May 14, 2025 • 37 mins
AI won't replace salespeople. It will replace salespeople who rely on templates | AI-Powered Seller EP12
In this eye-opening episode of AI-Powered Seller, Jake Dunlap sits down with Sean Mooney, Founder and CEO of Blue Wave, to explore the uncomfortable truth about AI's impact on sales teams.
While most leaders are panicking about AI replacing their teams, they're missing the real threat: AI will first replace the salespeople who've been trained only to follow scripts and processes without adding unique value.
Sean, who advises hundreds of PE firms and their portfolio companies on AI implementation, shares his insider perspective on:
- Why template-focused sellers are most vulnerable to AI disruption
- How the remote work era eliminated the learning-by-osmosis that created great sellers
- The three critical implementation roadblocks 80% of companies hit with AI
- Why creativity is becoming the most valuable skill in modern selling
- How custom GPTs are eliminating the need for complex prompting
If you're a sales professional worried about your future or a leader wondering how to implement AI effectively, this conversation provides the strategic framework you need right now.
The skills that make sellers irreplaceable in the AI era aren't what most think. Discover what they are in this must-watch episode.
📍 Chapters 00:00 Introduction to Sean Mooney
02:15 The Template Generation Problem in Sales
05:40 Who AI Will Replace First (And Why)
10:23 Three Implementation Roadblocks to Avoid
15:37 Making AI a Tactic vs a Technology
22:14 Why Creativity Trumps Process
28:45 Custom GPTs and the End of Prompting 34:19 Future-Proofing Your Sales Career
New episodes drop every other Wednesday @ 8 AM CT / 9 AM ET
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►Connect with Jake: https://www.linkedin.com/in/jakedunlap/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
All right, what's up everybody.
Welcome to another episode ofthe AI Powered Seller.
Today, my friends, we have gota great one.
I'm gonna be joined by SeanMooney, who's founder and CEO of
Blue Wave.
Also, his brother's oldest sonplays basketball with my son, so
I got a lot of connections withSean.
You know Sean and his firm.
They partner with hundreds ofPE firms in their portfolio
(00:22):
companies, helping them to findthe right partners, and so why I
think Sean is so unique forthis conversation is this guy
gets asked from the leadingfirms and then their port codes
what in the hell should we dowith AI?
And so today's conversation,we're going to talk big picture,
philosophical.
We're going to talk about thegoal.
We're going to talk logistics.
We're going to talk about thetemplate generation, which is a
(00:45):
generation of sellers that havegrown up being told to implement
templates versus sales process.
We're going to talk aboutmaking AI a tactic versus a
technology, and get into a wholebunch of other additional
topics.
There's, right now, a massivehype cycle around AI a massive,
massive.
What the heck should I do?
(01:06):
And Sean and I are going to getinto it.
So, sean, big, big appreciationfor you and the work that you
do.
Welcome to the show, excited tojump into it.
All right, sean.
So why don't you tell peoplejust a little bit about your
background and BlueWave?
Obviously, we've known BlueWavefor many, many years here and
really kind of the view that itgives you across so many
(01:29):
different industries.
Speaker 2 (01:31):
Yeah, great, jake, and it's great to be here with
you today.
I'm excited to be on the show.
What we are and what BlueWaveis is we're technically referred
to as a market network, and sowhat we do is, principally, we
work with many hundreds of thetop private equity firms in the
world, thousands of theirportfolio companies.
Speaker 1 (01:47):
This guy is connected .
I'm just going to tell you guys, if you want to get connected,
this dude is connected.
Speaker 2 (01:51):
We know a lot of people, but that's our job is to
bring really good together withreally good, and so what we're
doing is connecting the world'sbest business builders with the
world's best advisors, serviceproviders, interim executives in
this really intelligent, highlycalibrated way.
Most of our customers areprivate equity firms and they're
portcos, but everyday companiesuse this as well, and what that
(02:17):
gives us a vantage into is whatthe best business builders in
the world are doing, wherethey're doing it, why they're
doing it, how they're doing it,how it changes in real time.
And so it's this fascinatingkind of lattice of business
trends and how people reframerisk into opportunity and take
on challenges.
Just a two second backstory onme was this was the toy I wish I
had, and so most of my career Iwas in private equity and I was
like, oh, I need all thesepeople.
(02:38):
And I call my buddies and like,hey, do you know somebody who
does this?
And they're like no.
So I start googling and callingfriends like, hey, do you know
somebody who does this?
And they're like no.
So I start Googling and callingfriends like, hey, do you know?
You know, what should I do?
And then we would just squarepeg round hole and I said
wouldn't it be great if we couldcreate Amazon meets Gartner
Magic Quadrant for businessbuilders?
Speaker 1 (02:54):
Yeah, I love that.
Yeah, and I think, again, youdo a great job of you of
matching up the right partnerfor the right engagement, and so
you know, look, you get achance.
You know we're going to talk alittle AI, a little private
equity and you know landscapetoday.
But you know, let's talk maybea little bit about, like, what
you're seeing around AI adoptionand obviously I'll share a
(03:14):
little bit.
You know we run in the samecircles in terms of, you know,
private equity and you know,late stage venture, et cetera.
You know what are you hearingfrom your private equity
partners around AI in particular, and maybe talk a little bit
about, you know, some of thelike, the failures to like what
are you seeing that people arestruggling with the most?
Speaker 2 (03:35):
Yeah, and maybe we're seeing a lot, and I'll give you
a little bit of history, right.
And so, jake, we've talkedabout this and in my mind, mind,
you know, history doesn'tnecessarily repeat itself, but
it certainly rhymes, sure, andso I like that.
A lot of what's going on rightnow, it feels to me you know, to
date myself, this feels like1995, 1996, as we talked about
(03:58):
where netscape came out andfinally the internet was
available to mere mortals, right?
And then everyone was justtalking about internet, internet
, internet and it was all thiscraze and internet was a
strategy.
And then everyone eventuallychilled out and realized it was
a tactic, and so we're going.
So the internet used to be nuts, and then everyone overbuilt it
and then it all blew up.
(04:19):
And then, in 2001, beautifulcompanies came out of that that
are all the market leaders today.
2001,.
Beautiful companies came out ofthat.
That are all the market leaderstoday.
And so, for me, where thisstory begins is it's 2022.
This thing called ChatGPT comesout.
There were some early adopters.
This is really cool.
23, everyone's like AI is goingto take over the world, and
(04:42):
everyone's like AI, ai, ai.
But it's still kind of like agimmick in the beginning, right,
it's like, oh, this is cool, itdid my grocery list for me.
Yeah, exactly, it's like, oh,tell me about my something or
other, and it was cool, but itwould make a lot of stuff up.
And then what's happened even,I think, faster now than 1995 is
people have realized it's not astrategy unto itself, it's a
(05:06):
tactic, and so people are usingit very specifically to attack
certain work streams thatrequire synthesis, automation,
increasingly, decision andaction.
And so if you go back to whatwe saw in the early days think
about it last year, beginning of2024, pe firms calling us off
the hook and they're like weneed neural networks, and we'd
(05:28):
say, well, okay, that's great,but let's do some precursor
stuff.
Is your data organized?
Are you visualizing it?
Do you have your KPIs?
Do you have this stuff that'sgoing to really enable the cool
stuff?
Because unless your data isorganized, all this stuff
doesn't, you're never going toget to the frontier cool stuff,
because unless your data isorganized, all this stuff
doesn't, you're not, you'renever going to get to the
frontier cool stuff.
And so what is really cool waslast year we saw waves and waves
(05:50):
and waves of people putting inthings like snowflake, where
they previously didn't have that, organizing their data,
structuring it, putting intokpis, cleaning it, putting in
structures to keep it clean,which is the hard part, the
messy part and then visualizingit.
So you're the highest.
Our way, most people, is stillto visualize your data,
understand what your KPIs areand gamify your business and
(06:10):
then you know.
Concurrent with that, there's awhole bunch of other stuff.
But now it was interesting.
After that precursor activity,now we're getting waves and
waves and waves of P firmsactually doing stuff for their
port codes, activating the coolstuff.
But they were really thoughtfulabout like let's get the
foundation set so that we canbuild a house on it.
Speaker 1 (06:31):
I like that.
I think that that's a reallygood call.
Yeah, and we're seeing.
I mean, it's so funny.
Sean and I were talking aboutthis before.
I literally use the exact sameanalogy, except for I say 1996
around the internet and the waythat I describe it to people and
I've talked about it on thepodcast many times is, you know,
companies need to stop askingthemselves what's our AI
strategy.
It's like people are foundingSean, he's like AI officer, like
chief AI officer, and I'm likelook for product that makes a
(06:51):
ton of sense, right For productand how to integrate it.
But you know, your ITdepartment doesn't know what
your AI strategy should be foryour SDR or for your sales rep
or for your marketing team.
And you know, a lot of theconversations I'm having are
really with business leaders andceos being like guys, you, your
it team does not know, becauseit's about your roles.
(07:11):
I was like ai is a role, a roleand use case, specific
deployment.
Yeah, the same way as internetwas right, like you don't have
an internet, I I what I don'tknow in 95, 96, was there a
chief internet officer?
I bet there was.
Speaker 2 (07:25):
It was maybe 19, 1995 , probably not but 1998, if you
remember like every MBA was getan MBA in internet, you know it
was like we're going to be aninternet company and it was just
like this was a strategy untoitself.
And then eventually peoplerealized no, it's a tactic in
service of your strategy.
(07:46):
This is about movinginformation seamlessly from
place to place and up and down,and doing it much more
efficiently.
And so, as I think about thisand this is kind of a
provocative take that somepeople get upset this is just a
long progression between thefusion of human and machine.
