Importance of Natural Resources

HerdX With Ron Hicks and Austin Adams: GCPPodcast 191


[MUSIC PLAYING] MARK MIRCHANDANI: Hi,
and welcome to episode 191 of the weekly “Google
Cloud Platform” podcast. I’m Mark Mirchandani,
and I’m here with my colleague and
guest host, Gabe Weiss. Hey, Gabe. GABE WEISS: Hey, Mark. [LAUGHS] Same time. Let’s try again. Hey, Mark. MARK MIRCHANDANI: Hey, Gabe. GABE WEISS: How’s it going? MARK MIRCHANDANI:
It’s going well. How are you? GABE WEISS: I’m doing well. I’m glad to be here. MARK MIRCHANDANI: I’m super
excited to have you on. Gabe and I get to work
together every once in a while. But then we got a
really cool chance to interview one
of the customers that Gabe was working
with called HerdX. GABE WEISS: Yeah,
they’re awesome. MARK MIRCHANDANI: So we
have an interview coming up with Ron and Austin, talking
about what HerdX is and some of the really cool
things they’re doing with collecting data and
working with Google Cloud– very, very excited
to get into that. GABE WEISS: And how it’s
going to save the world. Like, that’s the
exciting part for me. Like, they’re totally
going to save the world. MARK MIRCHANDANI: I mean, it’s
such a grand gesture they’re trying to put into place
and say, look, we know a way to stop these common problems
with ranching solutions, right? We’ve got overmedication
being an issue. You’ve got introducing
new bacterial things. You’ve got measuring
all these kind of data. It’s a really, really
cool interview. So I don’t want
to spoil too much. We will get into that. GABE WEISS: Yep. MARK MIRCHANDANI: But
before we get into that, I know since you’re here and
you kind of know the IoT space pretty well– GABE WEISS: I do. MARK MIRCHANDANI: –I’ve
got a fun question for you talking about
how to connect an IoT device to a trigger
event in the cloud. GABE WEISS: Yeah,
so it’s the idea of having a physical
device in the world responding from the
cloud based on sometimes automatic data, sometimes
a trigger that you’ve done. And it turns out it’s
super easy to do. MARK MIRCHANDANI: Oh, good. GABE WEISS: We have
this cool product. It’s called IoT Core. MARK MIRCHANDANI: Well,
let’s get into that. GABE WEISS: Yeah. MARK MIRCHANDANI:
But before we even do that, let’s talk about our
cool things of the week. [MUSIC PLAYING] GABE WEISS: Because there are
some cool things on there. MARK MIRCHANDANI: Oh,
so many cool things. GABE WEISS: All right, what’s
the first cool thing, Mark? MARK MIRCHANDANI: Well,
my first cool thing is that Google has added
an integration where you can easily find podcasts. GABE WEISS: [GASP] MARK MIRCHANDANI: You may
be familiar with podcasts. GABE WEISS: Maybe. MARK MIRCHANDANI: I don’t
want to make assumptions. You might be listening to
this on an old ham radio. I don’t know if someone’s
rebroadcasting it. But if you haven’t
heard, this is a podcast. [DING] And now, when you type
in certain search terms into Google, it will
recommend podcasts for you. GABE WEISS: Whoa. So you can actually
search for podcasts, kind of like search for images. Now you can just say, hey,
find me a cool podcast. MARK MIRCHANDANI: Yeah,
you can be, like, look, give me a slow-cooking podcast. And that’s the reference they
use here in the blog post, but that’s a super cool thing. And I think it’s
been rolled out. So go to Google. Type in “Cloud Podcast,”
and hopefully we come up. I’m going to see if
that works right now. I’m going to be
really disappointed if it doesn’t work. GABE WEISS: This is very meta. MARK MIRCHANDANI: Wait,
wait, typing it in. “Google Cloud Podcast.” [TYPEWRITER CLICKING] GABE WEISS: This is
the best podcast ever. MARK MIRCHANDANI: There
it is, the podcast section specifically for us. GABE WEISS: Nice. MARK MIRCHANDANI: So good. All right, well, if
you host a podcast or if you listen to podcasts,
which I think most people do– GABE WEISS: Probably. MARK MIRCHANDANI: –at least all
of our listeners– then that’ll be a cool feature to check out. GABE WEISS: Awesome. I actually want to– I’m about to get a
3D printer, and I need to find podcasts
about 3D printing. MARK MIRCHANDANI:
Well, I got great news. Go to Google, type in
“3D printer podcast–” GABE WEISS: I’m going to search
for “3D printer podcast.” MARK MIRCHANDANI:
–and you will find it. GABE WEISS: I’m not going
to do it right now, though. MARK MIRCHANDANI: So what’s
your cool thing of the week? GABE WEISS: All right, so
the cool thing that I found, being in the IoT
space still, there is a group of
researchers in Japan– I’m going to butcher the
name– it’s Keio University. They have built a tail– MARK MIRCHANDANI: A tail. GABE WEISS: –for humans. MARK MIRCHANDANI: A human tail. GABE WEISS: Right? It’s a robotic
tail, and what they say the use for it is– and
I can think of a bajillion uses for this. But the use that they’re
specifically going after is to keep elderly
people upright. MARK MIRCHANDANI: Interesting. GABE WEISS: Right? So the way that it works
is just the same way as a tail does in nature,
which is to counterbalance the rest of the body. So cats can do all
kinds of crazy stuff. They balance all
sort of weirdly. And it’s their tail that
allows them to do that. The tail kind of
flares out and offsets the weight of their body. The idea that
they’re positing is they can attach
this tail to humans, and it understands the position
of your body and offsets it. They got some really
cool YouTube videos of people using this tail. It’s really– MARK MIRCHANDANI: I mean,
it’s so interesting, but it keeps making me
think about the inevitable exoskeleton future,
where we’re all just running around in mech suits. GABE WEISS: Well, and that’s
already kind of happening. MARK MIRCHANDANI: Right. GABE WEISS: You knew this. That’s happening in– MARK MIRCHANDANI:
But those mech suits didn’t have a tail until now. GABE WEISS: They should. MARK MIRCHANDANI:
Well, they will. GABE WEISS: And
now this university is going to make it happen. It’s really cool. MARK MIRCHANDANI: It’s
very, very interesting. It’s cool what
people come up with. And that’s a great
use case, right? Because some people who can’t
balance well are like, well, what’s a great way to do that? But also, it sounds like have
a little bit of fun with it– GABE WEISS: Sure. MARK MIRCHANDANI: –and use the
concepts brought up in nature. GABE WEISS: I wonder if it
responds to your emotions. MARK MIRCHANDANI: Oh, that’s
probably like V2 kind of stuff. Well, speaking of
nothing to do with IoT, I think one of the cool
things is a blog post that just came out, talking
a little bit more about how you can measure costs. GABE WEISS: Hm. MARK MIRCHANDANI: Now if
you’re doing something like building giant
robot tails, there might be a lot of costs
associated with that. And the Google Cloud
building part of the console can be a little intimidating,
especially for people who are more
concerned with code, and just, they want
to get in there and they want to run stuff– GABE WEISS: Right. MARK MIRCHANDANI: –not
necessarily understanding how to pay for it. Well, it turns out at the end,
you still need to pay for it. So this blog post really
highlights some cool features that have been released,
including some updates to the billing reports,
a new kind of report that lets you see a very,
very quick view of what you’re paying, what your taxes
are, what your discounts are. And I think the
idea here is really letting people get more
observability onto what they’re paying for and better understand
it so that they don’t spend as much time trying
to track down, oh, where did this
payment come from? I have no idea what project this
is– all these kind of things. Anything that makes
that process easier means you can go back to
doing the fun stuff quicker. GABE WEISS: Interesting. So is it kind of like a
Google Wallet add-on, almost, where it kind of can help you
