?
SPEAKER_01 (38:22):
Oh yeah, yeah.
We did we were asked to build aprototype for a military base uh
for snow removal.
Um we didn't so we builtsomething, but we weren't happy
with it.
So snow removal just has adifferent I I don't I guess I
can go deep in there, but likeit it, you know, I'm from
Toronto, right?
So it snows a lot.
My wife's from northern Ontario,so it snows a lot up there.
(38:44):
So I'm familiar with snow.
The thing about it is it's veryirregular, right?
So mowing, you're doing it atlike a week, two weeks, but when
snow comes down at a base, forexample, it needs to be cleared.
There's no option and all handson deck, but then it may not
snow for a while.
So it's very spiky.
And um, you know, the uh yeah, Iyou know, I I'm not uh this part
(39:06):
I'm not sure about.
The perception system might haveto be slightly more
sophisticated than what we have,but perhaps you can make it
work.
Yeah, snow removal is a terriblebusiness.
I don't want to say that, butlike it's just a different
different thing.
SPEAKER_00 (39:17):
I mean, for our thing, yeah it's hard.
I have family down in uh MammothMountain, California.
Okay.
And if it starts snowing, likethat's what you do.
You wake up every three hoursand you go and shovel.
Because if you get behind it,you'll wake up and you you have
seven feet and there's nothingyou can do.
You know, you have to wait tillspring, basically.
So yeah, snow removal is hard.
(39:39):
Or or like, you know, heresometimes we'll get one inch,
uh, and it's like justborderline, should you even
bother?
Um but all these snow removalcompanies are out there because
they're trying to bill for it.
Uh, and then on the days when wedo get two feet, then they don't
show up because they're too damnbusy at you know hospitals or
you know, these primarycustomers or whatever.
(40:00):
That's that's a hard business.
Um but Huntsville, it's so yousaid you had 600 customers,
right?
That's right.
600 lawns that we maintain, yes.
Dude, that's awesome.
Um are you thinking about youknow other uh other markets like
nearby?
Or I mean I guess there's alsomany, many more lawns to go
(40:20):
there.
Yeah, what's what's next?
Like what's uh what market isexciting?
SPEAKER_01 (40:24):
So, Jim, the way I think about it is Huntsville for
us is sort of like a like a testcase.
So we're kind of every uh everypart of the business from
building robots to running themto the sales and marketing, to
the operations, to theengineering, like all that stuff
is kind of like this is uh youknow, like the base case for us
in Huntsville and and thefinancial aspect as well.
(40:45):
Are we making money, right?
So we add all this together.
And for me, the objective is totake this and copy paste this is
and in many as many cities as asit makes sense, right?
So what's very attractive for usis any place where grass grows
eight to ten months a year ormore.
So everything starting fromTexas all the way across to
Florida is really attractive forus.
Um the next phase of ourbusiness would be to take this
(41:06):
and take it to one of thosecities and then see, you know,
what what what things, whatstrategies kind of continue to
work and what don't.
But I do the objective is tocopy it to maybe let's go to
Nashville or let's go to Austinor let's go to Orlando, set up
the system, set up the samesystem, set up, you know, and
we'd run it very lean, right?
So, you know, we we have astorage unit right now.
(41:28):
So, you know, this is our officewhere we hang out, you know, but
the the the law the robots andstuff leave from a storage unit.
That's kind of what our setupsystem setup is.
And you know, we can do this ina new city, so very low fixed
costs uh for us.
And um anyway, but yeah, longstory short, yes, to answer your
question, geographical expansionis a key element of a
(41:48):
landscaping company.
You know, all landscapingcompanies that are big,
billion-dollar companies,they're in 200 plus cities and
we want to be there.
SPEAKER_00 (41:54):
Wow.
Um what do you think is gonna bethe number one challenge uh of
the first new market that you gointo?
Okay, so let's see.
Because I have an idea.
SPEAKER_01 (42:06):
You have an idea.
All right, let's hear our ideafirst.
Let's hear it.
SPEAKER_00 (42:09):
I I think it's I think it's management.
I think it's leadership.
Okay.
Um when we open up a newfacility in a new state, yeah.
It's that the the operationsdirector, that's what we call
it, the guys who run ourbuildings, identifying that
operations director is key.
Um they will they will make youor break you.
And we've had nothing but goodones.
(42:30):
But now as we're we're trying toexpand into more cities, um
normally we could pull guys offthe bench.
You know, we're like, okay,you've you've been here long
enough, you've figured it out,you you have these qualities.
Um you know, if if you're ifyou're young and you want to
relocate, like we'll give youthis opportunity.
But what's happened is likewe've been going so fast that
(42:52):
our bench hasn't been able tokeep up the pace.
So we don't we have a lot ofguys that are awesome right now.
They just need like a couplemore years to learn.
Um, so once you deplete yourbench, then you have to go from
outside the company.
And then that's a a big gamble.
So we're trying to figure outlike what qualities do we want
to identify in these cities.
Um because relocating someone isreally challenging too.
(43:14):
Like you're taking a big gamble,you're asking them to do a lot
for you.
Um, you know, especially if theyhave like wife and kids or
whatever.
I'm like, hey, uproot your lifeand go to this city that you've
never been to.
Um so if I can recruit peoplefrom those existing cities, then
it's a little less risky.
Um anyway, sorry.
Sorry to give you your answer.
SPEAKER_01 (43:32):
But that's no, no, I I I like it.
So I wasn't, you know, to befrank with you, this is a
weakness of mine.
I was thinking about robots andmanufacturing, but humans are
always the challenge.
Um the only two things I'd saythere, Jim, is one, you know, I
uh the way you're thinking aboutit is how I think about it is
like, can we take our existingbench and ask them to do a tour
(43:52):
of duty in a new city and andbring it up essentially, right?
So they go out, the first 60 to100 to 200 customers they kind
of take care of.
Till we get local talent that'skind of ready to take on take it
on.
And the second thing, and thisis the sort of irrational
technologist problem that Ihave, is we have so much data.
Okay, we have video of everylawn being mowed across 12
(44:14):
cameras and every truck beingdriven, you know.
Can we leverage this data,perhaps, to make sure that
things stay straight?
But I don't know.
But that's kind of the you knowirrational optimism I have is we
can make it happen.
SPEAKER_00 (44:30):
No, I I love it.
We we spend a lot of timestudying um Starbucks and
Chick-fil-A and In N Out Burgeror whatever.
We're trying to figure out howhow can you expand, you know,
you go from 10 Starbucks to athousand Starbucks.
Like, what does their forwarddeployed team look like?
(44:51):
Um, how are they identifyingthese new managers who are going
to run that location?
You know, with Chick-fil-A, theyhave this crazy, you know,
audition hiring process.
Um, it's like a lotterybasically to get to run a
Chick-fil-A.
So I'm like, man, how do you howdo you build that?
Well, it starts with like areally cool brand, a really cool
culture that people want to getinto.
And then so I'm like, okay, soeverything that we're trying to
(45:13):
do to expand now actuallystarted, you know, eight years
ago uh in order to attract thattalent.
So I don't know what the magickey is.
I'll I'll let you know as soonas I figure it out.
SPEAKER_01 (45:22):
So thank you.