And so industrial revolutiongoes on.
(08:07):
You start getting machine andhumans working much more.
You get all the way down tolike the 70s and 80s.
Computers start coming out 80s.
You bring the computer toyourself, but you can't get any
data there.
It's just a computer.
And then you go into the 90s.
You get the internet.
Now you can bring the computerdata to your machine.
And then you get the internet.
Now you can bring the compute,the data, to your machine.
And then you get into 2000,.
(08:27):
You get these little iPhones.
Now you bring the data and thecompute power that was stronger
than the best cratesupercomputer in the 90s in your
hand.
And then in the 2010s, you getthe cloud, where it's all about
everywhere at mass.
And now where we're entering iswith AI.
It's it's about synthesis, it'sabout then doing something with
(08:48):
it.
So it's not just about movingit to places.
You are more efficiently in,greater data.
Now you can take the sum totalof human knowledge, synthesize
it in a second and help you makea decision and action
Increasingly.
It's going to do that actionfor you.
Speaker 1 (09:01):
That's right, yeah.
And the other big kind of kindof I, the other big kind of
evolution I I talk about too,sean, is, this is a.
You know, when I try to talk topeople, it's like this isn't a
technology, this is atransformation, how we solve
problems as humans.
And if you think about it, itwas, you know, we, we used to
have to, and, sean, you'reyou'll, you'll recognize the
Dewey decimal system.
It's like guys, you used to getanswers.
(09:22):
You either had encyclopedias atyour house or you went to a
library and you kind of likecomb through books and use it in
microfiche, right.
And then we got the Internetand Google, and then Google
summarized, kind of like, someanswer ideas, yeah, and that was
a huge leap in time, savings inthe quality of answer, etc.
This is the same leap, right15,.
(09:43):
You know, 20 years later nowwe're at that next bridge, where
now you give the machineeverything instead of like five
words of broken English.
You tell it everything and itgives you an answer.
And then, I think, you know,when you talk about the fusing
of the human, I think this iswhere a lot of people are
struggling.
I think that there's a lot ofpeople that say are like is this
going to replace me, and we'llget into that part of it later
(10:04):
but it's, it's AI is meant toget us, like, I can get to V1
and it's, and it's a really goodV1 so fast, and then I turn my
brain on and I turn it into V5,and the same amount of time it
would have taken me to get to arough draft, Right, and that's
the same thing as, like you know, when you had to go read a
bunch of books and half of themyou didn't even need the
insights in them.
You know you probably saved 10,20, 30, 40 X more time when
(10:27):
Google came out.
Well, guess what, guys?
That's where we're at now.
We're at our next 40 X in termsof time savings.
And the beautiful part aboutthis technology too, sean, is
this is the first technologywhere you know that we've seen
over the last 15, 20 years wherenot only can I do more of
something, the quality is alsogoing up.
That's exactly you know, andthat's unlike any.
And when I talk to a lot ofrevenue operations leaders,
(10:48):
go-to-market leaders, I thinkthat one thing is where they're
really their brain can'tcomprehend it.
It's like they're trying toautomate everything I want to
get you know.
I got into a quasi-argumentwith a on Monday and he's like,
yeah, but look, if this doesn'tintegrate, the salespeople
aren't going to do it.
And I'm like your salespeopleare already using ChatGPT every
(11:10):
day.
Speaker 2 (11:11):
They better be.
Speaker 1 (11:13):
Go survey your teams.
They're all already using itand they know more about it than
you do.
And so I think we're kind of atthis weird juncture around what
I really see happening to Sean.
I'd be curious.
I feel like the frontlinepeople are actually way better
at kind of adopting AI than alot of the leadership.
So I'm curious, you know, foryou, like what are the?
What are the things you'rehearing about, like the
(11:34):
portfolio company leaders?
You know, are you are youhearing about them kind of
jumping in?
You know, like, are you gettinga sentiment of?
You know, are we moving pastlike curiosity to execution or
somewhere in between?
Speaker 2 (11:47):
I think it depends on what segment and type of
business you have.
The tech ecosystem is runningtowards this as fast as it can
because that's natural to them.
The industrial world and eventhe business services world is
trying to figure out how you useit because it's not a native
tool to their everyday life.
And so one of the things that Ithink that we do a lot of these
teach-ins for PE firms or we'llbring in resources and groups
(12:11):
to them to help get them over it.
We're in this point for a lotof the economy, whether it's
Porcos or not, where they'relike, fear is overriding
opportunity and they just don'tknow where to start.
Is this friend or foe?
And we'll always say, like youknow, the first thing you got to
do is just start using it.
Get everyone in your companyshould have access to an llm
(12:31):
take your choices at.
Perplexity is a chat, gpt is agemini.
Just do one and make itavailable.
Speaker 1 (12:36):
And everyone should have like a sticker on their
computer like ask, chat, gpt,ask gemini ask john, you are
literally like, it's like I feellike you're saying I say my
stick it.
Note that I say is I tellpeople stop going to Google?
Speaker 2 (12:50):
Exactly when these first started coming out.
One little trick we did at ourcompany we were very early
adopters on all of these thingsand part of it is just we use
our business on our business andso just as like a family
business or a company or aportco or someone uses us, we
just do the same thing.
So we get to see these trendsearly.
And one of the things that wedid was we started we created an
(13:13):
api we put in so, becauseconfidentiality is still key I
know there's like some talkabout is it is the, the mix and
the soup, so big now you can'tpull out the parts, but we're
still pretty careful about itoverall because you can be if
you take just a little extraeffort.
But what we started doing waswe started in our Monday morning
(13:33):
kind of huddle what we do withall hands every week.
We started doing a league tableto see who was using it the
most.
We started making itcompetitive so we would track
the number of times peopleaccessed it and then everyone
started using it because no onewanted to be on the bottom of
the leaderboard.
So they all started playing andusing it and they all figured
it out.
Because if you realize thatparticularly the LLMs, which is
(13:56):
one tool they're parrotinghumans, and if you realize it's
like talking to a 22 old, it'sjust a conversation, then they
all start figuring it out.
When you realize it's not, it'snot Google command response,
it's conversation, and you'vegot to iterate and then you get
to the answer, just like whenyou're talking to a 22 year old,
like a really smart intern,like they're not gonna be able
(14:18):
to do it the first time, right.
But if you realize like no,it's the third part of the
conversation, and then youfinally take the end product and
make it the exact way you wantit at the end, then it becomes
really powerful.
And so I think we're seeing alot of that, like the industrial
world is picking it up reallyquickly Sales and marketing
teams, everywhere.
It's phenomenal, as you knowbetter than anyone, as you've
(14:39):
talked about this on yourpodcast.
It's a huge game changer foreverything in sales and
marketing, in particular as itrelates to content, call centers
, totally changing the game.
Speaker 1 (14:52):
Oh, especially inbound, Inbound.
Anything is the first to go.
Speaker 2 (14:59):
It's funny when they built these things initially
they thought it was going tohave robotics applications in
the manufacturing center.
That's always been the mindsetwith any innovation.
Really, in the history of ourlives and beyond, that has been
like frontline tip of the spearproduction that get impacted
first.
This is the first technologythat is impacting white collar
(15:19):
first.
That's exactly it.
Speaker 1 (15:20):
Man, that is exactly it.
Yeah, I think that that's areally great point right.
Actually, it's a reallyinteresting point, right when
you think about that, and that'swhy, you know, for me on the go
to market side, and then what Iwant to do next is we'll get
into some like really tactical.
Like you know, for me, likemost AI investments right now
(15:40):
are a complete waste of money,like what people are touting as
AI is still like V1, but now,because of Gen AI, they just
kind of pile it on and mostpeople don't kind of comprehend
the difference between the two,and so I see a lot of people
just spinning their wheels on onthat side of it.
But on the on the go-to-marketside, you know, sean, one of the
things that I am most worriedabout is that you know this
COVID generation of people thatcame into sales and leadership,
(16:04):
um, and you know people thatstarted their jobs probably late
2010s, um.
The issue I see is is salesleaders and maybe RevOps is
driving this too.
They're really focused oneverything is uniform,
everything is did you do thisstep in the process and we've
started to train, and I think wemissed the office environment.
So all these sellers didn'tgrow up listening and getting
(16:26):
better faster, and so what I seehappening is you have a
generation probably, like youknow, anybody with like seven
years of experience and underreally of go-to-market people
that really have not beentrained into how to have quality
interactions and they're beingtold to follow this methodology
that I feel like what's going tohappen is AI.
(16:48):
Those are the people that aregoing to be replaced.
You know, on the go-to-marketside, you know, and are the
people that are going to bereplaced.
You know, on the go-to-marketside, you know, and are the
people that have learned.
You know they're copying andpasting someone else's stuff and
then just parroting it, and I Ireally feel like that is what
we're gonna.
You know, I think that that'sgonna be a really tough time.
So if you're in sales right nowor any go-to-market function,
you've got to say like, hey, amI?
(17:08):
Am I using AI to its fullcapability?
Do I know how to use it?
And then am I turning my, mybrain on and so how do you see
it impacting?
Go to market.
Speaker 2 (17:17):
It's a really good point and I think what people,
you're right, we've kind of,particularly in COVID it went
everyone went to like templatesbecause no one was around humans
anymore.
We, you know, we've been atribal species for tens of
thousands and millions of yearsand then they broke everyone
into these little digital boxesand at first people loved it,
(17:39):
but we're, you know, we're amostly in-person office and
we're we're getting peopleknocking on our door because it
is awful lonely.
You know, particularly if asalesperson where your, you know
, particularly if a salespersonwhere your, your job is to be an
intelligent connector and inconsultative seller of solutions
and problem solving and thensuddenly you're on these little
(18:00):
islands all by yourself.