watch what you’re spending? MARK MIRCHANDANI: Well, you can
set up budgets for your billing account. I’m certainly working
with the billing team, and there is going to be some
content coming out soon that helps people understand
more about what you can do with the billing reports. GABE WEISS: Sweet. MARK MIRCHANDANI: Or not
billing reports, but actually the billing. GABE WEISS: The
billing itself, yeah. MARK MIRCHANDANI:
Yeah, the whole thing– setting up an account, setting
up quotas and budgets on it, and understanding all
those kind of things, all from within the GCP console. GABE WEISS: That’s cool, because
I’ve seen the calculator. We have that
calculator for costs, and it’s, like, a bajillion
tabs because each product has its own tab. And each tab has a
dozen things that you have to know about your system. Like, oh, OK, I need to know how
much network traffic I’ve got. Well, I don’t know. I’m going to throw in a bunch of
terabytes because I don’t know. And that kind of spits
out a monthly number. So is this kind of a
way to help automate that a little bit and
kind of help people guide to figuring that out? MARK MIRCHANDANI: It’s going
to be a little bit more on the post side. So after you’re
spending this money, you have a better idea
of where it was spent– GABE WEISS: What it was. MARK MIRCHANDANI:
–and what projects and what resources it was on. But the idea here is that
with these tools in place, you should be able to
put better cost controls and really do cost optimization
and management so that you don’t have surprising things
and that you can actually do predictions like that. GABE WEISS: Totally. MARK MIRCHANDANI: There is
that GCP pricing calculator that you can use ahead of
time to kind of estimate– GABE WEISS: Get
a ballpark, yeah. MARK MIRCHANDANI:
Yeah, to ballpark what you’re going to be spending on. I think it’s got
a lot of features. But I think here we’re talking
about more stuff like measuring where your costs are. GABE WEISS: Yeah,
post-morteming it, sure. Yeah, cool. MARK MIRCHANDANI:
Very, very cool. So some variety of cool
things for the week, but I don’t want to hold back
the main interview any longer. So let’s go ahead and jump
into it with Ron and Austin. [MUSIC PLAYING] Super excited to have our
guests, Ron and Austin, here. As we get started, tell us
who you are and what you do. RON HICKS: I’m Ron Hicks. I’m the founder
and CEO of HerdX, and this is our CTO, Austin. AUSTIN ADAMS: Yep. MARK MIRCHANDANI: So first
questions first, what is HerdX? RON HICKS: HerdX is a fabulous
idea, if I can say that. MARK MIRCHANDANI:
Self-proclaimed. RON HICKS: I’m somewhat
biased, but I will be honest. I came out of
retirement to do what I hoped would be a change for
livestock across the world. One was to become an
environmentally friendly solution– not easy to
address, but something we feel like we’ve accomplished. So HerdX is a
third-party provider, a way in which we can take
livestock, improve the system, and bring it to a consumer,
and improve the transparency. That’s basically HerdX. MARK MIRCHANDANI: So that’s
a pretty broad statement. What does that actually entail,
for the livestock especially? RON HICKS: So it’s a big change. So livestock in general
is, I would call, less transparent by design. They’re typically animals raised
in areas that are very, very isolated, and by design, it
has to be because they’re out in the middle of nowhere. So they’re somewhat hidden. In general, there are
pretty good ranches out there, pretty good feedlots. And they have no real
way of broadcasting a positive performance animal. So the goal for HerdX is to
authenticate that through data. GABE WEISS: So
how does it do it? Like, I first talked to you– I looked in my email
to find out exactly how long we’ve been chatting–
it’s been over a year now. So June of 2018 was
our first contact. And at the time,
you were describing the vision part of it. But mechanically, what
is it about livestock? You and I have had a
lot of conversations about what it does. And we know part of it is
that transparency to even all the way to the end consumer. So how does it do that? That’s what we’re
kind of looking for, is, what does the system do? RON HICKS: So there would
probably be steps in there. I think the initial approach is
what was working 50 years ago? What worked 100 years ago? And if you look back,
you’ll see kind of an art to raising animals, even in a
more quarantined or confined area. And it was this pen rider. You had this gentleman
that would show up– long-sleeved shirt. He was kind of the premier dude
of the day, and he’d come in and he would cut
through cattle– his goal to find animals that
were moving in somewhat of a what we would call an anomaly. If he could separate
that one animal, then he could determine
that that animal may need some additional assistance. Maybe check and see if there’s
a temp, some other things. They don’t have that today. So if you look for
a pen rider, it’s the rarest of the cattle raise– what I would call key– to good animals. What we’re doing at HerdX is
we’re replacing that with data. GABE WEISS: So the pen
rider was a person. RON HICKS: It was,
riding on a horse. GABE WEISS: It was a
physical person that would go down into the herds,
and walk amongst the animal, and be like, all right, this
cow or this lamb or whatever, they’re acting a little strange. Let’s pull them out of the
herd and let’s do something. So that’s been
replaced now by, what? Just mass antibiotics, right? Like, that’s the answer,
is we’ll just pre-medicate all of our animals. RON HICKS: Absolutely. GABE WEISS: And then who
cares if they’re sick? It doesn’t matter. They’re not going
to affect anything because they’re all medicated. RON HICKS: They’re medicated. You’re not going to
cross-contaminate. It’s an easy
management decision. Again, there’s less
information, so you cover it. I think the beauty of
a HerdX type system is you show an
animal’s movement. If that anomaly occurs,
you go back to, basically, in time, a more natural
method, and you’ve automated the entire system. GABE WEISS: Nice. So brass tacks now, you
talk about the HerdX automated system. RON HICKS: Mm-hmm. GABE WEISS: I don’t want to
infringe upon any IP here, so if there’s company secrets,
I don’t want to touch on that. But what is the HerdX system? So there’s no pen
man anymore, right? RON HICKS: That’s right. AUSTIN ADAMS: Yeah. GABE WEISS: So what
does the system do? AUSTIN ADAMS: So we have
monitoring solutions that are deployed out
of the feedlot that are able to monitor the animals’
movement to food and water. And through that, with
different algorithms in the processing of
their movement data and their movement
to water or to food, as opposed to each other,
we’re able to detect who’s the anomaly. And as that system
grows, we’re also able to kind of forecast
anomalies in different breeds and in different weather
scenarios and things like that. MARK MIRCHANDANI: What’s
an example of an anomaly? AUSTIN ADAMS: So an
example of an anomaly, once you understand the
supply chain of cattle, is many animals are coming
across the border from Mexico or across several
states on trucks. And they were weaned
off of their mother so they’re off of milk
and onto water and grain. So they’re just a big
transition in life, so there’s a lot of stress. So those animals might not be
exhibiting a temperature, which is a fever or indicative of some
immune response in the animal, which is trying to fight
a bacterial infection or some stress
response in the animal. But they might be
moving weirdly, meaning they might spend
a lot of time laying down, or they might not go
to the water trough as often as other people. Or they might not
go to food as often. And there’s a lot
of noise in there, based on temperature
