Pricing and Packaging for AI Products with Sandhya Hegde, General Partner at Unusual Ventures

Started here in another great episode of Workshop Wednesday.

Hello.

Hello everyone. Welcome Saster Community. We are so thrilled to have you here with us for another amazing Workshop Wednesday. Just a couple of quick announcements before we get going. We have so many exciting in person opportunities coming up in March and also later this year. So just want to remind you guys if you, if you happen to be in Miami, we are going to be there next week for a meetup and would love to see you there. I'll drop in the link for you to sign up and snag your ticket. We're selling tickets fast and make sure that you pick yours up this week. And then we also have our AI Digital day coming up at the end of this month again, I'll drop that in the slot or in the chat for us too. And Amelia is running an awesome alumni special for annual tickets that is going through the end of this month tomorrow. So it's never too early to buy those annual tickets. This is probably the best promo you're going to see leading up to annual, so it's a great opportunity to snag those and we would love to see you in person in San Mateo. So hi everyone and welcome. And we are thrilled today to be joined for another amazing Workshop Wednesday session with Sandia Hedgehog, general partner at Unusual Ventures. We are so thrilled to have her and she's going to be doing a really great, interesting session about pricing and and packaging for AI products today. I know a topic that a lot of people are grappling with and also just very curious about with everything that's going in in the market. So Cynthia, we are so thrilled to have you.

Welcome, welcome.

And would love to get kicked off with some VC quick fire questions.

Oh no, go for it.

So we always like to ask in the past year, what is one investment that you've been really excited about?

I think if I had to call out one, it would be this really exciting design startup called Viscom. I'll be talking a little bit about their pricing as well and our case study during the workshop. Yeah, they have just been really amazing to work with, doing very well.

Amazing. Yeah. I'm excited to see some more info on their case study coming up. So saw that in the slides. What have you been most surprised about in VC from the past year?

I'd say honestly a lot of surprises. The biggest kind of surprise for me has just been how quickly we forgot all of the kind of regrets and mistakes we had from 2021, especially in the land of investing in AI startups. I think I see a lot of us kind of struggling to keep disciplined with pricing, struggling to like, you know, remain kind of strong investors in terms of like how we support founders and how we, you know, keep, keep the foundation strong for companies instead of just focusing on kind of the hype and the valuation. I think it, I was a little surprised how quickly it kind of boomerang back to 2021. So probably not like the most positive surprise, but, you know, an important thing to keep in mind for us as investors.

Yeah.

And we'd love to hear for looking for 2024. What are you most excited about?

So, great question. So I think, especially as an enterprise software investor, for me, I think 2024 is kind of the year when we are really seeing enterprise adoption of new AI tools and software start to go from prototyping and concepts and pilots to real implementation, real roi. People starting to build some early frameworks of how they can actually deploy, rollout these new tools reliably within really big companies and, and get value that they can be certain of. I think this is the first year where we are really seeing that happening. So I'm excited for more of that over the next two years.

Wonderful. Well, we are going to let you get into your presentation and share with us more about your best practices and the best things that you're seeing for how folks are doing packaging and pricing for AI. I do want to just flag to everyone that Cynthia is going to be asking some questions throughout. She'd love to hear from you. We're going to have dedicated time for Q and A as we always do, but she's going to ask some questions throughout. So if you want to come off mute and answer any of those or drop them in the chat, please feel free to do so and I'll let you take it away.