You know they say you are theextroverts of the world and now
you've been locked into yourbedrooms and so I think that
templatization it's kind ofthere's, there's a whole
generation that's been done ahuge disservice, because they
kind of think like, okay, justfollow a process and do it and
then get to SoulCycle and thenwhen, in reality is, you know,
(18:22):
your job is to create alpha perhour.
You know you need to be goingout there and understanding and
doing, and these AI tools aretremendous tools, but it's not
soup to nuts and if it's soup tonuts, like call center, you're
out of a job.
That's right.
If you don't have, if you don't, if you don't have the judgment
part, you can't demonstratethat you're the first to go.
And so I was to like runtowards this stuff as a tool,
(18:46):
but don't forget the humanityportion of it, because if you,
if you don't not doing thehumanity portion, that gets
automated real.
You, if you're not doing thehumanity portion, that gets
automated real quick.
Speaker 1 (18:52):
Yeah, if you're not turning your brain on I mean
that's what I try toconsistently say is the guy you
know bucket the same amount oftime that you would to do
something and then get to V1 andV2 really quick, and then you
turn the brain on and say I likethat.
And then you prompt and thenlike hey, give me two more
really good ideas.
Ooh, I like that.
That one won't fit.
Delete that out.
I mean again, we'll get that.
I think that's a good precursor.
(19:13):
Let's talk like tangibleexamples, right?
So what you know, are there anyconsistent use cases that you
are seeing?
You know PEs you know comingand asking about?
You know, hey, can we automatethis or can AI help with this?
Like, are there one or two?
Speaker 2 (19:34):
kind of tangible use cases that you're seeing more
frequently.
Yeah, I think the big one wetalked about already is call
centers.
That is getting replicated veryquickly and really what it's
enabling is better service andfaster service for your
customers so your costs could godown.
But really it's that whenthere's going to be 20% of those
use cases, it'll always requirealmost always require human
(19:57):
intervention.
Speaker 1 (19:57):
Or human in the loop, right, it's just maybe later in
the process.
That's how I think about this.
People are going to be so fardown a certain funnel.
It's like I still want a person, but it's like here versus back
here, yeah.
Speaker 2 (20:07):
And it's the stuff you want the people like doing,
like the easy stuff.
They don't like doing thatstupid answer.
So, like the, the metaphor Iuse in my mind is like radiology
, like five years ago, if you goto a radiologist, they would
look at every single slide andthen it would take you like
three months to get your answerand like, oh my god, now I'm
like really sick.
And then they started usingmachine learning, which is now
(20:29):
called ai, and you know theylook side by side each other.
And then, but you know, fastforward to today, the machines
do all the easy stuff, theradiologists do the hard stuff.
They still need moreradiologists.
It's just the difference is youget your answers back the same
day now on the really hard stuff, versus waiting three months.
So it's the same way, like inthe call call centers, where
(20:51):
it's the easy stuff that you cando it.
I don't know about you, butwhen I'm on those I'm like first
like pretty resistant and thenlike that was pretty good, like
I didn't wait three hours ofgetting ticked off, or even like
30 minutes, which is probablymore, like the answer Right,
exactly.
Speaker 1 (21:11):
And then if I needed it, it got me to that person
quickly and I was just like Ilove this company.
Well, that's it too Right, likethat.
You know it's getting bettertoo.
Right, that's the.
I think the key is, you know,yes, you hated it before because
it was, like you said, machinelearning, but the call center
one now.
And the example I always use is,like, the inbound SDR is the
first role to go on and the B2Bside, right, like wouldn't I
would much if I'm already atstep three.
(21:33):
I'm like I know who you are, Iknow what the comp I already use
your competitor.
Like do I want to be qualified?
Like, wait, wait, wait, you'requalifying me.
It's like I want to talk tosomeone.
I would rather talk to AI,where I can say, hey, great.
Hey, Jake, I'm going to get youbooked with someone.
(21:57):
Let me answer a few questionsfor you.
What brought you here?
Okay?
Well, yeah, actually here's theanswer to that question.
And so I feel like, look, Iwill choose that option if I can
get answers, you know and likegreat.
And then make sure that then,when the sales rep gets looped
in now, the sales rep alreadyhas their SC and their customer
success person on the first call, and so I just I think that
there's a speed to um, you know,in the inbound world and what's
?
Speaker 2 (22:15):
what's so key about this and I think you've done a
great job talking about this onyour show is that you know it's
and you've made the point hereagain again.
You got to use the word and notor it's not like you're just
going to turn this all over tothe robots, right, you've got to
.
The human's going to stay inthe loop.
You're going to have the robotsdo the stuff the robots are
good at, but you got to keep thepeople in for the judgment, the
(22:36):
service, the nuance, theoutliers.
But if you could have therobots do the really just basic
stuff that no human wants to do,why wouldn't you and why don't
your people want to do that too?
And so like run towards thatstuff.
Don't, don't like we.
We make it, we make everyone,we don't make we we.
Everyone in our company readsthis book at least once a year,
(22:58):
called who moved my cheese.
Speaker 1 (23:00):
I've heard that it's a good one.
Speaker 2 (23:01):
And it's this great little parable about mice in a
maze and the cheese gets movedand then the world changes and
you eat, the smart ones just runand get, and they still get fat
and happy, eat a lot of cheese.
He doesn't love cheese, I lovecheese.
And then but the other oneslike resist, you, like I'm not
gonna, it's gonna come back, andit's the people and they wither
away and at the end of thestory, like you know, you make
and and some they make some kindof like hints that those two
(23:23):
mice are no longer with us.
But it's like you can.
You can be afraid of this stuffand just change, will catch up
and win, or you can run towardsit, and so I choose to run
towards it, like get excitedabout it, just start a little
day, every day.
Speaker 1 (23:36):
I love that.
So we got call centers.
What other tactical you know?
These kind of like in bat, likewe'll call these, like you know
.
I mean they are getting morenuanced but I'm gonna call it
like interactions that aren't,you know, overly, overly involve
lots of people necessarily, andI can get basic back and forth
on what are there other kinds oftactical use cases that people
(23:59):
are?
You know that you're kind ofhearing bubbling up.
Speaker 2 (24:01):
Yeah, I think the agents are at the early days.
There's everyone's trying tofigure this out.
So you know N a N is like oneof the acronyms that everyone
knows, and it's great where it'sno code, low code, right, and
so even like code is going away,you know.
And so what we're seeing is on,you know, like can you, almost
(24:23):
can you?
You know, mere mortals now cando these things that used to
take months of direct coding.
Speaker 1 (24:27):
It's 100%.
I can do it.
I can build something in innatein right, if that tells you
just the the low efficacy thatit takes.
Speaker 2 (24:33):
And even a little story.
Like I know nothing aboutcoding whatsoever, I, I've, I've
, never, I you know maybe otherthan basic and eighth grade,
when they made us make a littlegame, you know?
Speaker 1 (24:42):
man, that's I mean guys.
Do you want to know?
Oh OG, coders respect basicyeah.
Speaker 2 (24:48):
Until I was curious about it.
It was a rainy day in Nashville, I had nothing to do.
My kids are older and my one ofmy son does this thing called
quiz bowl, which is likecompetitive jeopardy in high
school and like part of thechallenge is you can't keep the
score, like no one knows what itis.
They don't show it to you.
So I was like you know what?
I bet you I could just make anapp.
And then one afternoon I wentto I used this program called
(25:11):
Vercel and it had me make anentire app.
That was the exact thing I everwanted in my life, all in one
afternoon.
That probably would have takenour dev team a year ago,
probably like a month to make.
It was 2,000 lines of code andit was the perfect thing I
wanted.
(25:31):
The hard part was getting itinto the damn app store.
You know that took me twice aslong.
But making the app I alwayswanted was with zero coding
skills and they just embedded a,a, an LLM into the dev shop and
I just had conversations withthem.
No, I want this, not that, andit's the same thing that's
coming out with with that.
So I think the agents arecoming.
I want this, not that, and it'sthe same thing that's coming
out with that.
(25:51):
So I think the agents arecoming.
I still think they're not quiteready for prime time, but
they're getting there fast.
Yeah, I'd love your perspectiveon that, because I think you're
probably farther ahead than weare on that.
Speaker 1 (26:00):
Well, there's some cool stuff, man.
Most people don't realize.
I mean ChatGPT.
I think I'm going to do afuture episode of the podcast,
probably in the next, you know,a few weeks here.
Chad dbt just released 40 tasks.
So if you're using the paidversion, I feel like this is
some feature I don't can't likenobody's talking about this and
you know it's kind of v1 of likea built-in agent where you can
be like uh, I want you tomonitor uh for octa's quarterly
(26:24):
filings on a consistent basis.
It'll go immediately.
Be like oh, octa last did this?
Do you want that one?
I will go and check octa forthat.
You know.
Go like, so you can like I meanimagine future go to market.
Is you know what we're doingwith our clients?
Dude, we're gonna have everyrep set up like 500 of these
freaking things, every singleone.
So the agents, you know andagain the word agents gets
thrown around it can mean likeyeah, we kind of delineate.
(26:45):
There's like the agents arelike the proactive things.
Assistants are the things thatare like that prompt you like
discovery call prep.
It's like the agents are likethe proactive things.
Assistants are the things thatare like that prompt you like
discovery call prep.
It's like give me a link to thecompany and the job title,
here's your discovery call prep,and then automations are things
that you know say, oh, I justsaw this new thing, let me run
that custom GPT and then I'llpipe it in to you.
