May 13, 2025 • 28 mins
EPISODE 8
In this episode, serial entrepreneur Ritwik Pavan joins Kevin Henrikson and Jason Shafton to explore the rise of AI-first product design and its profound implications on consumer hardware, smart cities, and modular construction. They discuss the essential strategies founders need to adopt, including the importance of hyper-personalization, overcoming challenges in training data, untapped opportunities in lifestyle and public safety sectors, and why AI is turning hardware into recurring revenue businesses.
CHAPTERS
00:34 – What It Means to Build AI-First
03:53 – Ritwik’s Journey and AI Consumer Innovations
08:41 – Smart Cities, AI, and the Future of Urban Life
17:08 – Modular Construction and AI’s Role in Housing
22:20 – Underrated Opportunities: Lifestyle and Public Safety
LINKS
Connect with Ritwik Pavan
Hardware Herald • LinkedIn • X/Twitter
Stay Connected with Founder Mode
Subscribe to our newsletter: foundermode.kit.com
Connect with Kevin
Connect with Jason
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
That's why Tesla was ahead of the curve when they were doing
FSD. And, you know, then you look at
Waymo or you look at, you know, in the past Cruise or Zooks
where they're having to basically send vehicles as test
vehicles to collect it. I mean, the reason Elon was
ahead of the curve with Tesla isbecause he was able to just have
drivers like us, You know, we were collecting.
(00:22):
Data for Elon for. A.
We're the Tesla training team right here at the Trio.
Exactly. Kevin, here we are founder mode.
Welcome back. Are you buddy?
So, so let's you know, let's talk about for you, given all of
the, you know, all of the stuff you're doing with AI right now,
(00:44):
like you, you're seeing a lot. Talk to me about AI first,
product design and, and the strategies that founders need to
kind of adapt, stay ahead. And just like not, you know, we
just talked about being too early.
Like it's very easy right now tokind of come upon it late.
Like what do you do? Yeah.
I think it's interesting. I think there's a couple things.
I think looking back, you know, historically, like there was
(01:05):
opportunities, you know, we had this kind of machine learning
sort of like Benton you, you'd have a data scientist, you'd
have like machine learning engineers and they'd build
features and then you'd train a model and it was incredibly slow
and incredibly manual. And I think the magic now is
these LLM's understand obviouslylarge language models,
understand natural language. And so a lot of the way we
design and think about things today is like, how can you
(01:25):
collect enough context and how can you collect that in a
natural way so that you can let the LLM just do the work.
And what you're sort of amazed with is like, okay, if you give
it a reasonable response or a reasonable sort of request with
enough context, the actual responses are pretty good.
And so I think that's the first thing is how do you get enough
context and how do you just likelet the LLM do its thing?
(01:48):
And then you start getting into the nuances of like, you know,
how do you make sure that it responds in the guardrails that
you have? Like one of our companies is
doing AI voice for medical. And so they're like the chance
of it getting off track and likeconfusing one very complicated
prescription regimen with another one is high.
So you have to be very careful of how you structure those sort
of kind of calls into the LLM orsomething.
(02:10):
That's a highly regulated space.You have to be really careful
that you don't. But I think, wow, the reason
though is that I think you know,and you've seen this like, you
know, things that are easy are now just not even, not even, you
know, that are just gone. And things that are sort of like
hard are now easy and things that were impossible are now
hard. And I think the part that is
important and what I think, you know, is we debate internally in
(02:33):
our companies a lot is like, arewe doing stuff that used to be
impossible or is on the edge or fringe of hard to impossible?
Because if you're not, there's agood chance that whatever
momentum, whatever traction you have since it's brought to
market in public will be, you know, essentially an AI wrapper
company, it will get not. Adopted that's debate.
Is it is it interesting if it's just a wrapper?
(02:55):
I think I saw a post from the from YC being like, you know,
they're all rappers, like every single one on the list of the
lightest batch was like they're rappers.
So is it is it enough to build awrapper or do you need your home
grown LLM and and data set and data model?
I, I love what you just said a moment ago about things that,
you know, things that were, thatwere hard are now easy.
(03:18):
Things that are impossible are now hard.
And I think that's, that's, and are you working on things that
used to be impossible that are now hard because like that is a
Moat. Let's get into it with our guest
today. We're thrilled to have Ritwick
Pavan on with us. He is a serial entrepreneur.
He started a company called Vaid, then Krava, and now he has
his third venture called Fragile.
We'll talk about some of that work in a moment.
And he's just tackling these hard problems, especially in
(03:40):
hardware, software, smart cities, modular construction,
Exciting, exciting guests on thepodcast today.
And and I'm just stoked to have me.
Ray Wake, welcome to founder mode.
It's great to have you here. Great to be here.
Thanks, Jason. Awesome, man.
Well, yeah, it's cool because wemet what, a while back now.
We met through Hampton and actually met Jason through
(04:00):
Hampton, which is kind of cool. Little Hampton Pod.
We were at the a retreat and so,yeah, little Hampton, shout out,
shout out. But it was good.
And yeah, so I think jumping into it, I mean, what is
building AI first product mean in practice?
And like, you know, I don't know, how does that change the
user experience? I know you've got it done a ton
of stuff with talking to companies and he just came back
from an international trip. And I'm, I'm curious kind of
(04:21):
what you're seeing with, you know, AI and how that compares
to the sort of traditional approach.
It's a it's a good question. So I think that the most
interesting part about AI first when it comes to let me get into
like consumer products specifically is just there is no
one-size-fits-all. I'm seeing so many different
broad applications when we're looking at AI first.
I mean, today I just featured a consumer product where it's
(04:43):
basically a tennis, it's AAI tennis player.
So basically this, it's able to do all sorts of different swings
based on like what you're tryingto practice for.
And it's AAI device that basically gives you a tennis
player and then eventually they're evolving to pickleball.
There's another AI device I've featured before where it's a
chess, it's AAI chess player. So you know, I don't know if
you've ever played chess.com, but you know, you play on your
(05:06):
phone and you're moving the pieces.
Now imagine actual chess board where the pieces move on its own
and it's like it's magnetic and it moves on its own.
And then, you know, it's able toteach you instead.
So basically you have a trainer across the world, AAI trainer
too, where it's like training you on how to play.
So it's got the voice and everything else and it and it
(05:26):
shows you how to play so tennis.Pickleball one also physical
world like on the other side of the court with you.
Yeah, yeah. So it's like it's on other side.
Yeah, it's it's robotics. Ball machine because I was like,
yeah, absolutely. We had like this little turtle
ball machine back when I was playing tennis and like it would
just spit him out at you at like, you know, some relatively
just one. Thing we do so exactly and I
(05:47):
used to play tennis and that wasthe standard right?
Like you could go to a tennis center, you could rent one of
those tennis machines and it just like throws balls at you.
