How Do You Integrate AI Into the Workplace? | Kraken CMO

On today's show, we're covering all the AI questions you've been too afraid to ask. What we're doing is we're breaking down with Maya Gupta, who's the CMO at Kraken, one of the best crypto exchanges in the world. We're going to go through how do you use AI? Which model is better? How do you get started? Do you just need one person to get started? How the heck do you actually make transformative progress in a business with artificial intelligence? That's what we're breaking down today. You're going to leave this episode with a whole new framing for how to move forward with AI. Let's get to today's show.

Hey, Meyer, welcome back to the show. We are happy to have a, you know, a second time tendee on marketing against the Green.

Thanks for having me. K. Thanks both you and Kip. Great to be here.

Kind of reintroduce you to our audience. We, I think you were our very first guest. So you are, you've had a pretty incredible career. You're currently the CMO and chief Growth officer over at Kraken, one of the most impressive companies in the crypto space, but just a very impressive company in general. And we wanted to get you on because you are a forward thinker. You were hitting up growth in Spotify. You've led marketing and growth and freshly like just a ton of incredible roles usually in companies that are actually trailblazers in their industry. So of course we wanted to get you on to talk about AI. We want to know how smart leaders and just smart people in general are using AI. So maybe we could just kick off with you giving our audience a little bit of like context on where you are seeing AI get used across your marketing and growth teams. What are some of the use cases that you particularly find pretty interesting?

I think it always is where AI is helping us get smarter and come up with more ideas. Idea generation versus actually coming up with the end asset. So what we are learning is it's great to give us ideas, it's great to give us different variations. But when we are zooming in in certain angles, that's where you still need, you know, your more traditional way of producing, creating content. Now one area where, you know, we've, we've taken a certain path where we've created local instances, let's say of ChatGPT because there's confidential data, you know, we are obviously for the nature of the business that we are in. So what one of the things that we are doing at Kraken is we've created our local instances where all the unstructured data is actually being pushed into that local instance and is being trained. And then you know, you're just asking a lot of questions and queries even just to understand user behavior because over a period of time, historically these have been PDFs, decks and docs with tons of data. But I think creating a local incent just give that access to thousands of thousands of crack nights pretty instantly.

This is actually a super important point. I think Kip and I have talked about this. I'm not sure Kip has thoughts. But the number one way to make this the very impactful within your company is to be able to provide the ability to collapse all the unstructured data into a repository that then you can just have the employees easily access. Like so when they build things, it can access that unstructured data.

The unstructured data unlock is crazy, right? And if you look at some of the reports with Google 2.0 Flash, its ability to extract data and insights from PDFs for essentially 40, 50 cents is like unreal and kind of unmatched. There's two things we've decided recently to do on our team, Kieran. Right. Which is one, we're in the process of having technical writers document all of the workflow so that we can then be very clear on what we're going to automate and what we're going to automate now, what we're then going to automate in the future. And then the second thing is every beating is recorded, every meeting's recorded and we are going to, we're creating like a whole nomenclature for how those recordings and transcripts are stored. So it's just like everything's recorded. We are just going to unlock the creation of structure of unstructured data. So I think those are two things that we're doing here. And I think the other thing we could talk about for everyone that I don't think has been talked about yet that Meyer kind of brings up is like when do you like buy a business seat of one of these frontier models? When do you maybe build a app on top of their APIs that's specific to your company or when do you maybe like run an open source model locally? I imagine you, Kieran, have some opinions on that. I do, but I want to kind of hear what you think and hear what Meyer thinks there.

So that is actually a pretty good question. Right? The one that I thought about, the one that I thought about much more deeply is when you are trying to stand up AI use cases within your company. Do you build that custom, do you use a vendor or I guess build a split between open source and non open source. Like do you commit to a certain model? I think that's one of the harder things to figure out right now because that's like one of the things that I'm trying to figure out all the time across our go to market which is when you are trying to stand up things. In AI, the most important thing is to get set signal as quick as possible because it's really hard to know what the capabilities are of the model at scale. And so the open source one and the closed source one is actually a really interesting question. So I've always believed open source is going to be a big part of how companies use models internally. But to really use open source you need to have a layer on top that can actually automatically fine tune the open source models and then orchestrate between them based upon the use case you want to do. So you can imagine you're trying to do something in the future and you're using some sort of interface and that interface basically is able to capture the intent like the use case you want to do and then in behind the scenes it can orchestrate and send to the right model dependent upon your needs. Right. And that does not exist. Like I actually try to invest in some companies doing that, but it is actually a way harder problem to solve than I first thought.

