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Outcome pricing vs Usage pricing vs Seats · AI for Founders with Ryan Estes

0:00
Welcome back to AI for Founders. I'm Ryan Estes.
0:02
Today we're diving into the future of revenue with Mark
0:05
Walker, CEO of Nu dot IO, a company that's raised over 40
0:10
million and is powering some of the biggest names in AI,
0:13
including Open AI, Jasper and Glean.
0:16
Nu has delivered 3X year over year growth and created an
0:19
entirely new category called a revenue life cycle management.
0:23
In this conversation, you'll learn how the fast growing AI
0:25
companies are experimenting with pricing, why outcome based
0:29
models matter, and how to avoid being trapped in outdated
0:32
systems. If you're a founder, SAS leader,
0:35
or anyone navigating price strategy in a hyper competitive
0:38
market, this episode is for you. Get ready for high energy, big
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picture. Look at what's next.
0:43
Quote to cash, hybrid models and protecting your margin in a
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world where the pace of change is accelerating every quarter.
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2:58
How are you? Hey, Ryan, I'm great.
3:00
How are you? I'm doing good.
3:02
I really enjoyed digging into you and your company a little
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bit with the LLMS before we got going.
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So I'm really excited for this conversation.
3:09
So let's just start with a quick introduction to new.
3:14
Great. So news, you know really A next
3:17
generation business system for recurring revenue and
3:20
consumption companies, right? I didn't found it.
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I was introduced to it about 3 years ago and asked to become
3:26
the CEO. Whole story about that we can
3:28
get into later, but really what it what the thesis is is that at
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the pace of change that we work at today, you really can't be
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stringing together different systems, but you do want systems
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that have the same kind of power and integrity of the old
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systems, but they have to be much more adaptable.
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So you know, quoting, contracting, self-service
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billing consumption, whether directly or with our partnership
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with Metronome, really, really powerful.
3:55
Absolutely. So you know, the whole pricing
3:57
model now is kind of getting thrown on its head.
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You know, we had the golden era of SAS, you know, which now is
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being challenged from from different models.
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You know, it's kind of, and it's still in the air, right?
4:09
Everything's changing and everyone's experimenting.
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So maybe you can talk about this shift to kind of a usage base to
4:17
or from a subscription model, but also there's hybrids and
4:21
kind of how new is like helping companies adjust to some of
4:24
this. Yeah, it's a really good
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question and it's a really interesting question because we
4:32
work with most of the major AI companies.
4:36
You know, some of them are really well known, like you know
4:38
what, they're all really well known, but that we're working
4:40
with them as well known. So Open AI and Glean and Jasper,
4:44
you know, people know about those and then there's a bunch
4:46
of other ones. But together they compromise.
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They comprise like about 9899% of enterprise AI compute, right,
4:55
our customers or about to go live with us.
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And the really interesting thing is none of them are running the
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same experiments because we have to see all the pricing models
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that they're using, right? And so there's all these
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different variants that are emerging.
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And then what we're also seeing is that this is in the same way
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that SAS radically changed how pricing works for all software.
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The emergence of SAS did that about, you know, 20 years ago.
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That's having the impact on everybody else because we're
5:27
starting to see the same questions raised and you can see
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the reaction of Salesforce like reaction of Salesforce is kind
5:33
of like that we're seeing as cascading impact on on business
5:36
models. So it's really interesting we
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can get into it in more detail about what we're learning about
5:43
what experiments people are trying.
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I think it's too early to know what the right answer is.
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So I think it's and also remember a lot of these parts
5:52
already existed before. It's not like Snowflake wasn't
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running a token based consumption model like 15 years
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ago. They were right.
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It's it's really people are now optimizing and try to balance it
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out. Yeah, it's interesting.
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And if, if you are, you know, you, you have some of the
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biggest clients in AI in the world, you know, customers, you
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know, what are their challenges as they're thinking about
6:16
pricing? And, and because it seems like
6:19
the subscriptions for most, let's say, major LLMS, if you're
6:22
talking about Gemini, you're talking about ChatGPT, you're
6:25
talking about Anthropic, you're talking about all the big ones
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are all kind of competitive with each other.
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And it, they're all kind of vying for new products that they
6:33
can add and where can they have kind of like upsells
6:37
potentially. So what kind of challenges are
6:40
they looking at right now as far as pricing goes?
6:43
OK. So you don't get to play with
6:44
those people talking about what they're actually doing, right?
6:47
So, so I can only talk in generalities, but I think that I
6:51
think here's a really interesting pricing problem that
6:55
that I don't think has ever existed.
6:57
Well, it kind of exists in computers.
6:59
It's kind of like Moore's Law on steroids, right?
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So the cost of compute, meaning the cost of a token, is
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plummeting. So imagine that you're the, you
7:10
know, this chief revenue officer of one of these companies, or
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even just an AE trying to contract with one of these
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companies. And you go, OK, so you're going
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to spend $1,000,000 with us. And that's going to be, I don't
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know, whatever number of tokens. Well, meanwhile, the cost of
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tokens is going down. Yeah.
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So people try to put together pricing contracts that are fair
7:31
to the customer and fair to the provider while simultaneously in
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this vicious competition cycle driving down the cost of the
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thing you're selling. Imagine in the old world if you
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were selling seats and every, and you had comparable seats,
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because that's the other thing is some, some of these
7:50
capabilities are quite comparable and the other company
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starts going, oh, our seats are now half the price.
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And by the way, we're going to honor that for existing
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customers. That is definitely not a problem
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we've dealt with before. So, so that's that's, that is I
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think the a very common problem that these companies are now for
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some companies. This is why outcome is so
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important. If you're an outcome based
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company, like I charge you $10 per resolution or $10 per
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document processed or $10.00 for whatever, this is great news
8:22
because your input cost is plummeting, right?
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But if you are a token selling company and you're effectively
8:30
selling compute and obviously very very value added compute,
8:34
this is a a very interesting business model problem.
