Recorded at the NFX SF HQ, this episode features Madhavan Ramanujam (author of Monetizing Innovation and Scaling Innovation), alongside Pete Flint (GP at NFX, founder of Trulia) and Anna Piñol (Partner at NFX). Together, they break down how AI is changing the rules of pricing, defensibility, and growth. He covers the shift from services to venture capital, AI monetization, and how early discussions on willingness to pay shape value capture. Strategies for seed-stage startups, successful POCs, and enterprise deals are examined, highlighting simple pricing communication. The episode also addresses outcome-based pricing, AI's potential to reduce pricing bias, and tactics for profitable growth. As startups navigate AI adoption, the significance of the 20-80 axiom and overcoming resistance to price changes are emphasized.
Key Points:
- 20% of what you build in tech drives 80% of the willingness to pay, making it crucial to identify and monetize this core value early on.
- Framing POCs as business case validation exercises rather than just technical validation can help ensure that you are engaging with customers who are serious about the value your product provides.
- Outcome-based pricing can align incentives between the customer and the provider, ensuring that the focus remains on delivering measurable value, but it requires high autonomy and clear attribution to be effective.
Timestamps:
(0:00) Introduction to pricing in the AI era with Madhavan Ramanujam
(1:13) Transition from services to VC and the impact of pricing
(2:56) The role of pricing in scaling innovation
(4:35) AI monetization and go-to-market strategies
(7:29) Evolution of pricing models in the tech industry
(9:37) Early conversations on willingness to pay
(12:17) Value capture and margins in AI products
(17:16) Monetization strategies for seed-stage startups
(19:02) Tactics for successful POCs and enterprise deals
(24:01) Communicating value and the importance of simple pricing
(28:07) SaaS pricing evolution in the AI context
(31:10) Pricing models: Understanding and applicability
(34:14) Outcome-based pricing: Challenges and benefits
(41:23) Adapting pricing models as products evolve
(43:58) The future of outcome-based pricing in AI
(45:30) AI's role in reducing bias in pricing
(46:23) Innovative pricing against incumbents
(49:56) Founder archetypes in pricing strategy
(55:51) Achieving profitable growth in startups
(57:49) The 20-80 axiom in product development
(59:15) Overcoming resistance to price changes
(1:00:06) Acquiring and retaining customers
(1:01:21) Validating leads with charged POCs
(1:05:14) Durability of AI startup revenue growth
(1:07:34) Segment-based cohort analysis and KPI evaluation
(1:09:43) AI adoption in different market segments
(1:10:50) Network effects and revenue durability
(1:11:36) Episode conclusion and future topics
Madhavan Ramanujam: 20% of what you build in tech drives 80% of the willingness to pay. This I've seen it over and over again.
Anna Piñol: Why is AI making us rethink pricing generally?
Madhavan Ramanujam: I think products have very increased autonomy and very increased attribution. That actually gives you insane monetization potential. And if you don't capture that from the get go, you're training your customers to expect more for less and then that's a slippery slope. Right? So how do you capture that value?
Pete Flint: Alright. Madivan, so good to have you and so good to see you again. I'm Pete Flint joined by Anna Pineau from NFX. And so it's such a delight to have you back on the NFX podcast. We go back a long Way back.
Pete Flint: Way back. So a little bit of context. So we were together like a dozen years ago when you were at SKP doing some pricing work at Trulia. And I think you'll get into this. But we at Trulia, we started with a very simplistic pricing idea, like what can we sell and what is easily understood really, really quickly.
Pete Flint: And we left a lot of value on the table. And then we were lucky to be connected with you. And you came in and really helped to improve and add a level of sophistication about pricing and insight, which not only economically super valuable, but I actually found it fascinating professionally because it's just a mix of psychology and economics.
Madhavan Ramanujam: Yeah, And it was also a time date. It was before the IPO, I think.
Pete Flint: Exactly. So yeah, I think it sort of pretty quickly added 7 figures to the bottom line. We've stayed in touch over that dozen years. And so maybe share a little bit about kind of like what a little bit of kind of how you spend your time and perhaps what you're doing right now.
Madhavan Ramanujam: Absolutely. And it's super awesome to be back. Thanks for having me. So, I mean, the last, fifteen years, I pretty much spent working with tech companies on helping them with monetization. So I worked with over 250 companies, more than 30 unicorns, navigating monetization challenges.
Madhavan Ramanujam: I did this in a professional services capacity, SKP or Simon Kutcher and Partners, like you called it. You know, me and, my co GP, Josh, who was also at Simon Kutcher, we, left last year towards end of last year and we started a venture firm. That's what is keeping me busy now. I mean, combined the two of us have worked with our finite companies. But the reason we actually pivoted to like venture was to specifically also work with very early stage AI companies and we can unpack a bit of that.
Madhavan Ramanujam: But my, you know, life right now is invest in companies, roll up my sleeves and work with founders.
Pete Flint: And you've and you've written a couple of books. You've written the first one, which is monetizing innovation, which which I've highly recommended to so many founders. And then the the most recent one, which launched just a couple of weeks ago, is scaling innovation, which is really thinking about monetization in a AI first environment, which we're in now.
Madhavan Ramanujam: Exactly. I mean, not just building great breakthrough products, but how do you build a great business, especially AI focused.
Pete Flint: Great. So I think so today, we really want to pack in We spent a lot of time, Adam and I and the rest of the firm at NFX, thinking about how do founders think about pricing in this crazy AI world where you've got these breakthrough products. But you've also got a world where you're seeing software become increasingly ubiquitous and potentially commoditized as well. So maybe, Anna, you'll kick things off.
Anna Piñol: Yeah. Just to kick things off, so ever since this new wave of AI companies started, it was pretty obvious to all of us that this new technology was unlike anything we have seen before. By investing early in some of these companies, we were quickly exposed to the new possibilities in terms of value creation, and as a result, like emerge in terms of value capture. And we often find ourselves discussing pricing with our founders as I'm I mean and and one of the things that I wanted to kick this off with was how have your own conversations around pricing evolved over the last few years in which we've been in the era, and why is AI making us rethink pricing generally?
Madhavan Ramanujam: Yeah. I think the big there are a couple of big changes that have happened. Right? I mean, if you think about, AI monetization or pricing, when should I think about it has changed dramatically. Because in the previous vintage of companies, we could still say, let's just grow and figure out monetization when you're running a 80% SaaS margin business to some extent.
Madhavan Ramanujam: Right? You can't do that in AI for two reasons. One, there's cost dynamics to navigate from the get go, and there's also like value capture like you rightly said. I mean, if for the first time, I think products have very increased autonomy and very increased attribution that actually gives you insane monetization potential. And if you don't capture that from the get go, you're training your customers to expect more for less and then that's a slippery slope, right?
Madhavan Ramanujam: So how do you capture that value? If you're building something as an agentic, you know, AI product for the taps into labor budgets, labor budgets are 10x compared to software and IT budgets. So if you use use the old playbooks, you'll completely under monetize. So what we are seeing is monetization and GTM is becoming really really important even in the pre seed and seed companies. And even in the previous, you know, vintage of companies, we could say that if I have a two year coding head start, that is a mode.
Madhavan Ramanujam: You can't say that anymore. Right? I mean, you can probably code things up in overnight. So what is the mode? You need to have some, you know, proprietary training data, network effects, and also like GTM becomes a moat.
Madhavan Ramanujam: And in that perspective, your monetization model becomes a moat and how can you have durable revenue. So all these questions kick in from day one. So what has really changed is the focus starts much early. I mean, that's kinda also why I alluded to the fact that, you know, that's why we also pivoted to like work very early in a venture setting where we are investing and operating as opposed to being on a fee for service model ourselves. We we changed our own pricing model.
Madhavan Ramanujam: If you think about it, right, it was more on the usage, now we are on outcome basis.
Anna Piñol: Yeah. That's awesome. And have you seen this happen before? I guess there's been like prior waves, prior technology waves that have also been known for setting new pricing models.
Madhavan Ramanujam: Yeah. I think with every technology wave, there's been a new pricing model innovation that's kicked in. Right? I mean, if you take Salesforce back in the day, you know, SaaS pricing was a huge shift from, like, on on prem, you know, perpetual licenses. I mean, to take someone like AWS for instance, the, you know, pay as you go for infrastructure, that was a big wave that actually started.