and things like that. So that’s why the pen rider
was kind of an art form, because it was a human brain
able to filter out the noise and understand, this is
an animal that needs help. I can see by the way its
lips look that it needs water and it hasn’t gone
to the water trough. Because they can’t watch them
all day like our systems can. So our system
doesn’t need to know what the temperature
of the animal is. We can actually
see their movement and decide a couple of
days before they even have a temperature
that this animal needs to be pulled and taken care of. And what taken care
of can mean is just put on some lower
stress sort of behavior, meaning get them away from
other animals for a while. Give them the actual
load of antibiotics maybe that they actually
need, so not a mass treat, but a specific acute
sort of treatment. MARK MIRCHANDANI: So collecting
a tremendous amount of data per animal, and then
you multiply that by the number of
animals, and you probably end up seeing a lot of gigs
or terabytes of just raw data and then using that
to make predictions and understanding that
something’s off here. Let’s treat that specific case
with a much more strategic implementation, rather than
just kind of a broad hey, let’s just throw a bunch
of pills at the cows and see what happens. AUSTIN ADAMS: Yeah. Yeah. GABE WEISS: The prediction–
are you actually using machine learning and artificial
intelligence to do that? You’re actually training
all of the models? AUSTIN ADAMS: Yeah, and that’s
part of where GCP comes in, is we’ve been able to
throw large volumes of that behavioral data into
systems like BigQuery and Cloud Dataflow, and able to
run models over that and also test different models
to see what sort of negatives they would generate
to understand how we can continue to grow
and learn this thing over time, as the system gets smarter
with putting in breed data, putting in other different
variables that could affect the outcome of the animal,
which are all things that these feedlots are
trying to do with their food. But they’re not looking at
it from a health perspective. They’re going out of
their way to decide what’s the most effective
food so that they can spend the least amount of food for the
largest amount of weight gain. Because that’s
what a feedlot is. GABE WEISS: Totally. AUSTIN ADAMS: And
it sounds horrible, but really, it can be
done in a very humane way. And there’s a lot of them
that do it in a humane way, but it gets hidden by
one bad actor, so– GABE WEISS: It’s all
trade-offs, right? You’re trading off– as harsh
as this is going to sound, but you’re trading off a
certain amount of humanity for, really, the business. I mean, ultimately,
they’re all ranchers. They’re all in it
for their business. That’s what they do. So I can imagine that there’s
a lot of kind of trade-offs when you’re talking about that. So the cool thing for me, and
why I got interested in HerdX when we first chatted,
was this feels like a way to mitigate that trade-off. You can still be humane
with these animals, but still get the gains
and the profitability so you can, like you said,
transform the industry. That’s kind of the idea,
is how can you do that. So when did you know
you had something? You had this idea. You came out of
retirement for it. What was the magical
moment where you said, this could work? We’ve got something here. RON HICKS: I think– well, you were actually involved
somewhat in that early stage. We took over a small
feedlot, leased it, decided just like any disruptive
product, which is really my past, in order
to change a mindset, you’ve got to prove it. And so here I’m coming in,
really unbiased, no background on raising livestock. In fact, to be
honest with you, I’m not someone that really likes
something that’s not clean. Ask my wife. I mean, everything is
sterile at my home. And so moving out
to a feedlot area, leasing something from a very
famous style set of leaders that, in the past, have
done very, very well, this place was abandoned. So we went in and
said, OK, we’re going to bring animals in. We’re going to test. We’re going to try some
things, all with natural means, using data. And the woman that was
there was watching this and leased this property to us
started to become very excited. Again, when I first
went out there, she probably thought
we were nuts. And she began to say,
look, this is interesting. GABE WEISS: Was she
a rancher herself? RON HICKS: She was
a past rancher, so they had basically given up
on the process of the feedlots. A lot of the family
had moved out. That’s also very common. The younger generation
decides to move to the city and do other things. So you’ll see that older
generation trying to hang on, but how do you do that
without maybe the labor force or the means? So we went in and just
decided we’ll just make it our own ranch, and
we’ll create our own feedlot. GABE WEISS: And you had
never ranched before? RON HICKS: Never. GABE WEISS: So this
was totally fresh. I’m going to come in. Ah, I’m just going
to make a ranch. MARK MIRCHANDANI:
How hard could it be? GABE WEISS: Hard. RON HICKS: It was
harder than we thought. But I think the
issue is we realized if we do this in a way
in which we would call it the most humane way of raising
the animal, using natural means and bringing in
these tech systems that we were just going to place
out there and start to test, even the rancher herself
said, I thought you were in a different
world, and said, I think this is something that
I’d like to actually participate in. And if you remember,
you met with her. Sure. GABE WEISS: She was
the one sitting with us at the luncheon. RON HICKS: At the luncheon. GABE WEISS: Yeah, yeah. Almost a year ago– almost exactly a year ago now. RON HICKS: Yep. And she was proud of the
actual output of what we could call a high-performance
animal– never needed medicine, gained weight beautifully, and
was absolutely incredible– the flavor, everything about it. GABE WEISS: I’m remembering. RON HICKS: And remember this. I mean, we’re talking about the
middle of a bell curve here. That’s the primary beef in
this country is feedlot raised. So we changed the
basis of feedlots with someone that’d
never seen it before. And she ends up
becoming a superstar– sold everything immediately. All the animals were sold. So she’s in the practice now of
actually being one of the top– what I would call–
high-tech ranchers, just by the sheer fact that
we drove by one day and leased her place. GABE WEISS: That’s awesome. I love that. You told me a story
at one point, which I would love for you
to regale us with, of when you were kind of testing
the AI system against humans. It was like master’s students
in veterinary system, and you wanted to– RON HICKS: Absolutely, yeah. GABE WEISS: –prove that
your system could predict as accurately as they could. RON HICKS: Austin’s
talking about anomalies, or what we call
something that’s odd. And I think that you guys here
at Google understand this. If you can take in enough
data, you can really see a lot. But if you go into an area
that’s not ever been watched, you can surprise some of
the best in the world. And we went out to meet with
some of the high-end vets that actually do feedlot management. And they said, Ron, we
have a graduating class, and in the summer, we always
make the graduates work through the final year. And we do stuff like test them. Can we test you
with our graduates? We are the best,
and these graduates are what we consider
to be some of the top, so they’ve made it
through the program. Bring your stuff out. Let’s watch some animals, and
you let us know what you think. We literally set
the antennas up. No one told us anything. We drove away. We were probably 500
yards from the site. And we could see from
our system the animal that was, without a
