Latest / 1 to 100 / E50: The Future of Software Businesses with Maxx Blank from Triple Whale

Transcript

E50: The Future of Software Businesses with Maxx Blank from Triple Whale · 1 to 100

0:00
What is the newest version of triple or triple or such like a
0:03
widespread product, right? You've existed for so long, so
0:07
much of e-commerce uses you. And I imagine that today I saw
0:10
the new launch, by the way, imagine today it's nothing like
0:15
maybe doesn't even resemble the the product that it used to be a
0:18
couple of years ago. What's like the yeah, what's,
0:21
what's the story? Yeah, it's definitely, it's
0:26
definitely not like the little the dashboard that used to be
0:29
back in the day, Jeremiah, I remember like we used to, I
0:31
don't remember what it was, but we used to hang out a lot.
0:33
You know, yeah, I was going to say, I think the first time we
0:36
met was like probably early 2021.
0:39
I don't remember how we got connected, but we just started.
0:42
It was before you guys had launched and we were jamming on
0:44
like go to market pricing and how to figure that stuff out.
0:47
And then, yeah, you guys, I, I just want to say to you like as
0:49
somebody who was kind of like coming up at the same time, your
0:52
strategy was so good and you guys just completely blew it out
0:56
of the water in terms of like just the go to market approach,
0:59
all that kind of stuff. So anyway, I was, I've always
1:01
been very impressed, but it was fun to see.
1:03
I just remember talking about like, how do you price this
1:05
thing? And then you guys found like
1:06
this? There's still a question we have
1:07
today. What's that?
1:09
That was still the question. Yeah, exactly.
1:10
How do you price this thing? I actually feel like it's harder
1:12
now than it was five years ago. It's like a giant Rubik's Cube.
1:15
Like, you turn one thing over here and then, like, all these.
1:18
Yeah. Because, like, so much of what
1:20
we built is different now. Like, it's just different.
1:23
Like to your point, Jack, like, you know, at the beginning of
1:27
our journey was really just aggregate those like main KPIs
1:30
that I cared about running my brand and AJ was we working
1:34
together on a brand that my wife and I started and we ran that we
1:38
like rode that brand through COVID and it was like it was
1:41
crazy. But like I always wanted just
1:44
like, give me like a tell me what's going on in my pocket,
1:47
right. And there wasn't anything early
1:48
out there, you know, you either had, you know, the Shopify app
1:51
is awesome, you know, still is awesome.
1:54
You don't have everything else in there like right in front of
1:56
your face. Like there's a big pop in sales.
1:59
Was it the ads? I mean, probably not.
2:01
It's probably like an e-mail campaign.
2:02
I'm like, can I see all that in one place?
2:04
That's great. So it's really like a quick
2:07
aggregation of like just everything that I needed to get
2:10
a quick TLDR from a founder standpoint, right?
2:14
That was really the thing we launched with and what we
2:16
started to gain a lot of traction with.
2:18
And I think a lot of our customers today think that's
2:21
still like the main thing we've got.
2:23
He's not like a lot of our users, I wouldn't say our
2:25
customers, but a lot of our users today, I think that's like
2:28
what we are is just like a dashboard and they're like, oh,
2:29
we're going to vibe code this thing now.
2:31
We're like, OK, go for it. We'll actually give it to you
2:33
for free. So you don't need to.
2:33
But wasn't really until iOS 14.5 and like, you know, like the ad
2:40
apocalypse stuff and all that. And we went heads down for like
2:44
a good amount of time and built our pixel, our, our attribution,
2:48
you know, our customer journey solution essentially to help
2:50
advertisers understand what ads are working.
2:53
And that turned our business into like a whole.
2:56
It went from like side hustle thing that's like kind of cool
2:59
to like, oh God, we got a rock'n'roll go all.
3:01
Then I dropped that brand completely and just focused on
3:04
this. And this was 4 1/2 years ago or
3:05
this is probably like 5 1/2 years ago really.
3:10
So we went, it went from like just aggregate what's going on
3:12
to like granularly, what ads are working, pixel attribution.
3:18
And then we got into creative, what creators are working all
3:21
about visualization really. And then I would say like when
3:28
AI started to become a thing, like AJ has always been obsessed
3:30
with AI, like the beginning. I'm always very theatrical and
3:33
everything, you know, like I came from the operator, like
3:35
sort of scrappy entrepreneur, just like doesn't save me money
3:38
or make me money if there's nothing in like there's no in
3:41
between that just that's all I care about.
3:44
It's like AI was like, OK, cool, but like, show me the money, you
3:47
know, how does this thing actually work?
3:49
And it was so inaccessible and theoretical and ethereal and
3:52
like futuristic to me. And I think once ChatGPT came
3:54
out, we're like, oh, OK, kind of see how this works, you know,
3:58
And the whole world's like turned on fire, right?
3:59
Like the whole thing just became a real, a real story.
4:02
So I was like, oh, wow, we should really it just like we
4:07
should, we need to like get ready for this thing even more
4:10
so. And then I would say like maybe
4:13
2 1/2 years ago, we took the whole engineering team
4:16
essentially and rebuilt and we have like multiple backends and
4:20
this database for that and this database for this.
4:22
And then we finally unified everything into one giant
4:25
schema. This is like, you know, almost
4:27
three years now and just got really organized and organized
4:29
the data and hired the best people in the world to have us
4:31
do that. And we just had this giant
4:33
warehouse, you know, underneath all of our clients and give them
4:35
access to it as well. And they were able to make their
4:37
own visualizations and things of this sort and really queer their
4:40
data beyond what somebody would just need as like a typical
4:44
quick dashboard. You could really go into like
4:46
the meat of what's going on with the data around your business.
4:50
I was curious, you said we took the entire engineering team.
4:53
So I think, I think this is like AI think.
4:57
I mean, I think people who listen are like they're
5:01
operating their own business. They're like, what can I learn
5:04
from what other people are doing?
5:05
And I think this is a very hard question because at the time you
5:10
guys were doing 10s of millions of dollars of revenue and your
5:13
current customers are asking you for stuff and you're confronted
5:16
with this change that you're staring at in the face, right?
5:20
And like as you describe it, and you can tell me where I'm over
5:25
interpreting, but as you describe it ages like dude, this
5:29
AI thing is happening. We got to do this thing and
5:31
you're like everybody just wants to make money like and and and
5:36
so like you're, you're, you're internally feeling the the
5:40
debate that is being felt by the whole industry in some ways,
5:43
right. By the way, that's how do you
5:46
decide the resource allocation in that context?
5:50
Like actually like what? Like what was the debate and how
5:53
did you actually resource allocate to what should be
5:58
forward-looking with what needs to be a native versus
6:02
maintaining current revenue? That is like the question, you
6:06
know, and when we had this debate of like sort of focusing
6:11
on like a whole, essentially let's build a data warehouse
6:14
that is queriable for our customers and then hopefully at
6:17
some point like AI as well, right?