But I gotta also say, you'relike about 10 steps ahead of me.
My problems are much smaller.
But uh but yeah, sounds like Imean, congratulations,
obviously, everything you Jim,you've accomplished that second
step, but you guys are wayahead.
SPEAKER_00 (45:37):
So learning, man.
Overnight success takes 10years.
Um so we we got a ways to go.
Um actually, and that's that'skind of a great place to kind of
wrap this.
Uh I was gonna ask you our finalquestion here.
What's what's one piece ofadvice that can be about
anything that that you want theaudience to walk away with?
SPEAKER_01 (45:58):
Um don't give up too soon.
That's what I would say.
You know, a lot of times I'vedone something and then said,
Oh, I'm gonna give up on this,like Bitcoin.
You know, um and then exactly.
But maybe not Bitcoin's not theperfect example, but you know,
where I've kind of just made adecision too soon, but just
gotta stick out stick it out anditerate, and then things can get
(46:20):
better.
So cool.
SPEAKER_00 (46:21):
I love that.
Divya, thank you so much foryour time.
Yeah.
Um, if anyone in the Huntsville,Alabama area is interested in
getting one of these things intheir yard to flex on their
neighbors, uh, where should theygo?
Uh just yard.bot.
Yeah.
Yard.bot.
Oh yeah, go check it out.
Um editor Logan will put a bunchof photos here of the awesome
robots.
(46:42):
Um dude, thanks again for yourtime.
But this is where I say myoutro, which is actually Dom
just hitting a button.
And to be frank with you, everything just broke.
He's like, dude, what are youtalking about?
SPEAKER_00 (00:03):
You're not gonna be able to buy a house for a
million bucks.
If this whole Senkut Sen thingdoesn't work out, we're gonna go
back to mowing lawns.
That's how I made money for RCcars, which was like my my drug
addiction back in the early 90s.
SPEAKER_01 (00:14):
I was running a team of 120 engineers.
And how hard can it be to run asmall team of blue-collar l
workers?
SPEAKER_00 (00:20):
You can have a thesis, which is like my
freaking least favorite word.
Everyone has a thesis now.
Hey guys, welcome to the JustGonna Send It podcast.
I'm your host, Jim Belosik, andtoday I'm joined by Diviator of
(00:42):
Yardbot.
So Divya, thanks thanks forbeing on the pod.
Thank you.
SPEAKER_01 (00:46):
Happy to be here.
SPEAKER_00 (00:48):
Cool.
We're gonna dive into uh kind ofDivya's background and how he
finally got to Yardbot, uh,which is awesome if you haven't
seen it.
And so it's yard.bot, right?
That's the web address.
That's correct.
Yes.
Okay.
Logan Logan will put it righthere.
Um tell us a little bit aboutYardbot before we go into your
(01:08):
background and everything.
Like what's what's Yardbot allabout?
SPEAKER_01 (01:12):
Yeah, so uh Yardbot, or we sometimes call it Yard
Robotics, is a hybridlandscaping and a robotics
company.
So on the front end of thebusiness, we do what's
traditionally landscaping.
So mowing mowing lawns, weedeating, edging, blowing, and
also other activities likemulching and bush trimming for
residential and commercialcustomers.
So at the front end of thebusiness, we present as a as a
(01:33):
lawn care company.
And on the back end of thebusiness, uh we build robots, we
design and build robots that weultimately use for this uh
activity, primarily for themowing side.
Um so yeah, it's kind of acompany of these two sides,
essentially.
SPEAKER_00 (01:46):
Okay.
Um yeah, I I definitely thoughtit was more just like robots
that you could put in your yard,but it's it sounds like it's
yeah, a hybrid of an actual liketraditional landscape
maintenance company.
So are your guys are likehauling these things around on
trucks and then they're weedeating while these things are
mowing?
SPEAKER_01 (02:03):
That's correct.
So yeah, we we have a truck withthree robots and weed eaters,
Azures blowers.
We train our guys to use theserobots in remote control and in
autonomous mode.
So remote control when they'redriving it with a remote
controller or autonomous mode,let's say you see an open yard,
you can put the robot there andit can mow autonomously.
And at the on the other side ofthis, we're we're presenting as
(02:24):
a company that provides areplacement for a traditional
landscaping company.
So for the from a customer'sperspective, we are better, our
lines are straighter, our priceis lower, and on our side, we're
we're kind of doing this becausewe have these robots that we
built.
SPEAKER_00 (02:37):
Okay.
That's yeah, that's wild.
Um I've I feel like, you know,what, uh 10 years ago or
something, I heard about allthese companies that were trying
to do, you know, a Roomba foryour lawn.
And you know, I know a coupleguys who bought them and uh and
they sucked.
You know, they would like driveacross the street or or they'd
get stuck in a ditch orwhatever.
So it sounds like you guysdidn't try to um over-automate,
(03:00):
you know, it's a a mix of a mixof dudes and robots is is maybe
the magic.
SPEAKER_01 (03:05):
Yeah.
Yeah.
So so you know, when you thinkabout lawn care, Jim, the end
product is a good-looking lawn,right?
So the solution path can be arobot or can be a human being.
You know, the the insight thatsort of we built is that the end
customers, you know, these arelike moms, you know, they're not
they're they want a good lookinglawn.
They're not interested inlearning how to operate a robot
(03:27):
and make it do it correctly.
And even if even if it does aperfect job, there's human
activity, weed eating, edging,blowing the front drive, you
know, front of the house.
The robot's not gonna do that.
So, you know, ultimately thetrue solution, what we realized
is we people just want a betterquality lawn care service.
And it's a different mindset tothink about this from this
perspective versus let's we havea great robot and we want to
(03:49):
give it to people.
So that's that's sort of adifferent way.
unknown (03:51):
Yeah.
SPEAKER_00 (03:52):
Yeah, you're you're kind of speaking my language
because uh every day I see allthese companies that are trying
to just over-automate something.
They're like, oh man, we'regonna get rid of all of our
staff and just have robots.
Um, you know, or we're we'regonna have we're gonna automate
our forklifts, we're gonnaautomate, you know, all of our
lines, we're gonna havehumanoids, we're gonna do all
(04:13):
this stuff, and we don't needpeople anymore.
And we're just not there yet.
You know, and honestly, I don'tknow if we'll ever be there.
And, you know, one of the thingsI'm proud of at Send Cut Send is
we use dudes where it makessense to have a dude, and we use
robots where it makes sense tohave a robot.
So if it's repetitive or youknow, uh a very discrete problem
that's that's uh solvable in thesame way over and over and over
(04:36):
again, it's great to use arobot.
But uh I don't know, not a lotof companies are thinking about
you know uh what the mix islike.
You know, is did you did youknow that was gonna be your your
take from day one or you know,like the hybrid, uh, or or were
you trying to fully automate it,or how did you end up there?
SPEAKER_01 (04:56):
I mean, it was just lessons, right?
So I started off by thinking ifI design the perfect robot at a
great price, it's gonna solve myproblem.
And I built something.
And then I realized that it wasnot, it's not, it doesn't do the
full job.
So that you know, it was aseries of like lessons.
Then I, you know, the nextiteration of the business was
perhaps I can sell this totraditional landscaping
(05:17):
companies.
