April 29, 2025 • 28 mins
Setting big goals sounds great — but what happens when the hard part hits? Nick Askew shows us how perseverance, creativity, and curiosity can bridge the gap between ambition and reality.
In this vibey and inspiring episode, the crew hangs out with Nick Askew of Space Auto — a man who seems to effortlessly blend tech, music, and entrepreneurial grit. After poking some fun at Nick’s musician-worthy name, the conversation quickly gets real. Nick opens up about a personal challenge he set for himself: recording a song every single day for a month. He reflects on the physical and emotional hurdles he faced and how that endurance translates to running a startup in today’s hyper-accelerated tech landscape. Nick’s insights about perseverance, adaptivity, and commitment make it clear: doing the hard things conditions you to thrive when the stakes are high.
The conversation takes a sharp, fascinating turn into the world of AI and its evolving role in automotive retail. Nick breaks down how we're living in the "baby stages" of AI, what practical steps dealers need to take today, and why curiosity beats waiting for a polished, plug-and-play solution. The team also riffs on how creative minds might have a unique edge when adapting to rapid technological changes. If you’re wondering how to stay relevant — or just stay standing — in the fast-moving future of retail and tech, this episode's your jam.
Timestamped Takeaways:
0:00 — Intro with Paul J Daly, Kyle Mountsier and Michael Cirillo
3:50 — Nick shares lessons from committing to record a new song daily for 30 days, emphasizing perseverance and adaptability
6:40 — Why doing hard things daily trains entrepreneurs to survive the inevitable grind of building anything new
10:00 — The real state of AI today: baby steps, not magic wands, and how breaking tasks into small agents creates real progress
14:54 — Practical advice for dealers: get curious, experiment with AI tools now, and learn how to be your own “data brain”
23:42 — Why automotive retail is uniquely positioned to lead in AI application — and how to think of your operations as a series of AI-powered agents
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Michael Cirillo (00:00):
I feel like Nick needs to wear a shirt that
(00:03):
says, I'ma ask you something.
Unknown (00:12):
This is Auto Collabs.
Kyle Mountsier (00:16):
Oh, the way you started that. Like, you know
that, like, like, that 50millisecond Are you gonna pause?
And say that right now. Okay,
Paul J Daly (00:25):
no, that's amazing.
Let me ask you a question. I'mgonna ask you some ask you a
question that's really good, butlike,
Kyle Mountsier (00:35):
accent, please ask you it's all of a sudden
gonna turn like Texas Canadian,and it
Michael Cirillo (00:42):
turns into, like, old monocle wearing top
hat. I'ma ask you become kingthe monopoly
Unknown (00:49):
man. That's unbelievable. I
Paul J Daly (00:50):
don't know, but I'm never gonna look at Nick's last
name again without thinking ofthat. It's a problem,
Unknown (00:55):
guys. I'm sorry, no, I mean, you
Paul J Daly (00:57):
see the world in a way, this
Kyle Mountsier (00:59):
guy also, just as a note, like, he's a
consummate musician, and he hasthe perfect name to be a
musician. He really does likeNick, ask you, put that on a CD
and sell it. Man, he's
Paul J Daly (01:11):
got the accent too.
It's unbelievable in the studio.
It's kind of frustrating. Andhe's smart and he's talented,
and, I mean,
Michael Cirillo (01:18):
nobody, why does he hang out with us again.
What that says? Nick that saysMichael Cirillo, you know, I'm
saying you don't go on AppleMusic and be like. That sounds
like
Paul J Daly (01:28):
I'm looking for like a cover band of the
classics. I might do it likepolka. Like, no, no, like,
Unknown (01:34):
Sinatra. Oh, Sinatra,
Paul J Daly (01:37):
I could definitely see you like in a polka band.
Now that you mention it. That'sfair.
Michael Cirillo (01:41):
Well, you know, like, I recently, not, not too
long ago, I had Nick on ourother show, the dealer playbook.
And what I what I realize, isthat he is a really deep
thinker. I don't know if itstems from the musical,
Paul J Daly (01:55):
yeah, but have you listened to his music? It's not
necessarily bubble gum pop. It'slike
Kyle Mountsier (02:01):
feeler music. He can turn bubble gum pop into
introspective music. I don'tknow how he does it. Well, hey,
maybe you'll get some bubble gumpop, maybe you'll get some
introspective music. But we hopeyou enjoy this conversation with
Nick ASOTU. Nick, thanks forjoining us again. Man, excited
to chat with you. Always good tocatch up and just see what
(02:23):
you're doing in the world, forsure.
Paul J Daly (02:27):
Amy, besides, besides, just like walking off a
movie set, yeah, like, if youcan watch, if you're, if you're
only on the audio version ofthis, he's sitting in a
director's chair. He's got afull sleeve on his right arm.
He's got headphones on. There'slike a little light peeking out
in the back. There's like, apsych so you can't even see the
corner of the room, but he'slike, Oh, this. I just threw
(02:47):
this on, right?
Kyle Mountsier (02:50):
Well, okay, but this is this that actually
transitions to what I wanted toask you about, because I haven't
really been able to talk withyou about it since it happened.
If you're not following Nick on,I'm gonna call it Instagram,
don't you can follow Nick onLinkedIn, but you gotta follow
Nick on Instagram because itlike it pairs personal and
professional, really, reallywell. Yeah, you went through
(03:12):
this time when you recorded asong that you learned, and then
recorded later that day, everysingle day, for how long? A
whole month, right? Yeah, full30 days. I because obviously,
you're a musician. We know thatabout you, you're you're not
just a musician, but I wouldconsider you like an artist,
(03:33):
like you think about the worldcreatively, right? What when you
were going through that, whatdid you learn about yourself and
what that gave you in in yourlife and your business and
things that like maybe youdidn't expect coming out of just
like trying something like that?
Unknown (03:53):
Good question. That
Nick Askew (03:54):
is a great question.