8:38
Yeah, 100%. And this is kind of tip of the
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spear stuff too is like outcome as a service is here how I'm
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hearing it describe. We had kind of software as a
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service, then we have service as a software and now we're hearing
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outcome as a service. And I, I'm really interested in
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like kind of what that model is. So is this basically assuming
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kind of like agentic support that can then show attribution
9:00
for the the system that's employing it?
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So if you have outcome based solution and your your outcome
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you're looking for is 1 widget sold, then essentially what the
9:12
one of the outcomes of the agentic solution is going to be
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actually to show attribution for the outcome.
9:21
Yeah, I mean, yes and no, right. So and first of all, one of the
9:27
things I think we have to really anchor on is don't think of
9:32
agentic or AI based solutions as being separate from the rest of
9:37
the solution, the rest of the software milieu, you know, for
9:40
two reasons, right. 1 is, you know, AI or or probabilistic.
9:47
Probabilistic computing will never be as efficient or
9:50
effective at doing some things that deterministic or
9:55
programmatic solutions be able to do.
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So for example, the odds of you getting truly probabilistic
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accounting is probably very, very low.
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There's just no juice worth this, you know?
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There's no juice that justifies the squeeze.
10:11
You will probably get probabilistic configuration of
10:15
and data entry and a bunch of other things that lead to
10:18
accounting, but maybe not the accounting itself.
10:20
I mean, maybe maybe accounting because accounting is kind of
10:23
made-up anyway, but those other things that you might not want
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to do that with. But my point is that that these
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pressures will affect all software, right?
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So the problem with outcome based models is that for some
10:39
companies it's really straightforward and for other
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companies it's completely ridiculous, like hard to do.
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So give you a good example. If you are checker and you do
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background checks on people, they already do outcome based
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pricing like you buy and DocuSign.
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DocuSign is outcome based pricing.
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Did they sign it? That's the thing.
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That's what they're selling. They sold the you generate their
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document and they signed it and they sell it in envelopes.
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Outcome based pricing and checker, you know, background
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checks, right? It really simple.
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If you buy data enrichment from Apollo and or from Zoom info or
11:15
name the other, you know all that it seamless and all those
11:18
other people, same thing. You wanted an outcome and you
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got it. You got your data enriched.
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Now there's obviously lots of other things they saw that maybe
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less outcome. That's you know, that's another
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thing, but you can those are the examples where you can easily
11:32
imagine price per results, right.
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But think about new, for example, we have a challenge
11:37
where, you know, we're really accelerating the ability of
11:40
companies to do business in lots of different ways.
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But it's not like they got bigger solely because of us.
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Like we do have pricing models that basically say, yeah, we
11:49
don't charge you. Even our revenue based models we
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don't charge you for like how much you grew.
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That is your ROI. But it's not like we made you
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grow. We helped you.
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We were part of the answer, but not the whole answer.
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That's problem #1 so, so unless your system is doing a unitary
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thing, it's hard to do outcome based pricing.
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But then you can do variance on it where you can do step based
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pricing. Like I helped you.
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How many of this did you do? How many of that did you do?
12:15
But that's getting back more of the snowflake world, right?
12:18
The other problem without the outcome based pricing is, is it
12:23
a conflict of interest between the customer and the software
12:27
company around how many outcomes they would like to automate?
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And I and if you have, if you're interested, I'll give you an
12:34
example that I heard recently that's really kind of drives
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this point home. Yeah, please.
12:43
So I was talking with a senior executive in a very old
12:46
industry, you know, pretty much this like Fortune 1000 stuff,
12:50
right? And, and this CEO and he's very
12:55
forward-looking and he's so he, one of the things he does, he
12:57
takes all his senior managers out, or even the senior managers
13:00
and literally anybody in the company who who sticks around
13:03
for five to 10 years, He figures out some way to take them to a
13:06
BBQ or a dinner or some kind of event to celebrate their tenure
13:09
with the company. And the question he asks them is
13:12
what do you know? Because what you do that you
13:15
think I should know as ACEO that you think I don't know, right?
13:20
And there's a great freaking question, right?
13:22
Just so gives you an idea of who this person is, right?
13:25
It's like a Zen Co almost. Pardon me.
13:28
It's like a Zen Co on almost. Yeah, exactly right.
13:32
It's like, yeah, it's like, you know, the, the, you know, did
13:35
the wind move the Reed or you know, so has somebody in there
13:42
in their claims processing. It's insurance company.
13:45
So in the claims processing department and the, the
13:48
underwriting department says our AI based underwriting system is
13:52
killing us. And he says, sorry, how He said,
13:55
well, we used to spend 80% of our time bringing value to to
14:02
people in the hardest point in their life.
14:04
And that gave, we helped them with emergency funding for their
14:07
funeral and then we helped get them through the process of
14:10
getting their claims settled. And that gave great meaning to
14:13
what we did. But now that allowed us taken
14:16
care of by the AI. So the only people we get to
14:19
talk to you are people where the guy who died gave the insurance
14:24
to his girlfriend or they did, the person who died stopped
14:27
paying their fees or we can't figure out if the person's dead
14:31
or not or, or the person or it's not clear who the beneficiaries
14:34
are, right? That's our whole day now.
14:38
And that's a result. Nobody wants to do that.
14:39
As a result, the part that the AI can't do that the, the human
14:43
part, there won't be anybody to do that part because nobody's
14:46
going to stick around, right? And so they're having to salt
14:53
the significantly salt the work day of these people with happy
14:59
cases that the AI could handle. So if you're in the software
15:01
company and you're selling the outcome and now your company is
15:04
now going actually we're going to salt it back in, people are
15:07
going to have to build that in as a feature and sell it like
15:10
the salting is valuable, right? And I thought it was a really
15:14
interesting experience. The conversation went on to talk
15:16
about how other parts of the company had an even worse
15:19
experience. But but it was, I thought it was
15:22
super interesting and really probative to what we're thinking
15:25
about an outcome based pricing because the outcome you want may
15:29
not be as simple as what you think.