I think the path forward is determined, I feel based on a couple of things. One is, is that core to your core product offering, if it is core to your core product offering, you want to own the IP building and that is core within the product, so that plays a key role. Second is the, you know, the security and confidentiality of that data. So depending on the use case, if it's creative and if you're playing around with that, no brainer, I think Most of the SaaS platforms, most of the cloud based solutions today will fulfill most of your needs. And if there is a very unique edge case, then it's a big question. And Kiran, I do feel you have to ask is, hey, are you, is that is a cost benefit analysis strong enough where you go down that path where you customize or you build an adaptive layer on top of it? Or is it, is it reasonable enough that it can solve 80% of your efficiency and then 20% are edge cases? But the local instance point that I made earlier, it is because there is certain type of data that we just would not want to leave R4 for four boundaries. And we felt that, okay, we can create a local instance, continue to train it over time with all our unstructured data. So I think there are three or four different layers and I feel either which way you got to start with experimenting with what's out there and absolutely see some of the early signals.

What's going to happen is a lot of companies are going to be like, oh, I'm behind, I want to get into this AI game. And they're going to go by business seats of ChatGPT, of Claude, of Gemini, pick a core frontier model and that is a great place to start. Really, really good place to start. There's nothing wrong with doing that. What you will find is if you have very specific use cases, building small applications on top of the API is like orders of magnitude cheaper, right? Like way, way, way, way cheaper. And so if you are very focused in what you're trying to do, I think you're going to be, be better off to build custom. If you are general use cases trying to figure that out, then probably going and buying those seats is probably not a bad place to start.

You know, I do have a question from you guys because you of course have been digging so deep into AI and talking to so many leaders. From an operational standpoint, what have you seen being most effective when AI is assessed and adopted and evaluated within each team? Or are you saying, okay, actually find a person across the board, across teams, because look, there's so much innovation happening. It's also noise, right? Because you're also running your core business. You're trying to hit those KPIs, you're trying to hit outcomes, but this is all relevant. So operationally, what have you seen being most effective in terms of experimenting and identifying what are the right tools to play around with for what type of use cases?

I kind of divide it up into three parts in terms of AI adoption within the company. So we have these like top down goals from the company and it's basically just a stake in the ground. It's saying like we believe these places across the company should be transformed by AI because we have like, we know where AI capabilities are today. But the thing that I kept kind of built for is we build the use cases that we believe AI is going to be transformational for in the future, regardless of where the model capabilities are today. And what we mean by that is, I think a year ago we talked about this. Kip, which is the model capabilities is a solved problem. Do not worry that the model capability yeah, the model capability. Don't worry that it cannot do what you want to do today. Build the infrastructure and the setup to do that thing and the model will solve itself. That was 12 months ago. If you look what's happened in the last 12 months, that was like pretty accurate. Right? The model capabilities have like, transform. Like being transformative. They are. It's speeding up, not slowing down. So you. So I think putting the stake in the ground and having large big bets at the company level and then having a large pod that can go after those bets and they should be run. The big thing that I think, and what I've seen in other companies is it should be run like a growth project, not a company, not an IT project. You have to have a growth methodology to how you approach those. Because AI in and of itself is an iterative technology. It is not a deploy the technology like regular software. And there you go, right? It is a very like, learn as you go. Then the second thing is team enablement. And then the. And then the third thing is employee in general enablement. So I'll give you a really somewhat take on employee enablement that might, might not go down well with folks. Right. I think on the employee side of things, the job of the team and the company is to provide the tools and the, and the kind of permission to go, go and play with AI. Integrate AI into your workflow, figure out what works, what doesn't work. Like play around with it. Here's the tools, here's some, like, courses that you might want to go take. Nothing is mandated. And if you don't understand that this is a paradigm shift in how you do work and you don't want to integrate it into your work and you are not curious about it, that's on you, not the company. You should not have to force people to understand that this is one of the most important things that you can do. The second one is the hardest one, actually, and that's the one I want to like, pitch over the kip, which is what do you do at the team level? Right. So should teams understand how AI can transform what they do, or should there be a central AI team that goes into those teams, spends time with them, looks at their workflows, and it is the AI specialist team and it understands how it can transform what they do. And I think actually either or of those could work. I don't know if there's a right option.