So then I started knocking ondoors there.
You know, the reality is thelandscaping industry is a you
know, it's a different world.
And the kind of talent thatworks in landscaping, I mean, I
came from software, hardwarebackground.
So it's a different type oftalent.
And they'd rather hop on a zeroturn and whip through 10 yards
than figure out how to sit infront of a computer and run a
fleet of 20 robots.
That's, you know, it's adifferent kind of person.
(05:39):
So it was a difficult time doingthat.
And then ultimately I realized,hey, if I can just be price
competitive and competitive froma quality perspective, I can
just start a landscaping companyand you know the robots can make
it happen.
So that's kind of the currentiteration of the business, and
this is the this is the onethat's been the most successful.
So to answer your question, it'sjust gone through a couple of
learnings here and ended upwhere we are today.
SPEAKER_00 (06:00):
Cool.
Yeah, I think that's that's ourour favorite motto here is uh
learn by doing.
Uh, you can have you can have athesis, which is like my
freaking least favorite word.
Everyone has a thesis now.
Like, no, just hey, I got arandom idea.
I have a gut feeling, I'm gonnago test it, I'm gonna go get
beat up.
And you you just have to knowthat where you're starting and
(06:20):
where you end are gonna be twocompletely different places, and
the most successful people arethe ones who can pivot and
adapt.
So uh it's it's funny.
My CTO and myself, um, we bothhave a background in
landscaping.
His he worked for like aprofessional big landscaping
company during college.
Okay, and Jacob Graham.
Um, and then I had a lawn mowingroute since I was like 11 or 12
(06:46):
or something.
Um so that's that's how I mademoney for RC cars, which was
like my my drug addiction backin the early 90s.
Um so he and I have always jokedthat you know, if this whole
Senkut Sen thing doesn't workout, we're gonna go back to
mowing lawns and uh and kind ofmake a new business model for
it.
So it sounds like you're alreadydoing it for us.
(07:07):
So hopefully I don't have to uhyou know run a blower all day uh
ever again.
Uh that that gets a little alittle crazy sometimes.
Um so what's what's next forYardbot?
Like I I looked at your robots,they're rad.
Um I see a lot of send cut sendparts on there.
Uh what generation are you on?
Like what how many how many uhiterations is this?
SPEAKER_01 (07:29):
So we're on our fifth iteration right now.
Um and it's um you know it's a36-inch deck, which is uh
perfect.
Most of our customers are sortof subdivision homes, like
residential homes.
We do about 600 locations in theHuntsville area, Huntsville,
Alabama area.
Um so we have a fleet of 20robots today.
We're building 10 more very,very soon.
(07:51):
We just actually got the orderin.
Thank you, Sankat San,yesterday.
Um, so uh these robots um uh areyeah, so they're 36 inch, 250
pounds, they have a fivekilowatt hour battery, uh triple
blade, um, and it's got anautonomous system that's lighter
and camera based.
And let's see, and yeah, it canrun all day.
(08:11):
It can do one robot can do 10yards, uh, and we put three, so
the guys are you know a littlebit, yeah, they they're a
little, they have too many, butum and yeah, that's kind of the
high level, the little specs ofthe system.
SPEAKER_00 (08:23):
Dude, that's that's amazing.
Um 36-inch deck is no joke.
Like, usually that's uh youknow, many horsepower gas engine
to to cut all that.
And then you're in Alabama wherethat that's like that's like
real grass.
That's not this dry ass, youknow, Nevada fake grass that we
have.
So uh like what are what aresome of the challenges you've
(08:45):
had?
You know, you're on fifthgeneration.
Like tell me about the failuresof one through four.
SPEAKER_01 (08:50):
Yeah, so you know, um a critical person in the
company is Nathaniel Chong.
He's my uh you know, he's headof engineering hardware.
So I first started designingthem, Jim, and you know, I just
3D printed stuff because I camefrom a software background.
That was my first step intobuilding something.
And to be frank with you,everything just broke pretty
much right away.
So that's when I discovered SenConsent, to be frank, and I
(09:11):
realized I could just actuallyget metal parts and started
building the first couple ofgenerations.
The two areas I would say mostiteration we've done is the
drivetrain and the deck itself.
So the drivetrain, uh, westarted off with skateboard
motors, so e-skate motors, um,which were direct drive.
Over time, then we added areduction with a belt, and uh we
(09:31):
realized the grass gets into thebelt and tears the belt apart.
Next we moved to the chaindrive.
Well, uh, we moved to gears,actually, and uh we used
planetary gearboxes uh for thenext iteration.
Um we were using five to one,that was not enough, so we went
to 20 to 1, but the planetarygearboxes, the pinions were too
small, so they would they wouldshear off.
So then we finally moved to achain design, and that's a
(09:52):
current iteration, and it'samazing.
It's actually really, reallygood.
So it's uh it's a five to onereduction on a gearbox and then
a three to one on a chain, andwe do a it's a differential
drive.
So every every wheel is powered,and uh, we can basically climb
any hill or do anything we wantwith the robot at this point.
So it's it's really solid.
SPEAKER_00 (10:10):
Um what's that make sense?
Yeah, no, that's that'sincredible.
It goes back to like learn bydoing.
Yeah.
Um, you know, you can you canspend six months going through a
bunch of iterations in CAD orwhatever, or you spend six
months you know building fivedifferent versions, you'll have
a way better product if youactually get your hands dirty
(10:31):
and go see what's what breaks.
Um I've seen so many people thatare like, oh yeah, this is this
is gonna be super, super strong.
And then yeah, they there's theunknowns.
Um oh, I didn't realize grasswas gonna get stuck in there, or
I didn't realize, you know, thatin muddy conditions the damn
thing's gonna tip over.
So I gotta readjust my center ofgravity or whatever.
So just yeah, learn by doing.
(10:52):
Your your modeling in CAD iscute, but go out and actually
like get shit done too.
Um what's okay, so tell me tellme what is what is generation 10
gonna look like?
Like what's your dream?
SPEAKER_01 (11:04):
So I think okay, that's an interesting question,
Jim.
So um I think there's obviouslylike mean time to failure, which
we need to bring down across thefleet.
And we're already pretty good,but every year it surprises us.
We find new failure modes thatwe have to have to drive down.
Um I think the platform, Jim, Ithink about it more than just uh
(11:26):
mowing platform.
So uh the way it looks, you cankind of tell it doesn't look
like a traditional zero turn.
It's designed to hold a deck,but perhaps hold a spray system,
or perhaps hold a something topick up garbage.
I think about it as like a unitof automation that you can
deploy in your community andthen you know achieve like a
(11:46):
better quality of life.
That's kind of how I think aboutit mentally.
So um I haven't thought throughgeneration 10 yet.
I'm a little bit in theiteration mode right now, but a
platform that's super reliable,can run autonomously, and can do
multiple tasks across a largearea is kind of what what I'm
looking to do here.
SPEAKER_00 (12:04):
I'll I'll tell you that's that's a fun exercise
that I do all the time is whereare we at now, but then where
are we at in 10 years?
Or, you know, what doesgeneration 10 look like?
I've like throughout my career,I've always thought very small.