Madhavan Ramanujam: Even companies like Uber started like dynamic pricing as a wave on its own. With AI, what we are seeing is there's probably a move in impetus to be more outcome driven because we are moving from a, you know, buy software for access to buy software for work delivered. And I think that is where the industry is heading, the way I see it.
Anna Piñol: Mhmm. And you are you are a big proponent. I mean, in your book, you talk a lot about encouraging founders to have willingness to pay conversations early, a little bit aligned with this idea of, like, pricing is very important since the get go. What are some tactics that you can share to do a good job at that? Yeah.
Madhavan Ramanujam: Sure. I mean, early willingness to pay conversation is really important. I mean, let's talk about the why and then we can talk about the how. The why because if you just build a product, slap on a price and you throw it out, you're just hoping, you just don't know. I mean, what we talk about testing for willingness to pay is like testing for just like product market fit.
Madhavan Ramanujam: Right? I mean, entrepreneurs know that. I mean, if someone comes and asks me, do you like the sparkling water? I like it. Do you like it for $25?
Madhavan Ramanujam: The whole conversation is different, right? So unless you put actually pricing as part of your product market fit, you often hear what you wanna hear, right? So willingness to pay is critical, so you can at least identify even before you're launching your product. Is this product something that people need value and are they willing to pay for? And then architect the product around it.
Madhavan Ramanujam: If if you find there's no willingness to pay, the most important question is to ask why? And then you start hearing all kinds of things that you can actually productize around jobs to be done and unmet needs. So that's the why, it's very critical. It's like test and learn, you know, monetization before just slapping on a price and hoping. On the how to do it, in the book monetizing innovation, we devoted an entire chapter to that, it's chapter four.
Madhavan Ramanujam: That's the most important chapter to read for the listeners. But I might like, let me just unpack maybe one specific tactic that actually Rahul Wara used when he came up with his own monetization for superhuman, right? So what we call is, it's like the acceptable expensive and probably really expensive questioning to understand psychological thresholds. So the way that works is you know, you take your product, know, your wireframe, blueprints, demos, free trials, whatever. Mean, just put people through the experience of the product, so you're having your same sales and marketing conversation that you'd actually have, you know, and then and then have the pricing conversation.
Madhavan Ramanujam: So once they understand the value, ask them what do you think is an acceptable price. You know, clock that answer, then ask them what do you think is an expensive price. Clock that answer and ask them what's a prohibitively expensive price. This is a very stylized way of asking. If you just go and ask something like, you know, how much should I charge for this project?
Madhavan Ramanujam: You know, I should charge for this product, you'll probably get garbage bag. Right? I mean, that's your job. But if you ask it this phase after you pitch the, you know, entire product, the sales marketing conversation, you start hearing something reasonable because acceptable price tends to be the price where people are negotiating with themselves. That's the price that they love, not just your product, so they're gonna lowball all day long.
Madhavan Ramanujam: Expensive pricing tends to be where it is around your value price, and prohibitively expensive tends to be where they'll laugh you out of the room. Now if you do this a bit statistically with like, let's say even hundred, two hundred people on an online study or how you administer this, you start seeing that these demand curves have cliffs. Like for instance, after 29 if you go to like 31, suddenly 20% of people actually think it's expensive or you know, or 40% don't find it acceptable, etcetera. So then you start seeing these psychological thresholds. That's how you know that okay, you need to be right around that price point.
Madhavan Ramanujam: If you cross it, it's gonna be a threshold. So Rahul actually used this for superhuman and found that, you know, $30 was a great price point for the product that he had and that's also how we actually launched it at a $30 price point. And he was talking about this in an a 16 z podcast, that's where I learned that he read Modernizing Innovation and did this and we've been great friends since then.
Pete Flint: Out of these out of those three different segments, acceptable, expensive, prohibitively expensive, like, where do you wanna anchor?
Madhavan Ramanujam: Yeah. So if you're in the look. If you're if you truly wanna be price value aligned, it's typically on the expensive. Probably expensive is like, you know, you shouldn't be there. That's like, it's a price premium paradox where you just wanna overprice thinking it's good, you're actually gonna hurt yourself.
Madhavan Ramanujam: If you're in the, acceptable zone, maybe in the growth phase it's still okay to be there because you can actually then you're gonna get a lot more acquisition. Yeah. As long as you have a land and expand strategy, it might be just fine. Yeah. Okay.
Madhavan Ramanujam: Yeah. But if you wanna start off with a value price, then the expensive price is probably more closer to your value price. It's the price where, you know, people don't hate you, they don't love you, they're just neutral, they'll pay you. Yep. So Mhmm.
Anna Piñol: I'm curious, thinking about, like, popular like, ChatGPT, Cursor, there are there are many claims that they might be not fully capturing the value that they're delivering. Like, what do you make?
Madhavan Ramanujam: Yeah. I I think it's there are some self inflicted anchors in that space. Right? I mean, is a $20 price point actually, you know, good or can it be more? I mean, back in the day, Copilot, GitHub, everyone started at ten, they moved it to like twenty, thirty.
Madhavan Ramanujam: I mean, there's some anchors. Right? I I really think that for for that, you need to really understand what value you're actually bringing to the table, and can you charge a value price based on that? And can you contextualize that, you know, pricing? I mean, we talked about superhuman, but just to drill down a bit on that, one of the chapters we write in scaling innovation is called beautifully simple pricing.
Madhavan Ramanujam: How do you keep it simple? And when you think about it, when Rahul actually introduced superhuman, he was competing with free alternates, Gmail and others. Like, why would people even pay money for another email app was a question mark. But he was actually delivering core value, which is, you know, I can free up your hours and increase your productivity because you can, you know, log into an outbox, etcetera. But the key there was the beautifully simple pricing talk the value story.
Madhavan Ramanujam: So he didn't just say it's a $30 price point. He said it's a dollar a day to get five hours of, productivity back in a week. Now that $30 doesn't look too expensive. Right? And that's actually how everything took off because, okay, will you pay the price of a latte to get five hours back?
Madhavan Ramanujam: Absolutely, I would do it, right? But so the entire premise was on the value. Where many of these coding agents, white coding, etcetera, while they're emphasizing, you know, the fact that you can do things fast, they're not emphasizing necessarily the value. So instead of a $20 per month, if it's a $30, $11 a day to get incredibly efficient at coding and save you, like, five hours or ten hours a day, would you pay for it? You would, but you probably never charge, so why should I?
Madhavan Ramanujam: So I think if you start wrong, you kind of end wrong. Then, of course, the different strategy, you can say I wanna grow, I mean, you you see some of, you know, some of these companies having, like, very fast ARR, but we can talk about whether that's durable, what are the margins, there are a lot of question marks.
Pete Flint: Do you have a do you have a point of view on margins? It's because it's it's such a it's such a competitive environment out there, and any good idea is replicated very quickly. And with the cost of software coming down, it seems the sort of incumbent SaaS businesses are kind of holding onto margins. But are we in a world where, you know, the you you see many of these AI companies launching and their margins are 90 plus percent because they're first mover and they're rep and they're replicating labor budgets. Whereas you're mentally like, well, how sustainable are these margins long term?
Pete Flint: Do you have a point of view on how things will evolve?
Madhavan Ramanujam: Yeah. I think the the key is to focus on, you know, both market share and wallet share. Right? That is that's how I see it. So it's more profitable growth.
Madhavan Ramanujam: It's not profits, it's not growth, it's profitable growth. That's also the subtitle of the scaling innovation book, how to architect profitable growth. And what that means is it doesn't mean that you need to have, you know, equal efforts at any given point in time on market share and wallet share, but you need to have equal attention in the sense that, you know, even if you, gave away on price to actually acquire customers, are you thoughtful about the fact that you can land and expand later and you have a clear vision on how to actually, you know, go towards wallet share? And similarly, you know, if you started more on the wallet share side because you think there's a new market that you can start shaping, do you have an alternate to actually create a low end product to actually gain more market share? So it's being thoughtful about it and having equal attention, but not necessarily equal efforts because at certain points in the company, you might actually wanna index more on one level or the other.