But now taking that to the next level, it's basically an AI
tennis machine that reacts to the shots you're taking.
Then they've got an entire app. They've got an app that
basically understands all of your shots and then gives you a
write up at the end of your tennis session is like, this is
(06:08):
what you need to improve on. This is where like you're
lacking. And so to answer your question,
Kevin, I think it's like AI first to me is like just
ultimately a better customer experience.
It's taking everything that before was like, in my opinion,
like not the best customer experience and now it knows you
as an individual and is basically able to improve the
(06:30):
customer experience per, you know, whatever it is you're
looking for. A few months ago I had the
chance to meet with a company called Matic Matic robots and
and I think they're a really exciting AI first product
because they're effectively theybuilt this robot vacuum, right.
So at first glance it looks likea Roomba.
It doesn't look like one, but itsounds like a Roomba or AU fee
or any one of these robot vacuums.
(06:51):
But when you actually look into it, it's learning all of the
behaviors around your home and is then to know that, OK, your
kids are coming home at 4:00 PM and they're going to make a mess
around the dining table. So let me go out the.
Floor, it's like, get ready. Exactly.
And so just, you know, ultimately I think AI first is
going to allow for humans to have a better customer
experience and every product is going to be focused on the
(07:15):
individual themselves rather than this kind of standardized
product that we used to see in. The past personalization first
is like we could be another way to say it's like now this AI is
now enabled personalization at this like new and incredible
sort of rate that we wasn't possible before.
Yeah. Absolutely.
And I think it also allows for better outcomes where, you know,
before we were OK with mediocrity, but now it's like,
(07:37):
how much can we actually push these products to their limit to
deliver the best experience? I love it.
I think what's interesting when you juxtapose that against AI
later, right? So AI first, amazing.
We talked, we're talking about hyper personalization of product
experiences, but for founders that are kind of falling into
(07:58):
the trap of adding AI later and even companies have been around
a long time and like they just weren't AI first.
How do you think about that mindset of building AI 1st And,
and maybe a good example of thiswould be, you know, your days at
starting vade and then also likeanother player in the space, a
very big one sidewalk like smartcities in the pre IAI explosion.
(08:21):
And now how how you'd think about that?
I'm just super interested in this space because it there's so
much that we don't even think about when we walk around in the
world in our in our urban or suburban environments.
And like this is stuff that I can imagine going off of what we
just talked about that that hyper personalization and AI can
make our world around us so muchbetter.
And I know you have a strong opinions about this, so I'm
(08:41):
eager to hear them. Yeah.
So you had mentioned my first startup, Vaid, which was a Gov
tech startup where we built cameras that used computer
vision to collect real time parking data and then
effectively improve traffic congestion, help citizens, you
know, with finding parking spots.
And if I were to build that company today, it would look a
lot different than it was back in 2017.
(09:01):
And that's just because of the tools that are available today
to, you know, everything from being able to use AI to predict,
you know, when a parking spot would be available.
Also on that note down the road,I mean, I think there's it's
it's going to be inevitable, youknow, bike lane safety, getting
traffic lights to actually reactto all of this behavior that is
(09:24):
being learned around like, what are the prime times when, you
know, people are looking for parking spots?
When is like that? What are the busiest times when,
you know, these curbs are taken?And then being able to, yeah, in
real time, and then being able to connect to this hardware
where it can now be AI first as well.
Because today there's effectively no real time data to
be able to know, you know what Iconsider parking spaces as a
(09:47):
city's inventory, right? There's no way to balance the
supply and demand, even when like some level of data is
available to the parking meters.So if we can actually use all of
that data and then connect it across through like pedestrian,
for example, sample with like the pedestrian science, right?
And then the traffic lights today, it's kind of just it's
kind of all just like a guess. But if we can use AI, I mean,
(10:08):
the company would look a lot different today than it was even
six years ago. So I think AI first is, yeah,
sorry, go ahead. No, no.
Have you seen approaches to thisstrategy in terms of like
training data feedback loops? Because I think part of what
you're getting into is like, youknow, we didn't have the models,
but then it's also like, did we have like the product road map
to make that strategy work whereit's like, hey, you need, you
need smarts on both sides, right?
(10:29):
Like especially in a city case where like there's this legacy
tech that you're trying to like kind of like light up with AI or
personalization. Yeah.
I mean, I'm seeing it on the road map for a lot of companies.
I think it's a good and bad because I think there's some
companies that are just trying to insert this AI first when it
doesn't make makes sense to go close a a fundraising check or
investor check. And then there's the.
Company. No, nobody's doing that.
(10:50):
Just by the way, anybody, just. Legitimately it's like golden
20X right? Exactly right.
Throw some AI use case in there and then you've got a yeah,
check. So I think there's a good and
bad to it. I think there's like real
applications where it makes sense.
But then there's also a stretch where like, I mean, ultimately I
do see a lot of these companies predominantly having a positive
use case. But you know, it's of course a
(11:12):
bubble right now where there's alot of companies and he's kind
of just throwing it out as a buzzword.
So let's, I mean, let's double click there, right?
Like when, when you think about that building of that initial
high quality data set for a truly AI first product, not a
pretend AI first product like wejust talked about, how do you
solve that that cold start problem and, and really get
(11:34):
there without the scale, right? Yeah, it's a good question.
And I think like the first use case or the first AI or the
first product that comes to mindis humanoid robots.
Because when you know, you've got all these companies kind of
fighting, you've got optimists, you've got, you know, unitary,
you've got a handful of these humanoid companies that are out
there where people think that the bottleneck is actually the
(11:57):
hardware. When in reality it's not the
hardware, It's the real like time data and the software side
where there's just simply not enough models to kind of train
it on, right? Like for example, like when
you're talking about a humanoid robot in every single persons
home, every single home is like very different layouts.
You've got like different washing machines.
(12:17):
So so it is an ongoing challengethat I you know, that I mean,
that comes to mind when we're looking at training these
models. I think it's far more complex to
be able to like just add this layer.
I mean, even with like the robotcompanies, right?
There's so many challenges around, like every home has a
different layout. And so I think it's just, you
know, it's going to come with time.
(12:38):
I mean, AI is moving at lightning speed right now, but I
think when some use cases, there's just not enough out
there to train these models on. I mean, with software, it's a
lot different because you can just train it off of X or Reddit
or like you know, with Grok, forexample, just pulling from all
the X posts. But I think to get real life
footage is, you know, that's whyTesla was ahead of the curve
(12:59):
when they were doing FSD. And you know, then you look at
Waymo or you look at, you know, in the past Cruise or Zooks
where they're having to basically send vehicles as test
vehicles to collect it. I mean, the reason Elon was
ahead of the curve with Tesla because he was able to just have
drivers like us, we. Were collecting data for Elon.
(13:20):
For the Tesla training team right here, the trio.