15:31
Like, you know, I resolved the ticket.
15:33
Yeah, successfully or unsuccessfully.
15:35
That's a big example, right? Yeah.
15:38
So in that case, is that the the AI is actually taking the joy
15:42
out of the work experience for the staff?
15:44
Is that kind of what he was describing?
15:47
Yeah. And when you take the joy out of
15:49
doing what you do, you then take the enthusiasm out and
15:52
eventually you have staff churn and you have no experts anymore.
15:57
So if anything changes, there's nobody at you understand or
16:00
there's nobody to deal with the edge cases and there's nobody to
16:02
deal with the rest of it. And I think we all have to be
16:05
cognizant of that in, in, in deploying these solutions.
16:09
You make it better, but you have to think about it.
16:12
What's the ultimate objective of the company?
16:13
Is 0 human humans the objective of the company, or is it
16:17
actually a cadre of enthusiastic people with high speed decor who
16:24
are looking after your customers?
16:25
In the hard cases, that's going to be the hard balance, yeah.
16:28
You know, that's kind of like, I think cross category.
16:31
You know, it's like the promise of robots is they take the
16:34
drudgery out of our life, but it it kind of the robots came for
16:37
joy first a little bit. If you're thinking about like
16:40
design and art and music and like these, the creative
16:44
pursuits right now is where is really struggling.
16:48
It's really interesting. One thing I wanted to ask too
16:51
about like outcome based pricing because it what what's kind of
16:54
nice about it too is it's, it is derivative of the price of
16:59
compute. Now it seems like, you know,
17:03
because the desire and need for compute is so great and power,
17:08
it's basically going to run, it's on a trajectory to run the
17:11
power for unlimited energy to 0. You know, at least let's say on
17:15
like a 100 year scope is a result of positive outcome of
17:21
like AI. Maybe the top one will be like
17:24
free energy for everybody once AI figures out how to give it to
17:27
us. So you know it.
17:29
It makes. It makes.
17:30
Me wonder how that's going to kind of affect pricing.
17:32
If you kind of can unbundle the, the price of compute with the
17:37
price of your product, then kind of what is the, what is the
17:41
strategy and tactics then about like setting pricing or even
17:45
what is it today? Because you know, I, I've spoke
17:48
to, you know, dozens of founders and like really nailing where
17:51
and how to price becomes like a major pain point for them.
17:56
Yeah, I I think you need to think about it as what is your
17:59
differentiating value over the raw compute.
18:02
So for example, there's all sorts of things we use.
18:07
We use tools that are based on that, use the large language
18:11
models from the customers that we actually for our own
18:15
customers. So the people you don't expect
18:17
to say for right now to say Anthropic and an open AI, but
18:20
obviously there's other people and those tools.
18:24
And obviously, we have enterprise accounts and we use
18:28
AI across the company in many, many, many different ways.
18:31
But we don't use necessarily Anthropic directly in all use
18:36
cases or Open the Eye directly in all use cases.
18:40
And we are often using them through mediated solutions,
18:44
right? So this raises 2 pricing
18:45
challenges. 1 is actually, I'll give an example from new in a
18:51
second, but one is, are you differentiated from the other
18:55
providers of that experience? In other words, it's not that
18:59
hard to differentiate yourself from the raw capabilities of the
19:04
underlying models because they're not packaged for
19:06
purpose. So yes, they can be used like
19:08
for example, you can prompt, you know, open the eye or entropic
19:12
to do amazing things if you are an expert in doing that.
19:15
But we use tools that automate the prompting in effect or the
19:20
agentic reach out and provide results that I don't need to
19:23
train an AE to be ex expert at that.
19:26
It just enriches data for them in a then that's the value of
19:29
that software. So in that case, if that's what
19:31
it's doing for me, it's not connected to the drop in
19:36
compute. It's connected to how well does
19:41
that save my AE time? Now, maybe the drop in compute
19:44
may eventually change how many AES I need or something like
19:46
that. But but that's that's, I say a
19:48
good distinction. If you're selling by way of, you
19:53
know, lovable and bolt right, going straight at each other and
19:58
cursor and a bunch of other companies, the competition
20:01
between those companies and they're all great companies, by
20:03
the way, they're all interesting and great companies, but they
20:06
are differentiating on being amazing at different things,
20:09
right. So if you're if you're, you
20:12
know, fundamentally thinking of it like a design designed to
20:17
prototype even to delivery model.
20:20
You are probably in the lovable bolt end of things and and then
20:26
if you're in the hard coding and the hard engineering end of
20:31
things like we are by the way, we'll use all these tools, but
20:34
but they they are used in different ways for different
20:40
purposes. So the people were using cursor
20:43
and and all the other IDE based things are not using, they're
20:49
not using in the same way as the teams that use lovable use
20:51
lovable or Bolt or whatever makes sense.
20:54
So you're differentiate, you have to drive differentiation
20:56
that is unique to you and then you can price it based on the
21:01
value and the increase efficiency for the end customer.
21:04
And if you don't do that, you are going to get either eaten by
21:11
the model providers, which I think is a relatively low risk,
21:15
But getting eaten by the other by the other competitors is a
21:19
very, very high risk. Yeah.
21:22
And regardless of how you price it, like even if you get away
21:24
for free, if you get away for free, if there's a better tool,
21:28
people will pay for the better tool rather than use the free 1.
21:32
Sounds good. Well, in.
21:34
Isn't everybody just scrambling for that now?
21:36
Because Google is just notorious for just making the the best
21:41
tools in the Internet with the best business model and making
21:43
everything free. You know, whether that was Gmail
21:46
or that was like Google Docs and like the the things they provide
21:50
Google Maps, it's you. When you think about the product
21:52
roll out for Google, that's really what they do now.