doubt, in trouble. GABE WEISS: The system knew. RON HICKS: The system knew. GABE WEISS: Yeah. RON HICKS: We were
actually embarrassed because we knew so quickly. So our chief engineer said, we
need to go back and tell him. I said, we need to
wait about 30 minutes. [LAUGHTER] MARK MIRCHANDANI: So
you said that you knew within a minute or seconds– RON HICKS: We knew. Oh, yeah. MARK MIRCHANDANI:
You’re just like, oh, that’s the problem
one, but we can’t go back and embarrass them. RON HICKS: We were sitting
in a Suburban up the road. We didn’t want to
turn around because I was embarrassed myself
for the poor students, who were out there with
their notebooks and trying to look almost
like a pen rider would because that’s a very
strong point here. A pen rider can give
you one viewpoint. But if you look at
data on a general basis and it’s recalculating
and reformatting, it doesn’t take long for us
to see what comes to the top. And we did it. GABE WEISS: So did you just
go drive in circles for a bit before you came back? RON HICKS: We did. GABE WEISS: Yeah. RON HICKS: We did. We picked up some barbecue
at a little place here. And again, I’m not a
big ranch type person, so walked in, got some barbecue,
stalled, went back to the lot, told the vets, we
know the animal. And we obviously put it on
a little piece of paper, handed it to him. He’s shocked. And he’s trying to slow it down,
but he said to those students, let’s come in. They turned in their results. We turned in ours. We were the only
correct answer– GABE WEISS: Hm. RON HICKS: –the only one. GABE WEISS: That’s
unfortunate for them. MARK MIRCHANDANI:
But it’s hard, right? That is the reality
of this situation, is that it can be really
difficult to observe these sorts of things. And being able to collect,
like you said earlier, tremendous amounts of data and
then make predictions on it is something that just
hasn’t been possible up until recently. RON HICKS: That’s right. GABE WEISS: I mean, these
pen riders have the lifetime of doing the job, right? So they’ve got just a
lifetime of experience of gathering that data. So in essence, they’re kind of
the hard drives or the storage, whatever you want to call it. When one of them retires,
all that tribal knowledge is [WHOOSH] gone. It’s so cool to me that there
is no leaving the system. The data is there. The data is there. So it’s really more
of a factor of, this can just be
applied everywhere. There’s no reason not to
just apply this all over. AUSTIN ADAMS: It’s way
more possible to export a model from AI than it
would be from a person. GABE WEISS: Human
knowledge is tough to dump. Anyone that’s left a job knows. I’ve got two weeks to
dump all of my knowledge. That’s not a thing. How you do that? You can’t do that. MARK MIRCHANDANI: And if you’re
looking for another business idea, finding a way to
export human-trained models would be fascinating. RON HICKS: So on
that, obviously, we– MARK MIRCHANDANI: Oh,
are you working on it? [LAUGHTER] RON HICKS: We can’t discount
the years of experience that the pen riders that
do still work in feedlots. So we have built features
into our ecosystem, apps, whatever you call them,
that do allow human feedback. So we’ll make what we call a
prediction or recommendation. But we still allow and track the
negation or acceptance of that from an actual pen rider. GABE WEISS: I’m
guessing that then, that gets folded into the model. RON HICKS: As a lot of people
are aware, machine learning, you have to put
weights on things. This big weighted
average machine’s just churning at high speed. GABE WEISS: Yep. RON HICKS: So you
put a specific weight on that, a feature
or that data point, and it can get folded in. So it’s a function
of understanding how important the
different variables are in different cases and then
making good predictions that are not noisy, they’re
not false positives, and they’re also
manageable by the staff. Because when you go into
a feedlot that might be really not doing so great– MARK MIRCHANDANI: You need
a lot more antibiotics. RON HICKS: Maybe,
or in the sense of being able to provide
relevant and manageable predictions. GABE WEISS: So we have
this concept of greenfield versus brownfield. The idea is, does
your system work? You mentioned a feedlot
that’s in bad shape and lots of sick animals
or whatever it is. Can the system be deployed
and fix brownfield so you could take a feedlot like
that that’s in really bad shape and be like, hey,
bring in our system. We’ll help fix your situation. Can it do that? RON HICKS: Oh, yeah. Yeah. AUSTIN ADAMS: We
think that if you deploy our system, our
monitoring, in the right way on a feedlot like
that, if you have a good strategy for treating
those animals, which we have a couple of other technologies
that can help you with that, we think you could
turn that place around. And I think there’s evidence
of that in some past research that Ron and people
before me have done, yeah. MARK MIRCHANDANI: And
that’s kind of the idea here is not to completely
automate the entire farm, right? Or the ranch, in this case. The idea here is
to actually gather all of this data, which
wasn’t possible before, make predictions on this
data, which wasn’t really possible before minus
human intuition, which is an astoundingly powerful thing. But like you said, it’s
difficult to export. It’s difficult to transfer
that to different people. But it’s also limited in
terms of what one person who comes in and sees it is. So with your system, you
collect all the data, you make the prediction, and
then you give it to the humans, and they can implement
changes based on that, right? Like a lot of AI, it’s not
trying to replace humans. It’s trying to make
them much more efficient at not only getting, but also
predicting, based off of data that just wasn’t possible. RON HICKS: Yeah, I mean,
if you think about the fact that we showed an early
tool to a university that’s great at what they do,
that tool becomes better. And as soon as the
input is, I think, shared across a number
of users, it even improves through
the human interface. GABE WEISS: It’s artificial
intelligence, right? RON HICKS: Absolutely. GABE WEISS: So the idea is
the more inputs you get, the better the system gets,
the more accurate it gets. So broader adoption
just means it’s going to be better and
better at predicting the anomalies of an animal
that’s not behaving properly. It’s just going to get
better at doing that. RON HICKS: And
this is something I think we should maybe
talk a little bit about, the whole global impact. If you can imagine now,
converging information from different
parts of the world, looking at different
animal movement criteria, you’re talking about
global perspectives here. And I think we can automate
a really nice system based on the fact that
there is a desire to have animals that
are high performance, clean, and retailed
properly because we’re bringing the transparency
now to the retailer. MARK MIRCHANDANI: And I
imagine that these models have a lot in common,
but how different is that data from
a ranch in Texas versus a ranch in,
let’s say, Australia? RON HICKS: So the difference– I mean, Austin
could go into this– but we obviously have
environmental changes, and obviously, the
breed, the genetics. Surprisingly, the
fact that movement is so vital to an
animal, especially if they’re in a stressed
condition, is somewhat similar, but you’ve got all the other
pieces that come into play. GABE WEISS: So a
stress situation might look very different
in a hot, humid environment like Texas versus,
say, an arid situation. RON HICKS: Oh, yeah. AUSTIN ADAMS: Day
one, implementing, Australia and somewhere
in Wyoming, whatever, totally different
temperature, we can still predict the animals that are
sick just on the way they move. The goal is to get better