Exactly right. And so I think that was a
brilliant move on his part because everyone else is is
behind, right? Like so it is an ongoing
challenge, but I think like as different applications come up,
it'll be interesting to see, youknow, how the models improve.
It is good because I think, you know, we're working on a lot of
software stuff in voice and, andone of the things for us is like
(13:41):
building simulators for that. When it's software or things, if
you, if you can mock, you know, the other side of that
transaction or you can have synthetic transactions that
you're training against where I think to your point, like with
physical world stuff and humanoids, like it's hard to
mock, like how many houses can you mock or what?
You know, you can at some level,but it's trickier to kind of put
that in software. But pivoting a little bit to
(14:03):
like the teams behind this, because I think a lot of this is
the teams that are going to be required or the talent that's
required, sort of take some of these AI first challenges head
on. I mean, you've talked to a bunch
of founders across across these consumer hardware space.
Like what are you seeing in terms of the makeup of these
teams? And like what does it take to
win at an early sort of AI firsthardware startup versus say like
(14:24):
a SAS company that we will all worked in, you know, 5-10 years
ago? Now with tariffs that you know,
I, I think it's a lot harder to vibe develop a product than it
is to vibe code. Yeah, it's an interesting one.
One of my close friends ASA, he's building kind of
construction robots and you know, when he built his first
robot startup, which was drones,they like custom built boards
(14:44):
and custom cameras and like super highly custom firmware.
And now it's like literally off the shelf robots and like using,
like I said, more simple techniques, right, to sort of
put this stuff together partly Ithink fuzzes scale.
So I think it, it's interesting that you're seeing the same
thing when you and the thing that for him, it's about how
quickly can they ship. And so I think you mentioned
this like, hey, people buying stuff on Alibaba and kind of
(15:06):
like, you know, rebranding it and then just shipping it.
I'm curious what you're seeing in terms of the speed of getting
stuff to market. And then how does that impact
like the ethical concern of like, putting a vacuum in my
house that maybe, you know, chasing my dog down or my kid
around versus like, you know, spending a little more time
making sure that that thing's got the safety controls to not,
(15:26):
you know, Elon's had some challenges with this as well.
Yeah. I think the biggest thing that I
have seen is the recent is that people are rushing to get to
market. And because of that, often times
the product that you see advertised, you know, people are
are pushing towards like marketing first companies rather
than product first, which I see is like a big problem because,
(15:48):
you know, and of course, like Elon is Elon has done this in
the past. But I think with Elon like that,
you know that the product is going to get delivered.
It might be like 8 years late, but it's going to get delivered,
right. I think it's that's
unfortunately not going to be the case with a lot of these
other companies where, you know,they set the price even before
the product is out of manufacturing and then they've
got this entire video where it shows the product at its V10.
(16:12):
I don't understand when my cybertruck wasn't.
Was it 50 grand? What was the number it was
supposed to? Be maybe that's the vibe coding
of hardware is you release a YouTube video on X.
Yeah, which like I get it from, I get it from like getting
customer feedback as soon as possible before investing, you
know, millions of dollars to building the product.
But at the same time, I think like showing the V10 and then
customer getting the V1 version often times leads to like, like
(16:35):
lack of trust as well. I mean, this is something you
even see with the FSD, right? People are like, oh, why is the
car not driving itself and picking me up today?
You know, why do I pay ten $10,000 for this?
And so except there's some levelof trust that has been built up
within Tesla that has not been built up with a lot of these
newer AI wearable companies, forexample, right?
So they launched the product on X, it goes viral, gets a million
(16:58):
impressions, and then it's goingto take a year to get the
product right. So at that point, who knows
where we're going to be in AI atthat point, You know, Meadow,
you might as well get a Meadow Orion, so.
How do you think about the housing crisis and how
technology and AI can help create more affordable housing
at scale? And I'm just curious your
thoughts on other tech startups in this space, since this is a,
(17:19):
you know, a couple companies ago.
Like talk to me about cover, if you're familiar with that.
I'm, I live outside of LA and I,I know Alexis and that team.
Like, I think it's very interesting this space and you
have a ton of experience in it. Yeah, I think the I think
modular construction, you know as a whole is necessary to be
able to accelerate the speed at which construction is going to
(17:39):
be done over as an excellent example.
I in terms of AI in itself as anapplication here, I actually
think the number one use case will be in permitting and
regulations because just to siftthrough all of the regulations
and permitting that goes in housing per county, not even per
like city, right, like perk zip code is just such a tedious
(18:01):
process and the you know, and I think this goes in with like
dodge and. Everything.
I knew we were going to go there.
Everything to do, you know, thatcan be done to cut regulations
today we're not there. And, and a large reason why an
SF and these other cities you'renot seeing more housing is
because of the fact that it's socomplex to even go get a permit.
And so I think the biggest application initially is going
(18:24):
to be being able to understand, you know, what are the
regulations that exist within the zip code and then being able
to, you know, quickly get permits out.
And then also know whether you're in the confines of being
able to like, you know, get exhaler X design built.
And then to kind of take that tothe next level.
What would be really cool is, you know, the AI, basically you
(18:47):
use AI to understand all of the regulations and then it can put
together a floor plan for your house and say, OK, this is the
plot of land where you want to build a house, you know, build
me a house or build me a, you know, whatever in office based
on the restrictions that you understand in the zip code.
Like, you know, that's just an idea that came to mind, right?
It works like backs into the hard space versus the like
(19:10):
setbacks and all that sort of pre calculated.
I mean, it's pretty awesome because I mean, I think when
we've done some of our permitting up here in the
barriers, like it wild, right? Like how long, I mean this many
feet, Oh, the fence can't be here.
The pool's got to be offset here.
Oh, but it's grandfathered in sothat if it's there, if you do
it, but if you rebuild it, it doesn't move and you're just
like, man, it's crazy. So it was very straightforward.
You've talked to a ton kind of companies and I think like, you
(19:31):
know, we went down the kind of like the permitting stuff and
pieces. But where from like the hardware
perspective, consumer hardware, like where do you think are like
sort of the untapped opportunities?
Where do you think there's just not enough innovation or there
there hasn't sort of been a breakthrough?
And what categories or sort of even products or companies do
you think like, you know are kind of like under underrated or
sort of just haven't had their moment in time yet?
(19:52):
Yeah. I mean, I think it's like very
clear, the ones that are a little bit overrated or hyped up
right now. Yeah, the high ones are good.
It's like, what are the under, what are the ones that like
where we the product categories that haven't had the investment
or the excitement? Yeah.
I think one of the exciting underrated ones is like sports.
I'm seeing like a lot of early stage companies where I'm like,
why is this company not been able to or like, why is this
(20:13):
company not been seeked out by, you know, some of the bigger
investors is like there is a very real application in AI to,
you know, help in lifestyle, right?