21:55
You can see that maybe they've had a little bit of fumbles with
21:57
their products along the way as they're introducing all their AI
22:01
products, but man, they're on a roll now.
22:04
So how much of that is considered now when people are
22:07
thinking like, OK, if we look at history, Google's going to come
22:12
out and launch a competitor to our product for free in a matter
22:15
of time. So, you know, getting it right
22:18
and getting the value right, getting people locked into the,
22:21
the that certain platform is crucial.
22:24
So I'm kind of looking at this now and maybe everybody is
22:27
because there's so many amazing tools.
22:29
You know, I'm pretty committed to lovable.
22:31
I've built a bunch on there and I'm, I feel, you know,
22:35
familiarized with their machine, but I'm always trying other
22:40
stuff and comparing it to it so that, you know, the loyalty
22:43
base, at least for me, is probably just with familiarity
22:47
and then of course price consideration because at some
22:49
point I don't know what the Max is.
22:51
I'm willing to pay for all the AI subscriptions, but it's it's
22:55
probably pushing 200 bucks at this point, you know?
22:59
And that's that's. Just using kind of the pro level
23:01
entry point to all of these. So I I feel like no, go ahead,
23:06
no, go ahead. I was.
23:07
I was. I was, I I thought, just finish
23:10
your thought and then I'll no, you know.
23:12
Just and so it has how it pertains to the models and just
23:15
the the ability for companies, whether they're AI native or
23:20
they're spinning up AI products like that.
23:23
Why new is so important because you can experiment, you can find
23:26
the threshold, you know, you can show multiple pricing options
23:31
where people can make a decision like, oh, what feels it?
23:33
Does usage base feel better for me?
23:35
Is it just a monthly subscription?
23:37
Set it and forget it is what I want because I love the product.
23:40
I want to do that. Like figuring that out is like a
23:43
very complicated process. Not only that, if you don't have
23:47
kind of a centralized place to like organize all that things,
23:49
if you need to make changes, man, you got to find 20
23:52
different people that have the passwords to to change
23:55
everything across the board. And you got to explain your
23:59
change to each of those systems. In other words, that's that.
24:02
But something I was going to say digging into this, right.
24:06
So new both has this solves this problem and has this problem
24:11
which it makes it very meta super fun, right.
24:14
So we so yeah, absolutely. So new, one of the major
24:21
reasons, I would say actually the reason why we've won all the
24:24
AI companies is because they know that they don't know what
24:29
the next step is. They, they know that this is
24:31
true, like the pace of change or the pace of change has changed.
24:34
This little sort of calculus on my chest here.
24:37
They know that's true. And they, what they don't want
24:39
is to be locked into a system where they can't experiment
24:42
because the problem with experimenting in business models
24:44
is you're stuck with the experiments.
24:46
Like even if if you decide, hey, we're going to price this based
24:49
on the number of rabbits you own, guess what, you're now
24:51
stuck with counting rabbits. And that's what you have to do
24:55
at least for the, for the next three years, right?
24:57
And just think how intimidating that is, right?
24:59
So by having these unified systems or having systems that
25:02
play nice with others as well, then you, you could kind of de
25:05
risk that. But I'll give you an example of
25:07
what's going on for us and then why that and then how that
25:10
cascades into our, our, our thinking about how to build an
25:15
AI product, at least in our world.
25:17
So the first part is we sell ours the same products on
25:22
exactly on completely incompatible revenue models all
25:25
the time. So we have customers who buy CPQ
25:28
on seats. So you can buy our CPQ without
25:30
buying or billing or you buy our billing without your CPQ, but,
25:32
but they'll buy CPQ on seats but billing on percent of revenue.
25:37
And we have other people who say I'm give you a percent of my
25:40
revenue. I, I, I send $1,000,000
25:42
invoices. I'm not giving you a percent of
25:44
the of $1,000,000 to generate an invoice.
25:47
I'll pay you 10 bucks an invoice.
25:49
How people are saying I'm not paying you 10 bucks an invoice.
25:52
The invoice is only $200 like so.
25:55
So, but this flexibility and pricing model allows for us to
26:01
have conversations with people say, where do you see the value
26:03
in our software and how does and how should you buy right?
26:06
So, but we're not worried about people all of a sudden radically
26:11
switching. So this takes a lesson the data
26:14
model and owning the data and adding value off the data.
26:19
The more data we have, the more valuable the system is becomes a
26:25
very, very important defense. So if, for example, your
26:32
company, if your company uses lovable, right and or no say you
26:36
use lovable and you're amazing and you're great, right?
26:40
Well, if the company adopts Bolt, right, and now you have,
26:46
you know, 20-30 developers using Bolt, and I'm not saying Bolts
26:51
necessarily better than lovable, I'm just saying they're
26:53
competitors, then that ability to switch becomes a much more
26:57
complicated problem. So building things into your
27:00
products that make it easy for people to collaborate, to work
27:04
together. And you see this actually also
27:05
in the offerings of Open the Eye and Anthropic, their team or
27:08
team based products where they have combined workspaces.
27:12
These things actually at what anchor people because it gets
27:15
built into how you run the business and makes it much
27:18
harder to switch. So when you think about it, what
27:22
I would think about is that the other part I would think about
27:25
is if your value added is not the underlying model.
27:28
And certainly you were about to release our MCP servers and all
27:31
our agentic capabilities. And the real fun thing for us is
27:35
we get to build those and test them with the people who are
27:38
creating the technology that they run on.
27:41
But what we didn't do was say you need to buy, you can only
27:46
use one of them or even any particular model from one of
27:50
them. And the reason for that is, is
27:53
that different customers will have different preferences and
27:56
different use cases and will want different outcomes.
27:59
And actually when you run these things, they're materially
28:02
different, like not materially better, they're just different.