Look, if you've been using AI for your marketing but still haven't fully figured it out, we've got the playbook that is going to change the way you market this playbook. Cuts through all the generic noise, gets down to the nitty gritty proven tactics that actually drive results. These strategies come straight from us over at HubSpot and the leaders over at A16Z and Asana. You can steal our playbook at the link in the description below. Grab it right now. Now let's get back to today's show. I think the number one way to get AI to take off in a team or within an organization is to centralize some valuable unstructured data use case. Like the second our product marketing leader had a couple projects in Claude that was like here's everything we know about our Persona and you can just get feedback from that Persona because I've taken all the time to train it, upload the perfect all of our decks and docs and everything there and I've tailored the output and everything for you. She did another one about like removing any like business jargon and stuff from your copywriting. Like basic things that like everybody can use like that is how you get real adoption and you get. And so I think the individual teams are required to be the experts to find and gather the unstructured data, put it in the frontier models, Claude, chat, GPT, whatever you're using and, and make that accessible to their team and the broader team. I think you then need a team of specialists who our experts in AI and automation to say we can now automate 90% of localization. This is a solved problem. We got DeepML, we got a bunch of different tools where we can make localization 10 times better and we're gonna go run and build very specific workflows, custom software, whatever it may be to go and solve that problem that like your core team isn't going to have enough knowledge and expertise to go into. We can go and train that team and do everything we need. But I think both is the only possible outcome.

Yeah, I totally agree. I think it's a hybrid model and I was thinking through how are we running it and what are the teams that are running it successfully. The big outlier or the big difference in the scenarios is anytime the actual business team and that could be marketing growth product anytime. They are the ones generating the inertia, they are the ones pushing the use case. We are seeing better results and outcome. Now of course in some cases they'll have a dependency on, you know, somebody in a global data team or engineering to figure out, you know, a local instance of something or create customization but anytime they are the ones who are driving and pushing for the outcome because they are closest to the efficiency, opportunity in what they're doing, we are actually seeing success.

I think curiosity has never been more valuable than it is today. Your time has never been more valuable than it is today because the opportunity to apply it to AI is so huge. I have a really high bar for what I should spend my time on if it's not AI. Like, I think if I'm doing something during the day and it's not AI, I'm like, why am I doing this? Like, it has to be a really high bar for this to be important.

Well, the best quote I've heard on this is from Dan, Dan Shipper, founder of every friend of the pod. He was reviewing OpenAI 03 mini and high and he was like, these advanced reasoning models are a bazooka for the curious mind. And that is like the perfect metaphor of like, geez, if you are curious, just like you take out a whole big problem really quickly and learn something. And I'm somebody who's, for better or worse, addicted to learning. And so like that just becomes insatiable. And you're right here you just stop being like, why am I doing anything that is like a bottom 20% thing ever?

Yeah, you know, that is so spot on. And I almost feel it's like social. You know, when social became a thing, whatever, 10, you know, 14 years back, you could not just assume that you would understand the nuance of social and building communities if you yourself were not in it. You have to live and breathe it to then say, oh, I have a chance at building a brand that is distributed through social and build communities. So believe it or not, but I, of course I follow both of you and I read a lot of your content. Late last year, as I was getting into the break, I actually dove really deep in Claude myself with my financial data, personal financial data. Me and my wife, we always end up overspending and it's a cluster F. When you actually look at your statements, there are thousands of rows and your Excel eventually breaks, so you can't really do anything. And then you look at all kinds of pie charts that typical financial systems give you, but it doesn't really tell you anything. So I read a lot about Claude and I saw one of the posts from Kiran where you had shared. Here are the five or six platforms and tools you're using. So, you know, I used three or four weeks to get into Groq and Claude and Gemini. So what I did was I cleaned up all my data, I removed all the PI, man. I dumped all the CSV files into Claude, and I went crazy asking all kinds of questions because why are we overspending? Where are we spending? You know, and what are the repeatable spends? My wife thought that she's overspending on Amazon. And I corrected that because, you know, this clearly said, no, this is how much you're spending on Amazon versus, you know, all of the restaurants. And we were surprised that despite having our own car, we were spending so much on Uber in a span of three months. And then we re. So I think the only way you unlock AI in your professional life is when you're actually living and breathing it and finding incrementality and finding those use cases in your personal life as well.

I do something similar where I plugged in my portfolio to OpenAI and then asked it to diverse like, how can I diversify this more? And I've made my first. Like, I actually should check in on that. I've made my first investment two months ago in a clean energy fund. And it was all. I had no idea what this clean energy fund was. Nothing. Like, it was all recommended by AI, broke down by AI, added to my portfolio for diversification in climate change. And I just said it, I'm gonna, like, just invest in this and see what happens, right? Because I'm so committed to it. I'm like literally putting my money into. And so I. But I think. I think that that is like, that that is the start. There's actually a use case I wanted to show you, Myra, because I think you're the best person in the world to show this too, which is one of the things I've been fascinated by, is how the reasoning models have become better with strategy. One of the things I want to take you through this, but because I want to go right down to the bottom, because the bottom part's the most interesting. And like, just to give an example of how incredible AI is, do this now. One caveat is this is with synthetic data because I can't show real data on the show from. So I've been trying to get good at trying to create synthetic data to show use cases.