and better at that, adding in the other variables. GABE WEISS: And better and
better translates to how soon– how fast? AUSTIN ADAMS:
Better and better– how fast, also being able to
filter out any noise that we might be creating
based on other factors, such as a different
terrain, blah, blah, blah, things like that, or
heat, humidity levels, the moisture content of
the grass or their food, different things like that. So those are some
of those variables. But given the difference
of hours or days on their behavior, we can still
predict which ones are sick. And that’s been proven through
different temperature scenarios and things like that,
that, again, the research that early HerdX did. So a lot of what’s happened
in HerdX is amazing technology and ideas have been
developed, and now the phase that we’re in now is
actually taking that, realizing the data value of it,
bringing it into the cloud, and scaling that piece. So a lot of this cool
stuff has actually been implemented on farms
and things like that. And what we’re now
doing is packaging it up where it can be run in a
modern way and on the cloud, and scale out to the
entire globe, too. GABE WEISS: Yeah, we call
that the lift and shift. AUSTIN ADAMS: Yeah. GABE WEISS: Once
you’ve got it all set up in your
local environment, and you have it
all humming along, now it’s time to get rid of
the infrastructure requirements and just get it out, right? So I know that
we’ve been talking a lot about the first
part of this business. But I know there’s more. You talked about
originally the transparency all the way to the consumer. So let’s talk a little
bit about what that means. We’ve now got a
good idea of what it looks like on the ranch. So we’ve got the livestock’s
all being taken care of. They’re getting improved health
conditions on the feedlots. How does this help the consumer? Where does the consumer come
into this story for HerdX? RON HICKS: Austin and I
could both jump into this. It’s kind of fun. We’ve been watching this happen
over the last year for him, and the last five years for me. The retail experience
is radically different than it was five years ago. The amount of data that’s
necessary to make a purchase has to be high speed
and has to be relevant. So your buyer is more aware,
in certain categories of food, as to what they’re eating
and how it was raised. In the case of US beef,
that’s our goal now, is to really bring
that transparency all the way through a– we’ll call it a
blockchain perspective, but it’s the methodology
of participants that all work to make sure
that the food is high end, treated in a more natural
way in the beginning, and then processed in
the best conditions, and then moved and trucked
under the right conditions. By putting all those
pieces together, you’ve created a really
awesome system for retailers. GABE WEISS: It feels like–
and correct me if I’m wrong– the concept of
the farm-to-table. That’s what we’re talking about,
is people are really enamored of the farm-to-table concept
of knowing this food came from the farm a mile away. So it sounds like what
you’re kind of, in essence, doing is extending the
reach, the range, of what that farm-to-table means. The food doesn’t have to be
next door to you anymore. It can be at a ranch
hundreds of miles away. But you’re giving the ability
to see, for those end consumers, what the origin point looks
like and where it’s from. What does that look like? For instance, I’m going to
go buy some meat, right? RON HICKS: Yep, and let’s
do this in a way in which– I think Austin can
help break this down– from a market perspective, or
maybe the niche that HerdX has. I think there’s a given on
anything that’s pasture raised, that’s out there just
running and roaming, it’s a good animal. It has not been
removed from its mom. It’s running out at a
really well-run pasture. So the given is it’s safe. It’s clean. It’s a great animal. High performance. What I’m saying that it’s a
high performance end good. In the case of the feedlot,
again, the majority of the entire US business– 24 million animals on feed
here in the US today– how do you take care of those? So that’s what this
system is all about. And I think the
advantage is it’s the majority of this
country’s livestock and beef that’s raised on a feedlot. That is not a
pasture–raised animal. And that’s what we’re basically
putting a mechanism in place to allow that to be the future
of how beef will be raised and how it’s made
available to the retailer. AUSTIN ADAMS: Yeah, so we
analyzed the cattle supply chain, the meat supply
chain, and looking at the different
countries involved. And they all want
a tracing program, and there’s so many
hurdles to that, and people who don’t want
to share data with who, or people who are kind of on
the fringe of the supply chain. So we selected the technology
and the methodology of just having each
person just have a way to manage the transaction
on the batch of animals. Although we can with
the data– we’re not trying to be one of those
people that shows you the nose print of the cow
that you’re eating– we don’t think that the
consumer really is into that. GABE WEISS: That
might be a bit much. AUSTIN ADAMS: Yeah. I think what the
consumer wants is a connection with
another person, not a connection with an animal. GABE WEISS: The rancher, yeah. AUSTIN ADAMS: Yeah, so we’re
trying to basically lift the rancher, the producer
where the animal has been most of its life,
because they’re only at a feedlot for maybe three
months, maybe five months. So the majority of
its life, it was raised as a great animal on
a pasture in some small town with some just super
quality people. And then it gets moved
into the supply chain where it gets hidden. And those people, they’re
having to fight for business all the time. So we’re trying to
highlight those people, create more incentive for people
to have longer term contracts with each other because
they’re working together on a supply chain, which
ends up making them all more profitable, while
at the same time, not changing the middle
of the bell curve of meat. Because we want basically the
pasture-raised farm-to-table for every family. We want that for everyone. So how we do it is we outfit
the different supply chain partners, or we integrate
with technology. So we integrate with
their technology, or we outfit them
with technology. And they’re able to
check in, check out at that transaction time, at
the moment of transaction. Where the animal or the
product is moving hands, we’re able to take their paper
processes, make them digital. But then because it’s going
into a distributed ledger– because I hate that
word blockchain– it’s something that we’re trying
to build for 50 years to come. We’re trying to select
a technology that’s future-ish proof, something
that can be managed by the industry moving forward. That’s the reason for the
distributed nature of it. It’s not because blockchain
is the hot thing. It’s because that actually
makes sense in this case because they will start to
manage the industry long after maybe HerdX– in 20 years, maybe
HerdX isn’t a thing. Who knows? But the cattle industry
all over the world needs a distributed
method for having a single source of truth. GABE WEISS: Right. AUSTIN ADAMS: Because that’s
what the consumers need, and that’s also what agencies
need, health agencies need, people need in recall scenarios. There’s a lot of– what’s the opposite
of collateral damage– GABE WEISS: [INAUDIBLE] AUSTIN ADAMS: –that happens? Yeah. No, I mean,
collateral improvement when you get a system that can
know the source ranch and all the steps in between
that something’s taken. Because it’s not the
producer that has a problem. It’s many steps down the road
and many changes of hands. It could even be
in a bad scenario. Obviously, HerdX’s