28:05
And you may decide to test both of them and go or three or any
28:09
all three of them and go actually for our company, this
28:12
one's better, right? The other thing is, is that they
28:16
consider whether you really should be including the compute
28:20
in the cost of your product at all, right?
28:23
Because if there were multiple providers and you don't know
28:27
which one of them is going to be the best, it might be a good
28:30
strategy as we are doing it new to say, hey, you can bring your
28:34
own tokens, right? You can bring your own tokens.
28:37
And why would you want to do that?
28:39
Well, if you are very sensitive about what data you're exposing,
28:43
you might not want to just dump it into a general machine.
28:48
Like say, you know, nobody literally wants to put their
28:50
product and pricing into an account.
28:52
They don't know who else. What is learning from like the,
28:55
you know, you mentioned like, oh, now the accounts going, I
28:58
recommend you price it this way because your competitor X is
29:02
pricing it that way. Like that would be a complete
29:05
disaster. So our view of it is, is that
29:09
security and portability is going to be really important in
29:13
the competition. OK, everyone loves to talk about
29:17
vision, the pitch, the funding round.
29:19
But you know what gets 0 love? The actual product build.
29:23
That magical, terrifying phase where nothing works, the docs
29:26
are out of date, and you've asked your dev team how's it
29:29
going? For the 47th time today on
29:31
Slack, that's where Code Story comes in.
29:34
It's a podcast for startup founders who know that building
29:36
The thing is the thing. Hosted by NOAA Lab Heart, Code
29:40
Story goes deep with tech leaders who've been through it,
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shipping real products, surviving messy launches, and
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figuring out what actually works.
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And when you're on the edge of chaos, if you're building now or
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ever plan to, you'll want these stories in your ear balls.
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Search code story wherever you get your podcasts.
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Or visit codestory.co. Because founders talk vision.
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But builders? Builders tell code stories.
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This episode. Is brought to you by MO Burst,
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defining together. 100%. Now you mentioned the shirt.
31:29
I need you to unpack the shirt a little bit because I've been
31:31
looking at it and I mean, black T-shirt, gang, it's Friday,
31:34
let's go. So explain kind of the meaning
31:37
of the shirt, OK. So the back of it is the actual
31:42
words, and it's something I've been using to explain, oh, by
31:46
the way, anybody can have the shirt.
31:47
It's like, you know, get dot new dot IO slash change and he
31:52
asked, you know, to see what the big T-shirt you want, we'll send
31:54
it to you. It's not it's about a way of of
31:57
us all thinking about what's going on.
31:59
So the thing I was trying to explain actually when I was
32:03
raising money last time, but why all these AI companies were
32:08
choosing new and it was basically explaining to the
32:10
VC's. The other really simplified
32:12
things to VC's. But I also want to blow their
32:14
mind. So I said, OK, the thing you
32:16
have to understand is that the pace of change of the pace of
32:20
change has changed, right? In other words, things are not
32:24
just going faster, they're going faster in way, in more ways and
32:29
faster than they ever went faster before.
32:33
And the reason that's super relevant to why new is being
32:35
adopted is now it's the, it's not the, the, the length of time
32:42
you've been in business is actually potentially a bad thing
32:46
because you've got all these, you've got a data model, legacy
32:50
data model is not very adapted. In fact, maybe the answer is I'm
32:54
just seeing this in some of the emergence very, very high growth
32:56
companies, the new entrant may be at in the best strategic
33:02
position to take the market, right.
33:04
And so you have to think of very, very short planning
33:07
horizons, very, very short delivery horizons.
33:11
And, and think about the fact that whatever thing that you
33:15
create, unless it's really deeply well thought out and
33:20
complicated to vibe from a better term, somebody else is
33:24
going to be able to catch up in feature.
33:26
So it's better be something more than just like feature function
33:28
that you're selling because that's going to that's going to
33:31
be really, really important. So, so certainly a lot of the
33:34
chief executives we talked to this is actually the sleep, the
33:39
sleep at night problem is this. And then because we're a heavily
33:42
engineering product based company, we wrote as math,
33:44
right. So by the way, the actual new
33:46
the the realtorship were shipping.
33:48
One of my team came to me and said that should be AV, not an
33:51
X. I'm like, well, then he goes,
33:55
no, it's the velocity, it's the pace of change of the velocity
33:58
that's in shape it. And then we go second derivative
34:00
of velocity that's actually really changing.
34:02
And I said, OK, you're right, you're right.
34:03
It should be, yeah, yeah, the the.
34:05
The the physics guys are always ruining my my UFO talks too, you
34:09
know? Yeah, but it was also, they
34:13
were, it was the same conversation as like, can I get
34:16
one of these shirts? I can't get some from my
34:17
friends, right. So it's like, yeah, yeah.
34:20
Where? Where can you?
34:21
Get it again. Oh, just go to get dot new dot
34:25
IO slash change and you just ask for it.
34:28
It's it's not a product, it's just it's just fun.
34:31
It's just fun thing too to wear what it it's great too because.
34:34
It is the can't sleep at night problem, right?
34:37
If if you you can really only plan 1/4 at a time because boy
34:41
in three months the world might look really crazy.
34:44
How do you sleep? I think I've.
34:49
I think I'm. I'm in the same.
34:51
Boat as everybody else, I, I, I in some ways really well, I
34:55
think that having a company, you know, first of all, you know, I
34:59
didn't found this company. I came in because we have an out
35:02
now genius running the company and she's amazing Tina Kong and
35:06
and she never sleeps so that helps me.
35:09
She's just an incredible bundle of creative energy and and
35:14
engineering brilliance. But you know, Tina and I openly
35:18
talk and, you know, people say to us, you know, hey, what's
35:20
your road map like? Are you crazy?
35:22
Like, you know, the road maps, like the road map on the at
35:25
least on the AI side is like 3 months long because the case if
35:29
we built it three months ago, the capabilities that we have to
35:33
work with are so different. It's a bit of a joke.