(27:05):
So yeah, and what I'm seeing isyou know it's interesting, you
had hinted to this before in thevery beginning is a lot of PE
firms I feel like are finallymoving toward, like we just need
to get our portcodes helped,like they're not going to do
this shit, like they're notgoing to do it, like we're
training them Like I can'timagine, sean, I've probably in
the last nine months, spoken atat least 12 different PE
(27:26):
conferences about about Gen AI,and the consistent is either
like one, the revenue leadersmany of them are like what in
the hell is this right and thentwo, it's like we follow up,
like oh yeah, we haven't done,we still are kicking tires, and
I feel like many of the PE firmsthat we're talking to are
finally like you just have tohave somebody do it for you.
(27:48):
And it's like which is good forour business, right?
I feel like what with ourbusiness?
How it's transitioning is likeyou know, we've always been kind
of this rev performance, somemix of like strategy, enablement
operations and kind of how doyou bring these things together?
And now I feel like we've gotthis, this part of our business
that's like website hosting 92,where I'm like let me explain
the internet and websites to you.
(28:09):
And then there's again a lot offear in the market, a lot of
these and for a lot of thesecompanies.
I think it goes back to whatboth of us said, which is, you
know, it doesn't matter, likefor your company in particular,
identify the use case right,like one of the ones that we
found, you know, with with someof our like more non-tech is
like, uh, what's the nextproduct I should recommend?
You know, we worked with acompany called Koozie Group HIG
(28:31):
back company and we helped themto develop an agent that's like
you know they got a ton ofdifferent SKUs.
It's like, hey, this is whatthey're doing with us.
What should they do with usnext?
And it's saving the reps threeto four hours a week.
Like the math is real.
You're talking about getting amonth to two months of
additional productivity out ofevery single person in your
go-to-market team with one likelittle assistant.
Speaker 2 (28:52):
Well, I mean, here's an example, even like a year old
example.
This is like the beginning.
Actually, this is the beginningof.
August.
This was like.
This is like two years ago.
Actually, in our firm, our, youthink about every marketing
team there's huge advantageabout content creation.
Oh god, it's two years ago.
(29:12):
You know, our, our head ofmarketing came up to us and said
, hey, I want, I need morecontent writers.
I go.
Well, no, there's this thingcalled chat gpt that just came
out yeah and there's a thingcalled jasper.
You can use both of those.
Try that out.
Long story long, two years agoour content production went up
500% for $40 a month insubscriptions and the ask was
like multiple $100,000 ofcontent writers.
(29:33):
The content was better, it wasmore SEOed, it was more
well-written, once you kind offigure out how to, particularly
then to prompt it.
But now it's just, you know,but that's going to create new
problems, right?
The cheese didn't get movedbecause now the world's just
exploding with content.
Oh my God.
You know, seo is almostimpossible now because
everyone's creating so muchcontent.
Speaker 1 (29:52):
Well, the content one's a really good tactical one
for people and, sean, like protip, you know, for you and your
team, we upgraded so that abrand new the chat GBT, oh, one
pro reasoning model came out inlate January.
It's $200 a month when you, itis insane.
So, like our marketing team,now we pay two.
(30:15):
We have three or four people atthe company, five people now
that we pay $200 a month just sothey can have access to the pro
.
I mean that's a lot of money.
If you think about it.
Our marketing team, when youturn on the O1 pro reasoning
with deep research, it canliterally put together the best
five, six page white paperyou've seen.
Cite 29 sources.
(30:37):
And so now our marketing teamis putting out a white paper a
day, yeah, a day.
And the key is you know whatBecca does.
She then spends two hoursmaking it awesome, yeah, so it's
like she gets to V1 and thenspends two hours, you know,
refining the prompts, blah, blah, blah, a little bit more of
that.
And then just like editing,moving this, deleting, I mean
(30:59):
it's unfathomable, like most,most, I mean, and even for back,
like I think about my marketingteam.
It's like I I had to get hercomfortable, of like you can
like that this is even possibleright.
Of like you can do this andlike you're going to be better
for it, more productive, etcit's a lot of things like
creating, like case studyengines.
Speaker 2 (31:18):
It's.
It's amazing.
I totally agree with that, andso it's it you talked about.
It's a distillation in in speedenhancer.
You still have to check it.
You do your magic.
Like I said, it's like theworld's best 22 year old entry
level employee.
You know like yeah, but thenalso knows everything yeah.
Speaker 1 (31:35):
They have a matrix brain to where it's like I can
access.
I created my first voice agentin 11 labs and it's me and it's
trained on like discovery callbest practices and what it does
is.
But you can start to challengeit and be like well, jake, I
don't think you actually knowlike anything about the
discovery call for my quantumcomputing tech company.
(31:56):
Can you explain quantumcomputing to me?
And then I just start spoutingall this knowledge about quantum
computing and these things.
And then I just start spoutingall this knowledge about quantum
computing and these things.
So it's pretty, it's prettyinteresting we'll put the phone
number in the show notes so youguys can give me a call and
check it out and and I'll tell,like the one other thing you
asked, like what are peopletactically doing?
Speaker 2 (32:14):
that anyone can do any.
You know, virtually everycompany now today has software
right and and even manufacturingcompanies, and if they don't,
then now they can.
And the, the softwaredevelopment, is the other big
area where everyone has to be onthis, and so what we're seeing
is massive, massive, massiveproductivity enhancements by
(32:36):
using these LLMs.
To code, gemini's new one isamazing, and so you know, I
think we just doubled ourproductivity in the last month.
Speaker 1 (32:46):
And so that's wild.
Speaker 2 (32:48):
Now.
Now what's creating is forpeople are doing this, what you
have to be aware of now it'screating other bottlenecks, and
so it's just like any kind ofsystem.
So read the book, the goal,which is an old line, right
Manufacturing book, the bestEverything's a process right.
Speaker 1 (33:03):
Eli Goldrath, is that right.
Speaker 2 (33:05):
Yeah, eli Goldrath, exactly right, and it's a great
book and it's told through astory, and so I read this.
When I was a young pup investorin industrial companies, I read
that and I could walk any plantin the world, but really it's
just a process.
And so now what's happening isour devs are working at like
light speed.
They're cranking through somuch code it's impossible.
Now it's it's testing and, moremost importantly, product that
(33:29):
can't keep up.
Right, and so you're gonna haveto now, but but what's coming
right behind that testing?
Automated testing solutions arecoming very close.
Product is is like there'stools that are making our
product people faster.
So you know, one of the thingsyou have to be really thoughtful
about these things is look atyour entire system and map it
and understand where yourconstraints are, cause you're
going to make certain partsreally fast, yeah, you're gonna
(33:52):
make certain parts really fast,but the others I'm going to keep
up in.
The whole engine won't move asfast as the slowest piece.
Speaker 1 (33:57):
So shout out to the goal.
Yeah, I read that in businessschool years.
It's a great book.
Uh, everyone, I really feellike everybody should read the
goal.
It really helps in terms ofjust knowing how to tackle
problems and do like goodproject management, of just,
yeah, understanding the theoryof constraints but being able to
read it in like a fictionalenvironment, I don't know like.
(34:19):
It's like, oh, makes sense, andthen like now, every problem,
you quickly, you, you eliminatethe noise and you go where's the
bottleneck?
Speaker 2 (34:25):
Yeah, and it's.
It's so important now becausecertain part of people's
businesses now are moving sofast.
Right, but if you can't speedup the bottlenecks you're going
to, you're not going to keep it.
Your whole business won't movefast store, it's just parts of
it will.
And then you're gonna have alot of people like reading ESPN,
you know, dot com, nothing leftto do.
I'm waiting.
Speaker 1 (34:44):
I'm waiting on Herbie to catch up here.
Shout out, herbie, by the way,is the fictional kid that they
that creates the bottleneck, andso you can read all about it.
So, look, I've got a ton ofnotes here.
I've got this for go to market.
It's a tactic in service ofyour strategy.
I thought that was like really,really good.
I love the leaderboard idea.
(35:05):
I'm stealing that oneimmediately for our team.
Everyone on our team has accessto chat GBT teams as well.
So there's a lot of really goodstuff in here.
So, sean, you know oneappreciate you joining.
Take us home.
Final thoughts.
You know somebody who'slistening saying you know, I'm
in a go to market function.
You get a chance to talk to alot of these folks.
(35:26):
You know what's maybe the onething that either we didn't
cover or kind of one final pointyou think that you know people
need to hear.
Speaker 2 (35:33):
I think so much of this is a change management
issue and what I would say it'smore this is more kind of just
conceptual than a than a tacticor life hack is you've got to
like you can run towards thisand you can thrive, or you can
resist it and wither and for,particularly, go to market
people.
Those are people who looktowards the world and run
(35:54):
towards it and so take that samementality that brought you into
this job in the first place andget super excited about it.
And it's really really kind ofdaunting to take the first step
and I was in private equity,which we are the most
risk-averse people of all time.
That's right.
But if you can take you knowall it is is that first step and
then you take the next step.
Put a little sticker.
I got this from a guy namedConnor Grennan and he's like
(36:19):
just put a sticker on yourmonitor that says ask ChatGPT or
ask Claude or Gemini, and it'samazing how often I just look at
that on my own and I do it andyou get it's just and you go oh,
why am I doing what I'm doingright now?
Yeah, it's just better andfaster.
And talk to the LLMs like it'sa person.
Realize that they were trainedto parrot.
People train to parrot.
(36:42):
People ask it a question.
Tell them who you are, tellthem who they are, tell them
what the objective is, checktheir work, cite their sources
and ask questions they want toknow, and you'll get amazing
outputs from it.
It'll tell you what to do, andso just use it every day, run
towards it, take one step at atime.
And it's the old cliche.
I didn't come up with this, butchat, gpt or AI in general is
not going to replace your job,but someone who is using it will
(37:02):
.
Speaker 1 (37:03):
That's right.