technology is focused on removing the bad
animal before they get into the supply chain. That’s a big, huge goal. But there’s also the
ability to detect the issues within the supply chain due to
timing, temperature, control, things like that. Because a lot of times,
it’s just a package of meat sitting on an airport because a
truck is late, kind of a thing. That’s where sometimes
things go wrong, and people get sick
and stuff like that. And so what we’re
hoping to do is, first off, remove the bad
animal at the beginning. Because when you have a
higher quality animal, the shelf life of that
product is actually better because of the stuff
inside the meat. But then when it gets
into the supply chain, taking away the
hidden tactics that make that a little less safe. Does that make sense? GABE WEISS: Yeah. It’s being able to backtrack if
there is a bad actor somewhere along the supply chain. Like you said, it
might be a pallet that was left for a
little bit too long, and now everything
in that pallet’s bad, but everything in
that pallet might be forked all over the place
from different ranchers. If the ledger can identify
from all of the same meat back to one bottleneck
point, you now know that you found
your bad actor. So the core of the health
system of watching over these animals, that all
happens at the feedlot level. But you’re saying this
ledger– the blockchain, for lack of a better
way of putting it– the receipt of transfer
extends all the way back to the initial ranchers,
the raising of the animals. So it’s like raising of
the animals to the feedlots to the packing in. So you’re tracking
the whole chain. How far down does it go? Does it go all the way to the
end consumers, the stores? How far does it go? AUSTIN ADAMS: Yeah, so
think of it backward. So we’re working
as hard as we can to get as close to birth
of the animal as we can. The easier parts are
starting at the grocery store and working backward. So not that it’s
a huge challenge. I’m saying the harder part is to
get propagated all the way back to these ranches that are on
2G basically kind of a thing. So that’s where we
love our business model because it pulls us
into those places so we don’t have to
go look for them. They’re just, we get an address
basically kind of a thing. But it does go all the
way to the grocery store, and that’s really
the consumer side. Go ahead, Ron. RON HICKS: Yeah, the
grocery is needing to make sure they
retain customers. And so if you think about it,
if you can actually highlight a new meet product,
have a storyboard back to a hot producer, you’ve
completed the chain, but in a sense, you’ve
improved retail. And so I think there’s
a huge advantage. Again, in our case,
we recognize that when an animal comes through it’s
divided into many parts. And so you’ve got some that
go to a restaurant, some that goes to a grocer, some
will be ground into ground beef. And so the advantage for us
is providing that transparency all the way through. We believe that’s the actual
retail thrust of our company. GABE WEISS: Are
you matching people doing A/B testing with
different ranchers? And they’re like, oh, no,
the meat from this rancher is really where it’s at. RON HICKS: So it’s interesting
you’re saying that. I think it’s a new
market advantage. And I’m sure, if some
are listening today, that the retailer is always
looking for something, right? Especially the big market
managers or the product managers. If you can imagine, if we
go back to the original test ranch of the many that I’ve
had, she sold all of her meat immediately. So if you really ate at
that restaurant that night– and I think Gabe,
you were with us– if you think about a grocer
up the street, a small grocer here in Austin that
carries that same brand, they may ask for hers again. Think about that on the
backside coming back. So now we’re providing a report
back to the producer, who says we need to have so
many more of your animal, so much weight, so many cuts,
to come to this Austin grocer due to the restaurant
that was just up the road. MARK MIRCHANDANI: Well,
it’s like you said. This is visible throughout
the entire supply chain and– RON HICKS: Absolutely. MARK MIRCHANDANI:
–at each step, they’re going to look back
and say, I want this one because this one has more data. I know this is good. And here’s the
reasons why it’s good. I now have observability in
ways that I didn’t before. And that goes from all
the way from the ranch, up, up, up, all the way to the
person who goes and buys it, whether that’s a restaurant,
whether that’s just a person going to a grocery store. RON HICKS: Yeah, the thrill– I literally– even today,
our work with you guys, we could do this globally. So I’m in New Zealand. I leave for Japan on Monday. I’m in New Zealand
the following week. We’re providing the same
reports back to them of requests for those animals that has
never been seen before. We’re providing to the
retailer reports of information they’ve never seen before. We’re marrying, basically,
those two ends of the bookends. GABE WEISS: And I’m
assuming people are super excited about seeing that. And that’s got to be kind of
eye-opening for these folks that’s like, I can know what? RON HICKS: Exactly. GABE WEISS: How? That’s the magic, right? RON HICKS: That’s it. GABE WEISS: That’s
hopefully what’s offered. AUSTIN ADAMS: One thing we
talk about a lot with HerdX is HerdX isn’t the only
one that could implement a traceability program. There’s a lot of people
that are trying to do it. The USDA has been
trying for how long? RON HICKS: 21 years. AUSTIN ADAMS: 21 years. We love the USDA, and
we think they can do it. And we are here to help. But there’s something
different when you have a supply
chain that’s also got what we call
authenticated data, meaning data that’s collected
without human intervention or interaction. So we have automatic
monitoring systems that can verify the source
country of the animal. It can verify a number
of different things. It can actually also
play into however far we want to move
the movement data, even over to the retailer to
give them a stamp of assurance or some form of quality
metrics on this animal. When you think about
higher quality retailers, or when you think
about retailers that are just super risk averse– I’m thinking of a
name right now– they want as much data as they
can on mitigating their risk. So they want the highest
quality, lowest cost animals. So the data that we’re able
to provide, but not just data, but authenticated
data, data that isn’t just someone scanning
a piece of paper, which could be fraught with error. It’s authenticated data from
automatic monitoring systems that we actually know
if someone’s opened the panel on our hardware. So it’s like we can
put different weights on different data. So we think that
authenticated data or thinking about data in a way that’s
like, hey, this is not just manual data digitized. This is a new form of data
that didn’t exist before. And it’s collected in
an autonomous way, which I think heightens your ability
to trust it because there’s no human intervention. There’s no bad actors
available in there. GABE WEISS: It’s not
subjective anymore. AUSTIN ADAMS: Yeah,
it’s not subjective. GABE WEISS: So this brings up
kind of an interesting thought, which is that the USDA offers
a bunch of certifications, like organic and all
these other things. It kind of feels like your
system would kind of blow that out of the water because the
certification becomes automatic now. If you know every step of
how your animal’s raised, you know if it’s
organically raised. I don’t have to give
someone a bunch of money to get the stamp on it that
says I’m organic anymore. Have you felt any
pushback from that? MARK MIRCHANDANI:
You go to the grocer, and you just have the