35:36
Like actually, of course we know what the actual key features
35:39
are, but the but the capabilities of them are really
35:42
changing. I think the, you know, the
35:45
biggest concerns we have at new right now is, is how to is how
35:52
actually to work with as many people as want to work with us.
35:54
That's the is a big problem because we are doing major
35:57
enterprise system changes, but we're using a lot of AI to
36:01
compress that time period. But that's the I have a big, big
36:05
worry is, is how do we possibly do this?
36:10
And actually a funny story, we're building a new enablement
36:13
things that are driven a lot by AI and part of it, we were
36:16
training, part of it, we've introduced training for our
36:18
customers so they can be more effective and using the training
36:22
and some of the AI enablement capabilities that are in our
36:25
platform, one of our customers actually accidentally went live
36:30
like so at the end of the training, they went, oh, at
36:33
night, we built the entire thing and they were like 95% right.
36:36
And so that's super exciting for us because that's if you're, by
36:40
the way, if you're in a consulting business and you
36:42
think your bit, your job is hands on keys, that's going
36:46
away. Like if you think if you were
36:49
really Pete should be losing sleep is if you sell consulting
36:53
and you do a lot of typing, that's going away.
36:56
However, if you, if you do consulting and you do a lot of
37:01
advising about what to do and how to adapt, that's becoming
37:04
more important. And that's that's a really
37:07
interesting thing, But that's not something I worry about is
37:10
something that I know that a lot of our SI partners are focused
37:13
on amazing your. Customers are some of the
37:16
biggest, coolest companies out there and largely it seems
37:21
because you actually created a category revenue life cycle
37:25
management. And I was reading a great quote
37:28
where you said the old way of selling is already dead.
37:31
Maybe you can walk us through the new way of selling and how
37:35
that helped enable kind of to get these unbelievable
37:39
customers. Yeah, and by the old way, I
37:42
don't mean to say that nobody was doing this before, always
37:46
thinks are on a spectrum. But if you think your job was to
37:49
go out, browse somebody, make a relationship with them and talk
37:54
them into something because you, you know, I don't know, because
37:57
you have reputational strength or because you're a super nice
38:01
person or something like that. That's what I meant by the old
38:04
way of selling, right. Hey, I, I've, I hey, this is
38:08
what we do. Here's the problems you have.
38:11
Here's how I probably solves your problem.
38:13
Buy it, you know, you think, well, is that how can I be done?
38:17
That's actually why somebody should buy a product.
38:19
The problem is the process isn't going to be the same at all.
38:21
What's going to happen is your marketing is going to reach out
38:24
to somebody and they're going to and it's going to touch them
38:28
with some value offer, right. And the person is like,
38:31
actually, I do want to do that. Actually, frankly, I do want the
38:33
systems that open the eye and etcetera are using.
38:36
And and So what are they going to do next?
38:40
They're going to go on ChatGPT or, or on Claude or, or wherever
38:44
a young Gemini. And they're going to ask them,
38:50
you know, if they're smart, they're going to ask what are
38:52
the challenges? I meant considering product A,
38:54
product B, product C. And they're going to jump
38:56
straight to what are the problems that people are have
38:59
implementing this software? They're not going to go, what's
39:02
a happy path and what are the good reasons why people do that?
39:04
They already got that part from your marketing.
39:06
They're now immediately going to go, who failed and why?
39:09
Right. And what are the horrible side
39:11
effects here? Yeah, what?
39:14
What? What?
39:14
Bad things, you know, bad things can happen.
39:18
And so so this is always being true.
39:22
You know, HubSpot been talking about this for a long time, but
39:24
there's a big part of, of the original Salesforce culture, I
39:27
would say was this commitment to this idea that, that you, you
39:32
don't let you enable people, you help them be successful.
39:35
You, you do all this sort of stuff.
39:37
And so working with customers and listed them is going to be
39:40
super, super important. But the other part is you have
39:42
to be open. It'd be completely open now.
39:46
So what do they want you to do? What they want, what they come
39:49
to the, when they, they, they do want to talk to human beings,
39:52
but they want the human beings to guide them through an
39:55
analysis of why this might not succeed, right.
40:01
And so the real new way of selling is either nothing in the
40:05
sense that you just enable people to go down their own
40:08
journey and then facilitate the transaction at the end.
40:10
And that's what a lot of PLG and SLG is like.
40:13
Or. It's very, very collaborative.
40:16
Like, in other words, you know, we train our people to actually
40:20
talk about why you might not want to buy new like, so for
40:22
example, if you like NVIDIA came to us, right, imagine how hard
40:28
it is to turn down NVIDIA, right?
40:30
But actually, I'm not sure what they actually bought, but I, I
40:33
sent them to Logic because they sell basically very, very highly
40:38
configured compute platforms like the, the computing
40:42
equivalent to selling helicopter parts, right?
40:45
And they're infinitely configurable.
40:47
And we just said, you know what, I know you love our software.
40:50
I know it's amazing and it's applicable to part of your
40:52
business, but the big meaty part of your business, we are
40:56
deliberately not good at that. It's not that we're accidentally
40:58
not good at it. To be great at something, you
41:00
have to not try to be all things to all people like RCA is trying
41:04
to be. And we basically say, so we
41:07
actually say, you know what? We don't think we're the right
41:08
fit. Here's a bunch of vendors we
41:10
respect and you should go talk to those people.
41:12
Would you like me to make intros for you?
41:14
Which just then made me want to throw up in my mouth.
41:16
But, but, you know, but that was the right answer for that
41:19
person. That's what's going to actually
41:21
win you more deals. How does the story end?
41:24
We get lots of back channel from some people that we talked to
41:27
over there. Yeah, yeah, it's it sounds like
41:30
the new. Way of selling is integrity and
41:32
I'm I'm glad the old way is gone because I suck at golf, you
41:35
know, I, I just can't do that. Yeah, it's, yeah, I, I think
41:42
still, I think the human part is still important.