So I think first of all, thething I found out about myself
was I like to set really biggoals, and then about halfway
through, the pain sets in. Soprevalence through, through self
inflicted pain. But you know,like halfway through, I got my
voice gave out, like I did asong that I really wasn't
(04:18):
prepared to do, my voice gaveout, and I still stuck with it
anyway. And I think, for being astartup, right? How many
companies? I mean, we know thesuccess failure rate of software
startups, it's, it's no secretthat, you know, most of them
don't make it past their firstcouple of years, right? So I
think for me, one of the thingswas, you know, commitment to
(04:40):
doing something that was hardand proving to myself that I
could do it no matter what itwas that was a portion of it,
adaptivity in the moment, likeeven sitting there going, Okay,
so I've prepared this song. I'mgetting down to record it, and
I'm just not. Feeling it, or Iactually can't do it, or I
(05:01):
didn't learn it well enough, soI've got to pivot in the moment.
And I think that's justsomething I was able to figure
out about myself, is like that,adaptivity and then also the
perseverance of, you know, Isaid I'm going to do this every
day for 30 days. And whether it,you know, kills me.
Unfortunately, my wife, ifyou're watching, like, you know,
she wasn't a big fan of me goingto spending and doing that when
(05:25):
I was supposed to be there. Iknow, you know, it was like, Oh,
you're recording your songagain. I can't wait for this
month to be over. And it's like,but she had a point. But like,
it was a, you know, I'd made thepromise that I was going to do
it to myself, so I've reallyfelt good about following
through. And I think that wasthe perseverance part was the
(05:46):
biggest for me.
Paul J Daly (05:48):
I was just watching a video, a Casey Neistat video
he released a few months ago. Itjust popped up in my feed. And I
think the title of the videos dohard things, and in his very,
you know, quintessentialstorytelling method. You know,
it kind of shows early morning,him waking up the alarm clock,
and he starts, he's like, wakingup is hard. You know, waking up
(06:11):
early is hard. He's like, andthen going to run is hard. He's
and then he goes into thiswhole, like, monolog on doing
hard things and what it actuallyconditions you to do in all the
other areas of your life,because you decide to do the
hard things and and I think asan entrepreneur, and as an
entrepreneur in the autoindustry, there's, they're
(06:31):
always going to be hard things,like all the time, yeah? And if
you expect that, there won't be,or aren't conditioned to handle
it like, you will wash out very,very quickly, and especially if
you're trying to build somethingnew.
Kyle Mountsier (06:43):
Well, and that's what, like I wrote on LinkedIn
the other day that, you know,we, I entrepreneur 17 hours. I
love that post, right? That one,and it's only be, and the only
reason is, it's that it's lessis because I can't remember my
dreams. That's the only reason,right? And like, there is this,
there is this thing where, like,it doesn't matter what aspect of
(07:07):
my life I'm alwaysentrepreneuring. I'm always
creating or crafting somethingnew. And that's the like, you
know, I can, I can hear my wifelike, because I commit to dumb
things like that too. Don'tworry, Nick, we're all, I think,
in that corner. But I can hearmy wife being like, again
tonight, right? But it is that,that constant push and pull of
like, how much can I create, andhow much do I have to, like, get
(07:30):
resolved and stay steady and bebe available for my team that's
living in this, like, right nowscenario, and sometimes I'm just
living in this, what's nextscenario, right? And that's,
yeah, that's a constant balance.
Well, that
Nick Askew (07:44):
is a constant, especially when, you know, and
that communication, I mean, Ibring that back to work in my
team, right? Because, you know,in unless I communicate those
kind of goals, it can sometimesseem, you know, that, Oh, Nick's
got this new, fresh idea, right?
And it's like, oh, that's his.
That's his idea of the week. Andit's like you're just thinking
(08:04):
so far ahead and making thesebig picture commitments that
unless you are very strong inyour communication about your
intent to do those things, itcan come across off as just
impulsive, which I to be fair,you know can be but yeah, it's
(08:24):
fair. But yeah, I think do hardthings, right? It's like, I you
do those things because I toldmyself, I would, you know, and
sometimes you don't feel likedoing that. It's sometimes you
don't feel like getting out ofbed, getting up early, having
those meetings, but you do itanyway. Yeah,
Kyle Mountsier (08:44):
the conversation that you and I were having when
we were talking about, actually,ASOTU CON is actually, it's very
akin to this. Because when youthink about the things that the
world is building right now,and, you know, I'm gonna go
straight to AI because it's,it's kind of like the hot topic.
You know, earlier this week, attime of this recording, we we
(09:07):
saw GPT have a million newsubscribers in less than an
hour, just because they releasedimage generation, right? Like
the pace and the availabilityand the understanding and the ad
the adoption is, is pacing up inan all time record. But there is
this kind of point we're atright now where AI is like, it
(09:30):
looks like this sexy, easything, but it's also this very
hard unknown, like, have to getin the ground, and then there's
a bunch of people like, AGI iscoming. Uh, no, maybe not yet,
maybe not yet, right? And, andso there's always this vision
and what the practicality isright now, you're implementing a
lot of these, a lot of solutionswhen it comes to AI and thinking
(09:54):
about how they become practicalin a dealership operation. How
are you balancing that? Like.
Yeah, oh, I had this vision forwhat it can look like and what
we're gonna what we canpractically accomplish today
with the way the tools and andthe capacity of AI is right now,
I think that's
Nick Askew (10:10):
just how science works, right? It's in general,
it's like we're always 10 years.
For the last 40 years, we'vebeen 10 years away from nuclear
fusion, right? Like self drivingcars? Yeah, we're always 10
years away from self drivingcars, 10 years away from nuclear
fusion, 10. I mean this, this ishow it's going to be. But any
(10:30):
practical part of science ingeneral is that you've got to
create, you got to create testcases first. And if you think
about the practical use of AItoday, AI has very specific
jobs. And you mentioned open AIreleasing the image generation.