Madhavan Ramanujam: But the best CEOs have been the ones that can actually think about the interaction effects between, you know, acquisition, monetization, and retention. And the and then you start building towards profitable growth. So while we talk about some AI companies having high margins, on the flip side, some of them have really low margins, like, especially if you take, you know, some of the coding ones that we talked about. I mean, there was a TechCrunch article that it's either neutral or negative in terms of margin. Right?
Madhavan Ramanujam: I mean, and if you have a lot of ARR at negative margin or neutral margin, we can question, is that a great business? Or do they have a pathway to get to, you know, better margins? Would they build their own model? Is there efficiencies that we will see? Or if it's just hoping that the cost would come down, hope is not a strategy.
Pete Flint: So founders, you know, ask us every day, like, just how do you think about monetization strategies? And what are what are some of your frameworks to think about monetization strategies for early stage startups?
Madhavan Ramanujam: Yeah. I think there are two questions that come up, and maybe I'll be curious to see if it comes up in your conversations with founders. And if those are the right ones, we can unpack each of those. The first question that comes up is, you know, how do I charge for this product? Like, what's the pricing model?
Madhavan Ramanujam: Because often we say how you charge is way more important than how much. Should I be on consumption based? Should I be on seed? I saw someone else doing outcome, should I do that? So, like, what should I do?
Madhavan Ramanujam: Right? I mean, that's closely tied to your operating your business that it it comes up and that's a sort of choice that you take earlier on. And the other question that comes up especially with b to b AI companies is, hey, how do I navigate POCs? I'm getting into these commercial agreements. My buyer wants to see whether these products deliver value.
Madhavan Ramanujam: How do I charge for it? How do I navigate these big deals? Right? So those two questions keep coming up in the pre seed and seed stage.
Pete Flint: And so and so let's
Madhavan Ramanujam: just Is that similar same with your company?
Pete Flint: Yeah. It it absolutely. They're like, okay. We have a breakthrough product idea. We know that this is I mean, I think there's a in consumer, it's you know, that in some ways, the superhuman story is a reference point for some of these consumer applications.
Pete Flint: But in b to b, it's kind of more complicated because there's a sort of deeper engagement. So maybe just walk us through how you might how founders might think about kind of navigating the price discovery with with customers who are who are which we see today, like enterprises and and and SMBs are so open to AI. They've kind of they're experiencing it in their daily life. They say, I know this can be helpful. So they're very open and receptive to conversations.
Pete Flint: And founders are building amazing tools, but they are not clear how to navigate this dynamic between what is the value I'm delivering and what is the price I'm able to to charge.
Madhavan Ramanujam: Yep. No. Totally. I think let's probably then talk about navigating those big deals first, and then we can come back to the pricing models later. So these POCs have become critical because like you said, there is a lot of curiosity on the buyer's side.
Madhavan Ramanujam: Right? And also a lot of budgets to experiment. They wanna see if AI can actually help their internal efficiencies and things like that, but they don't know how. So they keep asking these AI startups and companies saying prove the value. So now the classic mistake that a founder makes is approaching the POC as a technical validation, Right?
Madhavan Ramanujam: Because if you're just approaching it as, will my tech work in the environment of your customer, you're not really proving out any business case at all. Right? So we actually talk about when you think about POCs is to frame a POC as a business case validation exercise. Tech is tech validation is part of that. So which means that, you know, you try to co create an ROI model with your customer.
Madhavan Ramanujam: So what that means is, you know, you start the POC and say, okay. It's a finite amount of POC, maybe a month, two months, three months, whatever, not more than three months, typically. And then you say, okay. For that period, we are going to jointly create a business case. Why is this important?
Madhavan Ramanujam: Because the buyer on the other side now actually participates in that co creation and becomes the smarter person in their organization where they can actually shepherd it after that, you know, two to three months and say, hey, this product will actually unlock x millions for us, and we should buy this. Right? I mean, so you also inevitably make them smarter. And the other reason to do this is if you just work on something and show up with an ROI model, no one is gonna believe you. If they work with you on the inputs, they will, you know, believe the outputs.
Madhavan Ramanujam: As simple as that. So framing the POC as a business case exercise where you're building a business case saying, is there value generated from that AI? And being on the same page with customers. This is the key thing and if you don't do that, it's a mistake. And when you actually do that, there are various, you know, things that you need to think about in terms of how do you value sell, how do you create an ROI model, how do you negotiate, all of those things become critical.
Madhavan Ramanujam: But you postpone the pricing conversation to after the POC. So the POC is typically a fixed engagement only to build the business case. Then you tell the customer that, okay, look, commercial discussions will follow because you want to clearly upfront state that. Otherwise, your POC becomes, you know, POC price becomes the anchor for your pricing. They just multiply by 12 times.
Anna Piñol: How do you handle, like, one of these customers being like, no. I don't wanna I don't wanna postpone this pricing conversations because it's important for me to know now.
Madhavan Ramanujam: Absolutely. So that's a that's a great question. If you're asked for price during a POC and pushed, what would you do? I would say you should talk about price, but in a, you know, more, let's say strategic manner. So there are a couple of ways to deflect that question and that's the most important thing.
Madhavan Ramanujam: Right? So you can say, hey, look, no, the goal of the POC is to actually, know, create that value case and we can talk about, you know, what portion of that we deserve. Let's assume the buyer on this other side says that looks theoretical, I still want a price, right? So another way to deflect this would be, you know, you can say, you know, customers like yours have been able to unlock, you know, tens and millions of values, and we are typically on a one is to 10 x in terms of, you know, your investment in us and the ROI that you get out of it. So you basically just communicated that you're probably a million dollar deal and actually it comes to it, but without actually saying it.
Madhavan Ramanujam: Some customers will be satisfied by that because they're like, yeah, one is to 10 sounds fair. We will deal with it. We'll finish the POC and we'll come to it. If they push you even more, then don't give just a price point because that'll be the most artificial thing you can do because you're also guessing. Right?
Madhavan Ramanujam: So in that case, I would give a range. I would say, look, it can be anywhere from 500 k to a million for the first year. Where we would be will depend on how much value unlock we can actually do for you, and that is why we are bringing that is why we need to do the value case. So in all of these conversations, you're bringing the focus back to the POC is a business case exercise, and how do we co create value. But I just answered that, okay, it's a range.
Madhavan Ramanujam: I gave you predictability. Beyond that, you don't need to. And where you are depends on the business case. And if you unlock 10,000,000, you can be on the right side. If you're unlocking three, you're probably on the left side.
Madhavan Ramanujam: Right? So I think that's also how you can navigate this, more. The best negotiators or the best, you know, founders who are selling hardly reveal their, you know, price because if you don't understand value, what is price?
Pete Flint: Is there any are there any tactics to communicate value? Because I think it's like you might be able to Mhmm. It may be that I don't know. There is the VP of sales which is Mhmm. Using the product Mhmm.
Pete Flint: But they the CFO may deeply understand Mhmm. The kind of value story. And so just in incorporating into the product, the demonstration, the value, have you seen any companies that do a really good job of demonstrating to constituents the value that they create?
Madhavan Ramanujam: Yes. I think, absolutely. And the lesson learned in those situations is being, you know, very structured and systematic in terms of how you communicate value. Right? Random statements of I'm actually saving you, you know, this or that, just it's kinda lost.
Madhavan Ramanujam: It has to be systematic. So you're almost training your buyer to repeat the message that you're giving them internally, and they become internal champions. So the best ones actually say, hey. Look. There are three categories of, you know, value that we actually provide.
Madhavan Ramanujam: The first one is incremental on your top line based on your business KPIs. So that could be we generate more revenue for you. We reduce churn. So these are all incremental. Right?
Madhavan Ramanujam: So that how are we participating in that? If you are, communicate that. That's separate from every other value element. So we actually improve your top line. The second one is we actually save costs.
Madhavan Ramanujam: That could be other license cost savings. It could be headcount reduction. It could be, you know, some tangible cost savings that just happened because of AI. Right? So these are the cost savings, which a CFO would actually like.