41:44
I don't think it's not important.
41:45
I think that, you know, you get out of the office and go see
41:47
people. It's still important.
41:49
It's just get out of the office and go see people and listen.
41:53
Yeah, I love that. Amazing.
41:56
Mark, you've had an unbelievable career as a founder and a
41:59
leader. Multiple exits, super
42:01
successful. What was it about new that got
42:04
you off the bench to come lead this company?
42:10
Tina. Tina Kung So I was asked by some
42:15
of the early investors and just think about it like new is
42:17
basically like another financial system like NetSuite.
42:21
Like it's not the same as NetSuite, but still a very, very
42:25
big hairy ass goal of replacing multiple different systems of
42:28
one thing. So that's the analogy.
42:31
And I just sold the company TA We owned that company almost
42:35
completely outright. There was no VC in it.
42:37
So was great and and we just managed to make money like right
42:42
from the get go. So that was how we managed to
42:44
hang on to it. And and so I was planning to
42:48
just go kite, go kite boarding and not run a company for a
42:51
while. And then Tina gave me just the
42:54
most clear, you know, statement about what she wanted to
42:57
achieve. And it's so true to her and
42:59
true, like her commitment to our customers is bonkers.
43:03
Anyway, I once asked her because people try to buy us all the
43:06
time and we're not for sale, but but I said, how much money would
43:10
be enough Tina. And she goes no amount of money.
43:13
We're not done. I think it's just like committed
43:16
to the outcome for our customers.
43:18
And I'm just like, I love that about her.
43:20
I don't think that's actually true.
43:21
I think think anybody out there who's, you know, thinking about
43:23
a very, very big number, it's always possible.
43:25
But but I think, but I think the, but I think it's the ethos
43:30
of the clarity of vision, the amazingly high level execution
43:37
even at the early stage when I first joined them.
43:39
And then this complete obsession with the outcome for our
43:43
customers that is that I find energizing and joyful.
43:47
And and we have a thing about new, which we say all the time,
43:51
this is so hard that if you don't love it, you should leave.
43:54
Like what we're doing is so ridiculous, like climbing
43:56
Mountain Everest. If you don't really like
43:58
mountain climbing, you probably shouldn't do that, right?
44:02
Amazing, you know. Let's look beyond the the
44:06
quarter to the next 12 months. You know you guys have raised 40
44:11
million plus you guys have the biggest logos in the world as
44:15
customers. What are the big challenges you
44:17
see over the next 12 months? Well, I think again being
44:22
customer focused, you know, I think look new is a very young
44:28
company and we have tremendous appreciation and gratitude to
44:33
all the early customers. So one of the biggest things for
44:36
us right now is new is way better now than it was say 12
44:39
months ago. It's way more mature, it's way
44:42
more extensive. Working with some of these
44:43
amazing companies. If you ever heard the Bill Gates
44:46
interview about how IBM made Microsoft a massively better
44:50
company was one of the big strategic advantages of working
44:53
with people who demanded a lot, right?
44:56
Well, these massive AI companies because their pace of change
44:59
are, is, are that for us. So one of the big emphasis
45:03
embassies for the the next year is going back into our customer
45:06
base and say, hey, can we come back in and look at how we
45:10
implemented you and talk again about this.
45:13
And there's no charge for this service.
45:14
We're going back into all of our customers and saying, hey, can
45:17
we look again and think, is there a better way to do this
45:20
now? Can we help guide you through
45:22
that transition? Because we're profoundly
45:25
grateful. You know, like I said, God bless
45:27
the first people to buy enterprise software.
45:28
Like somebody has to do it and we're very devoted to them.
45:34
Why? Well, the reason is, is that in
45:37
order, if you really want to do what we want to do, which is be
45:40
the biggest business system on the biggest business systems on
45:43
earth and certainly the biggest recurring billing system on
45:46
earth, then you really have to be focused not just on the next
45:49
customers, but on your existing customers.
45:51
And so that's a huge push the on the new customer side, the big
45:56
concern is that right now it's a complex product.
46:01
So the demand for the product is completely right now
46:04
outstripping our ability even to respond to the phone calls.
46:08
So we are, I'm very fortunate to have a lot of, you know, senior
46:13
executives from, from major tech companies on our team.
46:16
We're, we're basically spearfishing some other great
46:19
people and, and really laying track for this.
46:24
They're delivering the same high quality experience to the next
46:28
generation of customers, the high touch experience to the
46:32
next generation customers, but not necessarily by us, right.
46:36
So that's the interesting thing. You need to scale it.
46:38
So it's how do you let other people build really successful
46:41
business models. So if you're an SI and you're in
46:43
the sales force space, we're super focused on helping those
46:46
companies get through that transition into a much more
46:48
profitable future rather than just taking their lunch, right.
46:52
So it's, but that's, you know, it's in, in 12 months, we'll be
46:58
a very, very significantly bigger company, but we'll have a
47:01
much, much bigger ecosystem. And that's the most exciting
47:05
thing going on right now. Romanis, thank you so much.
47:09
Mark, this has been awesome. Last question, based on where
47:12
you see the puck going now, if you were entering college as a
47:16
freshman, what would you study? If I was actually I get asked
47:22
this question all the time. I would say study whatever the
47:26
hack is going to make your college experience really,
47:33
really important to forming who you are going to be.
47:36
Don't make a call as to what the future economy in 20 years is
47:42
going to need. In other words, if you don't
47:44
think, or let me put it this way, if you go to college and
47:48
you're not so interested in what you're studying that they will
47:51
become an obsession and you'll learn to be just a a complete
47:56
nerd and geek about what you're doing.
47:59
And I don't care what it is, right?
48:01
Then don't do it because your future value is going to be that
48:07
you were able to go deep on stuff and that you are
48:10
thoughtful and that you have a perspective.