Well, if you think about all thelittle things that our brain has
(10:52):
to accomplish, right, you know,we've got image generation, you
know, in the fact that we canclose our eyes and imagine
something, we've got, you know,optical recognition and the
processing of that auditorywe've got long term memory,
we've got short term memory,we've got motor functions. And
what you're seeing right now inAI is all of these little pieces
(11:13):
becoming useful. So, like,there's a lot of practical
applications for like the chat,GPT style knowledge, text based
LLM, that is a useful thingbecause we need to comprehend,
store and understandinformation. What we're going to
(11:35):
see right now is a lot of theseindividual AI applications be
built to where it's imagerecognition, audio recognition,
it's going to get faster andbetter, and all of these little
pieces, much like how our brainworks and the way we're trying
to implement that is jobs to bedone with inside automotive and
(11:57):
harnessing the power of whatactually is available today, and
segmenting them down into evenfurther, which is, how can we
utilize LLM? How can we utilizeimage restoration recognition,
how can we utilize dataanalysis? How can we utilize all
of these things and make theminto little, practical agents
(12:18):
that just take the monotony outof doing the job and make car
buying experience a little bitbetter. So I think for the AGI
thing to finally happen, youknow, we've got, there's several
parts of artificial intelligencethat we have to master, and then
we have to tie that together,like all of our sections that we
have in our own brain, if thatmakes any sense?
Paul J Daly (12:44):
No, it makes a lot of sense. Yeah. And one, and one
of the things that I worry aboutthe fact is the fact that a lot
of the tech companies and a lotof the people who live and they
love to live over the horizon,right? And then they come back
and they tell us what's over thehorizon. They talk about it a
lot. Most of the people, 80%plus, live right here and are
(13:08):
looking at the horizon saying,like, I can't quite see what's
out there. And everything thatthey come back and tell me, I
don't know what to do with itright now. And so, I mean,
that's going to be a lot of whatwe're talking about at ASOTU CON
and and you're going to becoming in and sharing a lot
about like, yes, that's what'son the horizon, but this is what
the now looks like to preparefor it. Yeah.
Nick Askew (13:29):
And look, we're in the such baby stages of AI,
right? So there are practicalapplications that we can use
today, but what we can't do is,is, is, is sit here and look
into the future and say, This isthe vision of the future. One
day we'll get there, because wethe steps that we take today in
(13:50):
order to test and create actual,you know, real life scenarios
that either help or assist, youknow, in any industry, not just
automotive, are going to be thelearning experiences that paved
their way to that big horizon ofAGI and and that thing that
(14:11):
people think that just, youknow, AI is going to be one day
like these are the natural,scientific steps we have to take
in order to eventually getthere.
Paul J Daly (14:21):
So what are you saying? What are you saying
right now to your dealers. Youhave a lot of dealer customers.
What are you saying to them,even from and you obviously run
in like a singular lane, as faras your products and things like
that, you know, within a certainportion of the business. But
what is your general coaching toyour dealer clients about all of
(14:42):
this new stuff, about what theycan be doing today in order to
make sure they're prepared forthe future, or they're building
a house today that will actuallybe effective tomorrow as well.
Nick Askew (14:54):
There's a couple of things. So I first of all be
curious and learn about it. Imean that i. Think that every
you know, one of the things thatI'm speaking about at some, some
other conferences, is, is, iskind of like AI for the
everyman, right? Is how you knowhow to generate a bunch of
different reports and generate afew prompts to be able to ask
(15:17):
you reports some things, and askthe data questions, right? I
think that everybody, no matterif they're a GM, they're a sales
manager, they're, I mean,they're a BDC manager, and
analyzing scripts and whatworked and what didn't work. Be
curious and use these tools,because it's not just waiting
for the vendors as much as I'dlove to sign more dealers up and
(15:40):
solve that problem for you,like, unless you're curious
about it and understand how thisworks yourself. You're you're
never going to be able to fullyunderstand to strategize that
for your business. Because Ithink the the GM of the future
is able to take you know, theyare in charge of their own. They
are the CDP, right. Come on,right. It's, it's not go out and
(16:05):
buy a CDP and figure out how toharness it. It's, you have to
understand and control that. Soif you've got, you know,
ancillary, you know, products,you've got an F and I company,
you've got a body shopsomewhere, you've got whatever
that may be, you have to be yourown dealers, be harnessing your
(16:28):
own deal as CDP and build yourAI on top of that central brain,
right? But the only way that youcan do that is actually
understand it and be curiousabout it in the first place.
Kyle Mountsier (16:41):
I love that you say that to be curious about it.
Because I know some people, I'veactually, I've sat in
dealerships and with otherpeople that are starting to use
AI. And I think what, what thecommon belief right now is that,
like, I can just ask itanything, in any way, and it'll
come back with the right thing.
And what, what I've learned, andwhat we've learned on our team,
(17:02):
is actually the way you ask it,how you clarify the expectation,
what you clarify as, as the theoutput you know, what, what you
know, additional feedback andadditional data you you feed
into it is extremely important.
What, what's actually happeningis, it's making me a better
communicator, because I lovehow, you know, we're using this
(17:25):
word agent a lot, and when itcomes to AI, and it really is an
agent, and that agent needsinputs to give you the right
output. And that's the samething with an employee. It's
like, Oh, if I'm really curiousabout AI, it gives me the
proving and training ground inhow to approach an employee with
my clear expectations on inputsand outputs, right? And like,
(17:48):
you actually said CDP a coupletimes there. If you go to a CDP
and you look at it like, tell methe segments that we have. It
doesn't like, there are CDPswith like, here's our core basic
segments. This is what we set upfor everybody. But there's some
like, like, very human levelthings that we have to
understand about querying thatCDP and saying, I want to
(18:12):
extract this data out now thatI've put all together in order
to operationalize it over here.
And those are like, those arestill the movements that we have
to make as the oper, as theoverarching operators of these
new tools.