Like there's so many like as everybody kind of shifts and,
you know, eventually AI takes over, you know, jobs and, and,
you know, helps you become, you know, easier, older or anything
(20:34):
else. It's like, how can we use AI to
actually improve people's lives,whether it be sports, whether it
be, you know, any activity, right?
I think there's like, I think one of the ones where it has
been used and is going to continue evolve is like health
tech, right? There's a lot of health tech
companies being funded like crazy right now and you know,
health wearables, but also like health platforms.
(20:54):
One I'm not seeing as much as like more the lifestyle side of
it. You know, as I mentioned the
earlier, like I think there's going to be so many different
products that can be applicable,whether it's sports or
activities or hobbies, which which gets very interesting
because you obviously have like companies like 8 sleep or aura
ring or woop right where where they will have this like they
will eventually become AI first if they if they already aren't
(21:15):
offering it. Another use case I think that's
very interesting is outside of the like kind of lifestyle side
is I'm trying to figure out the best way to to there's public
safety is a very interesting 1. So I think there's a company
that you guys might be familiar with called Flock Safety, which
is Flock is absolutely crushing it.
(21:36):
And I think there's going to be a very real use case in security
systems, even for homes. I'm not talking about like the
cameras for HOA and neighborhoods, but for your home
there. I forget that it's a very
prolific founder. And I think in Miami, I'm
forgetting who it is. I think is the the a star
founder, somebody that's doing like this deterrence of, you
know, crime in your home. But like there there could be we
(21:59):
could take AI to the next level for like break insurance, right?
Like what if we're now able to use that to actively like deter
criminals? So I think there's going to be a
real application in crime, public safety, but also like
just, you know, home access control, like what if we could
take ring or, you know, some of these other secure ADT or some
of these other security systems to the next level with AII love
(22:21):
the. Focus on lifestyle and public
safety. I think these are interesting
categories that I agree. I'm not seeing nearly as much
interest and excitement. So as we think about maybe just
shouting out to the founders outthere that are building this
right, like you've been very successful at raising capital.
So when we talk about how early stage founders in these AI first
companies that are doing super innovative, cool stuff, how
(22:42):
should they communicate that vision to their investors and
ultimately customers without overhyping and to land that big,
you know, first check or you know, maybe the A round, the B
round, right, the thing that gets them to escape velocity.
Yeah, I think it's so. Also you just reminded me one of
the other underrated ones is AI with manufacturing or just self
(23:03):
building. One of my favorite companies is
Bamboo Lab, if you guys are familiar.
It's like the top 3D printer company, but one of the exciting
parts about it is, you know, what if you could use AI but
also like this whole manufacturing site to basically
build products on your own without and like having to go
reach out to factory, you know, and get parts and components,
(23:25):
you know, done. And then it takes like 3 weeks
to deliver The reason I mention.The CNC business, basically.
Yeah. So the reason I'm mentioning
that point Jason is because to answer your question with
fundraising, I think by being able to have founders build
these ad first companies that allows them to potentially have
a recurring revenue channel. Often times hardware was looked
down upon because it's like one time cost very difficult to get
(23:47):
out to market. And then once you get out to
market like now how are you actually making money getting
like recurring revenue. But I think like when you have a
real use case where you're understanding customer feedback
or you're, you know, using all this data to basically improve
their life or you know, build a better experience for them, They
are going to be more willing to continue investing in the
product as well. Whether that be, you know, like
(24:10):
a monthly subscription, whether it be like, you know, a
different interactions. But I do think AI unlocks this
new use case or like this kind of feature set where people are
now going to be using those products on a more recurring
basis, which is something that investors look for, like when
they're looking at apps, for example, not only are they
looking for. Retention.
(24:31):
Yeah, and with apps, they're notonly looking at like retention,
but they're also looking at likehow frequently is this app being
used, right, like activity wise.And I think that just improves
radically when you're able to have this AI bot or like this AI
system, for example, with the tennis, like just showing, you
know, all all the like where allyou hit it.
But then like what can be improved in in real time?
So in terms of like how I would pitch it to investors, I would
(24:53):
just, you know, try to tap into the the long term use case of
the hardware products rather than just like, hey, we're
building this product and it's going to help people do XYZ.
Transactional. We're building a relationship
with the user. Yeah.
Now and I think the the cool thing about that is I mean it,
it kind of takes hardware from this one time thing to now be a
subscription of the there is a subscription that makes sense
(25:13):
there, right. And so the hardware can just be
like amortized over that and then personalization and all
that. So I think I mean, right, look,
this has been an incredibly insightful, you know, time with
you. And I mean, I really appreciate
you sharing all the different pieces here.
And I think sort of breath of knowledge of this consumer
hardware space in particular is pretty amazing.
And you know, seeing all the thevideos you do and and your
newsletter is obviously pretty impactful.
(25:34):
So where can people find you outand follow more about your work?
It's been great chatting with you guys.
I'm on Twitter, it's just at Rithwik Pavan.
So RITWAKPAVANI also write a a newsletter weekly sharing
exciting consumer product launches.
It's called Hardware Herald, so you can just go to
hardwareherald.com. It's been incredible to see that
grow over the last year or so. Thanks.
(25:55):
For thank you. Glad to have you on.
Thank you guys. That conversation with Ridwick
was like true master class and building, I mean, intelligence
on the ground up. And I think for me, if you're
just treating AI like a feature and you bolt it on later, like
hopefully that shifted your perspective, right?
And now you realize that like, you really have to think about
(26:15):
if you're going to do AI, have it lead with AI.
And then how do you have that integrated in a way that makes
the most sense? And so let's recap a few of the
things that he went over. I think first was this notion of
AI first hardware is really thishyper personalization, right?
Like every piece of hardware nowfeels like it's very much for
you. And it's that real time user
experience that just gets betterand better as you use it.
(26:36):
But to build that, we need really good training data.
And so he talked a lot about thequality of the training data and
not necessarily the hardware, but it's like that becomes the
chief growth bottleneck, right? What is that ability for you to
get accurate training data to then be able to convert that
back and the winning devices aregoing to really be integrated,
right? It's that hardware, software,
ML, it's like and the talent that you need to go put those
(26:58):
together, you're no longer goingto have just one type of
engineer. The full stack is now really
starting from like mechanical engineer, you know, electrical
engineer and all of that. And then I think that they
highlighted this notion of like,where's the white space
opportunities, right? So this idea of lifestyle,
sports and just lifestyle in general.
And then how does that bring AI driven stuff into the home
public safety, maybe self manufacturing tech.
(27:18):
And then finally, like I just converts these one off gadgets
that, you know, used to buy something off the shelf
hardware. Now it's a subscription business
and investors are going to love that, right?
And it's just the size of these businesses and the value of
these businesses is going to continue to advance.