Madhavan Ramanujam: Right? And the third one is opportunity cost, which usually a CEO would like. Right? Because you said, I'm also freeing up, you know, ten to twenty hours for the team on a weekly basis. That can also be quantified.
Madhavan Ramanujam: What would they do with that time? You know, what does that actually work for the organization? So when you put it into these three buckets, the incremental, you know, top line, which is really important, everyone cares about it, the cost savings, typically the CFO function and others care about it, the opportunity cost, definitely the management team actually cares about it, then you can start putting your value story across these categories and then train the buyer to actually go paraphrase that wherever they are in the organization.
Pete Flint: And when you say trained, you mean you mean prompts within the product? I mean, not prompts in terms of AI prompts, but just kind of visual prompts in terms Yeah. Of just just reinforcing the value that's being created at every interaction.
Madhavan Ramanujam: Correct. And I would even, you know, in a negotiation when I'm I tell founders this, you know, pause and ask them the questions. Hey, this is what I said, how we will add value. What do you think? What did you get out of it?
Madhavan Ramanujam: Like, you had to repeat that back to me, what would you say? I mean, this is a very like, I just wanna know you understood what we are saying. Right? I mean, like like, how do you think about it?
Anna Piñol: Yeah. So They say the best products are those that get people promoted internally. Right?
Madhavan Ramanujam: Absolutely. Like,
Anna Piñol: get the champions of yeah. Promoted internally. Exactly.
Madhavan Ramanujam: And and this is a I mean, if you ask it in the most, innocent possible way, it's a great test. The we talked about, you know, beautifully simple pricing. Right? One of the tests for whether you have a beautifully simple pricing is Monday morning, go back and ask one of your customers to play back your pricing strategy to you. If they cannot, you don't have a beautifully simple pricing.
Madhavan Ramanujam: It's just complex as hell and you probably somehow sold it, and they don't understand it. If they didn't understand how they were charged, they don't understand how the value is created. That's a churn situation waiting to happen at some point. But if they can articulate and say, hey, your pricing makes sense because we get this, The price value realization is aligned.
Pete Flint: So we've seen we've seen the the evolution of SaaS when and in this AI world move from this per seat licensing to a lot of exciting stuff in on outcome based pricing. Because how do and you've you've got this two by two matrix. Maybe break that down for us and and kind of what types of companies are kind of able to capture most of the value.
Madhavan Ramanujam: For sure. And I think it it the two by two, it goes back to the, you know, same two dimensions I talked about in the beginning, autonomy and attribution. Right? That's really what's changed with AI. So if you think about a, you know, two by two with autonomy on the y axis and attribution on the x axis Yeah.
Madhavan Ramanujam: And low high, low high. If you take the, you know, bottom left quadrant where autonomy is low and attribution is low. When we say autonomy is low, that means you still need a human in the loop. You're operating as a copilot. Attribution is low in the sense that, you know, your product probably is good, but you cannot it's not directly tying into any kind of business metrics that your customers are tracking.
Madhavan Ramanujam: Right? So if you are in the if you're in that quadrant, you're kind of relegated to a perceived model. Right? I mean, even if you take a company like Slack, for instance. You you can say, you know, I use Slack and my productivity went up, but you can't measure it, you can't meter it, you can't monitor it, you can't charge based on it.
Madhavan Ramanujam: Yeah. Right? So the attribution is low and, it's autonomous autonomy is also low at Scopilot, so that's necessary in a seed based licensing model. But if you take the bottom right quadrant where attribution becomes higher, and autonomy is still low, you're still in Copilot, but you're adding a lot of value. This is also where, you know, some of the coding platforms have started moving, like Cursor, for instance, is here or Clay is here for instance, right?
Madhavan Ramanujam: Where it's a hybrid model makes sense. So it's still a seat, but then you also overlay a usage component. So like certain packages might come with certain number of AI credits And if you use it, after that, you know, you can buy AI credits on top. So there is a blend between a seat and a usage model. That makes sense because it's still Copilot, but the more people actually use it, the more value they're getting, so I wanna monetize on that.
Madhavan Ramanujam: Right? If you're on the top left quadrant, autonomy is high, but attribution is low. What that means is there's no human in the loop needed for these AI products. They kind of live in the background and do do their stuff, but they're not directly influencing the KPI of your businesses. So they are more background infrastructure tools, platforms.
Madhavan Ramanujam: So they have to be purely on a usage based basis because that's your best proxy for value. There's no seats involved, so there's no point, and they it'll be bad. But a usage is the best proxy for value. Like, in a company like even the classic Twilio would be probably in that kind of quadrant. Right?
Madhavan Ramanujam: If you take the top right quadrant, that is where autonomy is high and attribution is high. That's the kind of exciting quadrant if you can be on. What that means is your AI is, you know, fully autonomous and can also do stuff that is highly attributable. Then you can be on an outcome based model. That's, you know, the holy grail of, AI monetization.
Madhavan Ramanujam: It's not for everyone because many products, you can't show attribution. You I mean, even if you show it, do do people believe it? Is the loop closed? Is it fully autonomous? So I think there's still work to be done.
Madhavan Ramanujam: We are seeing about, you know, less than 5% probably around 5% of companies that are right now in AI on outcome based. We do predict that this will go to, like, 25 to 30% in the next three years, and I think that's kind of where people are moving, and we can talk about that. But a good example of a company here would be, like, if you take Intercom, they develop FinAI. So the way it actually works is if the AI agent is able to, like, you know, autonomously resolve a customer support ticket without any human intervention, then they charge for it. If a human intervention is required, then they don't charge for it.
Madhavan Ramanujam: And there is self attribution loops built in here because the customer might say, I'm satisfied with this ticket. They're actually closed with an AI agent. No human in the loop. It's clear outcome, right? So they charge for that.
Madhavan Ramanujam: Or there are companies that actually have demonstrable savings, like for instance, a company that, you know, recoups chargebacks. They do a 25% of the recoup money. So that actually is directly, you know, because they measure it, they they understand it, they take a portion of it. Right? So if you're, and it's auto autonomous.
Madhavan Ramanujam: So if you're highly autonomous, highly attribute attribute attributable in the on the top right quadrant, you can be an outcome based. So I think this is the key thing. The the the thing is to really understand, you know, what is the right, you know, pricing model or archetype for you based on your product in terms of, like, how autonomous an attribute it is. Where I see founders making a mistake is chasing, you know, shiny objects. Like, they just heard from some, you know, networking event that they went to that outcome based is cool.
Madhavan Ramanujam: Like, I need to be an outcome based. And actually talking about outcome based pricing, when the attribution of the product is incredibly unclear, that's setting yourself up for failure. So, like, understanding where you are in this quadrant is super important. Then you can build pathways to also move around if you need to. But if you stop wasting time in trying to, like, just copycat and, like, see really what is the right situation for you.
Pete Flint: It's it's also the the outcome based pricing also creates an alignment internally between the product efficacy and and tools and also customer expectations. And so that alignment is just terrific because it rather than start focusing on like, okay, how do we sell more seats? It's like, does the product actually deliver? But as you say, I think it's more more challenging.
Madhavan Ramanujam: Yeah. But that's a great point because for instance, if you take these, you know, customer support ticket resolution, like, right, I mean, if I when I started, for instance, only 20% of them got done by AI without any human intervention, then everyone in the company is now incentivized on how the hell do I make that, you know, 60 or 80% because that's when the product is actually delivering on its value. So you're completely tied in destiny in terms of, like, your vision, everything else with the customer, and you're partaking on that. That is where it becomes a very beautiful model where you switch to, like, you know, charging for, like, work delivered as opposed to access to software.
Pete Flint: You talk about wallet share and market share. And I you can see that outcome based pricing can be critical because it or or capture a huge amount of value. But at the same time, the the concern is that unless there's a real commitment, which is like, you're paying whatever, a particular flat fee, people are less invested in the product. They'll just use it episodically or as an option against many other channels. Do you see that in practice as a real challenge?
Pete Flint: Or do you see strategies for companies to kind of mitigate this sort of one of many outcome based companies?