Um and sometimes that's bit mein the ass a little bit.
You know, when I was uh my myprevious life, I was a graphic
(12:25):
artist and you know had a littletiny advertising agency.
And I was like, oh man, I wantto be the best agency in Reno,
Nevada.
And then I was like, oh, maybeon the West Coast, and then the
nation, and then you know,global or whatever.
And but I always thought inthese very, very small steps.
And I think it's a good, it's agood way to think, okay, how
what do I look like when I'mdominating the globe and then
(12:49):
work backwards and then go backand focus on your little small
town.
So if you can think about, youknow, what does this thing look
like uh generation 50 and thengo back to like generation six
or something like that, it'llit'll help your roadmap a little
bit.
It exercises your mind a littlebit, and then and then you'll be
discouraged because you'llyou'll never make it to that
(13:11):
that crazy um you know hundredthgeneration or whatever.
But uh our our uh VP ofoperations, Brian Wolf, he
always says, he says, aim forperfection and you'll achieve
excellence, or something likethat.
It's like aim for the stars andyou'll still get the moon or
whatever.
Like you'll still be prettysatisfied.
So um okay, so you guys are inyou're in Huntsville, Alabama
(13:32):
Alabama.
Lots of grass to cut.
Um is that where you're from, orhow did how did you end up
there?
SPEAKER_01 (13:39):
No, I've I've I've I'm from a lot of places, but
the last place I was in was uhSan Francisco.
But sorry, uh but just Jim, justto touch on that, yes, the the
vision for us is can we takethis and replicate it across the
country, right?
But that's still not V10.
Okay, just so I'm thinking aboutyou, you've encouraged me to
think broader, but I'm stillthinking about it.
But somewhere in there isreplicate this across the entire
(14:02):
country.
But yeah, I don't know if that'sV10.
SPEAKER_00 (14:04):
But okay, so but yeah, a little bit excited about
the sprayer too, because uh frommy my you know 12 to 13 year
old, 14-year-old self, um, Iwhen I realized that I could use
the spreader and put outfertilizer and then upsell that
fertilizer, like it doubled myrevenue.
Um and that was it, that was thefirst experience I ever had to
(14:27):
learn about like margin increaseor whatever.
I was like, oh man, if I, youknow, I'm already there, I'm
gonna do it for you know 15bucks, but I could actually make
30 bucks if I did thisfertilizer service and this
fertilizer only cost me five orwhatever.
Um all of a sudden I was like,oh man, I'm making like double
per hour or whatever.
That was really cool.
So yeah, add-ons like that on aon an existing platform, that
(14:49):
would be that would be prettyamazing.
SPEAKER_01 (14:51):
We just started doing it.
So we started offering sprayingservices.
So weed control, fertilization,you know, weed control is
liquid, so we're still trying tofigure out how to get that on
the bot.
Um, but yeah, I'm I'm I'maligned to you thinking your
13-year-old landscaper is livingin my 40-year-old body, I guess.
But yeah.
Do you guys have to aerate in inAlabama?
Yes.
Okay.
Yes.
So we we have a toe-behindaerator for the robot.
(15:14):
So we we kind of hitch it on andwe can run the bot.
Um it's not as big a service asI I would think, but uh when
customers ask for it, we do it.
SPEAKER_00 (15:24):
Okay.
Yeah, and usually it's a goodway to destroy sprinkler heads.
That's that's the best use ofaeration, I guess.
Or to locate sprinkler headsthat you forgot about because
you'll find it.
Yeah.
Um yeah.
Yeah.
Well thanks for those ideas.
SPEAKER_01 (15:37):
Yeah.
Yeah.
You were in you were in SanFrancisco.
Yes.
So just going backwards here.
So um let's see.
Maybe I'll start.
I I moved to the uh let's see.
Okay, let's start with SanFrancisco.
So I was in San Francisco forabout seven, eight years.
I worked at a company calledCruz, which is a self which is
was a self-driving car company.
It was bought by General Motors.
Um I started there as you know,when it was pretty small, about
(16:00):
60, 70 people.
I started as a front-endengineer.
Okay, that was my previous,previous life as a software
engineer.
And then uh the company grewreally fast.
It grew to about 2,000 people bythe time I was done with them.
And it was um I was uh you know,I was running a team of about
100 engineers and I ran um uhproduct engineering.
So that was all websites, apps,mapping systems, some machine
(16:22):
learning systems, some you know,uh collect fleet management, a
collection of sort of uhsoftware, software pieces.
So I did this in San Francisco,and then during COVID, I
decided, okay, you know, I wantto I want to live somewhere new.
Um and uh I just kind of youknow built a spreadsheet of
fastest growing cities in the USwith great quality of life and
low cost of living.
(16:43):
And I convinced my wife at thattime, like my wife, like, hey,
listen, let's move toHuntsville, Alabama.
And we've never been, we'venever been to Alabama before.
But you know, yeah, yeah.
Yeah, but remember, COVID was atime of like, you know,
rethinking everything, right?
So so we flew out out toHuntsville, we liked it, we saw
a couple of houses, we're like,all right, we you know, let's
get this house.
(17:03):
And we got a house, which wecould, you know, we try to buy a
house in San Francisco, it wasimpossible for us.
SPEAKER_00 (17:08):
Yeah, can you can you talk to me a little bit
about that?
Like tell me, yeah.
Like you know, the price of athree-bedroom, two-bath house in
San Francisco versus Huntsville,like what does that look like?
SPEAKER_01 (17:20):
Yeah, so today, uh so I live in a three-bedroom,
two-bath or three-bath house.
And, you know, I paid, I think,$350 for it uh when I moved here
and during COVID.
Now Huntsville has seen a lot ofgrowth, so it's probably closer
to$500 today.
Um I was living in SanFrancisco, I was paying about
$4,000 in rent for a one-bedroomloft, uh, I guess, you know, so
(17:45):
really just one room.
Um and I tried to buy a house,you know, because I was getting
older.
I was like, I told my wife,let's get a house.
So it was tough, man.
It's really hard to buy a place.
I remember the funniest thing,interaction, was I went, I was
taking a cab in San Francisco tosome place, and the cab driver I
was asking, Do you like SanFrancisco?
I love San Francisco.
(18:05):
He's like, Well, I'm look- Itold him I'm looking to buy a
house, and um I I can spend upto a million dollars.
And for me, that was a stretch,okay, because that's a lot of
money.
Okay.
So I I told him and he laughedat me.
He's like, dude, what are youtalking about?
You're not gonna be able to buya house for a million bucks.
And that I kind of realized likesomething was wrong.
Uh now this was during COVID,things might have changed.
Maybe San Francisco's moreaffordable.
I I don't think so, but maybe itis.
(18:26):
But um, I think cost of livingmatters, you know, just to be
frank.
SPEAKER_00 (18:32):
Yeah, if if you can lower your your monthly nut, you
know, down to, you know, uh asas low as you possibly can, like
your health improves, you know.
Otherwise, you're making amortgage on a million dollars or
whatever.
Plus, you know, in SanFrancisco, it's like seven bucks
for a black coffee and eighteenbucks for a burrito and all this
(18:53):
other stuff.