6:18
That's the whole is the bet really the common theme that
6:22
kind of helped me get over that hump because I want to see
6:24
something in front of me that's like, OK, this thing will make
6:26
money. AJ is way more like, let's focus
6:29
on like the new tech that's coming out more like, you know,
6:34
visionary on that end for sure. I'm more like, OK, let's make
6:37
money off this stuff. OK.
6:38
What helped me get over that hump is that we worked very
6:41
closely with some of the bigger customers that were, you know,
6:43
in a, you know, in a customer base.
6:45
And the common themes between all of them is that they just
6:47
need new visualizations and they need their own, they need
6:50
bespoke visualizations. And like that's extraordinarily
6:53
hard to maintain a software company, right, when you have to
6:56
have different visualizations that you need to manage like on
6:58
a global side. So we're like, OK, let's just do
7:02
this whole data warehouse play. Let's make this data query able.
7:05
Let's make a good UI around it. Let's you know, the schema is
7:08
relatively defiant. So it's not like a, a
7:10
generalized sort of data warehouse tool where you have to
7:13
go and take it out the shelf and then like make it work for the,
7:16
the language, the semantic language or the schema language
7:18
for that industry. DTC is very, very well known in
7:22
a sense. So we, we, that's the way I
7:24
justified it and people wanted that.
7:28
So that's how we did that sort of back and forth of when do we
7:31
do this? How do we do this?
7:31
Why do we focus on this? Who focuses?
7:33
On it and what percent and, and how did you think about
7:35
allocating budget or resourcing? Like was it 80207030?
7:40
Like what's the what's the guiding principle on on on
7:46
resource allocation? They used triple wall to see
7:48
where the revenue were coming from and they were like, you
7:50
know what here? What are you saying, Jack?
7:53
I said you guys use triple wall to see where the revenue is
7:55
coming from. You're like, OK, let's spend
7:56
more here. It's a good question.
8:00
I I want to say like I have some grand like OK, we did exactly
8:03
20% here, 8% here. I want to say it's more like a
8:05
focus from our teams. You know, we had like the best
8:10
we had our, our, our infrastructure guy.
8:12
You know, we had our data team really focus a lot of their
8:16
effort on this thing and to just iterate as fast as we could.
8:19
And not necessarily was it so calculated like we're going to
8:22
allocate this amount here. We did put some like, let's say
8:25
revenue goals or usage goals to help us kind of get along the
8:27
way. Like as soon as I had that as a
8:31
semblance of like what value this could bring right now to
8:34
some of our key customers, I just got on the phone with them
8:37
and started like pitching them. As you can start to see with a
8:39
lot of these customers that they have a very similar sort of
8:42
trajectory story that, you know, you have the startup that's just
8:46
trying to get product market fit around their store and their
8:48
products and the, you know, maybe that's zero to 5 million.
8:52
Then they get to 5 to 10 and they're like really starting to
8:55
move and they have to start to standardize things and
8:57
operationalize things and the founders kind of in all this
8:59
stuff and starting to bring in agencies and hiring people.
9:02
Then we start to break out a 10:15 to 20.
9:05
That's say the 15 to 2025 range is when you start to really you
9:09
need a data team or you or you need maybe like a data more of a
9:13
robust reporting solution. So we start, we just follow
9:16
these customers to look that trajectory and just met them
9:21
had, you know, and then, and then we offered a way to build
9:23
reports for them as well with the, with the team, with the SQL
9:27
team internally that we have. So Max, you redid the data
9:31
structure for the purpose of reporting, not not for AI or, or
9:36
was it the AI stuff in the back of your mind as well?
9:39
Like how are you thinking about that?
9:40
Because obviously you doing all that work has a massive has
9:44
massive implications on how AI ready that data is and how
9:47
usable it is in that context. But it doesn't sound like that
9:49
was the primary objective. It sounds like the primary
9:51
objective was actually solving a more near term problem that your
9:54
customers were having. I would say that the the
9:59
direction we were going is to get it ready for AI.
10:01
OK, But it wasn't so clear what that means just yet.
10:04
You know, we knew we had to go in the direction of making it
10:07
what a human needs to do. Now with that data, there will
10:09
be a lot of patterns that are similar to what the AI is going
10:12
to need. Now.
10:13
You need more of like definition around things, a lot more labels
10:16
and things like that for the AI that maybe doesn't have all the
10:18
context of what these rows and things actually mean when you
10:21
actually operationalize it. We just went that direction.
10:24
We kind of just like we're going, you know, and went all in
10:27
on that. That's interesting, hey,
10:29
adapting, but you don't know what you're adapting to.
10:32
That's what it. Is right.
10:33
The world's changing. The product's going to be
10:35
different in the future, but like, we have to have to be
10:37
ready for it, right? That's what it is.
10:40
And that's like this between AJ and I, it's like, OK, how much
10:44
focus we put on this new thing versus the current thing.
10:47
That's the current tension between he and I, you know, And
10:50
it's, it's a good little yin and Yang, you know?
10:53
You, you briefly said we took our best people and then you
10:56
like named certain types of people who you took to work on
11:00
this project. I guess I have two questions. 1
11:03
is how did you think about who should work on this project?
11:05
And the second one was how much of your Finder founder mindset
11:10
because again, you said I was calling, I was calling the
11:12
customers constant. I think there's this like under
11:17
appreciated thing, which is if it has the founder attention and
11:21
the founder is completely in it and then there's some group of
11:24
people, however you pick them to work on the thing, it can be
11:28
20%, thirty percent, 40%. Like it kind of ends up not
11:32
mattering because the energy of the company is on that thing.
11:37
And is that a fairway to articulate how you thought about
11:40
the energy allocation? Percent.
11:43
I couldn't agree more. And the thing here is that AJ
11:45
and I both have that since we're, you know, Co founders
11:47
here. And so he focused heavily on
11:50
engineering and product. I was essentially like the
11:53
practitioner of the product at the beginning and came from the
11:56
industry and through just my history, I'm just always had
11:59
been marketing or selling. And so I, my intuition was
12:02
around that. So we're building something and
12:04
then I would quickly like how can I come in at, let's say 85%
12:09
done, start to pick out the things that I believe are sort
12:12
of moldable to some type of offer conversation or value
12:16
conversation and then create that loop, bring it back to him
12:19
and then go from there. But the way you characterize
12:24
that, I would say is right. How often do you find yourself
12:29
do how? How fast are the iteration loops
12:32
today relative to last year? Oh, man, I don't know.
12:37
It's like night and day. You know, you can go, right?
12:40
I mean, I'm sure you're experiencing.
12:43
Yeah, I mean, look, obviously I have my own view on these on
12:46
these things, but I, I, I like asking these questions because
12:49
I, I think like this is what like when I call Jeremiah
12:55
one-on-one, this is what we talk like, you know, this is when I
12:58
call my CEO friends, this is what we talk about on the phone.
13:02
We're not talking about random like niceties.
13:10
So not not that I'm not trying to be nice.
13:14
I'm it's just that like, you know, the world kind of is
13:17
moving pretty fast and I don't have a choice because like, you
13:20
know, you run a business, you don't have a choice.