Nailed it It's a it's a lot of wisdom packed into a short
amount of time. So if you found this
conversation valuable, do us a favor share it with another
(27:39):
founder or builder who's navigating the world of AI and
and trying to build AI 1st and make sure you're subscribed
because the next time on foundermode we're we're also tackling a
pretty interesting trend hackingdirect to consumer growth with a
surprising strategy. So you can find links to
everything that we talked about,including how to connect with
quick in the show notes. And just thanks again for tuning
into Founder mode. Remember, don't just add
(27:59):
intelligence building around it.Build smart and build now.
That's why Tesla was ahead of the curve when they were doing
FSD. And, you know, then you look at
Waymo or you look at, you know, in the past Cruise or Zooks
where they're having to basically send vehicles as test
vehicles to collect it. I mean, the reason Elon was
ahead of the curve with Tesla isbecause he was able to just have
drivers like us, You know, we were collecting.
(00:22):
Data for Elon for. A.
We're the Tesla training team right here at the Trio.
Exactly. Kevin, here we are founder mode.
Welcome back. Are you buddy?
So, so let's you know, let's talk about for you, given all of
the, you know, all of the stuff you're doing with AI right now,
(00:44):
like you, you're seeing a lot. Talk to me about AI first,
product design and, and the strategies that founders need to
kind of adapt, stay ahead. And just like not, you know, we
just talked about being too early.
Like it's very easy right now tokind of come upon it late.
Like what do you do? Yeah.
I think it's interesting. I think there's a couple things.
I think looking back, you know, historically, like there was
(01:05):
opportunities, you know, we had this kind of machine learning
sort of like Benton you, you'd have a data scientist, you'd
have like machine learning engineers and they'd build
features and then you'd train a model and it was incredibly slow
and incredibly manual. And I think the magic now is
these LLM's understand obviouslylarge language models,
understand natural language. And so a lot of the way we
design and think about things today is like, how can you
(01:25):
collect enough context and how can you collect that in a
natural way so that you can let the LLM just do the work.
And what you're sort of amazed with is like, okay, if you give
it a reasonable response or a reasonable sort of request with
enough context, the actual responses are pretty good.
And so I think that's the first thing is how do you get enough
context and how do you just likelet the LLM do its thing?
(01:48):
And then you start getting into the nuances of like, you know,
how do you make sure that it responds in the guardrails that
you have? Like one of our companies is
doing AI voice for medical. And so they're like the chance
of it getting off track and likeconfusing one very complicated
prescription regimen with another one is high.
So you have to be very careful of how you structure those sort
of kind of calls into the LLM orsomething.
(02:10):
That's a highly regulated space.You have to be really careful
that you don't. But I think, wow, the reason
though is that I think you know,and you've seen this like, you
know, things that are easy are now just not even, not even, you
know, that are just gone. And things that are sort of like
hard are now easy and things that were impossible are now
hard. And I think the part that is
important and what I think, you know, is we debate internally in
(02:33):
our companies a lot is like, arewe doing stuff that used to be
impossible or is on the edge or fringe of hard to impossible?
Because if you're not, there's agood chance that whatever
momentum, whatever traction you have since it's brought to
market in public will be, you know, essentially an AI wrapper
company, it will get not. Adopted that's debate.
Is it is it interesting if it's just a wrapper?
(02:55):
I think I saw a post from the from YC being like, you know,
they're all rappers, like every single one on the list of the
lightest batch was like they're rappers.
So is it is it enough to build awrapper or do you need your home
grown LLM and and data set and data model?
I, I love what you just said a moment ago about things that,
you know, things that were, thatwere hard are now easy.
(03:18):
Things that are impossible are now hard.
And I think that's, that's, and are you working on things that
used to be impossible that are now hard because like that is a
Moat. Let's get into it with our guest
today. We're thrilled to have Ritwick
Pavan on with us. He is a serial entrepreneur.
He started a company called Vaid, then Krava, and now he has
his third venture called Fragile.
We'll talk about some of that work in a moment.
And he's just tackling these hard problems, especially in
(03:40):
hardware, software, smart cities, modular construction,
Exciting, exciting guests on thepodcast today.
And and I'm just stoked to have me.
Ray Wake, welcome to founder mode.
It's great to have you here. Great to be here.
Thanks, Jason. Awesome, man.
Well, yeah, it's cool because wemet what, a while back now.
We met through Hampton and actually met Jason through
(04:00):
Hampton, which is kind of cool. Little Hampton Pod.
We were at the a retreat and so,yeah, little Hampton, shout out,
shout out. But it was good.
And yeah, so I think jumping into it, I mean, what is
building AI first product mean in practice?
And like, you know, I don't know, how does that change the
user experience? I know you've got it done a ton
of stuff with talking to companies and he just came back
from an international trip. And I'm, I'm curious kind of
(04:21):
what you're seeing with, you know, AI and how that compares
to the sort of traditional approach.
It's a it's a good question. So I think that the most
interesting part about AI first when it comes to let me get into
like consumer products specifically is just there is no
one-size-fits-all. I'm seeing so many different
broad applications when we're looking at AI first.
I mean, today I just featured a consumer product where it's
(04:43):
basically a tennis, it's AAI tennis player.
So basically this, it's able to do all sorts of different swings
based on like what you're tryingto practice for.
And it's AAI device that basically gives you a tennis
player and then eventually they're evolving to pickleball.
There's another AI device I've featured before where it's a
chess, it's AAI chess player. So you know, I don't know if
you've ever played chess.com, but you know, you play on your
(05:06):
phone and you're moving the pieces.
Now imagine actual chess board where the pieces move on its own
and it's like it's magnetic and it moves on its own.
And then, you know, it's able toteach you instead.
So basically you have a trainer across the world, AAI trainer
too, where it's like training you on how to play.
So it's got the voice and everything else and it and it
(05:26):
shows you how to play so tennis.Pickleball one also physical
world like on the other side of the court with you.
Yeah, yeah. So it's like it's on other side.
Yeah, it's it's robotics. Ball machine because I was like,
yeah, absolutely. We had like this little turtle
ball machine back when I was playing tennis and like it would
just spit him out at you at like, you know, some relatively
just one. Thing we do so exactly and I
(05:47):
used to play tennis and that wasthe standard right?
Like you could go to a tennis center, you could rent one of
those tennis machines and it just like throws balls at you.
But now taking that to the next level, it's basically an AI
tennis machine that reacts to the shots you're taking.
Then they've got an entire app. They've got an app that
basically understands all of your shots and then gives you a
write up at the end of your tennis session is like, this is
(06:08):
what you need to improve on. This is where like you're
lacking. And so to answer your question,
Kevin, I think it's like AI first to me is like just
ultimately a better customer experience.