Madhavan Ramanujam: No, that's an excellent point because there's also some characteristics around when does outcome based model make sense of a company in a market, and also, you know, when do the other ones make sense? So we actually talk a lot about this in scaling innovation, but you talked about episodic, for instance. If my, you know, value delivered is episodic, you don't want to be on an outcome based model because that almost looks like, you know, holding someone hostage, like, when when the value was delivered, you're actually coming the, you know, bill collector is actually showing up. Think about this in the, you know, previous vintage of LifeLock. It's it's operating in my background, checking my credit, you know, like identity theft, everything else.
Madhavan Ramanujam: And then you say, okay, I found identity theft breach, I'm gonna charge you. It's like episodic. Like, it just happens once in a while. Right? That actually that but at that time, it's insane value.
Madhavan Ramanujam: You don't wanna monetize on that because that actually looks like a really wrong thing to do. But you're actually paying for the, you know, peace of mind, and it's like an insurance. So in those kind of cases, you wanna actually have a, you know, either a fixed fee or a recurring revenue basis. So, like, whenever it happens, it happens. But, of course, you need to choose your level of pricing commensurate with value.
Madhavan Ramanujam: But if it's like a more, let's say, you know, more frequent, you know, deliverables, outcome kinda makes sense like a ticket resolution. There is a ticket. It's resolved. You get monetized on it, and then it adds up. Right?
Madhavan Ramanujam: So I think there are some real characteristics on when each of these apply. If you're in an episodic one, probably not.
Pete Flint: Do you do you see any consumer based pricing model consumer applications which have outcome based pricing. You can imagine a fitness app, which is like, okay, if
Anna Piñol: you If you get feed, yeah.
Pete Flint: Yeah. If you lose 10 pounds or kind of like do you see anything happening there? The customer is perhaps not sophisticated or too much noise going on?
Madhavan Ramanujam: No. I mean, we've actually seen these, kind of models. Like, I that's more of a gamification for me. I don't know if it's an outcome based. You could claim it's, like, driving outcomes.
Madhavan Ramanujam: Like, if you lose weight, then we like, you start with a $100 a month for instance, just making it up, and then if you lose weight, then we give you like, you know, $20 back, or like if you study well in a course and you actually get a good grade, we've seen some of these things, we get some credit back because then you can actually have tie ins where people submit their scores and everything else and you know that the product actually improved their performance. It actually works well. It's like it's more of a gamification. I think an outcome based model in in the consumer side probably has not taken off as much as in the, you know, b two b side. Yeah.
Madhavan Ramanujam: Probably, more to come.
Anna Piñol: One of the key characteristics for outcome based pricing apart from autonomy is being able to prove I mean, there needs to be a key metric that you impact. And and the company needs to have some sense of, like, what's their current cost without AI or without your product. What what percentage of do they like, do these AI companies are charging? Like, how much are they capturing? Because, obviously, if you you charge the same as their current cost, they're gonna say no.
Anna Piñol: But I wonder what's the discount Yeah. That is common.
Madhavan Ramanujam: For for sure. I mean, like like you said, first of all, is super key. And the fact that you can attribute it and it's also closed loop in the sense that at the end of the day, your customer independently can say, I got this incremental revenue or cost savings or opportunity cost because of the company. And then, you know, the question then really is what is a if you can show that attribution and you unlock value, what can you charge? Right?
Madhavan Ramanujam: In the in the historical like, I mean, the previous vintage, we used to say if you're one s to 10 x, it's good pricing in SaaS. In AI companies, we're actually seeing that you could even capture 25 to 50% of that attributable revenue because it's true incremental and autonomous. Right? So 25 to 50% is the benchmark that we have for that we are seeing actually some companies actually do it.
Anna Piñol: And do you think this is gonna sustain, or are there any because another thing that that that makes me think, like, this outcome based pricing is that it's so simple and clear that it's also very easy to potentially switch providers. Mhmm. Right? Like, if someone comes and it's able to resolve that ticket as, you know, the the current vendor and it's not charging 50% or 25%, but is willing to go to 10. Mhmm.
Anna Piñol: Like, the switching costs, yeah, makes me think
Madhavan Ramanujam: switching cost is probably can be built internally with the product and and as opposed to, like, having your pricing model be the switching cost. In fact, I would argue that if your pricing is complex, that is when people actually wanna leave you to, someone else. If your pricing is simple and they understand why they're paying and is based on outcome, it's actually hard to, like, for someone to leave because you're putting skin in the game unless someone's gonna charge you lower for outcome. But then that's a different question because then your product needs to have some switching costs in the sense that, you know, it's very well integrated with things. It's hard to rip and replace.
Madhavan Ramanujam: It'll take some another AI agent to, like, do the training all over again, and you put some guardrails, and you build some new, you know, let's say, enterprise RL models that actually can solve things, then, of course, it's not easy to rip and replace because then there are models that you're building. There's training data. People get used to it. They understand the user interface. They understand how the agents work.
Madhavan Ramanujam: Then it's not that easy. The I I've seen actually, if pricing is complex, that's when people invite other people in the room. Right? Because then it's like, okay, I want, you know, simplify my pricing. But if it's outcome based, is a moat, is what I'm trying to say.
Pete Flint: Yeah. Everything's aligned, you certainly have also just this it's easy to communicate and easy to track and measure. And how do you see folks do you see folks moving around the map or revisiting pricing? Because it's there's you know, in this, you know, you you we see a number of AI sellers which are entering the market with an outcome based pricing, and then building these compound startups where they're like we're tacking on a bunch of other value after this very attractive initial wedge. How do you see how should founders think about that evolution and revisiting pricing?
Madhavan Ramanujam: Yeah. I think, like I said, it is when we when you start based on your, you know, product level of autonomy and attribution, you need to pick the right quadrant. But you also need to build pathways to, like, move around the quadrants if that is something that you can actually aspire to do. And and this has actually happened in many industries already. Maybe we can take an example to unpack that.
Madhavan Ramanujam: If you take, I mean, we talked about coding, you know, agents and coding assistants. So if you think about the classic, you know, GitHub Copilot for instance, they started in the bottom left quadrant, you know. Attribution was low, autonomy was low, so they were a per seat model. Back in the day, GitHub was per seat per user, right? Mean, we all know that.
Madhavan Ramanujam: When you take the cursors of this world, they actually move to the bottom right, which is, you know, attribution's higher, you're saving a lot of, you know, time for the developer, you're creating code that can be almost reused as code attribution, but it's still on auto, you know, copilot mode. So they are in a hybrid of seats and usage model. But you already hear, you know, companies talking about, I'm building an entire autonomous, you know, coding platform, like I'm gonna hire you a QA person who can actually do QA and you don't need Jim or Jen doing QA, you can have my AI do it. And I can actually show you how many bugs will fix everything else. Autonomy and attribution is increasing in that space that you could actually say, okay, then I might wanna actually charge based on outcome.
Madhavan Ramanujam: How many bugs did you resolve, and is that how's that compared to like a human, and should I charge for that agent differently? Then you get into more interesting conversations. But the key is to understand that where you are and what do you need to do. Like, how can you build more things in your product that will demonstrate more attribution over a period of time? And how can you build stuff in such a way that your product can get more autonomous over a period of time.
Madhavan Ramanujam: And for attribution, the some simple hacks are things like, okay, you actually wanna have some kind of, you know, dashboard where I log in as a buyer and I can actually see some charts saying how is this AI actually increased my top line, my cost savings, my opportunity cost, you know, sort of make that the front page. I mean, this hey. We we have actually saved these things. There's a report that's commonly available. We are training people that attribution exists, and how do you build pathways towards that before unlocking outcome based pricing, etcetera.
Madhavan Ramanujam: So I think you can move around, but you need to plan for it.
Pete Flint: The the the pace of AI is getting so much better so quickly. And, you know, we're we're you know, superintelligence is, you know, a tough definition. But as you kind of roll this forward a couple of years, do you see outcome based pricing being you you mentioned 25%, but do you see it perhaps kind of more prevalent than that over time as as sort of artificial superintelligence becomes kind of more pervasive? Because I think it we have to be building for this world because it's getting better at such an exponential rate.