13:22
You have to respond to the world.
13:24
And I guess, I guess I'm curious.
13:26
I. Think it's not, you know.
13:31
Well, that's what the that's what the money's for, Max, you
13:34
know what I mean? That's what.
13:36
Like it just, it went up such a high.
13:38
What you're saying has always been the case, right?
13:39
You always kind of having that trade off, but I don't know,
13:42
something happened the past like 60 days, 65 days.
13:45
I think it was one weekend when Claude bought like went nuts or
13:48
whatever and we're all like, whoa, this treadmill just speed
13:53
up and like you can't get off of it now.
13:54
And like you're not, you're not like.
13:57
What? OK I this is something I think
13:59
about a lot because I completely agree with you.
14:01
Like the a, the the speed at which you need to make decisions
14:04
out of so fast you have to iterate very quickly it maximum
14:08
with you. It feels exhausting.
14:10
I have a cold today. I'm not taking the day off.
14:12
I'm not that I probably would have before, but like I just
14:15
feel like I can take a break ever now and that's hard.
14:18
Like I actually don't think that's good long term.
14:20
I'm curious how you think about that.
14:22
Like how like at a certain level, we're all people and we
14:26
have different needs of the machine.
14:27
What does that look like in like this is a this is a pace that's
14:31
sustainable for a while, but like is it sustainable for three
14:34
years, five years, 10 years? What does that look like?
14:36
I'm curious what you guys think I.
14:38
Think it's important what are we doing?
14:41
What was that I'm? Not sure.
14:44
It's a it's a movie thing. It's a movie thing.
14:46
Three years, 3. Is it, do we do this or do we do
14:48
that? Like which three are we doing?
14:50
Yeah, yeah. It's, I can't remember which
14:52
movie it was from. It's like a famous scene from a
14:54
movie, but yeah. My view, I think it's important
14:57
to kind of detach X and Twitter from like reality, you know,
15:02
like, and maybe you guys are really good at that and you
15:05
know, I, I'm not the best at that, but I just could you have
15:08
all these new things launching and you think that all of a
15:10
sudden everyone's going to adopt them and everyone's going to be
15:13
every, every, everyone's just going to vibe code what you've
15:15
built. They're like this or that or
15:17
this solution is going to be set up and therefore we're all done.
15:19
It's like it's not the technology and the pace of
15:22
technology that that is going to keep going.
15:24
It's going to speed up. And I think the real challenges
15:26
people adopting it, using it, trusting it, actually getting
15:30
value out of it. I had a friend of mine the other
15:32
day who rents space for me upstairs.
15:34
And he's a he's a bro. He's a he's a real estate
15:37
broker. He sells storage units, right,
15:39
You know, like those storage units on the side of the road.
15:42
And he finally got into Claude, the Claude right.
15:45
And I'm called code just in regular Claude.
15:47
And he built like this cool CRM kind of tool to track his deals.
15:52
And he's non-technical orderly that, you know, the opposite of
15:54
what we're doing over here. And he's so empowered in the
15:59
blown away by what he's able to do.
16:01
And I see him the next day, he's like still so excited.
16:04
The next day, he's still so excited.
16:05
I'm like, how much work did you get done the past two days?
16:08
You know, you have so much of this stuff is very distracting.
16:12
It's exciting. It's like a kid in like a candy
16:13
store. But like, is the candy good for
16:15
you? I don't know.
16:16
It's like, get the thing that really works that's going to get
16:18
the results to you. And like, do we need to rebuild
16:20
it every time because something new came out?
16:22
I just think the adoption curbs are way longer than we think.
16:25
They're definitely faster than ever, but they're not like, you
16:29
know, someone launches something tomorrow and all of a sudden
16:32
done deal. Can I give you a provocative
16:35
counter to that? Yeah, I think that these curves,
16:39
well, first of all, you're correct at a meta level, these,
16:42
these things take longer than we say.
16:45
I do think that it is also correct to say that these are
16:49
going to be cohorted. So there's a cohort that's going
16:51
to adopt it extremely fat, just like every other technology
16:53
cycle, the core that it drops and then so on and so forth.
16:56
And then so let's just pretend that the labs are cohort 0,
17:00
right? So they're using it the second
17:01
that it releases because they're designing it or whatever.
17:04
And then let's just say that Healthcare is the last cohort
17:07
because you know, they, they're very risk off and so on and so
17:11
forth. So then, so then the question is
17:14
like, where is where's like tech, right?
17:18
Because like most of the people who listen to this are operating
17:20
tech companies and I think tech is pretty close to the front.
17:28
And, and the reason I think that is, and I'm sure you, and I'm
17:32
sorry, I, I'm curious what you all are doing.
17:36
I'll share what's happening at, at our company is every time we
17:42
encounter that a SAS tool cannot actually do something, we just
17:46
ask Claude to do it and then Claude can just do it.
17:50
And so it is not for it's not like I'm sitting here trying to
17:53
save money and therefore replacing SAS, which is
17:58
potentially true for other reason for other people why they
18:02
turn off of a SAS product. But I actually literally can't
18:05
do what I am trying to do because SAS was billed as
18:09
one-size-fits-all. Your, your custom dashboard
18:12
example is exactly the right example, which is like, it is
18:15
built, that was built for one-size-fits-all.
18:16
And you were like, this is the dash you get.
18:19
And then you described a set of customers who are like,
18:21
actually, we don't want that. We want this other thing.
18:24
And so now they can customize their software, right?
18:27
And, and they're doing, and they're doing that through you
18:30
for a variety of reasons that I'm, I'm curious too.
18:33
I, there's a, there's a separate question I have there, but you
18:37
can also do that. You, there are people who can do
18:39
it through Claude for other use cases.
18:43
And that's what we're doing every time we encounter
18:46
something that we cannot do with a tool.
18:48
And then, and then what ends up happening is you can tell
18:52
there's a big gap between software companies who
18:55
understand that they have to move at unprecedented speed and
18:59
those who are not doing that. And the ones who are not doing
19:03
that, we can see it. And we're like, I, I mean, like,
19:09
we're just not going to renew because they're going to be so
19:12
far behind. It won't be worth it.
19:15
I would say we, I still lean towards using a tool if it
19:20
exists, but we're, we're leaning very much into tools that have
19:24
an AI focus. So like we're, we're working on
19:28
getting set up with the CRM that's, that's an like a private
19:34
data right now because it's super AI focused.
19:37
We've got this like really small company.
19:38
I think we were like customer #4 that we're using is kind of like
19:42
the brain of our business. That's an AI tool.
19:45
Like we're not building it ourselves, but we're very close
19:48
to the ground floor on some of those things that we're working
19:50
with because they're giving us better support.
19:52
They're and they're, they're building things that are
19:55
extremely impactful for us. And then in some cases we are
19:57
actually literally going and building stuff like I've got
19:59
people on our team. We're doing like an AI challenge
20:02
right now. It's really cool to see the
20:03
things that people are putting together where we've got, you
20:05
know, an engineer who built their own little.