And then you go somewhere likeAlabama or uh, I'm a little more
familiar with Kentucky becausewe have we have a facility
there.
Uh we any anytime I travel, Iopen up Zillow and I try and
figure out like, okay, what'swhat's my even trade, you know,
for my home in Reno versus whatcan I get here?
And I think in uh, you know, wewe have a uh three-bedroom house
(19:15):
here in Reno, and I think thestraight trade in Paris,
Kentucky was like 80 somethingacres of like horse farm with
like a barn and all this otherstuff.
And I was like, oh my God, youknow, and that's that's a Reno,
which is a step down from youknow a big coastal city like
like SF or or uh Los Angeles orNew York or something like that.
(19:35):
So yeah, anyone listening outthere, your quality of life will
be better, your health will bebetter, your stress will be gone
if you can just yeah, lower,lower your nut and uh uh I don't
know, buy a buy a house in agood community.
That's small.
SPEAKER_01 (19:51):
But Jim, can we but Jim, can we say something nice
about San Francisco?
It has some magic, right?
It has magic.
It has a it is a magnet fortalent, you know, quality of
people is incredible.
So, you know, it there are someupsides, so it's not a straight
decision for everybody, you knowwhat I mean?
But no, I agree.
SPEAKER_00 (20:10):
You know, if you're when you're living in a hub,
there's just totally differentopportunities.
Right.
You'll meet people that youwould have never met otherwise.
Um, you'll have opportunitiesthat you've never had otherwise.
Um, you know, your neighbor willbe like, hey, you know, I got a
job with this new company.
Maybe I can get you a job.
Like that happens in those bigcities.
Uh just running into people onthe subway or something, it's
(20:33):
it's pretty cool.
Um, yeah, it's it definitelyhappens less, especially, you
know, if you have a little bitof land and then you don't
actually see anyone.
Uh which is that's kind of mygoal uh someday.
I I'm I'm a bit of a hermit uhdown deep.
But right anyway, so so you'rein San Francisco working at
Cruz.
Okay, so so yeah, I'm familiarwith Cruz.
(20:54):
That was that was a super coolcompany, um which makes sense
the jump into Yardbot.
Obviously, there's there's a lotof connections there, like the
LIDAR and and everything.
Um, but before Cruise, like youwere doing you were doing stuff
in like banking or something,right?
You were at Goldman?
SPEAKER_01 (21:10):
Yes, yes, yes.
So um so after I graduated fromcollege, I went to Toronto, so
University of Toronto in Canada.
Um I moved to the U.S.
So my first stop was Seattle,Washington.
So I moved to I worked atMicrosoft.
Um that was a short stint, butvery educational for me, but it
wasn't for me.
So I moved out of there.
(21:31):
I went back to Toronto and Itold my parents at that time,
like, hey, I want to I want totry a new industry.
I I I for like very quickly atan early age, no, I don't want
to be in the corporate, like bigcompany situation.
So I want to try a completelynew industry.
So I decided to focus onfinancial markets.
I try to study them, figure outwhat to do.
Ultimately I ended up in a in afirm called Ready, which was
(21:54):
part of Goldman, uh, which diduh electronic trading, so uh you
know, options for.
And I built systems for like youknow routing trades, interfaces
for placing orders, differenttypes of you know, order types,
like pair trades and differentoption types and different
option strategies.
So we we own that interface andthat system and our team.
(22:17):
And yeah, I joined that companyand that that was awesome.
I living in New York, you know,it was uh you know, seven, eight
years there.
And after that, um, I decidedthis was the first time I kind
of realized that, hey, I movedto America, I can do something
entrepreneurial.
So I decided started a companyin the Bitcoin space.
And the Bitcoin is 20, 20, 20,no, 2012, 2013.
(22:38):
Yeah, it's just coming up.
It's just coming up at thattime.
And for me, um, you know, theinteresting thing was that it
was kind of like a financialproduct, but not exactly at that
time.
It was a technical product, liketechnology product, but also a
financial product.
So I came at it from aperspective of like, hey, uh,
I'm working in Goldman's readysystem, and it's it it routes
(23:00):
all these trades from differentexchanges and buys finds the
best price.
And perhaps I should build asystem for um, you know, Bitcoin
across Mt.
Gox and BTCE.
There's like all these exchangesback there.
SPEAKER_00 (23:11):
I don't know how familiar I am with Bitcoin gym,
but you know, there's like I Iremember uploading my driver's
license to Mt.
Gox to get an account created,and it was so freaking sketchy.
I think I had sent fax at onepoint, which is like really
dating me.
I'm I'm 45 or 46.
I think 46.
Um 41, so yeah, about the same.
Yeah.
And yeah, we were miningBitcoin.
(23:32):
We had these little miners fromum uh Butterfly Labs, I
remember.
Yeah.
And they wouldn't send them tome because they were mining with
the miners before they soldthem.
And uh yeah, and then I Istarted losing sleep because I
think big Bitcoin went up tolike 200 bucks or something like
that.
And I was like, oh my god, thisis crazy.
(23:53):
I gotta get out.
And then uh yeah, sold, sold mystake.
So yeah, no more Bitcoin for me.
But my my health has beenbetter.
SPEAKER_01 (24:00):
Okay.
Okay, yeah, yeah.
Are you are you still do youstill have a stake?
I have a little bit of Bitcoin,but nothing, you know.
My company, my yard bot is worthway more for me.
So I'm not I've nothing nothingmeaningful.
I I mean I sold.
That's the that's the reality,you know.
You're supposed to hodle.
That's the that's the wholeidea.
SPEAKER_00 (24:19):
But uh I that's not for me, man.
I'm not a day trader.
I'm just not told for it.
Those guys are built totallydifferent.
Um I just couldn't do it.
I think yeah, you you hear aboutpeople spending, you know, uh 25
Bitcoin on a pizza or whatever.
And right, yes.
If I look back at how much Isold, it would be nice to have
today, but uh, you know, I don'thave a time machine.
SPEAKER_01 (24:40):
Hard to look, you know, I I ordered some commander
you remember Dogecoin, right?
It was uh it was a thing backthen.
So I ordered these uh theselittle gold Dogecoin things by
paying for them the Dogecoin,and obviously the amount that
the Dogecoin's worth versusthose little you know commander
com uh you know, just like coinsis is is way different.
(25:00):
So anyway, but just to just tokind of go there, you know, I
worked on the Bitcoin space.
I I but this was a big lessonfor me, Jim.
It was like I didn't I wanted todesign something that I wanted
to do, and I didn't care aboutthe customers, okay?
This is I know it's obvious,okay, but hard lesson is like I
wanted to design amulti-exchange Bitcoin clearing
system for the for thesophisticated trader trading
(25:21):
Bitcoin.
Okay, those things, none ofthose make sense.
Okay, they they in in 2024, 20,or sorry, 2012, 2020, whatever
time frame it was, none of thosethings were needed by anybody.
Okay, so I built a system, uh,nobody wanted it, and then yeah,
it was a struggle to try to makesense of it.
And it was a hard lesson.
And you know, uh at some point Italked to my wife, I said, Hey,
(25:43):
everything is uh, you know, bankaccounts are zero.
We you don't have anything goingon, so I think I need to get a
job at this point.