All right, thank you so much, Taylor. So we'll be talking about pricing and packaging specifically as it pertains to AI software. I focus largely on the enterprise, so a lot of my examples will be very enterprise as well. Just a bit of background about me. Have been in the startup ecosystem for about 15 years now. Started my own journey as a founder, worked in Sequoia Capital, Khosla Ventures as an emerging market investor, and actually then joined a very early stage company at the time called Amplitude as an executive and got to do all things product and growth at the company. We had a great IPO in 21 and then came back to Venture and joined Unusual Ventures. It's a lovely partnership and the last two years have been pretty much entirely focused on investing in AI native startups, primarily on the application and enterprise software side. So working with close to a dozen founders that have started building very compelling AI businesses. And the other thing that I've been doing is what I think of as case studies, case studies with really successful founders who have built incredible companies over the past 1512 years. And obviously given my area of focus, the last year has been a lot of deep dives into AI companies in particular as well as companies that are starting to release AI features and make that a part of their core strategy. Right. So notion is a good example of this. They were an early mover and releasing AI native features and doing it both as a proactive as well as a defensive move to make sure that they continue to be the market leader in their category. And a lot of the stuff that I focus on in these case studies is how these companies found product market fit, how they figured out what the right go to market strategy is for them, as well as how they think about product and pricing and packaging and, and especially when it comes to AI, I think our ecosystem has gone through this really fascinating rollercoaster. I remember last year, 2023, the way the analysts on Wall street or the way most people kind of analyzing the space were looking at it, were still thinking, oh, this is going to just kind of be like this massive growth driver for, you know, all of like the tech ecosystem. And lately, right, like if you think about more like November, December, people are starting to actually look at the margin profiles of some of these companies and notice that they are like, you know, between like 25, 55% for a lot of these AI businesses, as opposed to what you would expect, which is 80, 90% and starting to say, oh my God, what does this mean? And really I think this comes down not necessarily just to a value problem. It's not that, you know, there isn't enough value being created, it's more a combination of pricing and packaging being very challenging as well as obviously competitive pressure. You know, if you remember from like the Uber vs Lyft days, there's a lot of subsidized products today that are being subsidized by VC dollars and large investor dollars, as opposed to that being absent and every company being kind of super disciplined in pricing because they're trying to expand their market share as much as possible as fast as possible. So it's a confluence of all of those that I think is creating a very challenging dynamic. But even if the competition and the venture subsidy was not an issue, I think Pricing and packaging as is really it's harder than what pricing and packaging has been in the industry for many, many years now. And it's because of these three things, right? One, your costs are dynamic. There's like a real tangible cost. So if you're on the applied side, it is dollar per token, maybe the cost of hosting or like fine tuning a model. If you are a full stack or a model company, it's the actual cost of training, whether it's pre training, you know, retraining, the cost of the data itself that you need to curate. Even if the, even if you're using data from the Internet, you need to curate it and make sure you are only using high quality data that has a cost on obviously, you know, GPU hours have a real cost as you can see from Nvidia's returns. So all of these things are dynamic is kind of the challenge, it's kind of a moving target in terms of startups trying to figure out what is the cost of our engineering, what is the cost of shipping our product. And then you have volatile usage. So the adoption curve is still shifting a lot in terms of how much customers usage. So you have some companies that have AI features that are very low frequency in their usage, but they can't assume that they will remain low frequency. If I use for example, maybe continuing to use the example of notion. If I use notion, say our features right now like maybe once a week, they can't assume that will remain the same. Actually the best case scenario is that I start using it more often from a stickiness perspective, but not necessarily for them from a cost perspective. The fact that the customer behavior is new is also a challenge. Then another really important thing that not everyone has caught on to yet is just the fact that there's also a lot of potential for fraud. So if you have usage based pricing, you have to be very, very careful about how much a customer, quote, unquote a customer or a bot, can use your product before they hit a billing threshold, at which point they might disappear. So there's some real technical problems to solve to be able to keep not just your pricing and packaging strategy strong, but also its execution. And then on the customer, big challenges on proven roi, right? So how do I as a customer make, you know, big purchases when I'm not sure, you know, what should, what should my selection criteria be? How reliable is the investment going to be? Is it going to be effective? Like will this actually save me money? Will this actually make me money? And how much? The confluence of all These three, I think it's just all three are going to slowly get better over time and become more predictable over time. But we are certainly not there today. So I think, you know, in this kind of uncertain situation, like what was kind of a first principle design framework for this. So, you know, standing on the shoulders of giants here. So the way I think about it is, you know, this kind of simple structure which is, yes, you need a model for your company, then you need like an actual structure that you can iterate on and then you need operations that allow you to iterate, allow you to actually experiment, change and evolve your pricing. This has never been as critical as it is today given the pace of change of both your own costs and the way customers are adopting the tool. So we'll take these one at a time. So when it comes to model, I think AI SaaS companies are definitely kind of converging on hybrid, but not necessarily the way we used to talk about hybrids. So it's not just like, oh, seats plus some usage. Hybrid could even be I have seats, but then I also have service bundles. Right, if you are going after enterprise. So you know, I have kind of changed my own