HerdX number printed right on there, right? RON HICKS: Yes. AUSTIN ADAMS: So I think
some of those agencies aren’t necessarily aware of
what this sort of ledger, this sort of network can do. GABE WEISS: They haven’t put
two and two together yet. AUSTIN ADAMS: And
think about the fact that it could be co-operative. It couldn’t be as bad
as what you’re saying. It can be a cooperative
thing with them. That’s part of the
goal of the network, again, is this is
for the industry. Obviously, we’re a company. We have to make money. But this is for the industry. And you think about the way
that blockchains can work. We have our smart contract that
manages the change of hands on the network. But there’s other contracts
that can be written on top of the same network. So the organic certification
could be a function of data in, certification out on a
specific batch of meat. So those are some of the things
that maybe you still have an overhead cost or whatever. This is just ideation. Those are some things that
you can do with– weird thing to say– a data
network, a ledger. So how that works
out is you have third parties and
agencies involved in different parts of the data. Because, again, the beauty
of the distributed ledger is not all the data is
in everyone’s hands, but the fact that
there’s data somewhere is in everyone’s hands. The world’s data is distributed,
but not the actual data is distributed. GABE WEISS: Right, right, right. Cool. RON HICKS: Again, we’ve talked
about this with you guys, that I believe Google
brings a tremendous amount of B2C information
and knowledge. I think we have to
be aware of the fact that enterprise solutions,
information on the cloud, on multiple clouds,
whatever we’re setting up, we feel really
comfortable with the fact that search is a very
important part of retail. And I think it’s important
that we, as a company, take advantage of the fact
that the shoppers changed and that search and
what they read and some of the ad campaigns that we’re
going to place together with, I think, some of the top grocers
and restaurants in the world all fit with this new data. It totally makes sense. MARK MIRCHANDANI:
This is unique, right? There are so many people
talking about, oh, we can grab data, and analyze it,
and build machine learning. And then what? Right? This is a concrete scenario
where you said, look, we know that we can do this. Where are we going to use it on? And then you found an actual
case study or use case in this, I should say, and then
said, yeah, let’s do it. Let’s apply it. Let’s see what happens. Let’s test it. And then now you’re
coming up with a business model based on it. And now you’re basically
flying all over the world, trying to show people
that this is cool. And people care about it. And we’re making
animals healthier. We’re making the supply
chain easier to understand. We’re giving consumer choices. I mean, this is a
win-win-win-win-win. So that, I think, is
really the core model. RON HICKS: Austin’s
looking at me because my main thrust, every
time I come to the company, is everybody wins. And we really feel like that. Even the USDA piece that
Austin commented on, it’s a win for them. Is it cutting edge? It is. I spoke at the conference. They asked me to come and speak. I actually said I
didn’t have time. And I realized I’d better
just go say something. Because we could provide
a tremendous amount of information, even if,
as small as it might be, that is just the right
piece for a recall. They don’t have to pull
two weeks of supplies. They can pull two hours– GABE WEISS: One pallet, right? Pull a pallet of meat
off, soon as they know. RON HICKS: A pallet,
maybe a batch– that’s it. MARK MIRCHANDANI: Well, this
has all been super cool. I think we’re
running out of time. Are there any other
events coming up? What’s coming up soon for HerdX? RON HICKS: So I
don’t have to sleep. That’s what some people say. [LAUGHTER] We love the changes
that are going on, not only in our industry, but
in many others, due to data. My big event for me is the
Innovator of the Year finalist in New Zealand, due
to the fact that we’re taking all their
sheep and lambs, and placing this
technology out there, and helping them to propagate
what they have to propagate as a livestock export business. They have to keep that rolling. We’re going to
basically build that. We’re also nominated for
bilateral trade, which is an increase in New Zealand’s
business, which has already happened due to our product. So those are the
big wins for me. And actually, it’s
a long flight, but it’s well worth it
when you can come up against some of these guys that
launch rockets for a living and say we’re helping
to launch sheep. [LAUGHTER] MARK MIRCHANDANI: Not
launch sheep in rockets. RON HICKS: No, that’s right. MARK MIRCHANDANI: OK. GABE WEISS: I just
wanted to make sure. AUSTIN ADAMS: That’s next year. GABE WEISS: Yeah,
I was going to say, I kind of want to now
launch sheep in rockets. AUSTIN ADAMS: I want to
kind of need to do that now. GABE WEISS: Right? MARK MIRCHANDANI: Well, I think
you need to make a business model for it. GABE WEISS: I mean,
you are the CTO. This can happen. AUSTIN ADAMS: Quantum computers,
sheep rocket launchers. Ron will say yes to
anything I ask for. RON HICKS: Yes. MARK MIRCHANDANI: As long as
it still uses the blockchain– GABE WEISS: Of course,
yeah, yeah, yeah. MARK MIRCHANDANI:
That’s the key part. That’s how you know
it’s cutting edge. GABE WEISS: You got
to keep the buzz word. Got to keep the buzz word. MARK MIRCHANDANI:
Well, Ron, Austin, thank you so much for coming
in and talking about– I mean, this is
fascinating stuff. So thank you so much for kind
of shedding some light on it. And obviously, good luck
at the Innovator Awards. RON HICKS: Thank you. Thank you all. MARK MIRCHANDANI: Thank you
so much to Ron, and Austin, and Gabe for, of course,
making that connection and bringing them in. I mean, there’s a whole
world that I had never even thought of before. GABE WEISS: I think
Ron said it, which was, when you go into markets
and industries that haven’t really looked at the
data, it’s fascinating what you can get out of it. Once you can collect and look
at these huge amounts of data in aggregate, what you can
do with that boggles my mind. And I’m really glad that
HerdX was able to figure out, I’m just going to
look at cow data. And I’ve figured out
this really cool system based on just movement of cow. It’s really– it’s awesome. MARK MIRCHANDANI: Yeah,
it’s a cool system. And, like you said earlier,
it’s such a great idea they have to try and bring
a little bit of cleanliness, a little bit more
responsibility, a lot more observability into
the whole process, so that ultimately, they’re
solving some real problems. And I know that
Ron shared stories that he’s going off and
traveling and bringing this to a lot of people. So it’s super
exciting to see where this will be in the next
couple of months slash years. GABE WEISS: Totally. MARK MIRCHANDANI:
Super, super cool. Well, we talked a
little bit earlier. So let’s revisit our
question of the week. GABE WEISS: Oh, yes. [MUSIC PLAYING] MARK MIRCHANDANI:
And so let me ask, if I’ve got a physical
IoT device or some sort of mechanical
servo or something, and I want it to respond
to an event in the cloud– like, let’s say if somebody
uploads a file or someone changes some parameter
and that causes a trigger, what does it actually look like? Those are two very
separate concepts. So how does that work? GABE WEISS: So it’s interesting. It’s not as hard
as you might think. So we’ve got a product in
Google Cloud called IoT Core. And what it does
is it allows you to communicate between a
device in the cloud securely. It connects it in a
secure way to the cloud. And now that physical device
has a digital representation. In the industry, they sometimes