So, and that's where I ended upat Cruz uh and we decided to
change the scenery as well.
We went from from New York toSan Francisco.
SPEAKER_00 (25:56):
Okay, okay.
That's yeah, that's awesome.
I I'd love to hear aboutpeople's backgrounds and the
weird journey, because you know,uh sometimes like you know, a
new grad will come up to me andbe like, Jim, yeah, how did you
do this?
You know, you're yeah, you havethis this cool company, and you
know, man, you must have likeknown exactly what you were
gonna do.
I was like, hell no, man.
(26:16):
I was I was selling candy on thebus when I was eight, and I was
mowing lawns at you know, 12 orwhatever, and uh I was working
at um Office Depot.
Like I everyone has these weirdbackgrounds, um, but you you
learn a little bit, you know,everywhere.
So if you just had a straightline, like if I was always in
(26:36):
manufacturing or something likethat, um I think that that's
what people expect.
But really, I think it'ssampling all these different
experiences and then putting toputting them together in a a
different way um for a legacyindustry is really interesting.
So and that's that's exactlywhat you've done.
You've taken background from youknow Goldman and the the company
(26:57):
was celery, right?
But that was the Bitcoin.
That's right.
That's right, yes.
Okay, so and and then cruise andeverything, and you applied it
all into a very legacy uhindustry like landscape
maintenance, and and that's whyyou're disruptive.
So um I don't know, a hugeinspiration, man.
This is this is super, supercool.
Um however, I know it's noteasy.
(27:19):
Uh there's you we've talkedabout some of the the failures
along the way.
Can can you tell us any of thethe down and dirty behind the
scenes of like failures?
Um like we we talk about ourraccoons in the ceiling.
You know, we we bought a afacility, the ceiling was full
of raccoons, we had to figureout how to get them out, you
know, just crazy stuff that youdon't think about.
Um when you're like, I want tobe a business owner.
(27:42):
Uh what are some of the failuresthat you've had either with Yard
Bot or anything that that elsethat you can talk about?
SPEAKER_01 (27:49):
Yeah, I mean, I think um the first thing comes
to me, Jim, is like uh honestly,people, humans, you know, um the
kind of environments that Ispent time in at Cruz or in New
York and Goleman, it's a verydifferent type of environment
than working with landscapersand you know, and it's uh it's
(28:11):
just a different world.
Blue-collar work essentially isa different world.
So because of the hybrid natureof yard robotics, yard yard bot,
it's you know, I have to have asizable force of people.
Like we have nine right now thatkind of go out and drive the
trucks and and take the robots.
And, you know, everything I kindof I thought I was I knew
everything about managingpeople.
(28:31):
I was running a team of 120engineers, and how hard can it
be to run a small team ofblue-collar workers?
And it's it's just completelydifferent.
Their incentive structures aredifferent and interactions are
different.
And you know, when you thinkabout a corporate environment,
you're like, oh, you know, thiswould be unacceptable or
acceptable.
All those things change.
So I had a lot of failures therelearning about you know what
(28:54):
motivates people and what keepsthem happy in, you know, if
they're not getting paid thesame way as as we as people get
paid in San Francisco.
So uh yeah, I'm sorry, I'm notvaguely being specific there,
but you know, it's just a hardlearning uh on on the on the
blue-collar side.
On the robot side, I mean, youknow, we've had robots end up in
(29:15):
a lake, um, you know, and had tohad to kind of pull them out.
We've had robots fall apart, youknow, the deck falls off, you
know, all those kind of things.
So we've just had a bunch offun, random things.
Thankfully, every everything isrecoverable, everything is
everybody's understanding.
The customers honestly love therobots and seeing them in their
yards.
So nothing super crazy.
(29:36):
But yeah, it was it was asurprise when the robot was in
the lake and they were trying topush it up and they called me
saying, Hey, I think we needanother to get this out of here.
SPEAKER_00 (29:44):
It's it's definitely a flex uh to have the robots
mowing your lawn.
Like I would, I would definitelyshow that off to my neighbors
and be like, oh yeah, no, I gotI got robots who come in here.
Yeah, I was gonna ask you alittle bit about um, you know,
how you go about recruiting forthese teams, because it does
seem like a hybrid.
Like you have to, you know,you're you're gonna run this
(30:08):
piece of high technology thathas LiDAR on it and stuff, um,
but then I also need to throw abackpack blower on you and you
know go go blow leaves out ofstuff.
Um can you can you get atraditional landscaper to, you
know, uh get on board with theserobots, or or do you need
(30:29):
someone who is like a littlemore interested in technology,
or what's the ideal candidatethere?
SPEAKER_01 (30:34):
I think it works on both sides, right?
So we we have traditionallandscapers and we have guys
who've basically never mowed alawn before this.
Um the way I think about thisis, and this this is something I
do carry from my pastexperiences when when we think
about individuals to hire, wethink about skills, values, and
abilities.
And I think I was taught this inNew York, I think it was maybe
(30:55):
um uh Ray Dalio, but I'm notsure who kind of came up with
the system.
But it's basically skills beingsomething that's super uh easy
to acquire, three to four weeks,abilities taking about a year,
and values are something you'reborn with, right?
So values are stuff like do youwork well with others?
How much how big is your ego?
And you know, things like that.
So, you know, in engineering,when I when I recruited people,
(31:15):
it was always about values only,because I mean there's skill
tests and you know and abilitiesare kind of like you kind of get
them over time, but it's aboutvalues.
Can you work in a teamenvironment, things like that?
So that kind of came came, youknow, in the blue-collar work as
well, except there is aconstraint.
The constraint is because thejust to be frank about it and
honest about it, because of theway the system works, they're
(31:36):
not paid as much.
So money is really critical.
And a lot of incentives getwrapped around compensation,
right?
Uh it's it does so inengineering as well, but in
blue-collar work, it really,really matters, right?
So, you know, so I still lookfor values, but you do need
skills because we don't, youknow, we're we're not gonna be
able to teach you how to weedeat in a in the time frame that
we're looking for you toperform.
(31:58):
And the the key other element ofthis gym is training, right?
So we have a rigorous trainingprogram.
So every mistake we've madeever, right, is in our system.
Every time we forgot a modebehind a fence, every time we
hit a sprinkler head, we have anote and photos.
So it's like a dream of trainingmaterial, right?
(32:19):
And thank you, LLMs.
You know, this is one placethey're really good.
So we can take all this data.
SPEAKER_00 (32:24):
It sounds like a perfect application for LLMs.
SPEAKER_01 (32:28):
It's perfect.
So we have a handbook that hasthe 50 different things
everybody needs to be uh needsto keep in mind, everything from
like soft stuff like yourpresence in a neighborhood,
yelling loudly or smoking or youknow, things like that, to
precise stuff like what isedging?
What does that mean?
You know, how do you how do youscrew it up?
How can you make mistakes?
You know, we have photos andexamples of that across every
(32:48):
single aspect of the business.
And, you know, thank you, LLMsagain.
You know, we can aggregate allthis data and present a solid
training program that our guyscan take.
And then once they do that, wepair them up so we'd send a deal
truck out.
Our typical, our sort ofstandard procedures to send one
person per truck, but we'll sendtwo out when they're training,
and then we send one out.