That's right.
That's a good note.
And, by the way, I just wrotedown I'm replacing it.
I just wrote down Ask Chai GBT.
I'm literally doing it as wespeak here.
You know no need to, and again,I think I'm a power user, as is
, but again I constantly catchmyself being like, why am I?
Oh, oh crap, Go do this now.
So, sean, appreciate you, myman, we'll catch up next time.
(37:25):
I'm in Nashville, for sure.
Sorry I missed last week, buthopefully everyone out there
like I feel like this was asuper mix of I need to get my
butt in gear with you know sometactical ways.
You can think about gettingstarted as well too, which is
always you is always what we'retrying to bring on the podcast.
So, sean, big shout out, bigthank you to you, my man.
Speaker 2 (37:44):
Likewise, jake, great seeing you.
Speaker 1 (37:46):
All right, you too.
All right, what's up everybody.
Welcome to another episode ofthe AI Powered Seller.
Today, my friends, we have gota great one.
I'm gonna be joined by SeanMooney, who's founder and CEO of
Blue Wave.
Also, his brother's oldest sonplays basketball with my son, so
I got a lot of connections withSean.
You know Sean and his firm.
They partner with hundreds ofPE firms in their portfolio
(00:22):
companies, helping them to findthe right partners, and so why I
think Sean is so unique forthis conversation is this guy
gets asked from the leadingfirms and then their port codes
what in the hell should we dowith AI?
And so today's conversation,we're going to talk big picture,
philosophical.
We're going to talk about thegoal.
We're going to talk logistics.
We're going to talk about thetemplate generation, which is a
(00:45):
generation of sellers that havegrown up being told to implement
templates versus sales process.
We're going to talk aboutmaking AI a tactic versus a
technology, and get into a wholebunch of other additional
topics.
There's, right now, a massivehype cycle around AI a massive,
massive.
What the heck should I do?
(01:06):
And Sean and I are going to getinto it.
So, sean, big, big appreciationfor you and the work that you
do.
Welcome to the show, excited tojump into it.
All right, sean.
So why don't you tell peoplejust a little bit about your
background and BlueWave?
Obviously, we've known BlueWavefor many, many years here and
really kind of the view that itgives you across so many
(01:29):
different industries.
Speaker 2 (01:31):
Yeah, great, jake, and it's great to be here with
you today.
I'm excited to be on the show.
What we are and what BlueWaveis is we're technically referred
to as a market network, and sowhat we do is, principally, we
work with many hundreds of thetop private equity firms in the
world, thousands of theirportfolio companies.
Speaker 1 (01:47):
This guy is connected .
I'm just going to tell you guys, if you want to get connected,
this dude is connected.
Speaker 2 (01:51):
We know a lot of people, but that's our job is to
bring really good together withreally good, and so what we're
doing is connecting the world'sbest business builders with the
world's best advisors, serviceproviders, interim executives in
this really intelligent, highlycalibrated way.
Most of our customers areprivate equity firms and they're
portcos, but everyday companiesuse this as well, and what that
(02:17):
gives us a vantage into is whatthe best business builders in
the world are doing, wherethey're doing it, why they're
doing it, how they're doing it,how it changes in real time.
And so it's this fascinatingkind of lattice of business
trends and how people reframerisk into opportunity and take
on challenges.
Just a two second backstory onme was this was the toy I wish I
had, and so most of my career Iwas in private equity and I was
like, oh, I need all thesepeople.
(02:38):
And I call my buddies and like,hey, do you know somebody who
does this?
And they're like no.
So I start googling and callingfriends like, hey, do you know
somebody who does this?
And they're like no.
So I start Googling and callingfriends like, hey, do you know?
You know, what should I do?
And then we would just squarepeg round hole and I said
wouldn't it be great if we couldcreate Amazon meets Gartner
Magic Quadrant for businessbuilders?
Speaker 1 (02:54):
Yeah, I love that.
Yeah, and I think, again, youdo a great job of you of
matching up the right partnerfor the right engagement, and so
you know, look, you get achance.
You know we're going to talk alittle AI, a little private
equity and you know landscapetoday.
But you know, let's talk maybea little bit about, like, what
you're seeing around AI adoptionand obviously I'll share a
(03:14):
little bit.
You know we run in the samecircles in terms of, you know,
private equity and you know,late stage venture, et cetera.
You know what are you hearingfrom your private equity
partners around AI in particular, and maybe talk a little bit
about, you know, some of thelike, the failures to like what
are you seeing that people arestruggling with the most?
Speaker 2 (03:35):
Yeah, and maybe we're seeing a lot, and I'll give you
a little bit of history, right.
And so, jake, we've talkedabout this and in my mind, mind,
you know, history doesn'tnecessarily repeat itself, but
it certainly rhymes, sure, andso I like that.
A lot of what's going on rightnow, it feels to me you know, to
date myself, this feels like1995, 1996, as we talked about
(03:58):
where netscape came out andfinally the internet was
available to mere mortals, right?
And then everyone was justtalking about internet, internet
, internet and it was all thiscraze and internet was a
strategy.
And then everyone eventuallychilled out and realized it was
a tactic, and so we're going.
So the internet used to be nuts, and then everyone overbuilt it
and then it all blew up.
(04:19):
And then, in 2001, beautifulcompanies came out of that that
are all the market leaders today.
2001,.
Beautiful companies came out ofthat.
That are all the market leaderstoday.
And so, for me, where thisstory begins is it's 2022.
This thing called ChatGPT comesout.
There were some early adopters.
This is really cool.
23, everyone's like AI is goingto take over the world, and
(04:42):
everyone's like AI, ai, ai.
But it's still kind of like agimmick in the beginning, right,
it's like, oh, this is cool, itdid my grocery list for me.
Yeah, exactly, it's like, oh,tell me about my something or
other, and it was cool, but itwould make a lot of stuff up.
And then what's happened even,I think, faster now than 1995 is
people have realized it's not astrategy unto itself, it's a
(05:06):
tactic, and so people are usingit very specifically to attack
certain work streams thatrequire synthesis, automation,
increasingly, decision andaction.
And so if you go back to whatwe saw in the early days think
about it last year, beginning of2024, pe firms calling us off
the hook and they're like weneed neural networks, and we'd
(05:28):
say, well, okay, that's great,but let's do some precursor
stuff.
Is your data organized?
Are you visualizing it?
Do you have your KPIs?
Do you have this stuff that'sgoing to really enable the cool
stuff?
Because unless your data isorganized, all this stuff
doesn't, you're never going toget to the frontier cool stuff,
because unless your data isorganized, all this stuff
doesn't, you're not, you'renever going to get to the
frontier cool stuff.
And so what is really cool waslast year we saw waves and waves
(05:50):
and waves of people putting inthings like snowflake, where
they previously didn't have that, organizing their data,
structuring it, putting intokpis, cleaning it, putting in
structures to keep it clean,which is the hard part, the
messy part and then visualizingit.
So you're the highest.
Our way, most people, is stillto visualize your data,
understand what your KPIs areand gamify your business and
(06:10):
then you know.
Concurrent with that, there's awhole bunch of other stuff.
But now it was interesting.
After that precursor activity,now we're getting waves and
waves and waves of P firmsactually doing stuff for their
port codes, activating the coolstuff.
But they were really thoughtfulabout like let's get the
foundation set so that we canbuild a house on it.
Speaker 1 (06:31):
I like that.
I think that that's a reallygood call.
Yeah, and we're seeing.
I mean, it's so funny.
Sean and I were talking aboutthis before.
I literally use the exact sameanalogy, except for I say 1996
around the internet and the waythat I describe it to people and
I've talked about it on thepodcast many times is, you know,
companies need to stop askingthemselves what's our AI
strategy.
It's like people are foundingSean, he's like AI officer, like
chief AI officer, and I'm likelook for product that makes a
(06:51):
ton of sense, right For productand how to integrate it.
But you know, your ITdepartment doesn't know what
your AI strategy should be foryour SDR or for your sales rep
or for your marketing team.
And you know, a lot of theconversations I'm having are
really with business leaders andceos being like guys, you, your
it team does not know, becauseit's about your roles.
(07:11):
I was like ai is a role, a roleand use case, specific
deployment.
Yeah, the same way as internetwas right, like you don't have
an internet, I I what I don'tknow in 95, 96, was there a
chief internet officer?
I bet there was.
Speaker 2 (07:25):
It was maybe 19, 1995 , probably not but 1998, if you
remember like every MBA was getan MBA in internet, you know it
was like we're going to be aninternet company and it was just
like this was a strategy untoitself.
And then eventually peoplerealized no, it's a tactic in
service of your strategy.
(07:46):
This is about movinginformation seamlessly from
place to place and up and down,and doing it much more
efficiently.
And so, as I think about thisand this is kind of a
provocative take that somepeople get upset this is just a
long progression between thefusion of human and machine.
And so industrial revolutiongoes on.
(08:07):
You start getting machine andhumans working much more.
You get all the way down tolike the 70s and 80s.
Computers start coming out 80s.
You bring the computer toyourself, but you can't get any
data there.
It's just a computer.
And then you go into the 90s.
You get the internet.
Now you can bring the computerdata to your machine.
And then you get the internet.
Now you can bring the compute,the data, to your machine.
And then you get into 2000,.
(08:27):
You get these little iPhones.
Now you bring the data and thecompute power that was stronger
than the best cratesupercomputer in the 90s in your
hand.
And then in the 2010s, you getthe cloud, where it's all about
everywhere at mass.
And now where we're entering iswith AI.
It's it's about synthesis, it'sabout then doing something with
(08:48):
it.
So it's not just about movingit to places.
You are more efficiently in,greater data.