framework of how I think about hybrid models. But in general, you know, anyone here is kind of relatively new to pricing. There's kind of three big different pricing models you can think about. Just one is subscription, you know, per seat or per user. At its simplest, level, one is consumption. So it's based on how much of the product you're using. Snowflake is pretty much the kind of symbol of usage based pricing because that's kind of purely what they do. And then there's solution pricing, which is how I describe kind of pricing that comes with recurring services bundles. So that might be, you know, custom deployment that's on premise or you know, virtual private cloud. There might be personalization needed and especially for big enterprise, this is a very, very important element to consider as an enterprise software startup in AI because there's a ton of change management and a ton of handholding needed for your biggest customers to be able to adopt new products. So we are seeing that, I would say anecdotally a majority of AI SaaS businesses today are self reporting as having a hybrid model which is some combination of either subscription and usage. So consumption model or maybe even subscription and solution or all three. So that's definitely a very clear trend. And I would say in fact the large AI startups span all pricing levels maybe slightly differently for different products. And the exceptions are the ones that have Extremely simple single product offerings. So for example, character AI, you know, does only subscription, right dollar per active user, whereas companies like Hugging Face or Nvidia are much more complicated. Do a bit of all of these. Or take a company like Cohere, which requires a lot of like active service bundles to help their companies actually adopt cohere APIs, maybe have them be deployed on premise. So it's a combination. You can see this epitomized in OpenAI, which literally does all three of these across their products, such as ChatGPT and the set of APIs that they have. And so where do you start? How do you think about, okay, what is the right model for your company? So think about it as starting with figuring out two questions such as what is the nature of value that you offer and what is the degree of support that you need? So when it comes to value, there are kind of, I would say oversimplifying a little. There are two ways to think about value, right? So you have kind of are you saving time, right, giving people better, higher quality free time, but that can't be monetized. In some ways all of AI saves time. That is the central premise. It's automation, which means saving time is the value proposal. But the better question for value is what happens to the time that you save, right? So character AI gives me a higher quality free time, but I can't monetize it in any way. Whereas midjourney, if I'm a design contractor that does graphic design and content marketing, et cetera, the time that it saves me is time that I can support more customers. And so it could increase my revenue as a freelance designer, save costs if I'm a manager of freelance designers and now they need two, whereas I used to need five. So that's monetizable time. And this is really important. When you are not offering people monetizable time, you cannot do usage based pricing. So it's a very, very important thing to figure out upfront. If your customers require a fixed cost and cannot further monetize whatever time you are freeing up for them, you cannot do your search based pricing. So very important to understand that question up front. And then there's of course support, right? So take like two different legal AI startups based on the degree of support their target customer needs, they might have a lot of like service bundles versus they might just be a self serve seat based model. So again, this is almost nothing to do inherently with what the product vision is. It's a lot more about who you are enabling with Your products. We'll go into this a little bit more later as well. But the most important thing you want to do is put yourself in your customer's shoes and ask yourself, what is your customer optimizing for? It's the most important thing for them, a fixed cost, in which case you want to stick with a simple subscription model. If there is no way that they can have variable costs that they can't predict and they are very sensitive to, you cannot really experiment with other levers easily because your customer will not support that. And then of course, there's is your customer kind of caring more about fixed roi, not fixed cost. So a great example of this is everything in engineering and data infrastructure. They're not trying to keep their cost fixed. As if I'm a data engineer. I'm not trying to do fixed cost. If the amount of data I need to manage is growing, I'm happy to grow my cost base with it because the growing data means my company is growing and my revenue is growing, hopefully. So I'm trying to have a fixed cost margin, which means I can spend more if I'm making more. So that's what I think of as fixed roi. Now, obviously you want improvements over time, you want it to scale, but you're not trying to optimize for fixed costs. And then there's a really big companies where they have a lot of budget, but what are they optimizing for? They're optimizing for the job to really get done. They can't take the risk that the project is a failure. And that is where having services and having a solution package with service bundles becomes critical. Because at some level they don't care whether it's going to be seeps or usage. The thing they care the most about is are you going to make sure this project is successfully rolled out in my company? Because, you know, it's going to like take us like 6 to 12 months to like adopt this thing. It better work. And I think so. This is a very important lens to take when you're thinking about what you're optimizing for with your packaging and your company strategy, really. So have a few examples from these. Not really going into deeper case studies. We won't have the time, but just wanted to kind of refer to these few companies as examples of how to think about pricing. That's not necessarily counterintuitive, but has nuance in it. That's really important. So Viscom is in the image generation space. Mosaic ML is in the model training AI infrastructure Space writer is in the, you know, LLM language generation space. Right. So three kind of very different AI companies that are a perfect example of everything going on in AI right now. And the all of these companies are, you know, doing really well. So Viscom has had a lot of success with enterprise. They are very focused on industrial designers that make, you know, 3D physical products and design for that use case. Mosaic ML got acquired by databricks recently and is doing really well within databricks. And then Writer is kind of the enterprise version of maybe Copy or Jasper, if you're familiar with those companies and has these really good enterprise logos and customers they are working