20:10
I don't even know exactly how they did it, but it's a set of
20:12
skills and a bunch of stuff and cloud where they're going and
20:15
doing some bug triage stuff. And obviously there's tools that
20:18
could do that for us. But he's got a specific way that
20:20
works best for him. And so he's got this whole flow
20:23
that he built out that is really great and gets him all the
20:26
information he needs to go try to figure out what's going on
20:28
with this bug. Or somebody who's got like APM,
20:31
who basically built themselves a junior PM to scan and, and do
20:35
things like when a when Shopify change log event comes through,
20:40
we get grab that from the RSS feed, scan the code base, see if
20:45
it's relevant to what we need, and then basically make some
20:47
notes with that. So just just a lot of things
20:49
like that that we're doing that are like it just, it's, there's
20:54
no tool for that. Like that just doesn't exist.
20:56
That wasn't something that you would really pay that much
20:58
attention to in the past. And now you can fully automate
21:01
some of these things. And so, yeah, we're doing a mix.
21:04
I lean towards use the tool if it's available, because then we
21:06
don't have to maintain it. But obviously in some cases it's
21:09
better to just build your own thing and make it easier.
21:12
Yeah, I don't know. I think, I think it's a
21:13
transition for. I think it depends on what it is
21:16
like. I think we had this big
21:18
initiative and what's still happening is can we get
21:22
obviously engineering is doing tons of things with ejecta
21:26
coding, right? Product is doing way more
21:29
things, more mock ups that are interactive.
21:32
POC is almost things that are working.
21:34
You know what I mean? That that is happening on our
21:36
end all the time. And then we started getting the
21:39
GTM teams like what, what do you guys want to do?
21:41
How do you want to build things? What do you want to build?
21:43
You know, it's a challenge. What can you build?
21:45
What we're what we're finding is that like they want to build
21:47
lots of things, you know, and it's like almost freaking out
21:50
engineering and product like who's maintaining these things?
21:53
Who have access to these things what about security?
21:56
Rightfully so there's a lot of questions there.
21:58
You know, you got to connect this you want to test this tool
22:01
like there's like Victor or there's all these other you want
22:04
to give it access to everything and it just starts ripping
22:06
through stuff There's lots of questions on all that.
22:09
I'd say one thing that's working well for us and it's the
22:11
specific advantage to us is that Moby is essentially our you know
22:16
our AI tool, right and it's a Kindle like a a clod with your
22:21
data and it behind it are all the it's model agnostic.
22:23
You can switch to the models you can pick whatever you want and
22:26
it can create all the artifacts you're used to and more Gen. it
22:29
can do you know creative Gen. and anything it really but we
22:34
also early on put it on our data internally.
22:38
So we've been using, we've been using triple Whale and Mobi
22:41
internally for, for years and just that's, that's been
22:44
awesome. So that's more of a secure
22:47
environment internally if people want to build things, right.
22:49
So we've had people building like pricing calculators or, or
22:52
like quizzes or, or quarterly business review stuff for
22:55
clients because you can really get cool and personalized.
22:59
And then we, you know, we tag and we have all of our, of OMA
23:01
data like our call data, we have our e-mail, HubSpot, they are
23:04
CRM data and our, our team is really able to do a lot of not
23:08
like deep analysis, but reporting that we couldn't ever
23:11
do. I think the really like
23:14
challenging thing about this moment is understanding.
23:19
Yeah, I, I, I think the security part is definitely one part of
23:24
it. I think you something was said
23:25
earlier around you're like a kid in a candy store.
23:28
So where are you over engineering and where are you
23:31
like building stuff for the sake of it and because you can and
23:34
versus you should and it's actually a good customer need.
23:37
Where, where are you finding the product decisions are starting
23:45
to live, right. At the risk of asking like the
23:49
Twitter question, and I know we said like we're potentially too
23:53
deep in Twitter, but at the, yeah, at the risk of asking that
23:56
question, I, I am personally struggling with this right now
24:01
because we're building at such a fast rate, it is not obvious how
24:08
to think about the product role. And our current observation is
24:13
it is almost looking more like a curation role than it is a
24:19
planning role. So product used to be a planning
24:22
role. So it's like, I'm going to take
24:23
all of this time to plan what we should build because the cost of
24:26
building was so high, but it's so not high now.
24:31
I mean, like it, I think maybe 2 orders of magnitude difference,
24:36
right? Rough, rough, rough swags that
24:40
you're just like building stuff and now you're actually curating
24:44
how it is delivered and which ones are the ones you are
24:48
pushing and so on and so forth. You're curating and killing
24:51
faster than you are planning and and I think that's a very
24:54
different job and I'm not sure. Yeah, sorry, that's a big
24:58
preamble. Where do you find the product
25:01
job is is moving and how are you observing it changing inside of
25:04
your organization? I think a question that that
25:06
that's a. That's a fun one.
25:09
The product sort of product decisions have always lived
25:14
heavily, let's say between AJ and I indexed more on AJ.
25:19
And I would say kind of look at our business and say two or
25:23
three different ways. We have like our, it's almost
25:25
funny to say like legacy business, although it's not
25:27
legacy at all, but like our measurement dashboard business,
25:29
let's say, which has unique data set underneath it.
25:31
So it's like feels very, very like safe in the world of vibe
25:36
coding, OK, it's a data set. That's our attribution business,
25:39
right? And then we have our our AI
25:42
business, which I believe that that is really where that
25:44
curation conversation happens is because you can just build.
25:47
So you're right, you can build so fast.
25:48
It becomes almost like if you're going to have really good
25:51
product marketing internally, like you can target your
25:54
customers inside the product really well.
25:56
Who are they? What are their roles?
25:57
Because we have a vast index. Like you have a store and you
26:00
have like the media buyer, we have the founder, we have the
26:02
OPS guy. And so we can think about things
26:04
differently now and we almost have this situation where we can
26:09
market to one at scale with our product internally.
26:13
And so we can kind of throw things at the wall.
26:16
People like it. Amazing what we'll broaden it,
26:18
you know, we'll broaden it, we'll we'll go deeper on it.
26:22
And I think to the point you made before, which was like the
26:24
curation, it's much more of like a, it's much more of like an,
26:29
I'm way more like sensitive to the experience that people are
26:33
having. Like if you can make everything,
26:35
like should you make everything? First of all, if you have a lot
26:37
of different surface areas, how do you surface them up to the
26:40
right people at the right time? And if they were a dud, get get
26:44
rid of them quickly. You know, if we had these
26:47
template libraries for dashboards, right?
26:50
And they can never really sit so well.
26:51
We're kind of like, and now we've kind of like we're
26:54
thinking about a template library.
26:56
But for like these workspaces that are essentially like
26:59
agentic in nature, you have different maybe there's a
27:01
dashboard, there's an automation, there's an action
27:03
log and that you can do this thing now and you can select
27:07
from and pull them off the shelf and add them to your to your
27:09
dashboard or add them to your navigation and operationalize
27:12
them. But I think this curation piece
27:16
that you said is like is, is, yeah, speaks to me a lot.