48:13
It will not be that you learned how to do marketing
48:17
communications. Like the way in which marketing
48:21
communications will be done will be so different than the way you
48:24
learned it that you won't get any value out of it.
48:26
But you can still do marketing communications, but do it in a
48:30
way where you are not mailing it in, no pun intended.
48:34
You're you're you're you're you're actually, you know, going
48:38
there and and do socialize and do enjoy the college experience,
48:41
But just understand that what you are investing in is you not
48:47
in some companies future cog for you, because there's very little
48:52
chance that you will call that right.
48:53
So if you think if you were really into mechanical
48:56
engineering, you should go and become A and, and study
48:59
mechanical engineering. And I don't know exactly how
49:02
mechanical engineering will be in, in that time, but but you
49:07
will, right? You'll get to experience that.
49:10
But if you, but if you go there, because I don't know, somebody
49:14
told you, you should do mechanical engineering or, or
49:16
frankly, computer science, right?
49:18
And somebody says you should go into computer science because
49:21
there's going to be a lot of jobs.
49:24
That was never a good reason to go into Pierce Knights in the
49:26
1st place. And it's certainly not now,
49:28
right? By the way, can I ask you a
49:30
question? Do you think we're heading
49:32
towards the AI 2027 future or do you think we're heading to the
49:37
Jetson future? Like everybody kind of has a job
49:39
and but they don't work as much. Like what do you, where do you
49:42
fall on this boy? It really depends.
49:44
On if I've had my coffee yet or no.
49:50
Yeah, I think we're going to a Jetsons future, at least that's
49:53
the that's the future I want to believe, you know, part of the
49:58
the. Reason why I started.
49:59
This podcast is to explore that very conversation because I, I
50:03
tell you the truth, I was feeling a little pessimistic, a
50:05
little scared about it. But you know, talking to people
50:08
working in the space and building is very reassuring.
50:11
And also you seeing all the as great as some of these products
50:15
are, but they're also half baked in a lot of different ways too.
50:19
So I'm a, I'm a strong believer that humans are going to pull it
50:22
together. And I really like your answer
50:24
too, about studying in college. I've asked that question dozens
50:27
of times. It might be my favorite answer.
50:30
And it, it reminds me of the way Myomoto Masashi talked about the
50:35
way of the sword and how by studying the sword with such
50:39
intensity, he saw the way broadly in all things and was
50:42
able to become a expert flower arranger based on his experience
50:47
with with swords. So I really appreciate you for
50:50
that answer. Well, I'm going to go back.
50:52
And read that, that sounds, that sounds fascinating.
50:56
Look, I think it's the other side of this is 11 things.
51:00
If, if somebody is building and I start up and, and you're
51:05
wondering whether you, whether you have the right tools or
51:08
things like that as well. Make one last point is this is
51:11
really complicated and what you're doing is complicated.
51:16
Reach out to us. We're not going to sell you
51:17
like, frankly, if you're like under $20 million, you can just
51:20
have our stuff and we'll, you know, to help you grow your
51:23
company. We all have to accept that.
51:26
We all have to learn. So why would I want to be
51:28
working with like little tiny companies when I work with the
51:30
biggest companies in the world is because I want to learn.
51:33
I want to learn from them. And I think that's the other
51:36
thing we all have to do. Whether you're going to college
51:38
or you're going to, whether you're already an, you know,
51:41
established executive like myself, you have to understand
51:43
what this means is you actually don't know what you don't know,
51:49
right? And so we, the other thing I
51:52
would say to people is share your ideas, share your
51:55
objectives. The value of, of closeted
51:58
intellectual property has gone to 0 like, like nothing grows in
52:03
the dark, right? So just instead of saying, you
52:07
know, oh, I don't want to tell anybody because they're going to
52:09
steal my idea, they're definitely going to steal your
52:11
idea. That's, that's definitely going
52:14
to happen. What you want to do is build a
52:15
community around you of people who believe in you and one, make
52:19
you successful. Don't like sit a program in your
52:23
cave and hope that you know they'll come.
52:27
Go out there and and talk to people and understand and get
52:30
their perspective. And if it turns out they talk
52:32
you out of the idea, that's probably a good thing.
52:34
If it turns out they can't talk you out of the idea, that's a
52:36
better thing, right? But but I think we all got to
52:38
basically be very humble through the next, maybe forever.
52:43
Maybe we should always have been doing that.
52:45
But but I think that's the other big thing.
52:47
You try to be humble. Extraordinary, Mark.
52:50
Thank you. So.
52:50
Much What's the best place for people to connect with you
52:52
online? LinkedIn, you know, I, I live on
52:57
LinkedIn. It's where I recruit, it's where
53:00
I learn a lot. I don't really, I don't have
53:04
enough cycles to be really responsive on, on Reddit or on
53:09
on X. So so LinkedIn is easy to find
53:12
me. You know, it's easy to
53:13
triangulate on me and and look if there's anything.
53:17
My first question when anybody connects with me, I go and
53:20
connect to every everybody. If you send me a connection
53:22
request, tell me what I can do to help you, right.
53:25
If there's something I can do to help you, I'm super interested.
53:27
I'm more interested in what I can do to help you then then
53:29
what you can do to help me. But if what I can help you is,
53:34
is talk to you about your new product or your new service, or
53:36
maybe connect you with people who'd be interested in in beta
53:39
ING it or, or, you know, frankly, finding the right
53:42
person, my company to sell to, I'm open to that.
53:45
Just as long as you basically consider, you know, come to with
53:49
the same attitude of, you know, you're coming to help me and and
53:52
asking me to help you or but if you just need me to help you,
53:55
I'm totally fine with that too. Amazing, Mark.
53:58
You thank you so much. Thank you so much to.
54:01
Rank. This is super fun, thank you for
54:03
setting it up. You bet.
54:05
Bye. Hey founders, let me ask you.
54:08
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54:15
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