It's taking everything that before was like, in my opinion,
like not the best customer experience and now it knows you
as an individual and is basically able to improve the
(06:30):
customer experience per, you know, whatever it is you're
looking for. A few months ago I had the
chance to meet with a company called Matic Matic robots and
and I think they're a really exciting AI first product
because they're effectively theybuilt this robot vacuum, right.
So at first glance it looks likea Roomba.
It doesn't look like one, but itsounds like a Roomba or AU fee
or any one of these robot vacuums.
(06:51):
But when you actually look into it, it's learning all of the
behaviors around your home and is then to know that, OK, your
kids are coming home at 4:00 PM and they're going to make a mess
around the dining table. So let me go out the.
Floor, it's like, get ready. Exactly.
And so just, you know, ultimately I think AI first is
going to allow for humans to have a better customer
experience and every product is going to be focused on the
(07:15):
individual themselves rather than this kind of standardized
product that we used to see in. The past personalization first
is like we could be another way to say it's like now this AI is
now enabled personalization at this like new and incredible
sort of rate that we wasn't possible before.
Yeah. Absolutely.
And I think it also allows for better outcomes where, you know,
before we were OK with mediocrity, but now it's like,
(07:37):
how much can we actually push these products to their limit to
deliver the best experience? I love it.
I think what's interesting when you juxtapose that against AI
later, right? So AI first, amazing.
We talked, we're talking about hyper personalization of product
experiences, but for founders that are kind of falling into
(07:58):
the trap of adding AI later and even companies have been around
a long time and like they just weren't AI first.
How do you think about that mindset of building AI 1st And,
and maybe a good example of thiswould be, you know, your days at
starting vade and then also likeanother player in the space, a
very big one sidewalk like smartcities in the pre IAI explosion.
(08:21):
And now how how you'd think about that?
I'm just super interested in this space because it there's so
much that we don't even think about when we walk around in the
world in our in our urban or suburban environments.
And like this is stuff that I can imagine going off of what we
just talked about that that hyper personalization and AI can
make our world around us so muchbetter.
And I know you have a strong opinions about this, so I'm
(08:41):
eager to hear them. Yeah.
So you had mentioned my first startup, Vaid, which was a Gov
tech startup where we built cameras that used computer
vision to collect real time parking data and then
effectively improve traffic congestion, help citizens, you
know, with finding parking spots.
And if I were to build that company today, it would look a
lot different than it was back in 2017.
(09:01):
And that's just because of the tools that are available today
to, you know, everything from being able to use AI to predict,
you know, when a parking spot would be available.
Also on that note down the road,I mean, I think there's it's
it's going to be inevitable, youknow, bike lane safety, getting
traffic lights to actually reactto all of this behavior that is
(09:24):
being learned around like, what are the prime times when, you
know, people are looking for parking spots?
When is like that? What are the busiest times when,
you know, these curbs are taken?And then being able to, yeah, in
real time, and then being able to connect to this hardware
where it can now be AI first as well.
Because today there's effectively no real time data to
be able to know, you know what Iconsider parking spaces as a
(09:47):
city's inventory, right? There's no way to balance the
supply and demand, even when like some level of data is
available to the parking meters.So if we can actually use all of
that data and then connect it across through like pedestrian,
for example, sample with like the pedestrian science, right?
And then the traffic lights today, it's kind of just it's
kind of all just like a guess. But if we can use AI, I mean,
(10:08):
the company would look a lot different today than it was even
six years ago. So I think AI first is, yeah,
sorry, go ahead. No, no.
Have you seen approaches to thisstrategy in terms of like
training data feedback loops? Because I think part of what
you're getting into is like, youknow, we didn't have the models,
but then it's also like, did we have like the product road map
to make that strategy work whereit's like, hey, you need, you
need smarts on both sides, right?
(10:29):
Like especially in a city case where like there's this legacy
tech that you're trying to like kind of like light up with AI or
personalization. Yeah.
I mean, I'm seeing it on the road map for a lot of companies.
I think it's a good and bad because I think there's some
companies that are just trying to insert this AI first when it
doesn't make makes sense to go close a a fundraising check or
investor check. And then there's the.
Company. No, nobody's doing that.
(10:50):
Just by the way, anybody, just. Legitimately it's like golden
20X right? Exactly right.
Throw some AI use case in there and then you've got a yeah,
check. So I think there's a good and
bad to it. I think there's like real
applications where it makes sense.
But then there's also a stretch where like, I mean, ultimately I
do see a lot of these companies predominantly having a positive
use case. But you know, it's of course a
(11:12):
bubble right now where there's alot of companies and he's kind
of just throwing it out as a buzzword.
So let's, I mean, let's double click there, right?
Like when, when you think about that building of that initial
high quality data set for a truly AI first product, not a
pretend AI first product like wejust talked about, how do you
solve that that cold start problem and, and really get
(11:34):
there without the scale, right? Yeah, it's a good question.
And I think like the first use case or the first AI or the
first product that comes to mindis humanoid robots.
Because when you know, you've got all these companies kind of
fighting, you've got optimists, you've got, you know, unitary,
you've got a handful of these humanoid companies that are out
there where people think that the bottleneck is actually the
(11:57):
hardware. When in reality it's not the
hardware, It's the real like time data and the software side
where there's just simply not enough models to kind of train
it on, right? Like for example, like when
you're talking about a humanoid robot in every single persons
home, every single home is like very different layouts.
You've got like different washing machines.
(12:17):
So so it is an ongoing challengethat I you know, that I mean,
that comes to mind when we're looking at training these
models. I think it's far more complex to
be able to like just add this layer.
I mean, even with like the robotcompanies, right?
There's so many challenges around, like every home has a
different layout. And so I think it's just, you
know, it's going to come with time.
(12:38):
I mean, AI is moving at lightning speed right now, but I
think when some use cases, there's just not enough out
there to train these models on. I mean, with software, it's a
lot different because you can just train it off of X or Reddit
or like you know, with Grok, forexample, just pulling from all
the X posts. But I think to get real life
footage is, you know, that's whyTesla was ahead of the curve
(12:59):
when they were doing FSD. And you know, then you look at
Waymo or you look at, you know, in the past Cruise or Zooks
where they're having to basically send vehicles as test
vehicles to collect it. I mean, the reason Elon was
ahead of the curve with Tesla because he was able to just have
drivers like us, we. Were collecting data for Elon.
(13:20):
For the Tesla training team right here, the trio.
Exactly right. And so I think that was a
brilliant move on his part because everyone else is is
behind, right? Like so it is an ongoing
challenge, but I think like as different applications come up,
it'll be interesting to see, youknow, how the models improve.
It is good because I think, you know, we're working on a lot of
software stuff in voice and, andone of the things for us is like
(13:41):
building simulators for that. When it's software or things, if
you, if you can mock, you know, the other side of that
transaction or you can have synthetic transactions that
you're training against where I think to your point, like with
physical world stuff and humanoids, like it's hard to
mock, like how many houses can you mock or what?