27:20
So decisions I believe are still, a lot of them are still
27:23
like AJ and I are deep in the product decisions, but there's
27:28
great people on our team that have amazing ideas and it's like
27:31
go for it. Like what are you testing for?
27:33
Who likes it? Like, let's go, you know, let's
27:35
try it out and kill it quick if we need to.
27:36
Yeah, we shot. What's that look like in in your
27:38
org? Because I think that's an
27:39
interesting point. Like Max, you and AJ doing a lot
27:43
of the product decisions. I like to be very heavily
27:45
involved in product decisions too.
27:47
But obviously at a certain point like the decision making becomes
27:50
the bottleneck. I don't actually have that much
27:54
time for decision making on that front.
27:56
It's a few hours a week really. So I'm thinking about that, like
28:00
what does that look like? But yeah, it's like, are you
28:04
guys thinking this in terms of is there like a certain type of
28:08
decision that can be made, maybe like at the engineer level,
28:12
another type of decision that can be made at the the product
28:16
owner, whatever type of role level that is?
28:18
And then other decisions that are made at senior leadership
28:21
level? Like how are you guys thinking
28:23
about that? Because ultimately it does seem
28:25
like you need to almost give people more decision making
28:28
autonomy on the stuff at every level in order to be able to
28:30
move faster. Yeah, have you read Only the
28:32
Paranoid Survive? Yeah, a while back.
28:35
I mean, yeah. You have you have you, Jeremiah.
28:38
No, I haven't read it. OK, so.
28:41
Like a guiding principle. Yeah.
28:43
So the the one of the main takeaways from Only the Paranoid
28:49
Survive is that they they made decisions as systems instead of
28:53
singular outputs. And so they had this feedback
28:58
loop system as a decision making principle, which I've been
29:02
trying to push onto Fermat, which is extraordinarily
29:06
difficult. But you don't do things and
29:10
produce an output. You produce systems for decision
29:14
making that self regulate. So for example, in the case of
29:18
Intel, famously, the self regulating decision was, hey,
29:24
factory, if you see more demand for computer than memory, then
29:29
shift production over to compute over memory.
29:32
OK, so this this is like the story.
29:35
And what happened is senior leadership was not sitting there
29:38
making decisions about how much compute versus how much memory
29:41
you produce. They just were the recipient of
29:44
the observation that, wait a second, these five factories are
29:48
starting to produce more chips. Like what?
29:50
Like, why are they producing more chips than memory?
29:53
And then they were like, oh, my God, like, chips are the thing.
29:57
And then that led to the, like, massive boom at Intel.
30:01
OK, so, so, so like, why am I sharing that?
30:04
What I'm trying to do at Firma is help people understand that
30:09
they can't just like build a product and push it out.
30:12
And, and I mean, Max said something about product
30:15
marketing. And so I mean, there's like, I
30:17
mean, this is a massive topic that we could spend hours
30:21
talking about, but the simplest thing as an engineer is if
30:27
you're going to build something that is supposed to do a job, I
30:33
don't care what the measurement is for that job.
30:36
I'm not opinionated, but it has to have some feedback loop that
30:40
tells you whether or not it's doing that thing.
30:43
Whatever that feedback loop is has to be included in the push
30:47
of that product such that similar to this factory analogy
30:51
when you're like sitting back and looking at it.
30:53
So that way you can do this curation job easier because
30:56
otherwise you're never going to be able to do any curation.
30:59
You're like, how the hell do you even know what to do?
31:02
And so ideally, these things are making decisions on their own,
31:05
any given features making a decision on its own as to
31:07
whether or not it should be sort of like these things are,
31:10
because we're going to be able to produce so much.
31:13
The harder question is going to be, are you producing systems
31:16
that are self regulating such that either you can, it'll seek
31:20
to do more of it or it'll seek to do less of it because of what
31:23
it is receiving as feedback. And this I'm trying to do across
31:26
the whole companies. I'm trying to do it at the
31:28
engineer level, the product level, the marketing level, the
31:31
account management level, because the speed with which you
31:34
can do things is so fast. Now it also by the way, it makes
31:38
decisions this whole like, you know, one way versus two way
31:42
door decisions. It basically switches that quite
31:47
a lot because one of the biggest things with a one way door is
31:52
that it's only actually one way in the context of a given time
31:55
frame. So and if the time frame is
31:58
collapsing, then your one way doors all of a sudden become two
32:02
way doors. And, and so everything is a two
32:05
way door. And so if everything is a two
32:06
way door, you can actually push decisions out faster as long as
32:10
people who walk backwards. And so now you have to teach
32:13
people to walk backwards, right? Anyway, So this this is like,
32:16
and and I know you guys get it, but it is very hard to push this
32:21
type of thinking into the organization because it feels
32:25
abstract to people, but it is not abstract at all.
32:29
Like if you can actually implement this well, it will
32:32
change the pace of your organization in a in a massive
32:35
way. It's just at least I am finding
32:37
it very hard to do. It feels like, it feels like the
32:41
ideal is what you're describing and it feels like what was that
32:44
growth handbook, the high growth handbook?
32:45
Did you guys read that by it's like Eli Gill, right?
32:50
It's like the best book that I read for this game we're
32:53
playing. You know, he like opens up with
32:56
a lot of these system talks and I had a, you know, he gets into
32:59
how do you build systems and things like that?
33:01
And when you're in the thick of it, it's so hard to like move
33:04
yourself out to like get that, that view on things and build
33:09
these things. And you're kind of always like,
33:10
you should be doing that and maybe certain things go
33:13
sideways. Like if only I did do that, you
33:15
know, but did have the mission super clear to the whole company
33:18
all the time. I did review it all the time
33:21
with the company meetings and things like that really was
33:24
clear in the road map for everybody internally.
33:27
And I think it's like providing clarity, like it's like what
33:30
we're trying to do and it's all very ideal.
33:33
I almost feel as though you you have to do that more than ever
33:36
now because of how fast things can be.
33:38
And one thing you just said is teaching people to go back out
33:40
the door and back in like they have to be OK with the fact that
33:43
they maybe spent a lot of time building something and it's gone
33:48
different world now. And it can be so painful when
33:51
your thing's gone. You know, we've we've dealt with
33:53
that recently and it, it's definitely disruptive what's
33:57
going on. And it's being very clear in the
33:59
communication, like this is the world we're in and we're trying
34:02
to figure it out together and assume good injections.
34:05
I think in e-commerce SAS customers are feeling something
34:08
similar, right? I like this idea of competition
34:11
refining what's valuable, whether it's does the product
34:13
work and actually do the job properly or is this matching
34:17
what consumers are actually asking for, what demand is
34:19
right? And maybe there's a through line
34:21
there between the shift towards more curated as opposed to
34:25
planning, right? We're planning is supply side,
34:27
build something great and they'll they'll come right.