Madhavan Ramanujam: Yeah. I think that in the next three years also based on, like, studies that we have seen, we have done ourselves, stocks, etcetera, where the buyers are actually getting more comfortable, right, if you look at CIOs and organizations, even the ones who used to say, I want predictable budgets, they're like, you know what? I'm actually willing to, like, see if outcome would work because our incentives are aligned. Right? So that's also how we get to that 25 to 30% in the next three years.
Madhavan Ramanujam: If I take a more fall forward look of, let's say, you know, five, ten years, you know, superintelligence, AGI, we can debate what our worlds will look like. But I actually think intuitively outcome based pricing model should make a lot of sense there. Right? Because, I could even envision a world where AIs talk to AIs and they reconcile how much to pay each other, and they basically you know, it's based on the outcome, and there's actually no bias like humans. Right?
Madhavan Ramanujam: I mean, it can actually do that, and it has to be an outcome. If you if you take a world with less bias, outcome is the right model.
Pete Flint: Yeah. Yeah.
Madhavan Ramanujam: There's no debates.
Pete Flint: Yeah. There's no you know, let's say, I it's it's like, what is the value you're doing to me? And what is my willingness to pay? And Right. And it's a pure, like, individual economic decision, which lends itself to, you know, some sort of quasi auction type situation in a in a a real time basis.
Madhavan Ramanujam: Yeah. Exactly. My AI agent hired a, you know, recruiter agent because those models are actually better. I'm a general purpose AI, and I hire them, and they do the work, and I pay them based on the outcome. Yeah.
Madhavan Ramanujam: And and it all happens in the background. Yeah. Right? You know? Yeah.
Madhavan Ramanujam: It's a it's a yeah. It's a bit interesting to see how the world will evolve, but, it's anyone's guess.
Pete Flint: Yeah. But then it's in that environment, I think the founders need to think about what is the true defensibility beyond the the sort of the AI product that they're building, and which is a lot of data, network effects, workflow, etcetera. So so if you're if you're a start up competing against an incumbent, and you talk you know, when we spent time together a dozen years ago, there's a lot of thinking about bundling and kind of and and pricing and different kind of components. You can see an environment where you have a a sort of a an incumbent which arguably has a sort of bloated product set, adding an AI component, and they're looking to kind of, okay, this sort of like machine of like, okay, we need to kind of ratchet up the kind of ACV. And then you have a startup which is saying, okay, I'm going to monetize an outcome based on my high value AI product and then give everything else away for free.
Pete Flint: What are some of the merits of that approach and any tactics for have you seen that working? Have you seen and have you seen tactics for founders to execute
Madhavan Ramanujam: I on mean, like, look, when when you have a incumbent in in town that are and you wanna compete as a startup, there are probably, you know, few ways that you can actually do it. You can say I would try to be a low cost provider compared to the incumbent, and that's the reason to switch. But guess what? The incumbent probably has a lot more capital and they can drop the price faster than you can drop, right, in some ways. So that's a losing proposition.
Madhavan Ramanujam: It's always the other person who started the price war. Right? I mean, so it's that's one way you could think about it. But the more smarter way actually to think about it is to say, are there some strategic things that I can do in terms of, like, how I charge my pricing model? Can I actually make it different?
Madhavan Ramanujam: Are there some core pain points with the current incumbents that I'm actually solving and I how can I leverage that? Right? Even back in the day, when you think about Netflix, right, and Blockbuster used to have all these movies and, you know, you have price based on movies, late fees, everything else, but a subscription price on Netflix to keep your DVD however long you wanted was a different pricing model. It actually worked. I mean, and the rest is history.
Madhavan Ramanujam: So this has been done in various vintages, like, where a pricing model conversation itself is the reason for disrupting an existing incumbent. So if a incumbent because think about it. A a a really big incumbent with multiproduct, many different teams, you know, just building AI into their existing arc you know, tech architecture, which does not have AI, what are the chances they will move into an outcome based world? Zero. They're gonna have 20 debates, consensus, and they will shut it down as they know.
Madhavan Ramanujam: Let's just preserve our margins for our current business, and they won't do it. But if you're in if you're if you're coming new to the market, you have nothing to lose. If you say, look, I win when you win, and my model is outcome based, great. Maybe that's how you start stealing accounts. Yeah.
Madhavan Ramanujam: Right? So I think thinking about those things would be key, and I've seen it work all not just now. We're we're not just seeing it now. We've actually seen it historically.
Anna Piñol: Mhmm. And we're seeing this this trend, right, like, with AI at the end delivering work. Right? It's reducing the the need for so many people in a company, and and, like, a seat based pricing thrives when a company adds more headcount. Yeah, it's
Madhavan Ramanujam: like catching a falling knife if you're on seat based pricing.
Anna Piñol: Exactly, yeah. It's kind of like the classic innovator's dilemma.
Pete Flint: Yeah. So in the book, you talk about these founder archetypes and perhaps failure modes and kind of things that folks don't do so well. Can you talk through a little bit of those the failure modes and archetypes?
Madhavan Ramanujam: Sure. I think it ties back to the same market share and wallet share. Right? I mean, so I love two by two, so I gotta use one more.
Pete Flint: Oh, we love two by two. Fact that they're like frameworks.
Madhavan Ramanujam: Yeah. Exactly. So so when you think about, you know, market share on the y axis and wallet share on the x axis. Right? And let's take the top left quadrant first.
Madhavan Ramanujam: High focus on market share, as in a leadership archetype or a person who has high focus on market share but low focus on wallet share. You know, we In the book, we call them the disruptors, right? These are people who say, I'll grow at all costs and I'll figure it out in terms of monetization, right? If you take the bottom right you know, quadrant where wallet share is the big focus, but market share is not that big focus for the CEO or the leadership, we call them the moneymakers. These people actually think about from day one how to build a great commercial business, but, you know, not focusing more on the market share.
Madhavan Ramanujam: And then you have this bottom left where you're neither focusing on market share nor wallet share. We call them the community builders. Right? They're focusing on a core set of customers and they wanna do right by them and just keep working with them. And then the you know, if you take each of these archetypes, I'm sure all of us have seen many of these.
Madhavan Ramanujam: Right? Let's take the disruptor one, focusing more on market share, but not paying attention to wallet share. Not equal efforts, equal attention. You're not even paying equal attention to wallet share. And you're basically selling a dollar and 80¢.
Madhavan Ramanujam: Literally, that's what's happening. So you fall into two traps. You know, you're probably landing without expanding in the sense that you gave the farm away in your entry product, and then you're chasing your tails to, like, build something and hoping to monetize. That often does not, you know, sort of, translate, and that happens with disruptors all day long. And the other one is making the mistake of thinking that market share one is market share earned.
Madhavan Ramanujam: What that means is you're so focused on acquisition that you keep thinking about getting new customers. You're not focusing on keeping those customers, retailing them, adding more products, value to them, and retention is not the focus. Acquisition is the focus. So you won market share, but it's not durable. Right?
Madhavan Ramanujam: That happens with the disruptors. If you take the moneymakers, you know, focus is more on making, you know, money and or thinking about that before the, market share, they fall into a couple of, traps. One is the price premium trap, which is, hey. You know, I'm I wanna charge a premium because I learned that premium price means that it's value, and, you know, I'm the Nextiva. And, you know, a $200 wine is, value and whatever.
Madhavan Ramanujam: I mean, they've learned stuff that but the par I mean, while there are obvious connotations that have, you know, prices signal a value, the price premium paradox is when you charge it so high that you become irrelevant to, like, your audience. Right? So that actually happens all the time. Like, when you start charging for a, you know, a juice Aero machine at, like, whatever, $700 when you can probably squeeze the packets and you have the same result, you're on a price premium paradox. Right?
Madhavan Ramanujam: I mean, so that's different. Or you also fall into a, you know, nickel and diming kind of trap, which is you're so focused on the wallet share that you have these your pricing is incredibly complex, money, maybe even different, you know, elements of the fee structure, things like that, because you're only thinking about that, not the market share. If you're the community builder, this is an interesting one because there's also some literature around, let's just focus on a few customers, build a product, and scale it. CEOs who have actually done that have still been very thoughtful about wallet share and market share and paid attention even though they started community builders. But the community builders who don't pay attention to these, they fall into traps like, you know, giving away too much, for too less because they're so you know, wanna please their customers in that community.