call it a digital twin. They have this digital
object in the cloud that you can kind
of interact with. And there’s APIs,
where, depending on how you want
to trigger it, you can send admin messages,
configuration messages or commands, back down to your
device through this connection. So when the device
is online, you can basically say
things like, OK, I want a fan to automatically turn
on when the temperature hits a certain level. So it’s easy if
they’re in proximity. We’ve all got a thermostat
in the house, right? So the thermostat
hits a certain level, and either your heater or
your AC comes on in the house. Same concept, and now
you can divorce it from being physically connected. So you don’t have to have
a thermostat connected to your heater system anymore. The thermostat’s
connected to the internet. Your heater is connected
to the internet. And now based on the
temperature on your thermostat, your furnace can come on. So this is the
same idea of, Nest does this with the smart stuff
and sending all the data out. That’s kind of the same
idea of how you do this. So from a physical
standpoint, you’ve got some input, and that can
be, like temperature going up to the cloud. And those can be messages
going into Cloud Pub/Sub. And then you’ve got something
watching Cloud Pub/Sub that reads those messages and sends
through the IoT Core admin interface that says, for
this device, do this. And then IoT Core reaches
out to that device through the digital
representation that it’s got, and the device responds to it. So it’s a pretty simple– by APIs, I mean, it’s
like one function call. It’s not like there’s
huge amounts of code you have to write. It’s really concise. It’s nice. MARK MIRCHANDANI: So IoT Core
does a lot of the work there, and it kind of does
two different parts. It does the part where it
receives a signal, which I think you can call through
Pub/Sub or some other Cloud Functions, which,
hopefully, most people who are working in the cloud have
a good sense of how to do. And then on the IoT Core side,
it also creates a digital version– like you
said, a digital twin– that basically, you’re
sending signals to that. And then when that
gets the signal, it handles the mechanical
part of connecting that to the actual device. GABE WEISS: So my product
manager is going to freak out. Digital twin has a very specific
meaning in a lot of places. And this is not technically
like what the industry calls– I think of it as digital
twin because I haven’t been in the IT industry for decades. But people that have been, there
is a very specific connotation. I call it that. But it’s really just an object
in the cloud that represents this physical device. I wrote a blog post about
literal step-by-step if you wanted to do
something like this. I’m Gabe Weiss on medium.com. If you just look for Gabe
Weiss, you’ll find it. The show notes will
have a link to it. But yeah, I basically
came from the concept of, in the IoT world, there’s
two types of engineers. There’s hardware engineers
and software engineers. And the hardware engineers often
have no idea about the cloud. This is a broad generalization. It’s not always true. But Silicon hardware
people often don’t understand the cloud. And then cloud people
and then software people often have no idea about
the physical devices. So I came at it from a
let’s pretend no knowledge. Let’s pretend that
people that are coming to wanting
to do this have no idea how to do anything. So it’s step-by-step,
very clear– run this command. Make sure you see this output. The blog posts I wrote
for this are very detailed on how to do that. MARK MIRCHANDANI: So if
that’s something people are interested in,
go read the blog post or just yell at Gabe about
using digital twin incorrectly. GABE WEISS: I know, yeah. MARK MIRCHANDANI: I
mean, anything works, but it sounds like that’s
some pretty cool stuff. I haven’t gotten into
those blog posts, but maybe I’ll spend the
weekend reading over them. GABE WEISS: It’s fun. It kind of opens your
mind to, like, well, what can I automate in my house? What can I do? And that’s true. And then do it safely. Please, please, please, please. MARK MIRCHANDANI:
Yeah, test, test, test. And then be certain there’s
no loose wires or anything. GABE WEISS: Don’t
set the cat on fire. No, that’s bad. MARK MIRCHANDANI:
But usually, I would say most people can
agree that’s pretty bad. GABE WEISS: Right? I mean, dog people,
I don’t know. MARK MIRCHANDANI: So very,
very cool to check out those details. I’ll definitely be
looking at that. In the meantime, Gabe,
what do you have going on? Anything cool coming up? Any cool travel or plans? GABE WEISS: A couple of things. So I’m going to be at
Cloud Next in London. MARK MIRCHANDANI: Very cool. GABE WEISS: That’s
coming up in November– just officially signed on. I’m moderating a panel
on machine learning, which will be fun. Yeah. Also, I’m going to talk to a
potential customer of ours. They want to do some
IoT stuff there. It’s awesome. They’re doing heavy IoT data
on ocean current temperatures, and they’ve come up
with some cool tech to do the buoys better. So I’m going to go chat
with them about how to get that data in the cloud. MARK MIRCHANDANI: Just another
example of a lot of companies figuring out, now that
we have the ability to collect all this data and
now that we have the ability to process all this
data, what can we do? We can do anything. GABE WEISS: Exactly that, yeah. MARK MIRCHANDANI: We
just need to figure out what it is, and I’m
sure that measuring the temperature of things like
the ocean currents and what direction they’re going in and
all those other kind of data that I have no idea about,
but all those kind of things, it’s like, well, what
can you do with that? GABE WEISS: Right. MARK MIRCHANDANI: I’m sure that
the possibilities from there are going to be endless,
especially in the energy space and in terms of safety, all
kinds of things from that. GABE WEISS: Maybe
save the planet. MARK MIRCHANDANI:
Yeah, just generally. GABE WEISS: Yeah, just maybe. I don’t know. We’ll see. MARK MIRCHANDANI: It’s almost
like IoT is saving the planet. Thank you, Gabe. GABE WEISS: I want that
to be our new tagline. MARK MIRCHANDANI: You are
helping people save the planet. GABE WEISS: So what
about you, Mark? What do you got coming up? MARK MIRCHANDANI:
I am not saving the planet, unfortunately. I am going to stay pretty
local, working on some, like I mentioned, with the billing. I think we’re trying to
launch a couple of cool videos and other content items
to help people understand a lot more about billing. I’d like to think
that while people are using IoT to
save the planet, I’m going to be
helping them make sure that they pay
everything responsibly and that they can
keep their costs down. GABE WEISS: Well, it’s
important because we need those companies to stay around. MARK MIRCHANDANI:
You need all of that. You need everything in there. GABE WEISS: See, you’re
still saving the planet. MARK MIRCHANDANI: We’re
all working together to save the planet. Well, on that feel-good note,
I think we’re just about out of time. So thank you, everyone,
for tuning in, and we’ll see you all next week. [MUSIC PLAYING] GABE WEISS: Right
before I go on stage, I will feel like I have to pee. I could have gone pee
two seconds before. It doesn’t matter. That’s how my nerves manifest. MARK MIRCHANDANI: That’s
a nervous bladder, right? GABE WEISS: Yeah. MARK MIRCHANDANI: But I
think that’s something a lot of people can relate to. GABE WEISS: That’s what I mean. I just feel like it,
but I don’t have to go. MARK MIRCHANDANI: Oh, yeah. GABE WEISS: I could
have just gone. Like, there’s no pee. There’s no pee. MARK MIRCHANDANI:
There’s no pee. GABE WEISS: I know.


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