34:30
And demand side is curating and killing entrepreneurial judgment
34:35
from whoever, whoever his job it is to do product.
34:38
I can't help but like notice that between SAS founders mature
34:42
and, and startups, there seems to be like a, a common thread
34:46
and a response to AI. And that's the, to the customers
34:50
that we're serving. And that's the, you know, SAS
34:52
founders are looking for moats and the marketers are having to
34:56
burn the bridges. They're having to destroy their
34:58
own processes and adapt to to the new ones in a similar way to
35:01
the SAS businesses. You know, it started with
35:04
copywriters and they were all very poetic and flowery about
35:06
how, you know, with their ethics conversations about how AI can
35:10
do that job. But then it's everyone.
35:11
Suddenly it moved from copywriters to everyone, right?
35:14
Everyone has to reanalyze, whether it's the OPS guy, it's
35:16
the, you know, media buyer, it's the creative marketer.
35:20
Let's rewrite this playbook and understand how we can do, you
35:24
know, much more complex, systematic things that just
35:28
can't be done as an individual. If you think about like e-mail
35:30
with Clavio for example, you can't send out 100,000 emails
35:34
right as an individual. You need software and income
35:37
software to solve those problems for the marketer, right?
35:41
Understanding the marketer. Can I ask you about the really
35:44
hard question I'm going to like reveal to you guys like
35:48
literally an internal debate that was happening on my
35:50
leadership channel this morning that that both of your comments
35:53
is alluding to, OK, What you're both describing is showing to
35:58
the company, what is this new thing that is expected of them.
36:02
OK, You're, you're, you're both hitting on the same thing.
36:04
You're like saying, hey, guys, the world has changed
36:06
significantly. So Max, you're seeing it in the
36:09
context of, of the tech company that you run, Jack, you're
36:11
seeing it in the context of a brand.
36:13
It's like, guys, you are now expected to do like operate at
36:17
this whole new level, right? That's, that's what you're
36:21
saying. Not only are you saying that
36:24
like literally we are saying every month the level that
36:26
you're expected to operate at is shifting.
36:29
OK, We're seeing both of those things, right?
36:31
Because Jack, you, you implicit, you implicitly said it.
36:34
You didn't exactly say it, but you implicitly said it because
36:36
you said it started with copywriters and now it's
36:38
everyone. So that is that is in effect the
36:40
same thing. How do you land that without
36:44
coming across like harsh? And I'm asking this because
36:52
literally this morning, like I spent the last three hours
36:56
talking to my leadership question because they are
36:58
telling me that I am coming across this.
37:00
I mean, like I'm being hyper transparent to the company and
37:03
people are like, people are like Bishop, you got to cool your
37:07
jets. And I'm like, my jets are not
37:10
getting cooled guys. Like I was like, what I what I
37:13
am not sure how to do is articulate to the company.
37:18
I am just the messenger like this is happening with or
37:23
without us, with or without me. This is happening and I'm going
37:29
to do my best, my best to make sure that we are on the right
37:33
side of it and we'll see whether the whether my best is
37:36
sufficient. But that's that's like how I am
37:40
interacting with it. I'm very curious how you guys
37:42
are interacting with it because these are like, this is like the
37:45
guts of this is very hard leadership communication, very
37:50
hard leadership communication. I can take a stab at it.
37:54
You know, guys, it's like this is what I did all week.
37:59
I this whole thrash, I would call it like there's always
38:02
thrash in startups, especially at the beginning.
38:04
There's just like crazy thrash. This feels like just continual
38:07
thrash on on on things that, you know, we we used to think like
38:11
had a lot of solid staying power long term, you know, like it's
38:16
called dashboard SAS, let's call it that, right?
38:19
Like that felt like just something that's always I think
38:22
it's got it's got some life for a very long time and that's
38:26
compressing. And like just look at the market
38:28
right now, whether it's overhyped and a lot of it is.
38:31
But I think we all can agree that after the fact, it's still
38:33
going to come down, you know, and if you have really unique
38:36
data, really unique proposition around that data or whatever can
38:40
fit into this AI economy and context data organ, you know,
38:43
that's going to last, that's going to be great.
38:45
But there's real pressure and it's everywhere and everyone's
38:48
saying it and like, no one can debate that.
38:52
Like, so during all of this, you know, we, we sort of had to
38:56
like, I hate the word like I kind of like it, but the founder
39:00
mode and we all hear this, but there is an intuition you have
39:02
to just like put your seat belt on and like feel it, you know,
39:05
like you, you want to try to like quantitatively tell me to
39:10
explain myself. No, because it's like intuition
39:12
that entrepreneurs have to just like go and AJ and I had to jump
39:16
into that in the past, you know, all the more the past 60 days to
39:19
get some of our AI stuff like moving and we had to stop paying
39:22
attention to certain things, not like the main things.
39:24
And that caused a lot of thrash disruption, communication,
39:27
people feeling, you know, whatever.
39:29
Like, I don't want to say scared, but it is like it's a
39:31
different world we're going into.
39:33
Imagine speaking to grandparents about e-commerce, right?
39:36
I'm not sure if any of you have tried this, but like this is all
39:40
this is all so foreign to them. The idea of like people actually
39:43
purchasing most of their things online, they've already seen it,
39:46
right? They saw it move from billboards
39:48
and newspapers to suddenly everything's online and all
39:51
purchases are happening on, you know, smartphones and they don't
39:54
even know how to use it, right? So it's like, I don't know the
39:56
market moves and that's why we're.
39:58
That's happening right now. We're we can poke the
40:00
revolution. Yeah, we're in it.
40:03
This is, I mean, yeah, I think every finest.
40:06
That's right. I my approach has basically been
40:10
like a couple of things. One, like we're probably not
40:13
going to be in business in a few years if we don't figure this
40:15
out. So that's like one thing.
40:17
So it's like if you want to work here, we have to figure this
40:21
out. It's not an option.
40:23
And then two, I've been trying to really get in the weeds with
40:26
people too. So like I'll ask an engineer,
40:31
Hey, how did you use? Because we're, we're still
40:33
trying to get like everybody using these tools, right?
40:35
Like, Hey, what, what did you do for this?
40:38
Right. And, and if I don't like the
40:40
answer, then I'll get in and be like, Hey, can I show you what
40:42
somebody else is doing and how this works?
40:44
And like literally sit down, walk somebody through that and
40:47
be like, Hey, check this out. It feels like a very draining
40:51
use of my time in a lot of ways. But I know that if I can sit
40:54
down and actually like walk through that with somebody and
40:56
level them up, that has compounding impact.
40:59
And that takes me, I don't know, 30 or 60 minutes out of my day
41:02
to, to potentially increase their ability by 1020% in terms
41:06
of what they're doing. It's probably, it's not quite
41:09
that easy. But the point is, I'm trying to
41:11
do a lot of that kind of stuff, a lot more education, a lot more
41:13
showing how people are doing things.
41:15
Because I think the reality too is with these tools, nobody
41:18
really actually knows the best way to use any of these things.
41:20
We're all just trying to figure it out.