with. So they're all companies that are seeing success in the enterprise. That's the common thread. And we'll go into how their pricing structures are different. But one thing I'll call out is that they have literally different pricing models for different customer segments with the same product. So it's not one pricing model. They have maybe a subscription model for small businesses that want to use them, but a hybrid, full hybrid like consumption services and seeds like full hybrid model for enterprise customers using them at the same time while the same underlying technology. And I think that's a perfect example of what's working in AI right now. So that brings us to structure. So this question is, okay, if you have picked a model, how do you actually understand what's the underlying structure that you need? And one thing I want to call out here is I see a lot of people thinking of pricing and packaging as just like, oh, that's the product strategy and product owns that, or that's the go to market strategy. And maybe that's like a go to market leader and biz ops. Who owns that pricing and packaging is like literally full company strategy. If someone's done their pricing and packaging page, well I can just look at that one page and describe the entire company's strategy to you. So it's really, really important to think about it this way. So if you're the CEO, you own pricing and packaging. You might have people helping you, but you need to own a. If you're the head of pricing at a company, make sure that you're working with the CEO and pricing committee that represents everyone. Because you cannot make a big bet in pricing and packaging without it being led by the CEO. So what are the inputs that go into it? One is of course, what's the value create for your customers that determines ltv. What's your go to market strategy? What are the customers you're trying to acquire? How Are you going to acquire them? That's your cac. There's the engineering quality, right? Like that is a very important driver of your pricing and packaging strategy. And I think most people don't take advantage of it outside of dev tools and infra. And then there's competitive positioning. Your pricing and packaging says a lot about who you are trying to differentiate from and win deals away from. All of this needs to be represented in your PNP. It's not just about like okay, cost plus 80% margin. And so, you know, maybe take these one at a time. Right? So I think a lot of people make this mistake of saying okay, my, my product like helps people grow their business. So I should be able to like align my pricing to revenue. You can align your value and the reason to buy your product to revenue. But even like literally, Salesforce does not charge based on deals. One charges based on how many people are using Salesforce. Important to remember the difference between correlated versus directly attribute to same idea around being able to quantify improved performance. The degree to which you can really quantify performance and productivity versus it's correlated but not clearly quantifiable relies on the end user to do a good job of using the product. These are very important determinants of whether you can charge anything other than seed based pricing and how much. And then the other question such as okay, can I replace infrastructure or resources entirely? So this is nothing to do really with headcount per se. It's full infrastructure. This is where usage based pricing usually makes sense and you want to correlate it to the dollar cost of all the infrastructure that you can completely replace. Right. So I need to be able to say okay, I can like not do this, you know, million dollar investment instead I will spend 250k on this software tool instead. Of course then there's risk, which is a lot of what the cybersecurity companies focus on. So you know, this is kind of an important determinant of how you want to start adding structure to your pricing. And then we'll talk a little bit about go to market strategy and compare these two design companies. If your target customer segments and their willingness to pay is not a clear kind of function of your pricing and packaging, you definitely have a problem. But then you also need to be able to differentiate kind of what is it that enterprise buyers need and care about that self serve customers don't. And a small nuance here is you still want to be able to land with small teams in big enterprises, right? And protect your enterprise value while Being able to land cheap with small teams. So how do you pull all these together? I think I just do a mid Journey versus Wisconsin comparison because I think you can learn so much from it. Right now these are both companies that are using diffusion models to generate images. We'll do 2D. We'll probably Midjourney will also do 3D. I'm sure Viscom already does it. So this is kind of Midjourney's current pricing, right? If you look at it, you'll say, oh, this is seat based pricing, right? It's 10 months per user, 30 months for user 60. But I think what is really happening here is it's almost entirely usage based pricing. It doesn't look like that, but it is usage based pricing because the only difference between all of these packages is how much GPU time you're getting, right? So they're saying, okay, we'll give you $4 an hour of GPU time, but if you're willing to make upfront commitments, either monthly or annual, we'll give you discounted rates and maybe we will give you like $2 an hour per GPUR instead of $4 an hour. That's it. That's like the entire logic of most of their pricing, right? So it's free but slow for public dabblers. But if you're a freelancer making like running your business based on your segment journey, we can offer you this usage based pricing. Now the big question is, can this work for teams? Does anyone feel like this would translate well to a team? And I'm keeping my eye on time so I'll move forward and we'll do Q and A in the end instead. But I would argue that this will never work for a team. So if you're a design manager with 10 designers, you have no idea what your mid journey bill will be at the end of a month because it's a function of what these 10 designers are doing with their time. And that's terrifying for a design leader, right? So this is not going to work for enterprise. Ironically, even, you know, typically usage based pricing is much more common for enterprise products than for prosumer consumer products. But ironically it's the opposite dynamic happening here, which is that freelance designers or small team server couple designers are using it. Love this model. Pay for what you're using. And big companies will be terrified of taking advantage of this because it's unreliable. So now we compare that to Wisconsin. So Viscom kind of mirrors it a little bit on the starter professional side in the sense that they have a Free offering, just like midjourney. They have like this, you know, like $50 a month product for you know, basically tiny teams. It's mostly used by individual professional