BSMS 84 - Positioning for AI SaaS companies

• Stijn Hendrikse: Author of T2D3 CMO Masterclass & Book, Founder of Kalungi
• Brian Graf: CEO of Kalungi

B2B SaaS companies move through predictable stages of marketing focus, cost and size (as described in the popular T2D3 book). With people cost being a majority of the cost involved, every hire needs to be well worth the investment!

The best founders, CFOs and COOs in B2B SaaS work at getting the best balance of marketing leadership, strategy and execution to produce the customer and revenue growth they require. Staying flexible and nimble is a key asset in a hard-charging B2B world.

Resources shared in this episode:

• BSMS 67 - The impact of AI on marketing
• Unlocking the power of AI: Transform your content creation process
• The State of B2B SaaS SEO in the Age of AI [2025]
• T2D3 CMO Masterclass
• Submit and vote on our podcast topics

Brian Graf: Hi there, and welcome to Episode 84 of B2B SaaS Marketing Snacks. I'm Brian Graf. I'm the CEO of Kalungi, and I'm back with Antoine Vial, one of our very own fractional CMOs. Antoine has led marketing for over 20 SaaS companies and is at the forefront of positioning, packaging, and the pricing strategies for AI powered SaaS products.

This episode was originally supposed to be all about the pricing strategy in the AI vertical SaaS market, but Antoine and I got into positioning and the conversation was too good to break away from. So the episode ended up focusing more on positioning than pricing. We dive into the strategic challenges of standing out in an increasingly crowded AI SaaS landscape with over 14,000 AI startups launched in the past few years, and nearly every SaaS tool claiming AI capabilities.

How do you actually differentiate? And what should your go-to market strategy look like? We explore why technical features alone aren't enough, how to craft positioning around customer outcomes, and what pricing models are emerging as effective in this AI first era. Antoine also shares trends across industry, key verticals like customer service, sales, automation, and cybersecurity. And what early stage founders need to know to cut through the noise and drive adoption. 

Let's get into it. All right, welcome back everyone, and thank you so much for joining us. I'll say, I live in Seattle and if you're watching the video, you can see the sliver of sunshine behind me. It's the first time I've seen sunshine in recent memory, so it's a good day for me.

I have with me today Antoine Vial who's been on the show a couple times. He is one of Kalungi's fractional CMOs and we have a really interesting topic for you today, and that is the positioning, packaging, and pricing of vertical B2B SaaS AI companies. So that's a mouthful, but basically, as I'm sure you are aware, there have been a ton of AI startups in the last basically one to two years that we've seen come across our desk and show up in the market. Right. Just to put a number behind it, in the last five or so years there have been more than 14,000 AI startups that have been founded globally and in the US that accounts for about 40% of that.

So just tons of movement in the market. And as again, I'm sure you have seen almost every single company is slapping AI on their name or adding AI to their product. Which isn't necessarily a bad thing, but, what it has resulted in is a really interesting market dynamic that is worth highlighting and talking through a little bit.

So just from my perspective briefly, there's so many options now for consumers. It's almost a mix of there being a ton of different options out there and consumers having relatively little understanding technically of what AI does. It's just this magic black box that you can plug into solutions.

And also that being combined with these incumbents that are so capably strong that it makes it a tough road for an AI startup. How do you, how do you differentiate yourself from the pack? How do you even begin to compete with a ChatGPT or an OpenAI, a Grok or a Claude?

It opens the door for a really interesting strategic question that needs to be solved. And so Kalungi has seen a lot of these types of companies come across our desk with that problem, and Antoine has kind of been at the forefront of those efforts with those companies. And so he brings a really interesting perspective to this conversation.

So thanks for being here, Antoine. I'm excited to dig into this with you. 

Antoine Vial: Yeah, super excited about the conversation and happy you got a little bit of sunshine in Seattle for once.

Brian Graf: I'm in my basement, so I still don't actually get to see it, but it's out there. I know it. Okay, so maybe we just start out and just describe the current landscape of AI companies that you've seen.

Just to paint the picture and then we can go from there. 

Antoine Vial: Yeah, of course. Here the really interesting trend is that of course it's a highly competitive market, and there is a surge of AI powered SaaS applications. Which you mentioned before about 14,000 new AI powered SaaS, which is mind blowing.