Now you can take the sum totalof human knowledge, synthesize
it in a second and help you makea decision and action
Increasingly.
It's going to do that actionfor you.
Speaker 1 (09:01):
That's right, yeah.
And the other big kind of kindof I, the other big kind of
evolution I I talk about too,sean, is, this is a.
You know, when I try to talk topeople, it's like this isn't a
technology, this is atransformation, how we solve
problems as humans.
And if you think about it, itwas, you know, we, we used to
have to, and, sean, you'reyou'll, you'll recognize the
Dewey decimal system.
It's like guys, you used to getanswers.
(09:22):
You either had encyclopedias atyour house or you went to a
library and you kind of likecomb through books and use it in
microfiche, right.
And then we got the Internetand Google, and then Google
summarized, kind of like, someanswer ideas, yeah, and that was
a huge leap in time, savings inthe quality of answer, etc.
This is the same leap, right15,.
(09:43):
You know, 20 years later nowwe're at that next bridge, where
now you give the machineeverything instead of like five
words of broken English.
You tell it everything and itgives you an answer.
And then, I think, you know,when you talk about the fusing
of the human, I think this iswhere a lot of people are
struggling.
I think that there's a lot ofpeople that say are like is this
going to replace me, and we'llget into that part of it later
(10:04):
but it's, it's AI is meant toget us, like, I can get to V1
and it's, and it's a really goodV1 so fast, and then I turn my
brain on and I turn it into V5,and the same amount of time it
would have taken me to get to arough draft, Right, and that's
the same thing as, like you know, when you had to go read a
bunch of books and half of themyou didn't even need the
insights in them.
You know you probably saved 10,20, 30, 40 X more time when
(10:27):
Google came out.
Well, guess what, guys?
That's where we're at now.
We're at our next 40 X in termsof time savings.
And the beautiful part aboutthis technology too, sean, is
this is the first technologywhere you know that we've seen
over the last 15, 20 years wherenot only can I do more of
something, the quality is alsogoing up.
That's exactly you know, andthat's unlike any.
And when I talk to a lot ofrevenue operations leaders,
(10:48):
go-to-market leaders, I thinkthat one thing is where they're
really their brain can'tcomprehend it.
It's like they're trying toautomate everything I want to
get you know.
I got into a quasi-argumentwith a on Monday and he's like,
yeah, but look, if this doesn'tintegrate, the salespeople
aren't going to do it.
And I'm like your salespeopleare already using ChatGPT every
(11:10):
day.
Speaker 2 (11:11):
They better be.
Speaker 1 (11:13):
Go survey your teams.
They're all already using itand they know more about it than
you do.
And so I think we're kind of atthis weird juncture around what
I really see happening to Sean.
I'd be curious.
I feel like the frontlinepeople are actually way better
at kind of adopting AI than alot of the leadership.
So I'm curious, you know, foryou, like what are the?
What are the things you'rehearing about, like the
(11:34):
portfolio company leaders?
You know, are you are youhearing about them kind of
jumping in?
You know, like, are you gettinga sentiment of?
You know, are we moving pastlike curiosity to execution or
somewhere in between?
Speaker 2 (11:47):
I think it depends on what segment and type of
business you have.
The tech ecosystem is runningtowards this as fast as it can
because that's natural to them.
The industrial world and eventhe business services world is
trying to figure out how you useit because it's not a native
tool to their everyday life.
And so one of the things that Ithink that we do a lot of these
teach-ins for PE firms or we'llbring in resources and groups
(12:11):
to them to help get them over it.
We're in this point for a lotof the economy, whether it's
Porcos or not, where they'relike, fear is overriding
opportunity and they just don'tknow where to start.
Is this friend or foe?
And we'll always say, like youknow, the first thing you got to
do is just start using it.
Get everyone in your companyshould have access to an llm
(12:31):
take your choices at.
Perplexity is a chat, gpt is agemini.
Just do one and make itavailable.
Speaker 1 (12:36):
And everyone should have like a sticker on their
computer like ask, chat, gpt,ask gemini ask john, you are
literally like, it's like I feellike you're saying I say my
stick it.
Note that I say is I tellpeople stop going to Google?
Speaker 2 (12:50):
Exactly when these first started coming out.
One little trick we did at ourcompany we were very early
adopters on all of these thingsand part of it is just we use
our business on our business andso just as like a family
business or a company or aportco or someone uses us, we
just do the same thing.
So we get to see these trendsearly.
And one of the things that wedid was we started we created an
(13:13):
api we put in so, becauseconfidentiality is still key I
know there's like some talkabout is it is the, the mix and
the soup, so big now you can'tpull out the parts, but we're
still pretty careful about itoverall because you can be if
you take just a little extraeffort.
But what we started doing waswe started in our Monday morning
(13:33):
kind of huddle what we do withall hands every week.
We started doing a league tableto see who was using it the
most.
We started making itcompetitive so we would track
the number of times peopleaccessed it and then everyone
started using it because no onewanted to be on the bottom of
the leaderboard.
So they all started playing andusing it and they all figured
it out.
Because if you realize thatparticularly the LLMs, which is
(13:56):
one tool they're parrotinghumans, and if you realize it's
like talking to a 22 old, it'sjust a conversation, then they
all start figuring it out.
When you realize it's not, it'snot Google command response,
it's conversation, and you'vegot to iterate and then you get
to the answer, just like whenyou're talking to a 22 year old,
like a really smart intern,like they're not gonna be able
(14:18):
to do it the first time, right.
But if you realize like no,it's the third part of the
conversation, and then youfinally take the end product and
make it the exact way you wantit at the end, then it becomes
really powerful.
And so I think we're seeing alot of that, like the industrial
world is picking it up reallyquickly Sales and marketing
teams, everywhere.
It's phenomenal, as you knowbetter than anyone, as you've
(14:39):
talked about this on yourpodcast.
It's a huge game changer foreverything in sales and
marketing, in particular as itrelates to content, call centers
, totally changing the game.
Speaker 1 (14:52):
Oh, especially inbound, Inbound.
Anything is the first to go.
Speaker 2 (14:59):
It's funny when they built these things initially
they thought it was going tohave robotics applications in
the manufacturing center.
That's always been the mindsetwith any innovation.
Really, in the history of ourlives and beyond, that has been
like frontline tip of the spearproduction that get impacted
first.
This is the first technologythat is impacting white collar
(15:19):
first.
That's exactly it.
Speaker 1 (15:20):
Man, that is exactly it.
Yeah, I think that that's areally great point right.
Actually, it's a reallyinteresting point, right when
you think about that, and that'swhy, you know, for me on the go
to market side, and then what Iwant to do next is we'll get
into some like really tactical.
Like you know, for me, likemost AI investments right now
(15:40):
are a complete waste of money,like what people are touting as
AI is still like V1, but now,because of Gen AI, they just
kind of pile it on and mostpeople don't kind of comprehend
the difference between the two,and so I see a lot of people
just spinning their wheels on onthat side of it.
But on the on the go-to-marketside, you know, sean, one of the
things that I am most worriedabout is that you know this
COVID generation of people thatcame into sales and leadership,
(16:04):
um, and you know people thatstarted their jobs probably late
2010s, um.
The issue I see is is salesleaders and maybe RevOps is
driving this too.
They're really focused oneverything is uniform,
everything is did you do thisstep in the process and we've
started to train, and I think wemissed the office environment.
So all these sellers didn'tgrow up listening and getting
(16:26):
better faster, and so what I seehappening is you have a
generation probably, like youknow, anybody with like seven
years of experience and underreally of go-to-market people
that really have not beentrained into how to have quality
interactions and they're beingtold to follow this methodology
that I feel like what's going tohappen is AI.
(16:48):
Those are the people that aregoing to be replaced.
You know, on the go-to-marketside, you know, and are the
people that are going to bereplaced.
You know, on the go-to-marketside, you know, and are the
people that have learned.
You know they're copying andpasting someone else's stuff and
then just parroting it, and I Ireally feel like that is what
we're gonna.
You know, I think that that'sgonna be a really tough time.
So if you're in sales right nowor any go-to-market function,
you've got to say like, hey, amI?
(17:08):
Am I using AI to its fullcapability?
Do I know how to use it?
And then am I turning my, mybrain on and so how do you see
it impacting?
Go to market.
Speaker 2 (17:17):
It's a really good point and I think what people,
you're right, we've kind of,particularly in COVID it went
everyone went to like templatesbecause no one was around humans
anymore.
We, you know, we've been atribal species for tens of
thousands and millions of yearsand then they broke everyone
into these little digital boxesand at first people loved it,
(17:39):
but we're, you know, we're amostly in-person office and
we're we're getting peopleknocking on our door because it
is awful lonely.
You know, particularly if asalesperson where your, you know
, particularly if a salespersonwhere your, your job is to be an
intelligent connector and inconsultative seller of solutions
and problem solving and thensuddenly you're on these little
(18:00):
islands all by yourself.
You know they say you are theextroverts of the world and now
you've been locked into yourbedrooms and so I think that
templatization it's kind ofthere's, there's a whole
generation that's been done ahuge disservice, because they
kind of think like, okay, justfollow a process and do it and
then get to SoulCycle and thenwhen, in reality is, you know,
(18:22):
your job is to create alpha perhour.
You know you need to be goingout there and understanding and
doing, and these AI tools aretremendous tools, but it's not
soup to nuts and if it's soup tonuts, like call center, you're
out of a job.
That's right.
If you don't have, if you don't, if you don't have the judgment
part, you can't demonstratethat you're the first to go.
And so I was to like runtowards this stuff as a tool,
(18:46):
but don't forget the humanityportion of it, because if you,
if you don't not doing thehumanity portion, that gets
automated real.