creatives. But then as they go up market, they don't want to have, you know, usage based pricing with no limits. They just have a much higher price point for what unlimited usage could look like. And have usage and service bundles for enterprise with custom models for each company which is what the enterprise values. So you can see these are both companies leveraging the exact same underlying technology but have chosen completely different pricing models, almost entirely separate from each other because the customer segments that they are really focused on and their go to market strategy is so different. And then I won't spend too much time on this. But if you're in infrared dev tools, your engineering investment is really the most important thing determining pricing and packaging. The lower your cost basis is the more you can expand free and trial access, the more agile your team is with custom deployments, the more you can actually support enterprise use cases. Every time you ship a new feature you have to ask yourself am I going to use this to increase my ASP or am I going to use this to increase my win rate by not charging for it? And this is the race you see exactly that OpenAI and Google Gemini are going to be in for the foreseeable future. The next two years, these three questions are going to determine most of their API packaging and pricing strategy. And Mosaic ML is a great example of this. And you know what I would call out here is actually just the velocity. So they do usage based pricing, they train models. And this was like January 23rd where they said hey, everyone's thinking about diffusion. Everyone wants to train diffusion models. We are able to do it for 160k which is 2.5x less than what you probably will be able to do on your own. That's their value prop, right? It's an extremely commercial usage based pricing value prop. So that was January. By February they already have an update saying hey, by the way, it now costs 125k down from 160k. That's a great example of what you need to do if you are an AI infra company right now and how much you need to create predictability. If you're doing usage based pricing, people need to be able to plan how much they're going to eventually be spending on your platform. And then last but not the least competitive positioning I would say. One mistake I see a lot of folks making is they will have a freemium plan with a free version that's very limited, while their competitors have free trials of the best version of the product. This makes your tool look silly and I also see a lot of people going after the SMB segment when there's a good enough free alternative. The reality is any market that has a good enough free alternative, the SMB mid market segments are not compelling markets. You have to just focus on enterprise. So it's a very, very important learnings from years and years of trying to make different segments work. I think Jasper and Writer offer kind of significant lessons here as well. So Jasper, for example, started by just doing enterprise. They just had this fully hybrid plan, seats services usage, you name it, that was the only thing they were doing. They just recently announced a self serve try for free plan, whereas Jasper had done the opposite, had started by focusing on teams and is now trying to figure out enterprise. So you can see how different the pricing pages for both are and you can see how limited the free trial for Writer is. It's only up to five users. You just want someone to be able to dabble a little bit. Their focus is entirely what you see on the right. So even for something as simple as generating text, your go to market strategy needs to result in extremely different pricing and packaging plans. And then last but not the least, I'll touch on this. Overall, every company tries to have this monotonically increasing value for customers. If you're not doing that, you're probably not going to last. The disruption cycles and software are very rapid. However, your pricing needs to do, I would say a few different things. One, the cost of buying your tool should always be much less than the value of using your tool. So make sure that you have a good eye on that. And second, your cost does not need to increase monotonically. Right? There are times when maybe you decrease your pricing because you're trying to grow enterprise value, not asp. And maybe growing enterprise value at times requires you to decrease your pricing and increase your addressable market. So it's not monotonic. And you have to think about like am I doing it for win rate? Am I doing it for ASP all the time with every new feature released? So it could go up and down. The important thing is to be iterative and to be able to change it and improve it step by step all the time. I think that's incredibly important. And keep your eye on the cost of the alternative and whether you have enough value difference between you and the next best alternative. So I think I'll wrap up with that. So few kind of important takeaways here and then we'll do Q and A. The first successful AI native companies are really being full hybrid using different pricing models for each product, for each package, for each customer segment they are focused on. The important thing is customers want some predictability. It could be cost, it could be roi, it could be just job success, but they probably want all three. But you need to make sure you understand how they would stack, rank these and align your packaging for that customer segment to the most important thing they are trying to optimize for. And then good packaging and pricing is also about messaging. You need to kind of align with your customer's mental model of how they buy software, how they think about what this tool does for them and balance in all the cost based dynamic pricing with value based minimum commitments. Otherwise you won't be able to have any predictability in cash flow for your business. And then you know, don't forget this is an all company strategy. You can't delegate it. This is not something you know, one person in your company, no matter how brilliant, can do on their own. Because this really to make this work, it dictates you know everyone. Every functional strategy is aligned to kind of what you're doing with pricing and packaging. And you know, last but not the least, you have to be iterative. You have to like grow and evolve your pricing. And there's a lot of infrastructure you need in place to be able to execute well on that. There's a lot of good companies focused on helping do this as well, which is just exciting for me. And some resources, a few that I really like especially from Reforge and Open View. These are just kind of general SaaS pricing and packaging catalogs. Stig has a great developer guide to all of the modern SaaS monetization infrastructure. And and we also write a lot about this on the Startup field guide. So if you're looking to kind of go down some rabbit holes, feel free to email me as well. This is one of my favorite all time topics to talk about. I'm sandhyanusual vc.