And in order of magnitude, it's multiple fold above what we've seen in the past for the past five to 10 years. And at the same time, super exciting because anytime when there is this level of excitement and, and engagement and creation similar to a dot-com bubble there are a few big winners and it's definitely something that we are seeing unfolding at the moment with OpenAI leading the show, and of course other competitors that are blooming left and right and trying to get to that leading spot and be that leading solution whether it is cloud, or whether it is deep seek. 

We can definitely see the rush to becoming the leader in the market, but also this trend is leading to the commoditization of the LLMs and a real race to the bottom as far as pricing is concerned, where right now it's market share and trying to capture as much market share as possible. That's the goal of these big players. And so at our level, at the SaaS level, it's really interesting to see how companies are growing and which segments are actually or the vertical markets that are opening with this AI trend.

So far at Kalungi we've seen mostly about five main verticals. You have customer service and automation. So this one is led by Blend AI. If you haven't seen it all over the web, they are doing amazing marketing and you can see that old school phone: a red phone that they have as part of their branding.

But it's incredible, about 65 million rays so far. Leading with ai, voice agents, that's a really strong trend, The customer support side of things, customer service side of things, definitely booming. The second vertical where we're seeing a lot of movement and one that I'm particularly excited about and that we are all at Kalungi is the marketing and sales automation.

Here it's the place where you have a very early adopter mindset from this vertical. And you see a company like Clay leading the show with a valuation earlier this year at $1.3 billion. Ton of traction: agencies across the globe using Clay to build business on top of it. And recently giants like HubSpot are releasing some similar AI functionality on the marketing and sales automation side of things. So that's the second very strong vertical. 

The third one would be BI and analytics. So yeah, business intelligence analytics here. I'd say that this is mostly led by companies like DataRobot and the likes and the rounds of funding are massive. Here it's way beyond what we're seeing with the two previous verticals. And we have a DataRobot that has actually raised about 1 billion in funding so far. And because the complexity of the data lakes and the data and the opportunity for the companies that they're targeting is so big there's a lot of traction on this vertical.

Another one where you see a lot of cash flowing is cybersecurity and fraud detection. So SecOps as a category in the SaaS world is huge. Large enterprise, always scared of that, of that ransomware that will hit them one day on another. And you see very large private equity firms like Toma Bravo investing in companies like Darktrace.

They were acquired for about $5 billion which is really an enormous amount of cash put into this vertical. And then the last vertical is coding as a tool with companies like Lovable. So this one is definitely a great example of a successful European tech company being pushed to the forefront and being very successful.

But the output, it's quite phenomenal. It’s turning into a term vibe. Coding is becoming a thing. People are talking about how, now, supposedly anyone could just use a tool like Lovable and build a product. I have a strong opinion on how doable that is or not.

But that's not the topic of today. Regardless, there's a ton of traction in this space. The CEO of Enphilanthropic was recorded last week saying that in three to six months, 90% of the code would be AI-enhanced or written by AI, and maybe within a year. It would be fully written by AI.

Of course, it's a bit of a buzz, but still quite significant changes in this specific vertical. And yeah, that's really what we're seeing at the vertical level. And of course there are multiple ramifications and multiple opportunities for creating AI or finding a niche within these large verticals.

But definitely a trend that we're seeing more and more, I would say like about four out of five clients that are coming into our pipeline and that we have conversations with and that we serve are AI-first companies. And we don't expect that this is gonna reverse.

If anything, some of the companies that were not AI-first are heavily investing in product development and adding some AI features and capabilities into their existing tool, which is also very interesting. But yeah, that would be my high level overview of what's going on in the market.

Brian Graf: That's a great recap. What you've highlighted and what I've seen is that it's you can put the big players almost on in their own field, of like an OpenAI or a Claude or a Grok, where they have invested massively… just like unfathomable amounts of capital into their products in order to build these LLMs that are completely game changing and interestingly becoming almost commoditized in their own way, within their own tier. But then, that's its own tier. And then on the other side of things, it's these companies that are utilizing those tools and tools like them to apply them to more niche use cases. And it's getting interesting to me. It brings me back to the T2D3 theory of market makers versus market shakers. The big players and even like a play, you could argue, I'm not sure if they actually are, could be set as market makers.

Where they go and build the market themselves. But what's interesting is that the technical differentiation isn't quite as large. I feel like it's almost more about positioning. Keeping a focus on a subsegment of the industry and you just got there first, so you have had more time.