Madhavan Ramanujam: They keep giving. They keep giving. And they train their best customers to expect insane value for less, and they can never undo it because once those customers become reference customers, they'll be like, yeah, I got that for, like, $10. Are you kidding me? Like, it just doesn't make sense because p do you just train someone to, like, get insane value low price?
Madhavan Ramanujam: They're not even gonna be good reference customers because they're they're gonna say what price they actually got it at, and that's gonna be the reference point for everything that you actually do. That's a trap that you fall in. Or the other one is, you know, you fall into this trap of, you know, you're solving for, you know, current, let's say, base, but you're missing the frontier, which means you're so focused on the current customers that you're not even thinking other adjacent markets or others who look different to my loyal base that I should be building towards, so you miss adjacent markets, you know, opportunities to acquire new types of customer segments. So the best quadrant to be in is the top right, which is the profitable growth architect is what I call it, where, you know, you're focusing both on market share and wallet share. Like I said, not equal efforts at all point, but equal attention.
Madhavan Ramanujam: So profitable growth architect is actually a disruptor, a community builder, and a moneymaker all at the same time. How do you do that? And that's really the thesis of book as in, you know, we talk about nine strategies to build towards profitable growth and, you know, demonstrate that you could become a profitable growth architect if you follow those strategies. Four of them for the, you know, zero to one startup phase and five of them for one to five when you're scaling up kind of thing. That's the whole thesis of the book.
Madhavan Ramanujam: We actually have this thing called Axioms. I wanna unpack that too if you guys are open for Yeah.
Anna Piñol: That was gonna be our next question.
Madhavan Ramanujam: Okay. I mean, so the the the Axiom related to the start is the what I call the 2080 Axiom. This is my favorite one. I've, you know, seen this over and over again with tech companies. I don't know if you'll agree with it, but let's let's let's see how you'll agree with it.
Madhavan Ramanujam: Right? 20% of what you build in tech drives 80% of the willingness to pay. This I've seen it over and over again. Right? I mean, any product that you say, okay, 20 if you ask people recall on, like, why do you buy this product?
Madhavan Ramanujam: What are you paying? It's always, like, 20% of, like, what someone has built. 20% of what you build drives 80% of willingness to pay. The biggest irony in tech is that this 20% is the easiest thing to build. So what happens?
Madhavan Ramanujam: If you're in that disruptor mindset, you will build that 20%. You'll say, okay, you know, that end is the fastest thing to build. You'll say, okay, let's build it, put it out in the market, let's call it MVP, give it away for free. So basically, what have you done? You've basically given away the form, and now you're gonna build 80% of ridiculously hard stuff that's only driving 20% of willingness to pay.
Madhavan Ramanujam: Right? So you're basically disrupted, but you have no pathway to, like, get to profitable growth or monetization. You just gave away, and you're hoping that something would happen. The right person would say, okay, this is the 20%, you know, that's driving willingness to pay. How do I probably get it in such a way that maybe there's some usage things?
Madhavan Ramanujam: I wanna give it away for free, but after a certain point, you have to monetize on that. So can I actually compensate on a land and expand? And you'll have that equal attention kick in. And then you'll actually put it in such a way that you will, you know, cross both market share and wallet share. But if you didn't, you'll just go on leaning on one side.
Madhavan Ramanujam: Right? I mean, so that we call it the, the twenty eighty axiom, which is is is so true. Every time people will give it away and and then they're just chasing their tails.
Anna Piñol: Yeah. And it's surprising how founders, like, have such a hard time sometimes, like Yeah. Being afraid to charge a lot for their
Madhavan Ramanujam: I mean, in this case, they don't even charge. They just give it Go charge a we should stop calling things minimum viable product. We should call it most valuable product. This whole MVP definition needs to change, I think. It's, it's, I also heard another definition, most lovable product.
Madhavan Ramanujam: That's a good one, actually. I think that's a better definition. But this MVP, which is that 20% that you can build, is the core of the willingness to pay if you gave that free. Tough luck.
Anna Piñol: Mhmm. What are other axi axioms that are your favorite?
Madhavan Ramanujam: Yeah. I think there is, the axiom that I talk about, you know, on the price increase, axiom. So usually I mean, the price increase Axiom is that to to do a price increase often, you know, the, reluctance to do it is internal and emotional. It's not external and logical. And we unpack that axiom in the sense that and I think Warren Buffett said this well.
Madhavan Ramanujam: He said if if you need to have, you know, for doing a 10% price increase in a company, if you need to have a prayer session, you have a terrible business. Right? I mean, so how do you do a price increase if it's a 10%? And we talk about that, like, you know, how to actually achieve that. But the, axiom there is that every single time we have found a price increase being more internal and emotional as opposed to external and logical.
Madhavan Ramanujam: There's always a way to do it because, you know, the price of, like, everything that you consume goes up year over year. Your software or AI cannot be on the same price for the last five years. There's something off. I mean, it's internal and emotional. It's not external logical.
Madhavan Ramanujam: So that's one that I
Pete Flint: That's why we that's why we love network effects. Right? Because it's like if you as you as you scale the business without necessarily providing more features from a software perspective, the value of the product increases over time. And so if you and, you know, you and then actually sort of pricing and increasing pricing over time is sort of like a natural part of the service you provide or increasing value within the product you're providing.
Madhavan Ramanujam: Oh, totally. And every person who brings in another person makes it valuable for the whole network, right? I love network businesses too.
Anna Piñol: Another accent that I like is it says something like attract customers who won't leave.
Madhavan Ramanujam: Oh, that's a good one, yeah.
Anna Piñol: And the importance of choosing your customers. I mean, always, but also especially early on.
Madhavan Ramanujam: Yeah. That goes to the, I mean, promotion example that I was giving. Right? I mean, if you acquired customers with three promotions who are gonna leave, that's not robust revenue. Right?
Madhavan Ramanujam: I mean, that's just revenue. It's not durable. The best way to stop churn is to acquire customers who won't leave. That is the axiom. Right?
Madhavan Ramanujam: Because most people try to stop churn at the time that someone says, I'm going. It's too late. You can prolong it by a few more months, but that person is gonna leave. Right? And it's reactive.
Madhavan Ramanujam: The proactive way of understanding churn is say, look at my customer base and say, who are the types of customers who tend to stay longer? Who are the ones who actually tend to, you know, engage with me more? The usage, buying more products over a period of time. Who are they? What are they you know, how can I find more of them?
Madhavan Ramanujam: Can I transfer all my acquisition dollars to find more of them? Yeah. Then you're stopping churn because you acquired the right set of customers.
Anna Piñol: Yeah. It's interesting how sometimes, like, a retention problem is really an acquisition problem. Yeah.
Madhavan Ramanujam: Know? That's it is it is totally right. Nice way to phrase it.
Anna Piñol: And last question on this one. How would you relate this to, like, the what we were discussing before about POCs? Like, it's very critical. Right?
Madhavan Ramanujam: For sure. All of these tie back because if you choose customers I mean, first of all, you know, you need to separate the, you know, tire kickers from the people who really want to use your product. For that, you need to charge for a POC. That itself is a lead qualification mechanism. Like, many founders ask, should I charge for a POC?
Madhavan Ramanujam: I'm like, hell, yes. Because otherwise, you're gonna get some curious buyer who will take you down a three to six months pathway. Might never buy. They're just curious to see the AI. How do you know they have budget or they wanna do something?
Madhavan Ramanujam: Right? So charging for a POCs and sell you know, at least identifying some customers who wanna partake in that because they have to put some effort for it. And also, like, how do you, you know, frame it as a business case so that your monetization is part of the value delivery that becomes key so that you're not giving the farm away. So all of these principles actually apply there. So what we talk.
Pete Flint: So if you're so let's just say you're an early stage founder, you've raised your PreSeed or Steve Steve Round led by NFX and forty nine Palms.
Madhavan Ramanujam: That's great.
Pete Flint: Let's do it. And you're building an AI product. Like, what are, like, what are perhaps three pricing tips that they should do in the first ninety days?