41:22
That's a big deal. That's a big, I think that's a
41:23
big point is like you almost don't want the seasoned guy to
41:27
tell you his. You want his opinion.
41:28
But like you also want to be like, dude, let's just be humble
41:31
for a second here. Like we don't know and nobody
41:32
knows. And like, let's go at it
41:34
together with trust. Yeah.
41:36
Can I show you guys something? All right, Did you guys see this
41:39
this morning, this post from Alfred Lynn?
41:45
OK, if for whatever reason someone is still listening to
41:48
this on like Apple Podcasts or whatever and not watching, we're
41:54
living through a period of compounding change.
41:56
And in moments like this, the biggest risk is no longer making
41:59
the wrong decision. It is moving too slowly while
42:01
the world moves around you. There are two paths We can play
42:05
defense, protect what we have, optimizable works, wait for
42:07
clarity, it feels safe. It isn't.
42:11
Or we can play offense, learn faster than the environment
42:14
changes, use new tools to solve old problems and better ways,
42:17
and create entirely new strategies and businesses.
42:19
That's where the opportunity is. Challenge yourself to do things
42:22
faster and better than you ever than you have ever attempted.
42:26
Stay uncomfortable, stay on the front foot.
42:29
OK and what can I tell you guys what what was said here?
42:34
That was the big insight. It's these two lines.
42:38
It feels safe. It isn't and stay uncomfortable
42:42
and what I am struggling with personally.
42:46
What does that remind you of You guys are all you guys are all
42:52
on. You guys are all entrepreneurs,
42:54
right? Yeah, Yeah, Like everyone's
42:57
became like a 10X like opportunities everywhere.
43:00
I don't know, like people used to say I just interrupted you.
43:03
I'm sorry. I'm just say one last thing.
43:04
But like back in the day, I remember all my friends were
43:07
like, you know, I went to college obviously, but I do
43:09
nothing related to my when I studied, you know, zero.
43:12
And I remember the time I like decided not to do what I
43:15
studied. I just come entrepreneurial, not
43:18
get the 9:00 to 5:00. I I was always telling myself
43:22
that it's actually riskier to try to find a stable, safe thing
43:25
than to just be making the bets because it's moving too fast.
43:27
All the more so now. Anyways, yeah.
43:31
No, no, no. This is exactly right.
43:32
This is exactly right. I, I mean, what I am wondering
43:35
though, is this CEO is not trying to make his team feel
43:38
comfortable. He's just saying I'm
43:41
acknowledging reality or she I have no idea who this person is,
43:45
right, right. Whoever this person is has
43:47
decided that they're not going to even bother trying to tell
43:51
their team like, hey, here's how you can get comfort.
43:55
They're saying like, no, guys, I hate to break it to you.
43:58
This is reality and and you have to embrace that reality, which
44:03
is equal to you think this other thing is safe.
44:07
It's not. And you have to get used to
44:10
being uncomfortable, which is a very hard message to send.
44:15
Yeah. I think really.
44:19
I think too. So what this is something I
44:20
think about a lot. So I'm in a CEO peer advisory
44:24
group with local businesses. So I'm here in the Denver area.
44:28
They're all in different industries, right?
44:30
It's not software businesses. And I look at the salaries that
44:33
we pay people compared to what all these other industries pay.
44:37
And I think that in tech for a long time, people have had very
44:42
cushy jobs with very high salaries.
44:44
And when you get into that frame of mind, it is hard to get out
44:49
of that. And as a founder, I'm used to
44:52
not making money most of the time.
44:53
I actually didn't. I did negative net worth until I
44:55
was 30, probably because the 1st 10 years of my career I just was
44:59
terrible at it, right. But, and so anyway, like that,
45:05
that was my experience. I don't, I don't really have a
45:07
problem with the discomfort I'm used to, I'm used to jumping
45:09
into things that are painful and may make me money, may may not
45:12
make me money. I mean, like, for example, I'm
45:15
taking a pay cut this year if we don't perform better than, than
45:18
we did last year, whereas I don't ask out of any of my
45:21
employees, right? They're all getting raises.
45:23
And so like, it's just a different way of operating.
45:26
And I think that like there's a it's, I think it's been so cushy
45:31
for so long. And I think we've it's at some
45:35
point in time, there's a correction that comes for every
45:38
industry. And the reality is like, I think
45:41
that's what we're seeing right now.
45:42
It's like, OK, if you know, if the typical person is out there
45:45
making, I don't know, like I've got a friend who owns a a
45:49
company and he like blows mulch all over the place, right?
45:52
Like his workers make, I don't know, 60 KA year if they're
45:55
doing really well, that feels like peanuts compared to
45:58
somebody in this industry. And so it's just like a
46:00
different way of, I think we're just seeing a correction
46:04
ultimately and and that's part of what's happening here.
46:07
And I think if you want to make a lot of money, it's going to
46:10
require a lot of challenge and hard work.
46:14
Do you think it's a correction or just like these are the tools
46:18
we have now? Is it humanity?
46:20
And it's like sometimes I think about it, before Shopify, right,
46:26
To make a store, it was like super hard to do, right?
46:29
You had to build it, you had to get an engineer.
46:30
And then Shopify is like out-of-the-box and now
46:32
everyone's got a store, right? I almost feel as though like
46:36
that's sort of similar to some of this right?
46:41
Now everybody can just like build a SAS company in a sense
46:43
or something build like a workflow that's like, so you
46:47
know, it's like everyone becomes an entrepreneur, like if you
46:49
want. And now it's like, can you take
46:50
the risk? Can you be uncomfortable?
46:53
It's more like a industry that has like our, this industry has
46:57
kind of become commoditized in a way, right?
46:58
Like it's not the hard to find somebody sort of drive a truck.
47:02
Lots of people know how to drive, right?
47:03
There's a little bit of a skill gap.
47:05
Right, everyone can kind of do like people can do the thing
47:08
that made you that much like more unique is like the playing
47:12
fields are kind of equalizing. It's like AI don't know.
47:17
There's something I saw about like the the rise of the demise
47:20
of white collar, the rise of blue collar.
47:22
Now it's like we almost have always been like these jobs that
47:28
nobody wanted to do, and you went to college to be someone
47:32
who could do something with information, and now you can get
47:35
into that something physical with your hands.
47:37
It's like you're now more valuable.
47:40
I think that being uncomfortable is like probably the cleanest,
47:44
one of the easiest proxies for alone time preference, right?
47:49
If your high time preference, you satisfy your immediate
47:51
desires instantly and then that kind of dulls your edge.
47:55
You become incapable of doing things that are harder and span
47:59
longer time frames. And I think that, yeah, that's
48:01
probably what we're seeing with developers.
48:03
They were a very scarce resource.
48:05
And now the skip, the resource of, you know, being able to
48:08
build is so much more abundant that it's like you've got to
48:12
adapt to survive or, you know, struggle, right?
48:18
Really struggle as you know, the the market catches up as like
48:22
industrious, hungry people actually work harder than you.