You, if you're not doing thehumanity portion, that gets
automated real quick.
Speaker 1 (18:52):
Yeah, if you're not turning your brain on I mean
that's what I try toconsistently say is the guy you
know bucket the same amount oftime that you would to do
something and then get to V1 andV2 really quick, and then you
turn the brain on and say I likethat.
And then you prompt and thenlike hey, give me two more
really good ideas.
Ooh, I like that.
That one won't fit.
Delete that out.
I mean again, we'll get that.
I think that's a good precursor.
(19:13):
Let's talk like tangibleexamples, right?
So what you know, are there anyconsistent use cases that you
are seeing?
You know PEs you know comingand asking about?
You know, hey, can we automatethis or can AI help with this?
Like, are there one or two?
Speaker 2 (19:34):
kind of tangible use cases that you're seeing more
frequently.
Yeah, I think the big one wetalked about already is call
centers.
That is getting replicated veryquickly and really what it's
enabling is better service andfaster service for your
customers so your costs could godown.
But really it's that whenthere's going to be 20% of those
use cases, it'll always requirealmost always require human
(19:57):
intervention.
Speaker 1 (19:57):
Or human in the loop, right, it's just maybe later in
the process.
That's how I think about this.
People are going to be so fardown a certain funnel.
It's like I still want a person, but it's like here versus back
here, yeah.
Speaker 2 (20:07):
And it's the stuff you want the people like doing,
like the easy stuff.
They don't like doing thatstupid answer.
So, like the, the metaphor Iuse in my mind is like radiology
, like five years ago, if you goto a radiologist, they would
look at every single slide andthen it would take you like
three months to get your answerand like, oh my god, now I'm
like really sick.
And then they started usingmachine learning, which is now
(20:29):
called ai, and you know theylook side by side each other.
And then, but you know, fastforward to today, the machines
do all the easy stuff, theradiologists do the hard stuff.
They still need moreradiologists.
It's just the difference is youget your answers back the same
day now on the really hard stuff, versus waiting three months.
So it's the same way, like inthe call call centers, where
(20:51):
it's the easy stuff that you cando it.
I don't know about you, butwhen I'm on those I'm like first
like pretty resistant and thenlike that was pretty good, like
I didn't wait three hours ofgetting ticked off, or even like
30 minutes, which is probablymore, like the answer Right,
exactly.
Speaker 1 (21:11):
And then if I needed it, it got me to that person
quickly and I was just like Ilove this company.
Well, that's it too Right, likethat.
You know it's getting bettertoo.
Right, that's the.
I think the key is, you know,yes, you hated it before because
it was, like you said, machinelearning, but the call center
one now.
And the example I always use is,like, the inbound SDR is the
first role to go on and the B2Bside, right, like wouldn't I
would much if I'm already atstep three.
(21:33):
I'm like I know who you are, Iknow what the comp I already use
your competitor.
Like do I want to be qualified?
Like, wait, wait, wait, you'requalifying me.
It's like I want to talk tosomeone.
I would rather talk to AI,where I can say, hey, great.
Hey, Jake, I'm going to get youbooked with someone.
(21:57):
Let me answer a few questionsfor you.
What brought you here?
Okay?
Well, yeah, actually here's theanswer to that question.
And so I feel like, look, Iwill choose that option if I can
get answers, you know and likegreat.
And then make sure that then,when the sales rep gets looped
in now, the sales rep alreadyhas their SC and their customer
success person on the first call, and so I just I think that
there's a speed to um, you know,in the inbound world and what's
?
Speaker 2 (22:15):
what's so key about this and I think you've done a
great job talking about this onyour show is that you know it's
and you've made the point hereagain again.
You got to use the word and notor it's not like you're just
going to turn this all over tothe robots, right, you've got to
.
The human's going to stay inthe loop.
You're going to have the robotsdo the stuff the robots are
good at, but you got to keep thepeople in for the judgment, the
(22:36):
service, the nuance, theoutliers.
But if you could have therobots do the really just basic
stuff that no human wants to do,why wouldn't you and why don't
your people want to do that too?
And so like run towards thatstuff.
Don't, don't like we.
We make it, we make everyone,we don't make we we.
Everyone in our company readsthis book at least once a year,
(22:58):
called who moved my cheese.
Speaker 1 (23:00):
I've heard that it's a good one.
Speaker 2 (23:01):
And it's this great little parable about mice in a
maze and the cheese gets movedand then the world changes and
you eat, the smart ones just runand get, and they still get fat
and happy, eat a lot of cheese.
He doesn't love cheese, I lovecheese.
And then but the other oneslike resist, you, like I'm not
gonna, it's gonna come back, andit's the people and they wither
away and at the end of thestory, like you know, you make
and and some they make some kindof like hints that those two
(23:23):
mice are no longer with us.
But it's like you can.
You can be afraid of this stuffand just change, will catch up
and win, or you can run towardsit, and so I choose to run
towards it, like get excitedabout it, just start a little
day, every day.
Speaker 1 (23:36):
I love that.
So we got call centers.
What other tactical you know?
These kind of like in bat, likewe'll call these, like you know
.
I mean they are getting morenuanced but I'm gonna call it
like interactions that aren't,you know, overly, overly involve
lots of people necessarily, andI can get basic back and forth
on what are there other kinds oftactical use cases that people
(23:59):
are?
You know that you're kind ofhearing bubbling up.
Speaker 2 (24:01):
Yeah, I think the agents are at the early days.
There's everyone's trying tofigure this out.
So you know N a N is like oneof the acronyms that everyone
knows, and it's great where it'sno code, low code, right, and
so even like code is going away,you know.
And so what we're seeing is on,you know, like can you, almost
(24:23):
can you?
You know, mere mortals now cando these things that used to
take months of direct coding.
Speaker 1 (24:27):
It's 100%.
I can do it.
I can build something in innatein right, if that tells you
just the the low efficacy thatit takes.
Speaker 2 (24:33):
And even a little story.
Like I know nothing aboutcoding whatsoever, I, I've, I've
, never, I you know maybe otherthan basic and eighth grade,
when they made us make a littlegame, you know?
Speaker 1 (24:42):
man, that's I mean guys.
Do you want to know?
Oh OG, coders respect basicyeah.
Speaker 2 (24:48):
Until I was curious about it.
It was a rainy day in Nashville, I had nothing to do.
My kids are older and my one ofmy son does this thing called
quiz bowl, which is likecompetitive jeopardy in high
school and like part of thechallenge is you can't keep the
score, like no one knows what itis.
They don't show it to you.
So I was like you know what?
I bet you I could just make anapp.
And then one afternoon I wentto I used this program called
(25:11):
Vercel and it had me make anentire app.
That was the exact thing I everwanted in my life, all in one
afternoon.
That probably would have takenour dev team a year ago,
probably like a month to make.
It was 2,000 lines of code andit was the perfect thing I
wanted.
(25:31):
The hard part was getting itinto the damn app store.
You know that took me twice aslong.
But making the app I alwayswanted was with zero coding
skills and they just embedded a,a, an LLM into the dev shop and
I just had conversations withthem.
No, I want this, not that, andit's the same thing that's
coming out with with that.
So I think the agents arecoming.
I want this, not that, and it'sthe same thing that's coming
out with that.
(25:51):
So I think the agents arecoming.
I still think they're not quiteready for prime time, but
they're getting there fast.
Yeah, I'd love your perspectiveon that, because I think you're
probably farther ahead than weare on that.
Speaker 1 (26:00):
Well, there's some cool stuff, man.
Most people don't realize.
I mean ChatGPT.
I think I'm going to do afuture episode of the podcast,
probably in the next, you know,a few weeks here.
Chad dbt just released 40 tasks.
So if you're using the paidversion, I feel like this is
some feature I don't can't likenobody's talking about this and
you know it's kind of v1 of likea built-in agent where you can
be like uh, I want you tomonitor uh for octa's quarterly
(26:24):
filings on a consistent basis.
It'll go immediately.
Be like oh, octa last did this?
Do you want that one?
I will go and check octa forthat.
You know.
Go like, so you can like I meanimagine future go to market.
Is you know what we're doingwith our clients?
Dude, we're gonna have everyrep set up like 500 of these
freaking things, every singleone.
So the agents, you know andagain the word agents gets
thrown around it can mean likeyeah, we kind of delineate.
(26:45):
There's like the agents arelike the proactive things.
Assistants are the things thatare like that prompt you like
discovery call prep.
It's like the agents are likethe proactive things.
Assistants are the things thatare like that prompt you like
discovery call prep.
It's like give me a link to thecompany and the job title,
here's your discovery call prep,and then automations are things
that you know say, oh, I justsaw this new thing, let me run
that custom GPT and then I'llpipe it in to you.
(27:05):
So yeah, and what I'm seeing isyou know it's interesting, you
had hinted to this before in thevery beginning is a lot of PE
firms I feel like are finallymoving toward, like we just need
to get our portcodes helped,like they're not going to do
this shit, like they're notgoing to do it, like we're
training them Like I can'timagine, sean, I've probably in
the last nine months, spoken atat least 12 different PE
(27:26):
conferences about about Gen AI,and the consistent is either
like one, the revenue leadersmany of them are like what in
the hell is this right and thentwo, it's like we follow up,
like oh yeah, we haven't done,we still are kicking tires, and
I feel like many of the PE firmsthat we're talking to are
finally like you just have tohave somebody do it for you.
(27:48):
And it's like which is good forour business, right?
I feel like what with ourbusiness?
How it's transitioning is likeyou know, we've always been kind
of this rev performance, somemix of like strategy, enablement
operations and kind of how doyou bring these things together?
And now I feel like we've gotthis, this part of our business
that's like website hosting 92,where I'm like let me explain
the internet and websites to you.