Nick Askew (18:28):
Yeah, and if not, you're just relying on the base
level reporting, or you'rerelying on base level
automations. You're not reallyoptimizing anything. You're just
you're just connecting datawithout a purpose. And, you
know, that's the thing aboutprompt engineering. I think
everybody should learn how toprompt engineer properly. You
know, we do so muchexperimentation internally, just
(18:51):
for for ourselves. We've, Ican't actually remember what we
call them. They're basicallyphantom settings. So you've got
this, like, uh, this distancebetween words, and you've only
got so many tokens that you canput in. And what we found is,
(19:11):
you know, in creating a prompt,is that, you know, if you say,
for example, tell me all of thecustomers that I have that have
bought a car within the last twoyears, or have been interested
in a Ford f1 50, like your yourdistance between your the object
(19:33):
that you're trying to create andquery, and the you know the
information is Like, thesentence is so long and it's so,
it gives the AI, well, it givesAI more room to make
Paul J Daly (19:47):
assumptions and the thinking, yeah, you met, yeah,
Nick Askew (19:51):
yeah. It's so what we actually end up doing is
figuring out how to to tooptimize that. Then it. Went a
step further in some of ouroptimizations. Did you know that
even in like, GPT, when you'regetting something back, you can
say like, you can make upsettings, and you could say,
(20:15):
return markup equals false, andit would just export normal
text, and you could say, returnmarkup equals true, and it will
format your text for you withheaders and everything. And you
just make up the settings. Andthose are, yeah, that's, that's
a practice that we use, is justcreate these ghost settings that
(20:37):
really help us refine certainprompts and certain things just
like you're saying, and theydon't, they just interpret that.
So you know that when I say becurious, it's like, when we're
trying to figure those kind ofthings out, is like, this is all
such new tech, and it's our jobin any industry, not just people
developing technology, to goand, like, kick the tires on it
(20:59):
and make it better.
Paul J Daly (21:00):
Well, I think there's a huge opportunity, and
I don't think it's beingleveraged enough. It's one of
the reasons we created the autoindustry.ai site and email,
because we use AI superextensively within our in our
organization, from foreverything you can imagine,
right? Like, it's, it's not likecreative things only. It's
(21:21):
everything, and we use it somuch, we feel like there is real
value and responsibility for thetech players. And I'll consider
us a tech player. Even though wedon't build a tech product, we
operate a lot like a techcompany. It's our responsibility
to bring everyone along, andthere's so much value in just
sharing the little things likeyou just shared as we're kicking
(21:43):
the tires for all the people whoare out there on the front lines
who don't have time to kick thetires, right, you know? And so I
love that you're saying this.
And I think, like, more of that,just like this is what I've been
learning with AI is useful to,you know, a much broader
audience. And I actually thinkit's, it's a great, it's
actually a great opportunity tobring people from outside the
industry to pay attention tothis industry and be like, oh,
(22:06):
like
Kyle Mountsier (22:07):
they are.
They're leading the charge onhow to operate. Yeah, let
Paul J Daly (22:11):
me get in on some of that. Yeah.
Nick Askew (22:14):
I think auto motive has the opportunity to lead the
charge on AI in practicalapplications, because it is so
complex. We have so many jobs tobe done. And if you think about
agents, right, what is an AIagent? It is just something with
a specific job, right? Whetherthat agent is, hey, do equity
mining for me and give me a listof all customers that I should
(22:36):
call today and organize them ina way that is, you know, the the
absolute best in terms ofprofitability, you know,
likelihood to answer equity. Youknow, the relationship the
sentiment of their response. Didthey tell us to go ourselves, or
did they, you know, have a niceconversation with us, like you
could take all of thesedifferent data points. That is
(22:59):
one agent, if people juststarted thinking and
understanding an agent is a jobto be done, you could start
looking at your businessholistically and go, How many
jobs to be done are there? Thinkabout them as roles and
responsibilities. I have a CFOthat has a job to do, and build
(23:19):
me these reports once everyweek. Let's automate that. And
that is a job to be done. And Ithink anybody in any role should
be, you know, looking at theirown job and utilizing these
tools to be able to create theirown mini agents to to help make
(23:41):
them more efficient, becauseit's
Kyle Mountsier (23:42):
just a toss before we before we roll out.
I'm going to toss a challenge.
If anybody's listening to thispodcast right now, do us a
favor. Find a job in your sphereof influence that could
potentially be replaced with anAI agent. Either begin building
that agent, or let us know ifyou're having trouble with that,
and between the three of us, atleast, we'll figure out either
(24:04):
the path to get you there, orthe person to talk to, or
something like that. Because Ithink that that would be a
really cool outcome from thisconversation. Nick, always great
to chat with you. You're filledwith just creativity and insight
and vibes and vibes, full vibes,Director vibes, I can't wait to
see you at ASOTU CON. You'regonna be doing some podcasting
(24:26):
with us. You'll be on stage.
It's gonna be a really funcollaboration.
Nick Askew (24:32):
Looking forward to it as always. Great to see you
both.
Paul J Daly (24:40):
I'll tell you what his studio game is really
stepping up on LinkedIn andeverything. I'm watching all
these things. I'm like, he gotthat production bug, and he just
went back into it. Yeah, no, youknow, because I think, I think
all of us have kind of wanderedin and out of that, you know,
I'm just gonna make content andbut he's, you know,
Kyle Mountsier (24:58):
what? I think, actually, this is right? To the
conversation about AI, I thinkthat there is this wonderful
balance of people that areleveraging technology like at an
extreme level, but also stillcreating very natively and
(25:18):
personably. And I think it'senabling like this, left, right,
blank brain unlock in somepeople that are able to do both
within a single day. And, youknow, maybe it's just because
we're all musicians too. Like, Ithink it's something that a
musician or a creator orsomething like that that is
(25:40):
tuned into technology is havingmuch more of an unlock, because
there has been this like, whatcan I create with this? What can
I do? What really
Paul J Daly (25:50):
good observation here, right? I think that the
creative minded people arenaturally ones that are good at
taking, I won't say chaos, buttaking elements and putting them
together. Even think of howcreatives are natural, like
musicians are naturally betterat language, yeah, making up
languages and things like that,because they can kind of hear,
it's almost like they they seeand hear the music in it, oh,
(26:13):
yeah. And it's almost likethat's kind of what Nick's doing
with AI and tech, and what thecreatives are doing with the
tech, how they see the music inthe tech.
Kyle Mountsier (26:21):
There's another that's a post, that's a podcast,
that's whatever, yeah,
Michael Cirillo (26:25):
well, well, as you're saying it, I'm thinking
about this, and I'm like, Yeah,this rings true because, I mean,
I know we've all written songmusic, uh, we've all probably
sung in choirs. We all know howto do the harmonies and all of
that. And there's something tothis where you can hear the
whole and I think, are we allsound engineers too? Yes, okay,
(26:46):
so, like we're all soundengineers at the same time,
there's something to thisability to hear the whole song
at once, yes, and alsosimultaneously hear pick out all
of
Kyle Mountsier (26:58):
the individual things. Yes, yes. Oh, see now
you just done got me fired up.