And so it is an interesting look at positioning and how important that can be. You might talk about this in a second, but even once we get past the first movers and we get into more competitive markets, especially with AI and it becomes such a commoditized thing.

It's almost less about the technical capability of the AI and more about how it's applied and how focused you can make it applied. It's just a really interesting point of differentiation. And it almost forces you to come back to product marketing and positioning and messaging in a way that you haven't really had to in the past with other verticals.

Antoine Vial: Yeah, a hundred percent. I'm glad you mentioned the clear example. 'cause that's one that I'm a little fascinated about and love to look at, but if you look at how they go to market or how they went to market, you can clearly see the evolution of the niching down and then expanding and broadening their positioning.

Not even like a year ago. The homepage was personalization at scale. I'm paraphrasing, but personalization at scale was very much the positioning. And they were targeting agencies, you know outbound agencies because they knew that these guys were the early adopters and could really get a ton of business out of using that tool.

And that's how they went to market and on top of it, benefited from an enormous amount of brand awareness from these album agencies publishing online and on social media about how they use Clay to drive output. And that was the first go to market motion.

Then they brought on a little bit and then you get to today where they're branding themselves as the go-to-market tool. So you can see it's applicable for any vertical that, or any vertical markets that we just discussed before. There is a niche, there is an early adopter that is extremely important to identify if you wanna launch one of these AI SaaS products because without these early adopters, you don't get any traction and without traction, I.

But without traction, it's very hard to get to product market fit. Without product market fit, it's very hard to put fuel on the fire and then justify growth or any initiatives around growth. And so here the key takeaway is the importance of positioning.

Driving growth with these AI segments is really emphasizing the benefits: What is the job to be done of your tool? Whether or not it is AI, that has always been true for any SaaS product, Clients buy for two reasons: because it saves money or 'cause it saves time. These are very much intertwined.

And therefore, of course you don't lead with that. You lead with the job to be done. And we have a lot of content on how to think about positioning, messaging, and talk about the output or the benefits of using the tool. But it's especially true when it's a crowded space. And because the ability of people to create tools with less pretty much doing more with less, being enhanced by AI, you have more AI solutions every day, so you have more competition.

And even more important is even more important to, to drive the positioning, focusing on the benefits and, and really make it about the about the prospects and what they get out of using the tool. That would be my thoughts on how to leverage positioning to try to stand out in such a crowded space.

Brian Graf: It's interesting too that AI enhances a problem that I've seen a lot with B2B SaaS companies, even before AI. Everyone wants to be the Slack or the Salesforce of the world. That is applicable to everyone and a lot of companies will build a product that can be broadly applicable from a technical perspective.

It could service a lot of different companies and they don't wanna shut the door and so they on on opportunities, And so they want to keep themselves super broad, but of course, as we've seen that has a ton of pitfalls associated with this, and you end up being generic and not compelling to the broad market. I feel like with AI it almost becomes even more like AI's even more broadly applicable to so many different use cases, but again, because the market's so competitive, you become even more generic than you would have in a non-AI market. And so it forces that niche down and carves out methodology so much more than ever before.

Clay is a fantastic example. If you're thinking about building an AI company, you need to find the stepping stones of the market where you can, from a technical standpoint, meet the market where it is and add a ton of value to a specific subsegment that gives you the time to build out your product more and make it more broadly applicable or, or even more valuable in a different area.

Is that how you see things, or is it a little bit different? 

Antoine Vial: For this one I'm a hundred percent aligned and I would say it's especially true when you have OpenAI trying to become the Google of of LLMs and therefore there is this association with any AI features or any AI capabilities that, from a prospect point of view, it's lowering the expectation.

You know, there was actually a super interesting report that was shared by Irritational Labs, which did a survey on about 800 companies looking at what they think or did they even care about AI powered features. And really people don't care. Like they don't really care if it's if it's AI powered or not.

What they care is like, what is the output? What's in it for me? What are you actually gonna do for me? 

Antoine Vial: Yeah, exactly. Technically nothing changes, like that has been the same for previous solutions. You mentioned Salesforce. Salesforce is a very interesting company to look at, especially in the way they embrace AI.

There's a lot to be said on what is the job to be done of using AI when you're a Salesforce user, because what this survey shows is that people don't care. They wanna focus on the benefits. So here you have Canva doing it very, very well.