Thank you so much Sandhya. Super helpful. We're getting a lot of great comments in the chat with your resources. We had someone that had used the field guide before so was giving a plug for that and saying how helpful it was. So thank you for sharing those awesome resources that Unusual has and other ones that you use to have a few questions that are coming in. TK is on with us and they have a question about pricing for a partnership. Tk There.

Yeah, back from. Hey there. How you doing? Can you hear me?

Yes.

Yeah, awesome. Perfect. So, yeah, from last week, loving them. And I'm going to be a recurring guest hopefully with questions because these are really insightful. So my thoughts are. So when you're an early stage startup as you need the customers to get data to validate whether the LLM that you've created can solve. When I say LLM is in like the specific, like sort of the AI tool that you built can solve enterprise problems. You can't prove that it works until you get customers data. How would you price Almost like a design partnership whereby you can sort of work with them to validate where the product works but also at the same time use that money from them almost as like seed funding which is obviously like, you know, cheaper and more valuable than VC money in the early days. So like I hope that makes sense. It was. But, but, but yeah. So it's basically how, how do you, how do you, how do you deal with those conflicting factors and price it in a way that, that, that works with, with those two things in play or I might be completely wrong in my thinking initially. So.

Yeah, yeah, no, I mean I'm trying to think of what's the best short answer I can give to this. It's definitely a very important big topic. I think if you space where there are a lot of alternatives, I don't see enterprises paying for design partnerships. I just don't see it. So you know, your options are you either do try to raise VC funding or you are able to, you have a good enough early founder engineering team that you're able to build something viable that you know does not require your design partner to pay for a pilot or proof of concept. If it's very competitive because they have so many alternatives, they would rather wait for your product to mature and then try it even for like a free trial. They would rather wait for a product to mature. If there are literally like 20 alternatives that they see in their newsfeed when they wake up in the morning. If it's not, if they feel like, you know what, we have looked and looked, we had tried to build this in house, why is nobody doing this? If that is what your buyer is asking, they are much more willing to pay for a design partnership, do a paid pilot. However, for that situation, the founder that they, that is pitching that needs to be highly credible. Right. So they need to be able to say, yes, we are taking kind of a flyer on this person, but look at this person's track record. They have done, you know, enterprise grade engineering and product building for these other companies in the past. Right. So because the person who's doing this design partnership with you is taking a big risk. Right. They if it fails, they might not get promoted. Their job is on the line. Right. So you have to think about not just, you know, can I make this happen? It's a little bit like, you know, can I have my cake and eat it too? Right. You're lucky if you can do it. But just structurally, I think paid design partnerships in AI can only happen if you are going after a problem that like nobody's trying, nobody else is trying to solve right now. And so their customers, pretty desperate to try to like get some help, has already had internal projects fail when they try to solve it themselves.

Yeah. So maybe if I was able to give a follow up just for added context, it was that setting where it's, it's this is in healthcare where this is something that hasn't been done before. So they're like, so if that maybe gives a bit more context to that.

Yeah, yeah, I can definitely share some resources as a follow up on like kind of how to frame like what are the success criteria for a design partnership in that, in that situation it is possible. But I think, yeah, all of these factors need to be in play.

Thank you very much. Really enjoy the talk.

Thanks tk. Glad to have you as a follower of Workshop Wednesday. David is on the line and has a question about free trials and some of your opinions around that. David, I'll let you jump in.

Yeah, hi, thanks. A fantastic webinar again from Saster. So thanks so much for timely information for us. So I'm the chief Revenue officer for Picnic Robotics. So we create software for unstructured robotic environments. So we not a hardware player, etc. So we've got all the perception and AI stuff. So my question for you is we tried free trials. It didn't work out well for us. The reason was because there's so many different hardware configurations our clients would get stuck in the startup. Right. And so we pulled our free trial down and so but there's so much emphasis on free trials. Just curious if you've seen other companies that are successful not having free trials insisting on paid versions and I guess some example companies and when do you think it's appropriate or when do you think we just have to put in a lot of engineering to get to a self serve type model?