48:27
When I say work, I don't mean like, you know, put in more
48:29
hours. I mean like, actually want to
48:31
make things better than they were before, while you just want
48:34
to enjoy continuing to be comfortable, right?
48:37
Yeah. And the thing that's like
48:39
disrupting you, you can actually ask it how to get better.
48:41
Yeah, that's interesting, right? Could teach you something now.
48:44
But you have to want to. You have to want to ask me how
48:47
do I get better a? 100% I think that's what's
48:49
happening is like, do you chat to me, to your love letter to
48:51
your wife or do you write that thing?
48:53
You know what I mean? It's like, no, you got to write
48:54
that thing. You got to want to do it.
48:56
And that's the effort to make the relationships like it's the
48:58
same thing. I'm making value producing value
49:00
like bringing your unique thing that's going to.
49:04
Right. I think there's a like a really
49:05
interesting through line between, you know, our
49:07
customers, whether they're, you know, solo founders, you know, a
49:12
lot of founders are making a huge amount of the decisions in
49:14
their business about, you know, go to market and the SAS
49:17
founders that are listening to this podcast and that's the, you
49:20
know, a marketer, Shopify user, their job is the entrepreneur's
49:25
job, right? They have to find arbitrage in
49:27
the market, that favorable gap between what you put in and what
49:30
you get out your investment and your return on a reasonable
49:33
payback period, right? And I think that's how we ended
49:36
up here in e-commerce. The market moved in a small part
49:38
from retail to buying online and their attention moved from TV to
49:42
social media, right? You could spend a dollar to make
49:44
$4.00 on Facebook ads. And then everyone moves that is
49:47
arbitrage. And I guess you could look at
49:49
like a bunch of the different ways that this has taken shape
49:51
and organic social media back when it was like chronological
49:54
feed and you could just spam and you get sales and, and then
49:57
moving on to SEO and all of these different like hacks,
50:00
right? Where there was some benefit and
50:02
like you know yeah, I'm just capitalizing on the hacker and
50:07
getting a little bit of AAB, but I think that right now people
50:11
don't know how to get it right. The marketers, the founders,
50:14
they don't know how to get it so income software to like help
50:17
them solve those increasingly complicated problems, right.
50:21
I'm not sure if you guys saw this from Nick Shackleford, but
50:24
this is a big reason why I think that tools like, and I mean
50:29
forget the fact that it's actually already really hard to
50:32
use numbers adaptively. Most people are cherry picking
50:35
like 3 numbers, coming up with a conclusion that educates a
50:38
decision that is wildly off base because they don't actually know
50:41
how to use the numbers in the first place.
50:43
But then you layer in the complexity of not actually
50:45
getting the right numbers right and not knowing how to use them
50:48
in the first place. Don't get me wrong, I think that
50:50
it's going to catch up and this is all going to be super
50:53
accurate eventually. But then still you're going to
50:55
have the problem of people knowing, not knowing actually
50:57
how to use information that they're consuming adaptively,
51:01
right? A. 100% Mobi has this ability to
51:05
like forecast and it uses standard models like, you know,
51:10
from Google and has like the best sort of forecasting models.
51:14
Like I don't know how easy it's going to be for someone, even
51:18
for myself when I was running my business and just like asking
51:20
for a forecast and be like, OK, I'm going, you know, like you're
51:25
going to need a level of someone who knows how to explain to you
51:27
what's going on here and like make sure it's right and
51:30
accurate. And so you still need that human
51:32
element to it, right? You do, there's something about
51:35
it. You need that reassurance.
51:37
That was just an example of I think what you're hitting on
51:39
there. It's like people are so quick to
51:42
sort of not only outsource their their cognitive, like sort of
51:46
discipline, right, But also like the job you're trying to get
51:49
this thing to do is never job you were doing.
51:51
Were you like a data analyst, were you?
51:53
There's still, there's still room for all for a lot of these.
51:56
Yeah. It's very hard for me to kind of
51:59
think that all these jobs are just extinct and like, all of a
52:02
sudden AI is going to do everything.
52:03
I just think you can do a lot more now.
52:05
And it's an amazing tool. I have no idea, you know, Like,
52:12
I'm not going to sit here and tell like I know.
52:15
Totally. I think the critical thing that
52:16
was missed in that tweet, which I think Max is what you're
52:20
saying is there's a presumption that the output is the thing of
52:26
value. And, and so, and I think that
52:29
that's like a dangerous presumption. the IT is only
52:33
always ever the case that the thing of value is the right
52:36
question. And so of course, if you say
52:39
something like give me a forecast, you're going to get
52:41
the wrong answer because you're not even asking the right
52:43
question. It's like it's it's like
52:45
whatever hitchhikers got to the Galaxy, It's like, what is the
52:47
meaning of life 42? It's like, OK, well, yeah, you
52:50
ask a bad question, you're going to get a bad answer.
52:51
And, and I think that what is like what was missed in that
52:55
dialogue, which is why I did not engage, was, was like, no guys
53:01
actually, actually the problem is that most people who have
53:05
never done this before don't know the right questions to ask.
53:08
So, so like the value of experience has very quickly
53:11
become knowing what the right questions to ask are.
53:14
Like this is like Jeremiah's life, but knowing what the right
53:18
questions to ask are. Because if you know what the
53:20
right questions to ask are, then yes, of course, like the tactics
53:22
of like adding one plus one such that it equals 2.
53:25
You should outsource that right? Like we've had calculators
53:28
forever. There's a bunch of things that
53:29
you should not do. And now the span of the things
53:32
that you should not do has gone up a lot.
53:35
But actually, it kind of turns out like with any other system
53:39
in life, garbage in, garbage out.
53:40
You ask a bad question, you're going to get a bad answer.
53:43
That is obvious. That is also true when you
53:45
direct people like we we manage teams.
53:49
You ask your team to do something dumb, guess what?
53:51
You're going to get something dumb in response, right?
53:53
Like badly articulated direction receives badly articulated out
53:57
or bad outcomes. So yeah.
53:59
But anyway, this was Max, by the way, I'm for, I'm sorry I didn't
54:03
say in the beginning, but dude, I can't believe you've never met
54:07
before and this was incredible. I thought we did meet before at
54:10
the Whaley's, No. Didn't you come there?
54:12
Once. No, I've never been to a
54:14
Whaley's. Well, OK, you're going to come
54:16
this time. Would you come?
54:18
I'll come you. Hey man, if I'm your guest, then
54:20
I'll come I. Think I met your Co founder
54:24
then? Yes, you met my Co founder.
54:26
Yeah, yeah, yeah, yeah. He's better looking than me.
54:29
So the fact that you, you mistook me for him is like I.
54:33
Don't know. I don't think so.
54:35
It's like that's a good sign. I thought both of you were
54:37
there. I don't know.
54:38
But well, thanks, Max. Appreciate it.
54:39
It's been good time. Yeah, yeah.
54:41
Yeah, guys, that Yeah, should do it again.
54:43
You should hang out another time.
54:44
You know this is fun.