They dropped the magic design features. So, you know that that was trying to make it very easy. We are implementing a feature that uses AI and it's very clear what the outcome is or at least the benefit. You can now do more faster. Then you have I guess that quality impact.

GitHub here launched a copilot a few months ago, and the claim is that it helps you code 55% faster than before. So that's like very much like, okay, you can quantify the impact of using AI. Great. That's the job to be done. That's a very good way to think about it versus like, oh, it is gonna code for me.

But like, what is it gonna do? You know? And, and these are two good examples of what the market cares about. It's not the AI feature or AI powered feature, but rather like, if it helps me, great. If it doesn't, just because there's disassociation with this commoditization. Every day there is a new feature, every day there's a new solution.

It doesn't really matter. Tell me why it's gonna help me. That's really where the key is. 

Brian Graf: You bring up a really good point. Well, one, one you bring up the pitfall of like, it is dangerous to solve a gap for a ChatGPT when they very well could next month just come out with a feature that undermines your entire business.

And I've seen that a couple times, which is too bad, but also, I feel like one of the things in terms of like what the market wants and pushing it to jobs to be done, one of the interesting things that AI companies need to combat is credibility, because I feel like a ton of AI companies that I've seen have been, you can promise the world almost in terms of like in theory what it could do, but then when applying it specifically, there's a ton of caveats and use cases that the outputs may get you 60% of the way there, but not entirely. What do you think about that? With the companies that you've worked with and establishing that credibility early. 'cause I feel like it would be a crucial positioning point for a lot of companies.

I mean, to your point, going after the early adopters will get you some traction and you'll get people in the door, but you know, you'll inevitably hit the chasm. Right and just hit a ceiling that will be really difficult to cross unless you can establish that credibility early. 

Antoine Vial: A hundred percent.

And it's especially true because there are so many applications, it's almost infinite. The number of applications for AI is whether it is at the B2B level or the B2C level, it's just infinite. The adoption from an individual perspective is that people are very keen to think like, okay, I'm gonna use AI as a personal assistant.

Regardless of what the use case is. Coding faster writing better brainstorming, really, whatever it is, it's a personalized system. And that, that has a limit when you transfer it to an organizational level. So in the B2B SaaS space, how do you make it so it becomes more than just a personal agent that is just helping one person, but rather brings all teams together?

What we've seen so far with some of our clients at Kalungi is that there are three main challenges. The first one, you hint at, which is choosing the right LLM. Because it's a race to become the next Google because there's so much money poured into these companies without much confidence.

Really when you build your tool, is OpenAI still gonna be there? Is a new one gonna come up? Of course we have an idea of who has the biggest chance, but still it's very early to know. So the big challenge for B2B SaaS solutions, regardless of the vertical, is like which LLM to build on top of here.

Who is gonna be the winner, who is the winner today and who is the winner tomorrow and who is the long-term winner? That's the question that everyone wants to answer, but it's very hard. So, I would say the strategy here would be to be LLM, agnostic as much as possible, trying to integrate with as many as possible.

Make it available for the user to choose what is the cheapest option? What is the most powerful option? What is the new option? You know, this kind of level of agnostic would really bring your solution a long way because if things change, you can be very agile and you're already ready for it.

So that's the big one. The second challenge would be what we've been talking about for the past 20 minutes, which is positioning, cutting through the noise, repositioning, how do you position yourself?  How do you differentiate yourself with all these tools that are coming left and right?

Early on with one of our clients, we went on Software redirect and just popped up because of AI. There is an AI for everything or there's an AI for it. And it was insane, like mind blowing to see the amount of new solutions coming up, coming up, coming up and coming up.

Brian Graf: And what's funny about that is even like talking with them. Early on they were like, this technology is game changing, Nobody's ever done this before. This is brand new. And then we did a little bit of competitive research and we're like, oh no, people have not done it before, but they're doing it right now.

And it just moves so fast. 

Antoine Vial: Yeah, it moves at a pace. It's unseen before,  but within six months you can have tens of tens of competitors, hundreds of competitors that have just popped up because it's so much easier to do copycat solutions. The speed of execution is so much faster that here it's cutting through the noise, which is the strategy here to try to stand out. It's to build a very, very solid distribution engine. That's probably the hardest thing to do. I've seen a visual that stuck with me 'cause it was so powerful and it was like two bar charts.