(28:09):
And then there's again a lot offear in the market, a lot of
these and for a lot of thesecompanies.
I think it goes back to whatboth of us said, which is, you
know, it doesn't matter, likefor your company in particular,
identify the use case right,like one of the ones that we
found, you know, with with someof our like more non-tech is
like, uh, what's the nextproduct I should recommend?
You know, we worked with acompany called Koozie Group HIG
(28:31):
back company and we helped themto develop an agent that's like
you know they got a ton ofdifferent SKUs.
It's like, hey, this is whatthey're doing with us.
What should they do with usnext?
And it's saving the reps threeto four hours a week.
Like the math is real.
You're talking about getting amonth to two months of
additional productivity out ofevery single person in your
go-to-market team with one likelittle assistant.
Speaker 2 (28:52):
Well, I mean, here's an example, even like a year old
example.
This is like the beginning.
Actually, this is the beginningof.
August.
This was like.
This is like two years ago.
Actually, in our firm, our, youthink about every marketing
team there's huge advantageabout content creation.
Oh god, it's two years ago.
(29:12):
You know, our, our head ofmarketing came up to us and said
, hey, I want, I need morecontent writers.
I go.
Well, no, there's this thingcalled chat gpt that just came
out yeah and there's a thingcalled jasper.
You can use both of those.
Try that out.
Long story long, two years agoour content production went up
500% for $40 a month insubscriptions and the ask was
like multiple $100,000 ofcontent writers.
(29:33):
The content was better, it wasmore SEOed, it was more
well-written, once you kind offigure out how to, particularly
then to prompt it.
But now it's just, you know,but that's going to create new
problems, right?
The cheese didn't get movedbecause now the world's just
exploding with content.
Oh my God.
You know, seo is almostimpossible now because
everyone's creating so muchcontent.
Speaker 1 (29:52):
Well, the content one's a really good tactical one
for people and, sean, like protip, you know, for you and your
team, we upgraded so that abrand new the chat GBT, oh, one
pro reasoning model came out inlate January.
It's $200 a month when you, itis insane.
So, like our marketing team,now we pay two.
(30:15):
We have three or four people atthe company, five people now
that we pay $200 a month just sothey can have access to the pro
.
I mean that's a lot of money.
If you think about it.
Our marketing team, when youturn on the O1 pro reasoning
with deep research, it canliterally put together the best
five, six page white paperyou've seen.
Cite 29 sources.
(30:37):
And so now our marketing teamis putting out a white paper a
day, yeah, a day.
And the key is you know whatBecca does.
She then spends two hoursmaking it awesome, yeah, so it's
like she gets to V1 and thenspends two hours, you know,
refining the prompts, blah, blah, blah, a little bit more of
that.
And then just like editing,moving this, deleting, I mean
(30:59):
it's unfathomable, like most,most, I mean, and even for back,
like I think about my marketingteam.
It's like I I had to get hercomfortable, of like you can
like that this is even possibleright.
Of like you can do this andlike you're going to be better
for it, more productive, etcit's a lot of things like
creating, like case studyengines.
Speaker 2 (31:18):
It's.
It's amazing.
I totally agree with that, andso it's it you talked about.
It's a distillation in in speedenhancer.
You still have to check it.
You do your magic.
Like I said, it's like theworld's best 22 year old entry
level employee.
You know like yeah, but thenalso knows everything yeah.
Speaker 1 (31:35):
They have a matrix brain to where it's like I can
access.
I created my first voice agentin 11 labs and it's me and it's
trained on like discovery callbest practices and what it does
is.
But you can start to challengeit and be like well, jake, I
don't think you actually knowlike anything about the
discovery call for my quantumcomputing tech company.
(31:56):
Can you explain quantumcomputing to me?
And then I just start spoutingall this knowledge about quantum
computing and these things.
And then I just start spoutingall this knowledge about quantum
computing and these things.
So it's pretty, it's prettyinteresting we'll put the phone
number in the show notes so youguys can give me a call and
check it out and and I'll tell,like the one other thing you
asked, like what are peopletactically doing?
Speaker 2 (32:14):
that anyone can do any.
You know, virtually everycompany now today has software
right and and even manufacturingcompanies, and if they don't,
then now they can.
And the, the softwaredevelopment, is the other big
area where everyone has to be onthis, and so what we're seeing
is massive, massive, massiveproductivity enhancements by
(32:36):
using these LLMs.
To code, gemini's new one isamazing, and so you know, I
think we just doubled ourproductivity in the last month.
Speaker 1 (32:46):
And so that's wild.
Speaker 2 (32:48):
Now.
Now what's creating is forpeople are doing this, what you
have to be aware of now it'screating other bottlenecks, and
so it's just like any kind ofsystem.
So read the book, the goal,which is an old line, right
Manufacturing book, the bestEverything's a process right.
Speaker 1 (33:03):
Eli Goldrath, is that right.
Speaker 2 (33:05):
Yeah, eli Goldrath, exactly right, and it's a great
book and it's told through astory, and so I read this.
When I was a young pup investorin industrial companies, I read
that and I could walk any plantin the world, but really it's
just a process.
And so now what's happening isour devs are working at like
light speed.
They're cranking through somuch code it's impossible.
Now it's it's testing and, moremost importantly, product that
(33:29):
can't keep up.
Right, and so you're gonna haveto now, but but what's coming
right behind that testing?
Automated testing solutions arecoming very close.
Product is is like there'stools that are making our
product people faster.
So you know, one of the thingsyou have to be really thoughtful
about these things is look atyour entire system and map it
and understand where yourconstraints are, cause you're
going to make certain partsreally fast, yeah, you're gonna
(33:52):
make certain parts really fast,but the others I'm going to keep
up in.
The whole engine won't move asfast as the slowest piece.
Speaker 1 (33:57):
So shout out to the goal.
Yeah, I read that in businessschool years.
It's a great book.
Uh, everyone, I really feellike everybody should read the
goal.
It really helps in terms ofjust knowing how to tackle
problems and do like goodproject management, of just,
yeah, understanding the theoryof constraints but being able to
read it in like a fictionalenvironment, I don't know like.
(34:19):
It's like, oh, makes sense, andthen like now, every problem,
you quickly, you, you eliminatethe noise and you go where's the
bottleneck?
Speaker 2 (34:25):
Yeah, and it's.
It's so important now becausecertain part of people's
businesses now are moving sofast.
Right, but if you can't speedup the bottlenecks you're going
to, you're not going to keep it.
Your whole business won't movefast store, it's just parts of
it will.
And then you're gonna have alot of people like reading ESPN,
you know, dot com, nothing leftto do.
I'm waiting.
Speaker 1 (34:44):
I'm waiting on Herbie to catch up here.
Shout out, herbie, by the way,is the fictional kid that they
that creates the bottleneck, andso you can read all about it.
So, look, I've got a ton ofnotes here.
I've got this for go to market.
It's a tactic in service ofyour strategy.
I thought that was like really,really good.
I love the leaderboard idea.
(35:05):
I'm stealing that oneimmediately for our team.
Everyone on our team has accessto chat GBT teams as well.
So there's a lot of really goodstuff in here.
So, sean, you know oneappreciate you joining.
Take us home.
Final thoughts.
You know somebody who'slistening saying you know, I'm
in a go to market function.
You get a chance to talk to alot of these folks.
(35:26):
You know what's maybe the onething that either we didn't
cover or kind of one final pointyou think that you know people
need to hear.
Speaker 2 (35:33):
I think so much of this is a change management
issue and what I would say it'smore this is more kind of just
conceptual than a than a tacticor life hack is you've got to
like you can run towards thisand you can thrive, or you can
resist it and wither and for,particularly, go to market
people.
Those are people who looktowards the world and run
(35:54):
towards it and so take that samementality that brought you into
this job in the first place andget super excited about it.
And it's really really kind ofdaunting to take the first step
and I was in private equity,which we are the most
risk-averse people of all time.
That's right.
But if you can take you knowall it is is that first step and
then you take the next step.
Put a little sticker.
I got this from a guy namedConnor Grennan and he's like
(36:19):
just put a sticker on yourmonitor that says ask ChatGPT or
ask Claude or Gemini, and it'samazing how often I just look at
that on my own and I do it andyou get it's just and you go oh,
why am I doing what I'm doingright now?
Yeah, it's just better andfaster.
And talk to the LLMs like it'sa person.
Realize that they were trainedto parrot.
People train to parrot.
(36:42):
People ask it a question.
Tell them who you are, tellthem who they are, tell them
what the objective is, checktheir work, cite their sources
and ask questions they want toknow, and you'll get amazing
outputs from it.
It'll tell you what to do, andso just use it every day, run
towards it, take one step at atime.
And it's the old cliche.
I didn't come up with this, butchat, gpt or AI in general is
not going to replace your job,but someone who is using it will
(37:02):
.
Speaker 1 (37:03):
That's right.
That's right.
That's a good note.
And, by the way, I just wrotedown I'm replacing it.
I just wrote down Ask Chai GBT.
I'm literally doing it as wespeak here.
You know no need to, and again,I think I'm a power user, as is
, but again I constantly catchmyself being like, why am I?
Oh, oh crap, Go do this now.
So, sean, appreciate you, myman, we'll catch up next time.
(37:25):
I'm in Nashville, for sure.
Sorry I missed last week, buthopefully everyone out there
like I feel like this was asuper mix of I need to get my
butt in gear with you know sometactical ways.
You can think about gettingstarted as well too, which is
always you is always what we'retrying to bring on the podcast.
So, sean, big shout out, bigthank you to you, my man.
Speaker 2 (37:44):
Likewise, jake, great seeing you.
Speaker 1 (37:46):
All right, you too.
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