Yeah, I think the important thing to ask yourself is will engineering even get you there? Because there are some problems where it's not about your product that makes self serve and free trial hard. It's the circumstances of your customer. Right. So it's kind of like you're setting yourself up to fail. So the question you have to ask yourself is if you put yourself in your customer's shoes, can they actually try your product on their own? What would it take for them to be able to pull that off? Right. And so that's a function of not just like your customer's role but also the kind of organization they work in. So for example, I was in an analytics company before Unusual Amplitude. We had customers who would never send us their data without having a few months of meetings because it was sensitive data. There's no way they're going to just send it to us self serve. But then we also had 100,000 startups who were just sending it to us and using Amplitude Self serve, same product, completely different customer. Right. If I'm trying to like go sell to Walmart, I'm not going to expect them to send me their data in her self serve trial, no matter how good our engineering is. So it's really about your customer and you know, robotics, you know, not that I'm an expert, like sounds like, yeah, like that is something where it will be hard for your customer to put all the pieces together to try your product for free. You know, it's not about the money. Right. It's about the degree of support and handholding needed. And so the equivalent of a free trial in that setting is a proof of concept or a pilot. And you know, it's. Yeah, it's. That is the equivalent and it's not going to be completely self serve because your customer won't succeed.

Right.

Sandy, it's Jason Lemkin. Could I, could I ask a related question to what you've been going on, a little perpendicular, but one I've been thinking about a lot is these pricing for classic B2B companies with AI enhancements, with automation co pilots. Aaron Levy talked a little while back when we did one of these about how they're kind of doing piecemeal pieces. You get a little AI for free and then you pay.

Right.

In my portfolio, what I've found is a lot, at the end of the day, a lot of B2B applications are basically charging 50% more for automation. Right. And a lot of folks are charging 100% more for a co pilot.

Right.

It's kind of like however they describe it, they're charging at least 50% more, I guess. That my two questions one, what do you think of that? But two, at a meta level, is it sustainable? Like can everybody charge 50% more? In theory, yes. Right, because you're automating and replacing humans. But on the other hand, it budgets aren't completely inelastic, are they? I mean the money has to come from somewhere. So can everybody just inflate prices? Because there's cogs here. There's cogs here, right?

Yeah. So that's a great question. And I think a lot of the pricing right now is like aspirational. Like people are hoping that this works out as opposed to it's actually going to work out. And there are two big things to look at, which is why the best companies are all like adding AI features in a private beta and then looking at usage. So what can you learn from usage? The few things you can learn are a, is it a high frequency usage of the AI feature? What is it actually going to cost us? So for example, I'll say something a little maybe controversial or unfair, but Microsoft Office Shipping or Copilot is going to be low frequency usage for them. If they are thinking about okay, how much does this cost versus how much do I charge? I bet you they're not doing dynamic pricing. They're just going to like, you know, it's like okay, $20 a month. Like everyone gets Microsoft Copilot because the actual usage is going to be low enough that their margins stay high. And because it's low frequency usage, they cannot really charge too much for it. Right. And they will also have to go on a price war with Google Workspace or whoever else starts charging for it. So I think for any low frequency usage it will end up kind of slowly normalizing to you know, customers have figured out that a it does not maybe it improves their productivity by like 10%. It does not really replace headcount. Right. And then there are things which are actually genuinely replacing headcount. So I was reading this blog post from the Klarna engineering team yesterday which talked about how they have automated two thirds of their customer support queries using GPT4 and has replaced the equivalent of 700 full time customer support employees for them. Now you're not employees, contractors, whatever, but seven, they have a number, right? Which is okay, 700 people, I don't know, even at like 30k a year, that's you know, a lot about $20 million. So there are some use cases where it's actually full automation. The task is not something that you say, no matter what happens, we need humans in the loop and you can put a number to it. Then it becomes a question of do I buy the premium service for this full automation how much am I willing to pay for it? If I'm going to save 20 million am I willing to spend 2,3 million on it? Probably I think it's very use case dependent and wherever you can't actually fully replace the human that it's still a seat based model for a reason. You're not replacing that seat you're making them a few percentage points more productive. I think that it will be a slow race to the bottom where you can charge a little bit extra but you're going to have to compete hard with everyone else. And the low frequency use cases will get commodity pricing so you'll get a bit of a bump. The high frequency use cases are where I think all the money gets really made and there you know you if it's high frequency it has to be very specialized and customized to your particular you know industry. It can't be a general purpose thing and you know that's where most of the kind of value added dollars will get made.