And so far, honestly, this isafter a couple of iterations,
(33:09):
this has been the best systemfor us so far.
So this is not the a preciseanswer to what you're asking,
but these are sort of all theconsiderations when it looks to
hiring hiring people for ourfirm.
SPEAKER_00 (33:17):
Yeah, um, I agree with you a million percent on
values.
Like I I can teach anyone to runa machine, you know, or drive a
forklift or you know, um typeinto a piece of software or
whatever, but I I can't teachyou how to be friendly, um, you
know, how to how to answer thedoor when a customer is knocking
(33:38):
or whatever.
Like your parents should havedone that.
So we we try and identify thosethings during the hiring
process.
Um one of my favorite things todo is like I'll grab a tote of
heavy parts in it, and I'll belike, hey, you know, follow me.
Let's go out on the shop floor,and I'll walk towards a door
that needs to be opened for me.
And I'll see if they grab thedoor.
You know, if they just arethinking one step ahead, like,
(33:59):
oh, he his hands are full.
How's he gonna open up thatdoor?
Let me let me grab it for him.
And then there's some peoplethat'll just sit there and like
watch me struggle.
And I'm like, oh, come on, man.
You know, like who raised you?
So it all goes back to theparents, but um so yeah, we were
we were talking about, you know,AI is that's actually a perfect
use of this.
Um you take all this data, likejust log it, just log it, log
(34:22):
it, log it, and then use LLMs togo through it and identify these
major pain points.
So um, how effective has thatbeen?
Like, can you identify, oh man,um we need sprinkler head
detection in the next revision,or uh, hey, we've identified we
need to make the mower deck 35inches so it can fit behind a
(34:43):
gate better or something likethat.
Like, what are what are some ofthe big things that AI has
revealed to you that that arekind of unexpected?
SPEAKER_01 (34:50):
Yeah, so the uh so Jim, AI, um the way I think
about it is first is you know,there's different aspects.
So computer vision and LLMs,right?
So on the computer vision side,this is where I kind of learned
some things at Cruise.
You know, how can we use ourcameras to detect things like
there's a hose on the ground oryou know, like uh there's
there's a dog toy in the city.
SPEAKER_00 (35:10):
I ran over a lot of hoses.
Yeah.
SPEAKER_01 (35:12):
Yeah, yeah.
And you know, they cost 60, 70bucks, so it takes takes the you
know price of the mow away.
So um those are sort of sort ofthe foundational things that we
have, and we're iterating on,we're collecting data on, we
record, you know, all camerasacross all our systems.
And you know, we're very small.
So I do all the software work inthe company, so you know, we're
(35:32):
iterating on it still.
So it's not fully perfect, butthat's kind of what the AI uses
on the computer vision side forour for our robots.
The place where it's actuallybeen incredible to use LLMs in
the in the sort of the in thatpart of the AI is obviously
programming.
So, you know, when it comes tofront-end software development,
even back end, likerun-of-the-mill crud stuff, you
(35:54):
know, database stuff, you know,the low-hanging fruit of are,
you know, you're you can be somuch more productive today than
you could just honestly lastyear.
So that's changed thingscompletely.
And then the last sort of areawhich you're you're touching on
is when you have, you know, Icall it like context shaping.
I don't know if this is theright term in the LM industry,
but like you have all thiscontext, you have all this
(36:15):
customer data.
Can you use it to do, forexample, a training manual?
Okay, that we can.
How about how about we do asentiment analysis on every um
every phone call text and emailcoming from every customer every
single day, so we know by theend of the day which customers
are the most happy or mostunhappy, and use that to build a
model to see which one's mostlikely to churn because we get
the same mistake two times in arow, or you know, the language
(36:38):
from uh their text or the thetranscription of their phone
call.
You know, it's just uh somethingthat's honestly never been
possible.
And it's that gets me veryexcited.
Uh, but obviously it comes withlimitations, but that is where I
think LLMs have just beenincredible for us that we can
with it with a super small team,we can just provide such a high
quality of service, is what'sbeing different for first.
SPEAKER_00 (37:01):
It it allows a super small team to you know uh have
the same performance as a as alarge team.
You know, the the way that wesee it is uh we don't see LLMs
here to eliminate jobs, we seeit as a way to make the existing
jobs like you know better andbetter.
So then we can actually hire youknow more people, but they're
(37:22):
every time we hire someone andpair them with an LLM or
something, they become asuperhuman.
So um yeah, it allows it allowsthe teams to be lean.
And I actually think in thefuture, you know, if we're doing
it the yard bot method whereyou're allowing a small team to
act like a big team, I thinklayoffs will be much more rare
in the future because companiesaren't gonna overstaff and have
(37:45):
you know 10,000 accountants orwhatever.
They're gonna start with twoaccountants and then layer on
this like superhuman ability andthen grow at the right pace and
and hopefully you know not havehave to suffer from these like
massive layoffs that you see atthese big corporations.
So I think we're going through achurn time right now.
Um, but in the future it'll be alittle more predictable, I hope.
(38:06):
Dom was saying there there's nosnow in uh in Alabama.
Because we were we're talkingabout like, could these things
what else could they do?
Could they could they blow snow?
Could they uh you put a dozerblade on the front of it?
Like have you ever thought aboutsomething like that or in
different markets?
SPEAKER_01 (38:22):
Oh yeah, yeah.
We did we were asked to build aprototype for a military base uh
for snow removal.
Um we didn't so we builtsomething, but we weren't happy
with it.
So snow removal just has adifferent I I don't I guess I
can go deep in there, but likeit it, you know, I'm from
Toronto, right?
So it snows a lot.
My wife's from northern Ontario,so it snows a lot up there.
(38:44):
So I'm familiar with snow.
The thing about it is it's veryirregular, right?
So mowing, you're doing it atlike a week, two weeks, but when
snow comes down at a base, forexample, it needs to be cleared.
There's no option and all handson deck, but then it may not
snow for a while.
So it's very spiky.
And um, you know, the uh yeah, Iyou know, I I'm not uh this part
(39:06):
I'm not sure about.
The perception system might haveto be slightly more
sophisticated than what we have,but perhaps you can make it
work.
Yeah, snow removal is a terriblebusiness.
I don't want to say that, butlike it's just a different
different thing.
SPEAKER_00 (39:17):
I mean, for our thing, yeah it's hard.
I have family down in uh MammothMountain, California.
Okay.
And if it starts snowing, likethat's what you do.
You wake up every three hoursand you go and shovel.
Because if you get behind it,you'll wake up and you you have
seven feet and there's nothingyou can do.
You know, you have to wait tillspring, basically.
So yeah, snow removal is hard.
(39:39):
Or or like, you know, heresometimes we'll get one inch,
uh, and it's like justborderline, should you even
bother?
Um but all these snow removalcompanies are out there because
they're trying to bill for it.
Uh, and then on the days when wedo get two feet, then they don't
show up because they're too damnbusy at you know hospitals or
you know, these primarycustomers or whatever.
(40:00):
That's that's a hard business.
Um but Huntsville, it's so yousaid you had 600 customers,
right?
That's right.
600 lawns that we maintain, yes.
Dude, that's awesome.