Paul J Daly (27:05):
Well, listen, we hope you enjoy that
conversation. We hope you enjoylistening to us. Just pat
ourselves on the back at the endof this podcast. On behalf of
Kyle Mountsier, Michael Cirilloand myself, this would be the
perfect time to sing a threepart harmony if we rehearse, but
we are not so. Thank you forjoining us here on Auto Collabs.
Unknown (27:25):
Welcome to Auto Collabs. Sign up for our free
and fun to read daily email fora free shot of relevant news and
automotive retail media and popculture. You can get it
now@asotu.com That's asotu.comif you love this podcast, please
leave us a review and share itwith a friend. Thanks again for
(27:46):
listening. We'll see you nexttime.
Welcome to Annika.
I feel like Nick needs to wear a shirt that
(00:03):
says, I'ma ask you something.
Unknown (00:12):
This is Auto Collabs.
Kyle Mountsier (00:16):
Oh, the way you started that. Like, you know
that, like, like, that 50millisecond Are you gonna pause?
And say that right now. Okay,
Paul J Daly (00:25):
no, that's amazing.
Let me ask you a question. I'mgonna ask you some ask you a
question that's really good, butlike,
Kyle Mountsier (00:35):
accent, please ask you it's all of a sudden
gonna turn like Texas Canadian,and it
Michael Cirillo (00:42):
turns into, like, old monocle wearing top
hat. I'ma ask you become kingthe monopoly
Unknown (00:49):
man. That's unbelievable. I
Paul J Daly (00:50):
don't know, but I'm never gonna look at Nick's last
name again without thinking ofthat. It's a problem,
Unknown (00:55):
guys. I'm sorry, no, I mean, you
Paul J Daly (00:57):
see the world in a way, this
Kyle Mountsier (00:59):
guy also, just as a note, like, he's a
consummate musician, and he hasthe perfect name to be a
musician. He really does likeNick, ask you, put that on a CD
and sell it. Man, he's
Paul J Daly (01:11):
got the accent too.
It's unbelievable in the studio.
It's kind of frustrating. Andhe's smart and he's talented,
and, I mean,
Michael Cirillo (01:18):
nobody, why does he hang out with us again.
What that says? Nick that saysMichael Cirillo, you know, I'm
saying you don't go on AppleMusic and be like. That sounds
like
Paul J Daly (01:28):
I'm looking for like a cover band of the
classics. I might do it likepolka. Like, no, no, like,
Unknown (01:34):
Sinatra. Oh, Sinatra,
Paul J Daly (01:37):
I could definitely see you like in a polka band.
Now that you mention it. That'sfair.
Michael Cirillo (01:41):
Well, you know, like, I recently, not, not too
long ago, I had Nick on ourother show, the dealer playbook.
And what I what I realize, isthat he is a really deep
thinker. I don't know if itstems from the musical,
Paul J Daly (01:55):
yeah, but have you listened to his music? It's not
necessarily bubble gum pop. It'slike
Kyle Mountsier (02:01):
feeler music. He can turn bubble gum pop into
introspective music. I don'tknow how he does it. Well, hey,
maybe you'll get some bubble gumpop, maybe you'll get some
introspective music. But we hopeyou enjoy this conversation with
Nick ASOTU. Nick, thanks forjoining us again. Man, excited
to chat with you. Always good tocatch up and just see what
(02:23):
you're doing in the world, forsure.
Paul J Daly (02:27):
Amy, besides, besides, just like walking off a
movie set, yeah, like, if youcan watch, if you're, if you're
only on the audio version ofthis, he's sitting in a
director's chair. He's got afull sleeve on his right arm.
He's got headphones on. There'slike a little light peeking out
in the back. There's like, apsych so you can't even see the
corner of the room, but he'slike, Oh, this. I just threw
(02:47):
this on, right?
Kyle Mountsier (02:50):
Well, okay, but this is this that actually
transitions to what I wanted toask you about, because I haven't
really been able to talk withyou about it since it happened.
If you're not following Nick on,I'm gonna call it Instagram,
don't you can follow Nick onLinkedIn, but you gotta follow
Nick on Instagram because itlike it pairs personal and
professional, really, reallywell. Yeah, you went through
(03:12):
this time when you recorded asong that you learned, and then
recorded later that day, everysingle day, for how long? A
whole month, right? Yeah, full30 days. I because obviously,
you're a musician. We know thatabout you, you're you're not
just a musician, but I wouldconsider you like an artist,
(03:33):
like you think about the worldcreatively, right? What when you
were going through that, whatdid you learn about yourself and
what that gave you in in yourlife and your business and
things that like maybe youdidn't expect coming out of just
like trying something like that?
Unknown (03:53):
Good question. That
Nick Askew (03:54):
is a great question.
So I think first of all, thething I found out about myself
was I like to set really biggoals, and then about halfway
through, the pain sets in. Soprevalence through, through self
inflicted pain. But you know,like halfway through, I got my
voice gave out, like I did asong that I really wasn't
(04:18):
prepared to do, my voice gaveout, and I still stuck with it
anyway. And I think, for being astartup, right? How many
companies? I mean, we know thesuccess failure rate of software
startups, it's, it's no secretthat, you know, most of them
don't make it past their firstcouple of years, right? So I
think for me, one of the thingswas, you know, commitment to
(04:40):
doing something that was hardand proving to myself that I
could do it no matter what itwas that was a portion of it,
adaptivity in the moment, likeeven sitting there going, Okay,
so I've prepared this song. I'mgetting down to record it, and
I'm just not. Feeling it, or Iactually can't do it, or I
(05:01):
didn't learn it well enough, soI've got to pivot in the moment.
And I think that's justsomething I was able to figure
out about myself, is like that,adaptivity and then also the
perseverance of, you know, Isaid I'm going to do this every
day for 30 days. And whether it,you know, kills me.