Yeah that isn't the high frequency versus low frequency is interesting I don't have I'm just observing their I'm the largest investor in a company called Gorgeous which is like Zendesk for E commerce and so they're about 13,000 customers and they've seen a huge boost from automation and AI but honest what's interesting and maybe sad it's like Klarna and so is many folks in the post sales space is it's literally to replace humans for better or worse. For better or worse to get rid like the goal in if folks are out there doing this around the world when you go to contact centers in 2024 they want to get rid of half the people it's no longer then they're going to do it with AI whether you like it or even if the service is worse it's like and so if you look at anyone look at Intercom's homepage it's like we auto they're saying we automate 50% everyone's goal is to automate away 50% so that what and so what Anyhow just around by going to let others folk what so it's a force of nature and post sales what I'm trying to wonder is in three to four years going to your point can they still charge up for this? Right. Because what's happened with Gorgeous for example is they've been able to increase their ACVs 50 in six months. Not just like and and and people are happy to pay for it if, if I can not have 750 people at Clark or seven right. Seven at my E Commerce company. But will it con. Will all of this contract in 24 months? Like it's table stakes, right? And then we're stuck pain. It's all got to be in Office365 or the base Zendesk. I think it's like a meta question for investors and for founders.

Yeah, I, I do think there will be contraction but in a. In the sense that the cost of shipping AI products will also contract. Right. So if you look at like what GPT 3.5 cost when it was first made commercial versus what it costs now, I might be wrong here. Too many things to remember. But it's like 50x cheaper right? So I think it's substantially, substantially right. I think even the pricing models will change. I don't think Zendesk can really embrace AI without switching from a seat based model to ticket resolved model. For everything we resolve, we will charge you this much and the cost of using AI to resolve it is this much anything that we can't automatically resolve. Here's the seat based product. The business models will have to evolve where there is truthful automation but there are some models where there isn't. Right? Like notion is not going notion is to help make individuals more productive. They are not going to fully automate away anything because the whole purpose and mission of the company is to make me more productive. So they will continue trying to just be more powerful and powerful for me and have a seed based model. Anyone who's trying to do full automation needs to switch to like a success based pricing.

They do looking for success based. Right? Yeah, the one I'm wondering anyhow, I'll. I'll shut up after this. But the, the interesting thing just on the B2B side is historically when there's true cogs, when there's cogs for telephony, when there's cogs for messaging, it has actually enabled vendors to charge more because there are certain people are okay, they're like listen, I'm only want to spend $10 for Zendesk but I'll spend $40 for Voice because voice is expensive even though the cost of voice underlying contracted right. Never to zero, I'll pay more for messaging. That's why Braze and Klaviyo have exploded. Right? And so today people are happy. No one's happy. But people are more than willing to Pay for AI because they know it has cogs, even if it's gone down 50x or it's gone down an order of magnitude. Right. And so I think for all of us, will that last? Like can we charge more for AI forever? Forever because there's cogs or will it compress to zero? It's like. Yeah, I think it's a big question. And if we can charge more forever because there's cogs like telephony or messaging, it actually could be a boost for pricing long, long term. Right.

So I, I think it's a combination of multiple things, so cogs definitely matters. But you know, for example, if you look at like Klaviyo Brace Marketo. Right. Why can Marketo charge per customer record or like whatever while Salesforce charges per seat? Yeah, right. I think the, the. When you can, when your product solves something that is related to how many customers does the company have? How much data do you need to store and process? Like you can charge based on success because you are aligning your product to the growth of the company. Quite literally, one is to one, right. You have more customers and you have to service them every day as opposed to like, you know, cost center where you're trying to constantly reduce like dollar cost per company revenue. Right. So I think what function you are supporting and whether you're doing full automation are just as important. Like inputs into the pricing strategy you can execute on Ask the cogs. Like the cogs is more about, I think kind of just people recognizing that there's a reason why there is additional dollars. Right. There's a reason why I will assume it won't be free fair. But, but past that, how much you can charge, what's the margin is I think a completely a function of what problem you are solving and how aligned you are to the growth of your customer. Because they're always willing to pay based on success when you are like perfectly aligned to top line for sure.

Thank you.

Thank you so much for the great questions and for the great conversation. Cindy, we are out of time, so we're so thrilled to have you. We really appreciate you joining us. We have a couple questions that I'll share with you too. Just because I know you are always posting and you're always addressing this kind of stuff on your podcast. So I know a lot of people are wanting to hear similar to what Jason was saying of just, you know, how to incorporate this if you're not AI native and you're just having AI enhancement or an AI add on. So I know, that could be a lot of great content, too. And people really appreciate you being here. We've gotten a lot of great comments. So thank you so much again for an awesome presentation. And everyone, don't forget to sign up for some of our amazing March events. And also, we'll see you back here next week. We have Brett Goldstein, who was with Google for many years over their mergers and acquisitions. So he's going to be talking about the things you need to know if your company gets acquired. So thanks so much, everyone, and we will see you next week.

Thanks, everyone.