Um are you thinking about youknow other uh other markets like
nearby?
Or I mean I guess there's alsomany, many more lawns to go
(40:20):
there.
Yeah, what's what's next?
Like what's uh what market isexciting?
SPEAKER_01 (40:24):
So, Jim, the way I think about it is Huntsville for
us is sort of like a like a testcase.
So we're kind of every uh everypart of the business from
building robots to running themto the sales and marketing, to
the operations, to theengineering, like all that stuff
is kind of like this is uh youknow, like the base case for us
in Huntsville and and thefinancial aspect as well.
(40:45):
Are we making money, right?
So we add all this together.
And for me, the objective is totake this and copy paste this is
and in many as many cities as asit makes sense, right?
So what's very attractive for usis any place where grass grows
eight to ten months a year ormore.
So everything starting fromTexas all the way across to
Florida is really attractive forus.
Um the next phase of ourbusiness would be to take this
(41:06):
and take it to one of thosecities and then see, you know,
what what what things, whatstrategies kind of continue to
work and what don't.
But I do the objective is tocopy it to maybe let's go to
Nashville or let's go to Austinor let's go to Orlando, set up
the system, set up the samesystem, set up, you know, and
we'd run it very lean, right?
So, you know, we we have astorage unit right now.
(41:28):
So, you know, this is our officewhere we hang out, you know, but
the the the law the robots andstuff leave from a storage unit.
That's kind of what our setupsystem setup is.
And you know, we can do this ina new city, so very low fixed
costs uh for us.
And um anyway, but yeah, longstory short, yes, to answer your
question, geographical expansionis a key element of a
(41:48):
landscaping company.
You know, all landscapingcompanies that are big,
billion-dollar companies,they're in 200 plus cities and
we want to be there.
SPEAKER_00 (41:54):
Wow.
Um what do you think is gonna bethe number one challenge uh of
the first new market that you gointo?
Okay, so let's see.
Because I have an idea.
SPEAKER_01 (42:06):
You have an idea.
All right, let's hear our ideafirst.
Let's hear it.
SPEAKER_00 (42:09):
I I think it's I think it's management.
I think it's leadership.
Okay.
Um when we open up a newfacility in a new state, yeah.
It's that the the operationsdirector, that's what we call
it, the guys who run ourbuildings, identifying that
operations director is key.
Um they will they will make youor break you.
And we've had nothing but goodones.
(42:30):
But now as we're we're trying toexpand into more cities, um
normally we could pull guys offthe bench.
You know, we're like, okay,you've you've been here long
enough, you've figured it out,you you have these qualities.
Um you know, if if you're ifyou're young and you want to
relocate, like we'll give youthis opportunity.
But what's happened is likewe've been going so fast that
(42:52):
our bench hasn't been able tokeep up the pace.
So we don't we have a lot ofguys that are awesome right now.
They just need like a couplemore years to learn.
Um, so once you deplete yourbench, then you have to go from
outside the company.
And then that's a a big gamble.
So we're trying to figure outlike what qualities do we want
to identify in these cities.
Um because relocating someone isreally challenging too.
(43:14):
Like you're taking a big gamble,you're asking them to do a lot
for you.
Um, you know, especially if theyhave like wife and kids or
whatever.
I'm like, hey, uproot your lifeand go to this city that you've
never been to.
Um so if I can recruit peoplefrom those existing cities, then
it's a little less risky.
Um anyway, sorry.
Sorry to give you your answer.
SPEAKER_01 (43:32):
But that's no, no, I I I like it.
So I wasn't, you know, to befrank with you, this is a
weakness of mine.
I was thinking about robots andmanufacturing, but humans are
always the challenge.
Um the only two things I'd saythere, Jim, is one, you know, I
uh the way you're thinking aboutit is how I think about it is
like, can we take our existingbench and ask them to do a tour
(43:52):
of duty in a new city and andbring it up essentially, right?
So they go out, the first 60 to100 to 200 customers they kind
of take care of.
Till we get local talent that'skind of ready to take on take it
on.
And the second thing, and thisis the sort of irrational
technologist problem that Ihave, is we have so much data.
Okay, we have video of everylawn being mowed across 12
(44:14):
cameras and every truck beingdriven, you know.
Can we leverage this data,perhaps, to make sure that
things stay straight?
But I don't know.
But that's kind of the you knowirrational optimism I have is we
can make it happen.
SPEAKER_00 (44:30):
No, I I love it.
We we spend a lot of timestudying um Starbucks and
Chick-fil-A and In N Out Burgeror whatever.
We're trying to figure out howhow can you expand, you know,
you go from 10 Starbucks to athousand Starbucks.
Like, what does their forwarddeployed team look like?
(44:51):
Um, how are they identifyingthese new managers who are going
to run that location?
You know, with Chick-fil-A, theyhave this crazy, you know,
audition hiring process.
Um, it's like a lotterybasically to get to run a
Chick-fil-A.
So I'm like, man, how do you howdo you build that?
Well, it starts with like areally cool brand, a really cool
culture that people want to getinto.
And then so I'm like, okay, soeverything that we're trying to
(45:13):
do to expand now actuallystarted, you know, eight years
ago uh in order to attract thattalent.
So I don't know what the magickey is.
I'll I'll let you know as soonas I figure it out.
SPEAKER_01 (45:22):
So thank you.
But I gotta also say, you'relike about 10 steps ahead of me.
My problems are much smaller.
But uh but yeah, sounds like Imean, congratulations,
obviously, everything you Jim,you've accomplished that second
step, but you guys are wayahead.
SPEAKER_00 (45:37):
So learning, man.
Overnight success takes 10years.
Um so we we got a ways to go.
Um actually, and that's that'skind of a great place to kind of
wrap this.
Uh I was gonna ask you our finalquestion here.
What's what's one piece ofadvice that can be about
anything that that you want theaudience to walk away with?
SPEAKER_01 (45:58):
Um don't give up too soon.
That's what I would say.
You know, a lot of times I'vedone something and then said,
Oh, I'm gonna give up on this,like Bitcoin.
You know, um and then exactly.
But maybe not Bitcoin's not theperfect example, but you know,
where I've kind of just made adecision too soon, but just
gotta stick out stick it out anditerate, and then things can get
(46:20):
better.
So cool.
SPEAKER_00 (46:21):
I love that.
Divya, thank you so much foryour time.
Yeah.
Um, if anyone in the Huntsville,Alabama area is interested in
getting one of these things intheir yard to flex on their
neighbors, uh, where should theygo?
Uh just yard.bot.
Yeah.
Yard.bot.
Oh yeah, go check it out.
Um editor Logan will put a bunchof photos here of the awesome
robots.
(46:42):
Um dude, thanks again for yourtime.
But this is where I say myoutro, which is actually Dom
just hitting a button.
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Hey Jonas! The official Jonas Brothers podcast. Hosted by Kevin, Joe, and Nick Jonas. It’s the Jonas Brothers you know... musicians, actors, and well, yes, brothers. Now, they’re sharing another side of themselves in the playful, intimate, and irreverent way only they can. Spend time with the Jonas Brothers here and stay a little bit longer for deep conversations like never before.
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If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Las Culturistas with Matt Rogers and Bowen Yang
Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.