You know, you can at some level,but it's trickier to kind of put
that in software. But pivoting a little bit to
(14:03):
like the teams behind this, because I think a lot of this is
the teams that are going to be required or the talent that's
required, sort of take some of these AI first challenges head
on. I mean, you've talked to a bunch
of founders across across these consumer hardware space.
Like what are you seeing in terms of the makeup of these
teams? And like what does it take to
win at an early sort of AI firsthardware startup versus say like
(14:24):
a SAS company that we will all worked in, you know, 5-10 years
ago? Now with tariffs that you know,
I, I think it's a lot harder to vibe develop a product than it
is to vibe code. Yeah, it's an interesting one.
One of my close friends ASA, he's building kind of
construction robots and you know, when he built his first
robot startup, which was drones,they like custom built boards
(14:44):
and custom cameras and like super highly custom firmware.
And now it's like literally off the shelf robots and like using,
like I said, more simple techniques, right, to sort of
put this stuff together partly Ithink fuzzes scale.
So I think it, it's interesting that you're seeing the same
thing when you and the thing that for him, it's about how
quickly can they ship. And so I think you mentioned
this like, hey, people buying stuff on Alibaba and kind of
(15:06):
like, you know, rebranding it and then just shipping it.
I'm curious what you're seeing in terms of the speed of getting
stuff to market. And then how does that impact
like the ethical concern of like, putting a vacuum in my
house that maybe, you know, chasing my dog down or my kid
around versus like, you know, spending a little more time
making sure that that thing's got the safety controls to not,
(15:26):
you know, Elon's had some challenges with this as well.
Yeah. I think the biggest thing that I
have seen is the recent is that people are rushing to get to
market. And because of that, often times
the product that you see advertised, you know, people are
are pushing towards like marketing first companies rather
than product first, which I see is like a big problem because,
(15:48):
you know, and of course, like Elon is Elon has done this in
the past. But I think with Elon like that,
you know that the product is going to get delivered.
It might be like 8 years late, but it's going to get delivered,
right. I think it's that's
unfortunately not going to be the case with a lot of these
other companies where, you know,they set the price even before
the product is out of manufacturing and then they've
got this entire video where it shows the product at its V10.
(16:12):
I don't understand when my cybertruck wasn't.
Was it 50 grand? What was the number it was
supposed to? Be maybe that's the vibe coding
of hardware is you release a YouTube video on X.
Yeah, which like I get it from, I get it from like getting
customer feedback as soon as possible before investing, you
know, millions of dollars to building the product.
But at the same time, I think like showing the V10 and then
customer getting the V1 version often times leads to like, like
(16:35):
lack of trust as well. I mean, this is something you
even see with the FSD, right? People are like, oh, why is the
car not driving itself and picking me up today?
You know, why do I pay ten $10,000 for this?
And so except there's some levelof trust that has been built up
within Tesla that has not been built up with a lot of these
newer AI wearable companies, forexample, right?
So they launched the product on X, it goes viral, gets a million
(16:58):
impressions, and then it's goingto take a year to get the
product right. So at that point, who knows
where we're going to be in AI atthat point, You know, Meadow,
you might as well get a Meadow Orion, so.
How do you think about the housing crisis and how
technology and AI can help create more affordable housing
at scale? And I'm just curious your
thoughts on other tech startups in this space, since this is a,
(17:19):
you know, a couple companies ago.
Like talk to me about cover, if you're familiar with that.
I'm, I live outside of LA and I,I know Alexis and that team.
Like, I think it's very interesting this space and you
have a ton of experience in it. Yeah, I think the I think
modular construction, you know as a whole is necessary to be
able to accelerate the speed at which construction is going to
(17:39):
be done over as an excellent example.
I in terms of AI in itself as anapplication here, I actually
think the number one use case will be in permitting and
regulations because just to siftthrough all of the regulations
and permitting that goes in housing per county, not even per
like city, right, like perk zip code is just such a tedious
(18:01):
process and the you know, and I think this goes in with like
dodge and. Everything.
I knew we were going to go there.
Everything to do, you know, thatcan be done to cut regulations
today we're not there. And, and a large reason why an
SF and these other cities you'renot seeing more housing is
because of the fact that it's socomplex to even go get a permit.
And so I think the biggest application initially is going
(18:24):
to be being able to understand, you know, what are the
regulations that exist within the zip code and then being able
to, you know, quickly get permits out.
And then also know whether you're in the confines of being
able to like, you know, get exhaler X design built.
And then to kind of take that tothe next level.
What would be really cool is, you know, the AI, basically you
(18:47):
use AI to understand all of the regulations and then it can put
together a floor plan for your house and say, OK, this is the
plot of land where you want to build a house, you know, build
me a house or build me a, you know, whatever in office based
on the restrictions that you understand in the zip code.
Like, you know, that's just an idea that came to mind, right?
It works like backs into the hard space versus the like
(19:10):
setbacks and all that sort of pre calculated.
I mean, it's pretty awesome because I mean, I think when
we've done some of our permitting up here in the
barriers, like it wild, right? Like how long, I mean this many
feet, Oh, the fence can't be here.
The pool's got to be offset here.
Oh, but it's grandfathered in sothat if it's there, if you do
it, but if you rebuild it, it doesn't move and you're just
like, man, it's crazy. So it was very straightforward.
You've talked to a ton kind of companies and I think like, you
(19:31):
know, we went down the kind of like the permitting stuff and
pieces. But where from like the hardware
perspective, consumer hardware, like where do you think are like
sort of the untapped opportunities?
Where do you think there's just not enough innovation or there
there hasn't sort of been a breakthrough?
And what categories or sort of even products or companies do
you think like, you know are kind of like under underrated or
sort of just haven't had their moment in time yet?
(19:52):
Yeah. I mean, I think it's like very
clear, the ones that are a little bit overrated or hyped up
right now. Yeah, the high ones are good.
It's like, what are the under, what are the ones that like
where we the product categories that haven't had the investment
or the excitement? Yeah.
I think one of the exciting underrated ones is like sports.
I'm seeing like a lot of early stage companies where I'm like,
why is this company not been able to or like, why is this
(20:13):
company not been seeked out by, you know, some of the bigger
investors is like there is a very real application in AI to,
you know, help in lifestyle, right?
Like there's so many like as everybody kind of shifts and,
you know, eventually AI takes over, you know, jobs and, and,
you know, helps you become, you know, easier, older or anything
(20:34):
else. It's like, how can we use AI to
actually improve people's lives,whether it be sports, whether it
be, you know, any activity, right?
I think there's like, I think one of the ones where it has
been used and is going to continue evolve is like health
tech, right? There's a lot of health tech
companies being funded like crazy right now and you know,
health wearables, but also like health platforms.