Unfortunately, my wife, ifyou're watching, like, you know,
she wasn't a big fan of me goingto spending and doing that when
(05:25):
I was supposed to be there. Iknow, you know, it was like, Oh,
you're recording your songagain. I can't wait for this
month to be over. And it's like,but she had a point. But like,
it was a, you know, I'd made thepromise that I was going to do
it to myself, so I've reallyfelt good about following
through. And I think that wasthe perseverance part was the
(05:46):
biggest for me.
Paul J Daly (05:48):
I was just watching a video, a Casey Neistat video
he released a few months ago. Itjust popped up in my feed. And I
think the title of the videos dohard things, and in his very,
you know, quintessentialstorytelling method. You know,
it kind of shows early morning,him waking up the alarm clock,
and he starts, he's like, wakingup is hard. You know, waking up
(06:11):
early is hard. He's like, andthen going to run is hard. He's
and then he goes into thiswhole, like, monolog on doing
hard things and what it actuallyconditions you to do in all the
other areas of your life,because you decide to do the
hard things and and I think asan entrepreneur, and as an
entrepreneur in the autoindustry, there's, they're
(06:31):
always going to be hard things,like all the time, yeah? And if
you expect that, there won't be,or aren't conditioned to handle
it like, you will wash out very,very quickly, and especially if
you're trying to build somethingnew.
Kyle Mountsier (06:43):
Well, and that's what, like I wrote on LinkedIn
the other day that, you know,we, I entrepreneur 17 hours. I
love that post, right? That one,and it's only be, and the only
reason is, it's that it's lessis because I can't remember my
dreams. That's the only reason,right? And like, there is this,
there is this thing where, like,it doesn't matter what aspect of
(07:07):
my life I'm alwaysentrepreneuring. I'm always
creating or crafting somethingnew. And that's the like, you
know, I can, I can hear my wifelike, because I commit to dumb
things like that too. Don'tworry, Nick, we're all, I think,
in that corner. But I can hearmy wife being like, again
tonight, right? But it is that,that constant push and pull of
like, how much can I create, andhow much do I have to, like, get
(07:30):
resolved and stay steady and bebe available for my team that's
living in this, like, right nowscenario, and sometimes I'm just
living in this, what's nextscenario, right? And that's,
yeah, that's a constant balance.
Well, that
Nick Askew (07:44):
is a constant, especially when, you know, and
that communication, I mean, Ibring that back to work in my
team, right? Because, you know,in unless I communicate those
kind of goals, it can sometimesseem, you know, that, Oh, Nick's
got this new, fresh idea, right?
And it's like, oh, that's his.
That's his idea of the week. Andit's like you're just thinking
(08:04):
so far ahead and making thesebig picture commitments that
unless you are very strong inyour communication about your
intent to do those things, itcan come across off as just
impulsive, which I to be fair,you know can be but yeah, it's
(08:24):
fair. But yeah, I think do hardthings, right? It's like, I you
do those things because I toldmyself, I would, you know, and
sometimes you don't feel likedoing that. It's sometimes you
don't feel like getting out ofbed, getting up early, having
those meetings, but you do itanyway. Yeah,
Kyle Mountsier (08:44):
the conversation that you and I were having when
we were talking about, actually,ASOTU CON is actually, it's very
akin to this. Because when youthink about the things that the
world is building right now,and, you know, I'm gonna go
straight to AI because it's,it's kind of like the hot topic.
You know, earlier this week, attime of this recording, we we
(09:07):
saw GPT have a million newsubscribers in less than an
hour, just because they releasedimage generation, right? Like
the pace and the availabilityand the understanding and the ad
the adoption is, is pacing up inan all time record. But there is
this kind of point we're atright now where AI is like, it
(09:30):
looks like this sexy, easything, but it's also this very
hard unknown, like, have to getin the ground, and then there's
a bunch of people like, AGI iscoming. Uh, no, maybe not yet,
maybe not yet, right? And, andso there's always this vision
and what the practicality isright now, you're implementing a
lot of these, a lot of solutionswhen it comes to AI and thinking
(09:54):
about how they become practicalin a dealership operation. How
are you balancing that? Like.
Yeah, oh, I had this vision forwhat it can look like and what
we're gonna what we canpractically accomplish today
with the way the tools and andthe capacity of AI is right now,
I think that's
Nick Askew (10:10):
just how science works, right? It's in general,
it's like we're always 10 years.
For the last 40 years, we'vebeen 10 years away from nuclear
fusion, right? Like self drivingcars? Yeah, we're always 10
years away from self drivingcars, 10 years away from nuclear
fusion, 10. I mean this, this ishow it's going to be. But any
(10:30):
practical part of science ingeneral is that you've got to
create, you got to create testcases first. And if you think
about the practical use of AItoday, AI has very specific
jobs. And you mentioned open AIreleasing the image generation.
Well, if you think about all thelittle things that our brain has
(10:52):
to accomplish, right, you know,we've got image generation, you
know, in the fact that we canclose our eyes and imagine
something, we've got, you know,optical recognition and the
processing of that auditorywe've got long term memory,
we've got short term memory,we've got motor functions. And
what you're seeing right now inAI is all of these little pieces
(11:13):
becoming useful. So, like,there's a lot of practical
applications for like the chat,GPT style knowledge, text based
LLM, that is a useful thingbecause we need to comprehend,
store and understandinformation. What we're going to
(11:35):
see right now is a lot of theseindividual AI applications be
built to where it's imagerecognition, audio recognition,
it's going to get faster andbetter, and all of these little
pieces, much like how our brainworks and the way we're trying
to implement that is jobs to bedone with inside automotive and
(11:57):
harnessing the power of whatactually is available today, and
segmenting them down into evenfurther, which is, how can we
utilize LLM? How can we utilizeimage restoration recognition,
how can we utilize dataanalysis? How can we utilize all
of these things and make theminto little, practical agents
(12:18):
that just take the monotony outof doing the job and make car
buying experience a little bitbetter. So I think for the AGI
thing to finally happen, youknow, we've got, there's several
parts of artificial intelligencethat we have to master, and then
we have to tie that together,like all of our sections that we
have in our own brain, if thatmakes any sense?