Madhavan Ramanujam: So the first thing that I would probably, you know, look at is, you know, how do they charge or how do they plan to monetize? Because that's a more strategic and fundamental question as opposed to how much. How much can wait? Right? So that's a ninety day question.
Madhavan Ramanujam: Like, should I
Pete Flint: The tactics of specifics. Specifics around how they will charge, whether that's outcome, whether that's And
Madhavan Ramanujam: that's unpacking their entire product, seeing what value they actually offer to their customers, how the customers realize value. Do you have some value elements? For instance, you know, can you charge based on a resolution, tickets, whatever? Or is it like consumption on tokens? Is it like usage based?
Madhavan Ramanujam: Is it hybrid? What all of that stuff can be, you know, ironed out pretty fast based on the archetype and everything else that we talk about. So that's the first thing that I would do. The second thing that I would do in the first ninety days is to actively start, framing POC conversations as business case conversations so that to your point, Anna, before that I start selecting customers who actually wanna invest time to jointly investigate with these founders that there is value in this AI product as opposed to some just technical diligence. So I would probably, you know, coach them on on on that.
Madhavan Ramanujam: Right? And, that would be the, second thing. And the third
Pete Flint: thing By by case, you really it's really deeply understanding the the needs of the customer in terms of their expectations, their cost structure, business model, etcetera.
Madhavan Ramanujam: Exactly. And the third one is also, like, preparing the founders on how how to have these conversations with the the customer. Right? I mean, there are some because what I often find is founders will show up with a product and demonstrate what they can actually do from a technical standpoint. But, like, are they actually having the right value messages?
Madhavan Ramanujam: Are they actually talking to that? So, like, you know, coaching founders on how to articulate value, create needs, not just discover them. And how do you value sell and negotiate becomes key. So actually, like, for instance, the options that we talk about. Right?
Madhavan Ramanujam: I mean, things of that nature that you can actually start being more strategic in terms of how you negotiate. Yeah. And I think that's the other training that we can vastly do. I'd love to have a a situation of a joint capital with
Anna Piñol: you. So
Pete Flint: I'm sure. I'm sure. I'm sure. That'll great. So anything else that we missed as as founders thinking about pricing in this in this new world?
Madhavan Ramanujam: One thing that probably, you know, I I keep getting asked, and we sort of unpack that, but if there's interest, we can unpack it, was all these AI companies are getting to, like, you know, revenue pretty fast. Is that good? Is that have they cracked the pricing code? Is this durable? I think that's an interesting topic.
Madhavan Ramanujam: We kinda talked about the negative margin or neutral margin based on the TechCrunch article. I mean, so that's one aspect of that. But there are several aspects of it, is very interesting, I think, which even investors need to know. Right? I mean, there is there's a there's a there's also, like, you know, where am I getting that, let's say, revenue from?
Madhavan Ramanujam: Is it contracted revenue, or is it POC revenue? So there's a lot of, you know, things that you actually see. Okay. I'm I hit a 10,000,000 ARR, and, you know, I start unpacking it. It's probably more POC revenue than contract revenue.
Madhavan Ramanujam: I got contracts from, like, Tesla, Apple, Google, and other companies. Like, okay. It's a ninety day POC. That's not a contract. Right?
Madhavan Ramanujam: So I think so let's be all careful about, like, what these reported revenues mean. That's one aspect. And then there's also, you know, the other aspect of, like, is this revenue durable? That's also important question because there's a lot of curiosity on the buyer side to actually, you know, look at these AI products. Is it working for me or not?
Madhavan Ramanujam: So there's a lot of curious buying, which means are they gonna be there after three to six months? Question mark. So you cannot take something and extrapolate it to twelve months when you've only been there for, like, three months. You don't even know how churn is actually gonna affect you. Is that durable revenue or not?
Madhavan Ramanujam: Key question for us to think about. Right? I mean, this do they have foundational modes? Is it is it truly delivering value that people will actually have an ongoing monetization conversation?
Pete Flint: Yeah. It's you see it all the time where you've got the boards of directors pushing down into their kind of CEOs and and VPs saying, what's our AI strategy? And so they go out and sign a bunch of POCs or experimental budgets to try sort of things that tick the box, and then they and then they move on. Is there any and just from a founder perspective, is that really just looking at, you know, does the engagement and retention increase over time? Is it is it as as simple as, like, watching core usage and and core monetization within on a per customer basis on on specific cohorts?
Madhavan Ramanujam: Yeah. I would nuance that to say on a segment basis. Like, what are the types of segments of customers I'm attracting? What is their usage intensity? You know, are they actually who are the ones that are using?
Madhavan Ramanujam: Are they gonna tend to stay? So, like, doing cohort analysis, exactly, that's the that's the key. Because if you just look at it on on average, averages will always lie. Right? And there's also the all these pressures on, like, what is ARR?
Madhavan Ramanujam: I think we also need to, like, in especially in AI, think about is ARR the only right metric? No. We there are probably other things that we also need to look at for the health of the business. And even ARR, how is it even reported? I mean, some of the things that we saw, I don't know what you see, like, and we we we met a founder who was actually we talked about seasonal products, for instance.
Madhavan Ramanujam: Right? Seasonal products, seasonal product, I mean, we didn't come up in the conversation because it was seasonal. We understood that it was seasonal after talking. But if you look at the financials in terms of how the ARR is done, it's like, take the maximum in a five month period and multiply it by 12. I'm like, no.
Madhavan Ramanujam: That was the peak month. What happens the other month? You can't just do that to extrapolate your ARR. So, like, how is the ARR even computed is is a question mark. Right?
Madhavan Ramanujam: I mean, like, if you don't account for, like, Shannon, how do you do it? So I think being thoughtful about this as a community of, like, founders, investors, and others, like, what KPIs are we looking at? Usage, retention, you know, ARR? I think
Pete Flint: And the the seasonal one is an interesting one because you could look at let's just take auditing or taxes. Like, actually, you know, to many organizations, like, I don't wanna, like, staff up for a three month period and then staff down again. I like if there's an AI that can do that for me, then it's terrific. And so actually so your kind of your your kind of fixed labor becomes variable. And so and so you might look at seasonality as negative, but I think we see a lot of companies doing very well because they're just purely seasonal because they don't need to go through the headache of hiring and firing.
Madhavan Ramanujam: Yeah. This one was like usage exists in other months, but in certain months, the usage peaks. Yeah. Let me leave it at that, which is a bit different case than what you're saying. But, yeah, it could be negative or or positive.
Pete Flint: And then thinking through the the ICPs, we we see there a number of small companies intrigued by AI, but they don't necessarily have the economic sophistication to think through, okay, if I substitute this action with this piece of software, then I'm able to see an ROI, where you see a lot of the kind of mid market companies that are perhaps more aggressive or professionally run that actually see a lot of adoption for these types of products.
Anna Piñol: At the same time, like, it it's been I mean, it used to be that when in b to b in particular, like, you started selling to mid market more and, like, enterprise would come later. And now we're seeing all these founders being able to close deals very fast with with enterprise. But but to your point, I completely agree that it's a little given, like, how much curiosity it is. And it's also kind of our jobs to, you know, learn
Madhavan Ramanujam: Mhmm.
Anna Piñol: To assess also, like, the quality of that revenue and also Well, it changes. Well Yeah. In this process.
Madhavan Ramanujam: That's the that's the right
Anna Piñol: And because sometimes, you know, we don't have time to wait for a renewal. Right? Like, to Mhmm. Yeah.
Madhavan Ramanujam: For sure. For sure. But I think that's also where I mean, especially in the businesses that you're probably investing in, that network effects itself become a moat, and is that strong enough? Is something I'm sure is part of your, you know, thesis, and is it durable, and is it quality revenue? I think those are the diligence that needs to be done as investors, but also as founders being thoughtful about is my own revenue, you know, durable or am I just reporting something that do you actually believe it?
Madhavan Ramanujam: Ask yourself that question in the mirror. If you can sleep well, okay. If you can't, what do you need to be answering to actually see if it's durable or not and then work towards it.
Pete Flint: Thanks so much for joining. Great conversation, great insights. I'm excited to do some co investing together.
Madhavan Ramanujam: That would be awesome.