The first one was like in 2025, takes one day to build a product, but in 2025 still takes 10 years for marketing to create a brand. And of course that was kind of funny. But the idea is like if you don't build that solid distribution channel where prospects come to you because they trust you, because of all of the things that we talked about, because they have a high level of confidence that you are the solution if they engage with you.

They have the lowest risk possible. Back to what you solve, you make them spend less money and be faster or save time. It's very, very true here. So building that sole distribution channel or this distribution is the strategy to try to cut through the noise by differentiating yourself, by customer case, case studies by being out there positioning yourself as a thought leader and, and showing real output and proving that that it works. So that's probably the hardest. And then I guess the third big challenge is adoption for all of the verticals that we talked about. There is a world of difference between how fast the market is adopting AI solutions.

You mentioned the crossing the chasm model of Moore, but here it's even more critical in the vertical that I said I was the most excited about, which is marketing and sales automation. The chasm might be smaller because you have a lot of tech savvy or tech forward kind of people.

It's the entire market shifting to the left a little bit. 

Antoine Vial: Exactly. And then on the other hand, with cybersecurity and fraud detection, that's gonna be slower and just by design, just because the stakes are so high. Way higher. Way higher. Mm-hmm.

And any mistake here, especially at the large enterprise level can cost. Billions of dollars. And that increases the payback period for all of these investments early on for these SaaS investors that are playing in this vertical. Doesn't mean that the outcome cannot be fruitful, if not the opposite.

But who's gonna last long enough to prove that there is value to get people to get the market, to adopt these AI solutions? It's very early on, so it's hard to give strong recommendations. But so far what I've seen and what we've seen at Kalungi work the best is combining, especially because there is this huge, I guess it's natural.

It kind of feels like we are living in a science fiction kind of movie where the AI, the robots are taking control. So the strategy would be to keep that human element right and really drive part of your positioning and part of the solution that you're proposing. That the human element of it and the human control on this AI solution is extremely important.

It's true, with the cyber security vertical. But even with the customer support agents where AI agents are like the AI SDRs and all these things, no one wants to pick up the phone and talk to a robot. At least like, yeah, maybe I, I'm okay with it as long as I know that it is one, but not knowing and figuring it out in the middle, like that's probably the worst customer experience.

So keeping that human element and making sure that you always meet your prospects or your customers where they are. And that human element for AI solutions, probably the key to drive market adoption. So this yeah, this is a really valuable early lesson from this from what we are learning so far.

Brian Graf: Those are fantastic points. It's also interesting that thinking about who's it for, and like cybersecurity for example, one of the reasons why what you bring up is so poignant is that like the applications that those companies need to use, they need to be so bulletproof.

Right. The risks are so high, but the reason that a marketing and sales automation tool is more effective is that marketing and sales are all about making mistakes and documenting them and learning from them. So it's totally fine to be messy and iterative. And so it matches really well with the persona.

One other thing too is I've talked to a lot of AI companies that want to build for like a developer, which makes sense and there are absolutely good applications for it, particularly on the coding side. But what I've also seen is that the line from development to revenue is a longer line, I feel like.

From what we've seen other areas of the business and maybe a more technical, I guess, path that you have to walk. And that's been an interesting learning. So it's not to say that you can't build AI for developers, but it can be so much easier to showcase real value and tie your product to revenue delivered or efficiencies gained, et cetera.

When you're on a more of a front lines department, which has been just an interesting thing that I've seen. Doesn't mean that it's a hundred percent true, but, but it's something to consider, as you're building a product or maturing a product is: Who is it for? And how can you get your product to be as closely tied to revenue as possible, rather than just saving time.

Antoine Vial: And on this one we probably have the opportunity to have a conversation that is long enough for entire podcast episodes, but really the pricing and packaging and the go to market motion that you choose when you go to market with these kind of SaaS solutions, depending on who is it for and your ideal customer profile and the persona that you're gonna target is extremely important.

There's this conception, especially what you just talked about. Early adopters being the engineers or whatnot. And it's probably heavily impacted by a company like Cursor, which is probably one of the fastest to hit the one 100 million in ARR. In everyone's mind when they think about growing an AI first SaaS company.

People also forget how clean, how aesthetic, how easy to use, how perfect the product’s UX and UI is and allowed this exponential growth through PLG because the value was immediate. The people using the tool didn't have to go through too much convincing to use the tool. It was absolutely amazing and that worked.