Paul J Daly (12:44):
No, it makes a lot of sense. Yeah. And one, and one
of the things that I worry aboutthe fact is the fact that a lot
of the tech companies and a lotof the people who live and they
love to live over the horizon,right? And then they come back
and they tell us what's over thehorizon. They talk about it a
lot. Most of the people, 80%plus, live right here and are
(13:08):
looking at the horizon saying,like, I can't quite see what's
out there. And everything thatthey come back and tell me, I
don't know what to do with itright now. And so, I mean,
that's going to be a lot of whatwe're talking about at ASOTU CON
and and you're going to becoming in and sharing a lot
about like, yes, that's what'son the horizon, but this is what
the now looks like to preparefor it. Yeah.
Nick Askew (13:29):
And look, we're in the such baby stages of AI,
right? So there are practicalapplications that we can use
today, but what we can't do is,is, is, is sit here and look
into the future and say, This isthe vision of the future. One
day we'll get there, because wethe steps that we take today in
(13:50):
order to test and create actual,you know, real life scenarios
that either help or assist, youknow, in any industry, not just
automotive, are going to be thelearning experiences that paved
their way to that big horizon ofAGI and and that thing that
(14:11):
people think that just, youknow, AI is going to be one day
like these are the natural,scientific steps we have to take
in order to eventually getthere.
Paul J Daly (14:21):
So what are you saying? What are you saying
right now to your dealers. Youhave a lot of dealer customers.
What are you saying to them,even from and you obviously run
in like a singular lane, as faras your products and things like
that, you know, within a certainportion of the business. But
what is your general coaching toyour dealer clients about all of
(14:42):
this new stuff, about what theycan be doing today in order to
make sure they're prepared forthe future, or they're building
a house today that will actuallybe effective tomorrow as well.
Nick Askew (14:54):
There's a couple of things. So I first of all be
curious and learn about it. Imean that i. Think that every
you know, one of the things thatI'm speaking about at some, some
other conferences, is, is, iskind of like AI for the
everyman, right? Is how you knowhow to generate a bunch of
different reports and generate afew prompts to be able to ask
(15:17):
you reports some things, and askthe data questions, right? I
think that everybody, no matterif they're a GM, they're a sales
manager, they're, I mean,they're a BDC manager, and
analyzing scripts and whatworked and what didn't work. Be
curious and use these tools,because it's not just waiting
for the vendors as much as I'dlove to sign more dealers up and
(15:40):
solve that problem for you,like, unless you're curious
about it and understand how thisworks yourself. You're you're
never going to be able to fullyunderstand to strategize that
for your business. Because Ithink the the GM of the future
is able to take you know, theyare in charge of their own. They
are the CDP, right. Come on,right. It's, it's not go out and
(16:05):
buy a CDP and figure out how toharness it. It's, you have to
understand and control that. Soif you've got, you know,
ancillary, you know, products,you've got an F and I company,
you've got a body shopsomewhere, you've got whatever
that may be, you have to be yourown dealers, be harnessing your
(16:28):
own deal as CDP and build yourAI on top of that central brain,
right? But the only way that youcan do that is actually
understand it and be curiousabout it in the first place.
Kyle Mountsier (16:41):
I love that you say that to be curious about it.
Because I know some people, I'veactually, I've sat in
dealerships and with otherpeople that are starting to use
AI. And I think what, what thecommon belief right now is that,
like, I can just ask itanything, in any way, and it'll
come back with the right thing.
And what, what I've learned, andwhat we've learned on our team,
(17:02):
is actually the way you ask it,how you clarify the expectation,
what you clarify as, as the theoutput you know, what, what you
know, additional feedback andadditional data you you feed
into it is extremely important.
What, what's actually happeningis, it's making me a better
communicator, because I lovehow, you know, we're using this
(17:25):
word agent a lot, and when itcomes to AI, and it really is an
agent, and that agent needsinputs to give you the right
output. And that's the samething with an employee. It's
like, Oh, if I'm really curiousabout AI, it gives me the
proving and training ground inhow to approach an employee with
my clear expectations on inputsand outputs, right? And like,
(17:48):
you actually said CDP a coupletimes there. If you go to a CDP
and you look at it like, tell methe segments that we have. It
doesn't like, there are CDPswith like, here's our core basic
segments. This is what we set upfor everybody. But there's some
like, like, very human levelthings that we have to
understand about querying thatCDP and saying, I want to
(18:12):
extract this data out now thatI've put all together in order
to operationalize it over here.
And those are like, those arestill the movements that we have
to make as the oper, as theoverarching operators of these
new tools.
Nick Askew (18:28):
Yeah, and if not, you're just relying on the base
level reporting, or you'rerelying on base level
automations. You're not reallyoptimizing anything. You're just
you're just connecting datawithout a purpose. And, you
know, that's the thing aboutprompt engineering. I think
everybody should learn how toprompt engineer properly. You
know, we do so muchexperimentation internally, just
(18:51):
for for ourselves. We've, Ican't actually remember what we
call them. They're basicallyphantom settings. So you've got
this, like, uh, this distancebetween words, and you've only
got so many tokens that you canput in. And what we found is,
(19:11):
you know, in creating a prompt,is that, you know, if you say,
for example, tell me all of thecustomers that I have that have
bought a car within the last twoyears, or have been interested
in a Ford f1 50, like your yourdistance between your the object
(19:33):
that you're trying to create andquery, and the you know the
information is Like, thesentence is so long and it's so,
it gives the AI, well, it givesAI more room to make
Paul J Daly (19:47):
assumptions and the thinking, yeah, you met, yeah,
Nick Askew (19:51):
yeah. It's so what we actually end up doing is
figuring out how to to tooptimize that. Then it. Went a
step further in some of ouroptimizations. Did you know that
even in like, GPT, when you'regetting something back, you can
say like, you can make upsettings, and you could say,
(20:15):
return markup equals false, andit would just export normal
text, and you could say, returnmarkup equals true, and it will
format your text for you withheaders and everything. And you
just make up the settings. Andthose are, yeah, that's, that's
a practice that we use, is justcreate these ghost settings that
(20:37):
really help us refine certainprompts and certain things just
like you're saying, and theydon't, they just interpret that.