(20:54):
One I'm not seeing as much as like more the lifestyle side of
it. You know, as I mentioned the
earlier, like I think there's going to be so many different
products that can be applicable,whether it's sports or
activities or hobbies, which which gets very interesting
because you obviously have like companies like 8 sleep or aura
ring or woop right where where they will have this like they
will eventually become AI first if they if they already aren't
(21:15):
offering it. Another use case I think that's
very interesting is outside of the like kind of lifestyle side
is I'm trying to figure out the best way to to there's public
safety is a very interesting 1. So I think there's a company
that you guys might be familiar with called Flock Safety, which
is Flock is absolutely crushing it.
(21:36):
And I think there's going to be a very real use case in security
systems, even for homes. I'm not talking about like the
cameras for HOA and neighborhoods, but for your home
there. I forget that it's a very
prolific founder. And I think in Miami, I'm
forgetting who it is. I think is the the a star
founder, somebody that's doing like this deterrence of, you
know, crime in your home. But like there there could be we
(21:59):
could take AI to the next level for like break insurance, right?
Like what if we're now able to use that to actively like deter
criminals? So I think there's going to be a
real application in crime, public safety, but also like
just, you know, home access control, like what if we could
take ring or, you know, some of these other secure ADT or some
of these other security systems to the next level with AII love
(22:21):
the. Focus on lifestyle and public
safety. I think these are interesting
categories that I agree. I'm not seeing nearly as much
interest and excitement. So as we think about maybe just
shouting out to the founders outthere that are building this
right, like you've been very successful at raising capital.
So when we talk about how early stage founders in these AI first
companies that are doing super innovative, cool stuff, how
(22:42):
should they communicate that vision to their investors and
ultimately customers without overhyping and to land that big,
you know, first check or you know, maybe the A round, the B
round, right, the thing that gets them to escape velocity.
Yeah, I think it's so. Also you just reminded me one of
the other underrated ones is AI with manufacturing or just self
(23:03):
building. One of my favorite companies is
Bamboo Lab, if you guys are familiar.
It's like the top 3D printer company, but one of the exciting
parts about it is, you know, what if you could use AI but
also like this whole manufacturing site to basically
build products on your own without and like having to go
reach out to factory, you know, and get parts and components,
(23:25):
you know, done. And then it takes like 3 weeks
to deliver The reason I mention.The CNC business, basically.
Yeah. So the reason I'm mentioning
that point Jason is because to answer your question with
fundraising, I think by being able to have founders build
these ad first companies that allows them to potentially have
a recurring revenue channel. Often times hardware was looked
down upon because it's like one time cost very difficult to get
(23:47):
out to market. And then once you get out to
market like now how are you actually making money getting
like recurring revenue. But I think like when you have a
real use case where you're understanding customer feedback
or you're, you know, using all this data to basically improve
their life or you know, build a better experience for them, They
are going to be more willing to continue investing in the
product as well. Whether that be, you know, like
(24:10):
a monthly subscription, whether it be like, you know, a
different interactions. But I do think AI unlocks this
new use case or like this kind of feature set where people are
now going to be using those products on a more recurring
basis, which is something that investors look for, like when
they're looking at apps, for example, not only are they
looking for. Retention.
(24:31):
Yeah, and with apps, they're notonly looking at like retention,
but they're also looking at likehow frequently is this app being
used, right, like activity wise.And I think that just improves
radically when you're able to have this AI bot or like this AI
system, for example, with the tennis, like just showing, you
know, all all the like where allyou hit it.
But then like what can be improved in in real time?
So in terms of like how I would pitch it to investors, I would
(24:53):
just, you know, try to tap into the the long term use case of
the hardware products rather than just like, hey, we're
building this product and it's going to help people do XYZ.
Transactional. We're building a relationship
with the user. Yeah.
Now and I think the the cool thing about that is I mean it,
it kind of takes hardware from this one time thing to now be a
subscription of the there is a subscription that makes sense
(25:13):
there, right. And so the hardware can just be
like amortized over that and then personalization and all
that. So I think I mean, right, look,
this has been an incredibly insightful, you know, time with
you. And I mean, I really appreciate
you sharing all the different pieces here.
And I think sort of breath of knowledge of this consumer
hardware space in particular is pretty amazing.
And you know, seeing all the thevideos you do and and your
newsletter is obviously pretty impactful.
(25:34):
So where can people find you outand follow more about your work?
It's been great chatting with you guys.
I'm on Twitter, it's just at Rithwik Pavan.
So RITWAKPAVANI also write a a newsletter weekly sharing
exciting consumer product launches.
It's called Hardware Herald, so you can just go to
hardwareherald.com. It's been incredible to see that
grow over the last year or so. Thanks.
(25:55):
For thank you. Glad to have you on.
Thank you guys. That conversation with Ridwick
was like true master class and building, I mean, intelligence
on the ground up. And I think for me, if you're
just treating AI like a feature and you bolt it on later, like
hopefully that shifted your perspective, right?
And now you realize that like, you really have to think about
(26:15):
if you're going to do AI, have it lead with AI.
And then how do you have that integrated in a way that makes
the most sense? And so let's recap a few of the
things that he went over. I think first was this notion of
AI first hardware is really thishyper personalization, right?
Like every piece of hardware nowfeels like it's very much for
you. And it's that real time user
experience that just gets betterand better as you use it.
(26:36):
But to build that, we need really good training data.
And so he talked a lot about thequality of the training data and
not necessarily the hardware, but it's like that becomes the
chief growth bottleneck, right? What is that ability for you to
get accurate training data to then be able to convert that
back and the winning devices aregoing to really be integrated,
right? It's that hardware, software,
ML, it's like and the talent that you need to go put those
(26:58):
together, you're no longer goingto have just one type of
engineer. The full stack is now really
starting from like mechanical engineer, you know, electrical
engineer and all of that. And then I think that they
highlighted this notion of like,where's the white space
opportunities, right? So this idea of lifestyle,
sports and just lifestyle in general.
And then how does that bring AI driven stuff into the home
public safety, maybe self manufacturing tech.
(27:18):
And then finally, like I just converts these one off gadgets
that, you know, used to buy something off the shelf
hardware. Now it's a subscription business
and investors are going to love that, right?
And it's just the size of these businesses and the value of
these businesses is going to continue to advance.
Nailed it It's a it's a lot of wisdom packed into a short
amount of time. So if you found this
conversation valuable, do us a favor share it with another
(27:39):
founder or builder who's navigating the world of AI and
and trying to build AI 1st and make sure you're subscribed
because the next time on foundermode we're we're also tackling a
pretty interesting trend hackingdirect to consumer growth with a
surprising strategy. So you can find links to
everything that we talked about,including how to connect with
quick in the show notes. And just thanks again for tuning
into Founder mode. Remember, don't just add
(27:59):
intelligence building around it.Build smart and build now.
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