It's not true for every AI solution and even if the ultimate goal or the ultimate customer is the more technical persona, if early on the product is not where it must be for PLG, then it's important to, to, to acknowledge it because if it's not gonna work it is just, it's, it's just not gonna work.

Brian Graf: No, it's a great point. And you're gonna become a victim of your own success. I wanna tackle that topic really badly, but we should save it for its own episode. So I'm gonna drag you back here and we can talk about that. I will say that for an episode that was originally supposed to be about pricing, I didn't do a great job of organizing the conversation, but it was really good.

The positioning element of AI and companies in general, but you can also kind of equate that back to like hyper competitive markets and or commoditized services is really interesting and it becomes so critical. So I'm glad that we spent a lot of time on it. Let's cut it here and then we can have a different episode with you on it where we actually do talk about pricing and we can get into that as well. 

Antoine Vial: The very interesting part of all of this, and to close the conversation today on why positioning is so important and, and how pricing can impact that positioning, is that, beyond the very large players that are really running that race to the bottom, you see six different segments.

And you have the traditional SaaS pricing which is just like the subscription model. It's very true, very relevant still. Now, then you have the skill-based pricing, which is more like, okay, what level of features and capabilities. This is the regular subscription for ChatGPT, the charge GPT Pro for deep research or deep thinking.

This is also very traditional pricing. Then you have a hybrid: a little bit of a mix between usage seats, the platforms or the modules that you get access to, which is also very relevant. But then there is something super interesting, which makes me go back to I guess like the early days of when I used a phone to send texts and you are like credit based and it's awful because you've hit your 150 credit limit and, and it feels like you're going back to the two thousands. But this is happening live right now and it's like very, very true for, so. Many of these AI driven companies, and there's a huge misconception here on how that works for companies for P two B, because I guess like companies are more used to that, but at a personal level, at a B2C, that's like very, I don't know.

it brings very, a lot of bad memories. WhatsApp exists because of that. 

Brian Graf: It almost moves things back to cost-based pricing, right. For the company, because it is based on usage pricing that they have to deal with. From the LLM that they're using. Right. So yeah, they kind of equate it to value based, but it's, it almost has the opposite effect to your point.

It 

Antoine Vial: It is a very interesting trend, and so many companies are using this right now. I'm very curious to see where this leads. Clay is a great example, The Clay plan hit the credit. The two last ones, it's the per FTE type of capabilities, which is what even OpenAI is moving towards with like 20,000 plus for like this PhD type of reasoning and whatnot, which we're probably gonna see a lot more like per Q or SKU, compared to a full-time employee type of equivalence, which that's gonna be interesting too. And then the last one, which is my immediate favorite because it was already my immediate favorite for a regular SaaS pricing: value based and success based model. It's so much more relevant with AI.

Like, okay, how much do you get out of this? And then how I can use your pricing to get a portion of that is gonna be very much more aligned with all of the organization. The benefit driven, the job to be done type of thing, which is part of this positioning, part of this pricing and packaging. Which has a lot of potential, but again, it's early on, a lot of experimentation going on.

So we'll see what ends up being the winning solution. Probably there is no winning solution. There is like a specific output for every company and probably trends depending on the verticals as well, and depends on the adoption. So all of the topics that we talked about. But these are the six bigger big trends that we see on the pricing that are very representative of the unknown and the ever evolving trend around AI SaaS products. So yeah, that's a good way to close the episode today. And open it up maybe for more in depth into pricing. 

Brian Graf: Quick teaser, on some of the topics we can cover more in depth, but it is the market sorting itself out a little bit in terms of what pricing model becomes the standard and, and what is most readily adopted. But yeah, it's a nice teaser for the next one. All right, Antoine, thank you so much. It was a great conversation. Sounds like we got two more in the queue for us. So we'll see you back soon.

Antoine Vial: Thank you for having me and looking forward to our next conversation. 

Brian Graf: Alright. Yes sir.

Thank you to Adriano Valerio for producing this episode and the Kalungi team for helping us make this whole thing work and of course, you for choosing to spend your time with us. As a reminder, all the links we mentioned in this episode can be found in the show notes, and if you wanna submit or vote on a question you'd like us to answer, you can do that at kalungi.com/podcast.

Every time we record, we take one of the top three topics and jam on it. Thanks again.

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