So you know that when I say becurious, it's like, when we're
trying to figure those kind ofthings out, is like, this is all
such new tech, and it's our jobin any industry, not just people
developing technology, to goand, like, kick the tires on it
(20:59):
and make it better.
Paul J Daly (21:00):
Well, I think there's a huge opportunity, and
I don't think it's beingleveraged enough. It's one of
the reasons we created the autoindustry.ai site and email,
because we use AI superextensively within our in our
organization, from foreverything you can imagine,
right? Like, it's, it's not likecreative things only. It's
(21:21):
everything, and we use it somuch, we feel like there is real
value and responsibility for thetech players. And I'll consider
us a tech player. Even though wedon't build a tech product, we
operate a lot like a techcompany. It's our responsibility
to bring everyone along, andthere's so much value in just
sharing the little things likeyou just shared as we're kicking
(21:43):
the tires for all the people whoare out there on the front lines
who don't have time to kick thetires, right, you know? And so I
love that you're saying this.
And I think, like, more of that,just like this is what I've been
learning with AI is useful to,you know, a much broader
audience. And I actually thinkit's, it's a great, it's
actually a great opportunity tobring people from outside the
industry to pay attention tothis industry and be like, oh,
(22:06):
like
Kyle Mountsier (22:07):
they are.
They're leading the charge onhow to operate. Yeah, let
Paul J Daly (22:11):
me get in on some of that. Yeah.
Nick Askew (22:14):
I think auto motive has the opportunity to lead the
charge on AI in practicalapplications, because it is so
complex. We have so many jobs tobe done. And if you think about
agents, right, what is an AIagent? It is just something with
a specific job, right? Whetherthat agent is, hey, do equity
mining for me and give me a listof all customers that I should
(22:36):
call today and organize them ina way that is, you know, the the
absolute best in terms ofprofitability, you know,
likelihood to answer equity. Youknow, the relationship the
sentiment of their response. Didthey tell us to go ourselves, or
did they, you know, have a niceconversation with us, like you
could take all of thesedifferent data points. That is
(22:59):
one agent, if people juststarted thinking and
understanding an agent is a jobto be done, you could start
looking at your businessholistically and go, How many
jobs to be done are there? Thinkabout them as roles and
responsibilities. I have a CFOthat has a job to do, and build
(23:19):
me these reports once everyweek. Let's automate that. And
that is a job to be done. And Ithink anybody in any role should
be, you know, looking at theirown job and utilizing these
tools to be able to create theirown mini agents to to help make
(23:41):
them more efficient, becauseit's
Kyle Mountsier (23:42):
just a toss before we before we roll out.
I'm going to toss a challenge.
If anybody's listening to thispodcast right now, do us a
favor. Find a job in your sphereof influence that could
potentially be replaced with anAI agent. Either begin building
that agent, or let us know ifyou're having trouble with that,
and between the three of us, atleast, we'll figure out either
(24:04):
the path to get you there, orthe person to talk to, or
something like that. Because Ithink that that would be a
really cool outcome from thisconversation. Nick, always great
to chat with you. You're filledwith just creativity and insight
and vibes and vibes, full vibes,Director vibes, I can't wait to
see you at ASOTU CON. You'regonna be doing some podcasting
(24:26):
with us. You'll be on stage.
It's gonna be a really funcollaboration.
Nick Askew (24:32):
Looking forward to it as always. Great to see you
both.
Paul J Daly (24:40):
I'll tell you what his studio game is really
stepping up on LinkedIn andeverything. I'm watching all
these things. I'm like, he gotthat production bug, and he just
went back into it. Yeah, no, youknow, because I think, I think
all of us have kind of wanderedin and out of that, you know,
I'm just gonna make content andbut he's, you know,
Kyle Mountsier (24:58):
what? I think, actually, this is right? To the
conversation about AI, I thinkthat there is this wonderful
balance of people that areleveraging technology like at an
extreme level, but also stillcreating very natively and
(25:18):
personably. And I think it'senabling like this, left, right,
blank brain unlock in somepeople that are able to do both
within a single day. And, youknow, maybe it's just because
we're all musicians too. Like, Ithink it's something that a
musician or a creator orsomething like that that is
(25:40):
tuned into technology is havingmuch more of an unlock, because
there has been this like, whatcan I create with this? What can
I do? What really
Paul J Daly (25:50):
good observation here, right? I think that the
creative minded people arenaturally ones that are good at
taking, I won't say chaos, buttaking elements and putting them
together. Even think of howcreatives are natural, like
musicians are naturally betterat language, yeah, making up
languages and things like that,because they can kind of hear,
it's almost like they they seeand hear the music in it, oh,
(26:13):
yeah. And it's almost likethat's kind of what Nick's doing
with AI and tech, and what thecreatives are doing with the
tech, how they see the music inthe tech.
Kyle Mountsier (26:21):
There's another that's a post, that's a podcast,
that's whatever, yeah,
Michael Cirillo (26:25):
well, well, as you're saying it, I'm thinking
about this, and I'm like, Yeah,this rings true because, I mean,
I know we've all written songmusic, uh, we've all probably
sung in choirs. We all know howto do the harmonies and all of
that. And there's something tothis where you can hear the
whole and I think, are we allsound engineers too? Yes, okay,
(26:46):
so, like we're all soundengineers at the same time,
there's something to thisability to hear the whole song
at once, yes, and alsosimultaneously hear pick out all
of
Kyle Mountsier (26:58):
the individual things. Yes, yes. Oh, see now
you just done got me fired up.
Paul J Daly (27:05):
Well, listen, we hope you enjoy that
conversation. We hope you enjoylistening to us. Just pat
ourselves on the back at the endof this podcast. On behalf of
Kyle Mountsier, Michael Cirilloand myself, this would be the
perfect time to sing a threepart harmony if we rehearse, but
we are not so. Thank you forjoining us here on Auto Collabs.
Unknown (27:25):
Welcome to Auto Collabs. Sign up for our free
and fun to read daily email fora free shot of relevant news and
automotive retail media and popculture. You can get it
now@asotu.com That's asotu.comif you love this podcast, please
leave us a review and share itwith a friend. Thanks again for
(27:46):
listening. We'll see you nexttime.
Welcome to Annika.
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