This is.

Synthetic data is just not complex. So what I've given it is like a. A dashboard for a growing SaaS company at 30 million in ARR that wants to double its ARR over the coming years. And so the first prompt is, is I need to get to that number in 12 months. Of all the metrics that you see here. What are the three to five I should focus on that it picks out churn rate, gives me examples of why it picked at that tactics and trade offs. Pick that arpu, pick that conversion rate, free to paid. Again, this is a PLG company, so it has premium tier, pro tier, starter tier, pro tier and enterprise activation rate and then referral rate. And so then I ask it to put together this and this is where it started to get much better. Then it will take those, it will take those metrics, right? And all I just, all I'm saying is like, okay, create a growth plan and I'm going to go through the incremental improvements I've made. But it starts to get really interesting, right? It basically, I don't have to tell it to do hypothesis, right? It does its own hypothesis. It starts to give you pretty great experiments. I think it's as good as an average growth team, if not like a pretty competent growth team where it will give you, you know, the kind of experiments you can run to improve churn rate. There's one here that is actually pretty interesting. So it started to do things now that I, that I found it never used to be able to do, which is give me something that I hadn't thought of. So this one here, early warning outreach, it's a, it's telling us to like basically trigger usage signals and so when you see someone drop in daily active users, you take your daily active user cohort and then you trigger emails to cohorts where you see the daily active usage drop off over time, which is actually, which is actually a pretty smart thing to do.

One question, Kiran, this is my mind's buzzing. And two questions actually. One is, are you also uploading your UX and design flow for it to understand where the opportunities could be? That's one question. Let's start there because I'm very, very curious about how to leverage this.

This is a great. This is my. So this would be, this was going to be my ending point, but I, I'm glad you brought this up, which is just, just think about if you actually, if I actually went through this and so I'll go away to the end because I want to cover your point because it's, it's part of what I was going to cover at the end. I've asked it to continually think more deeply. If you ask it to think more deeply about these problems and do another version, you'll get better results, better results, better results each time. Now there's been a release late. There's been a release Lately on how deep seek and others and these reasoning models were managing to get better results. And one of the key things was just every time it thought it was finished giving you the answer, they would just type in the, they would just give it the word wait, that's it, wait. And they're doing that to say think more. And what it's doing in the background is it's looking through all of the things it's giving you and stack ranking them and trying to give a better one, a better one and better one. So that's the reason, the reason, the reason the models in the background are trying to reason out what is the best answer they can give you. Not too dissimilar from search but like I think somewhat more sophisticated. And then it's given me a big swing. But your point is really important because I'm pretty sure and I know because if I was showing you the internal use case, the more context you give it the better. So the way to actually make this incredible is you give it your actual real data, which for Kraken I assume is very complex. Right? Which would be better because then the reason the model would be able to decipher the first question of like really tell me the metrics that matter in this complex model. That is actually really important. The ones that's picked here are like pretty self explanatory. They're just like best in class PLG metrics. It would be interesting for a business like HubSpot or Kraken it would pick more interesting things. The second point is okay, we'll turn that into a growth plan. But actually here's all of the experiments we've run. So you have a library of all previous experiments which again speaks to the fact the most important thing to do for AI to be impactful is documentation. The most boring thing you could ever think of. It's really documentation and you load in all your experiments and then to your point, which is the next version I want to do internally, which I think is a great idea. We do have a ton of wire mapping and flows from our customer journey and so I would actually just add all of that into the context window as well. And I suspect you're going to get a first version of a growth plan that needs to be edited but does not in any way need to be rebuilt.

Here's what I would love to do, man, I would love to come back in four weeks because all I have to figure out today is and I'm going to tell you the type of growth questions we are Trying to answer that. Any business that is global is trying to answer and I want to bring some of those use cases back. But the only caveat is I need to figure out the local incident because we won't push that into cloud. I think that is, that is too scary for me to even think that I'm going to do. It doesn't matter what type of enterprise. But I get it. Here are some no freaking brainer questions that I can assure you this guy would or any of these platforms will help us get much faster. One is in a global company, I'm always looking at growth rates, difference between geographies. No brainer. Just tell me hands down, why is activation rate stronger here and worse there? Now they're all just looking at all the variables, all the inputs in the activation rate and telling me what the differences are. 2 We always look at, okay, who's a high value cohort and who's a lowest value cohort, but trying to figure out session analysis to understand okay, what is it the high value cohort doing beyond the aha moment, you know, that is driving maximize ltv. Now all that is doing is data crunching is just trying to figure out patterns, which is what AI can do a lot faster than a data scientist would running different queries. And then obviously we focus so much on payback period because we challenge ourselves to have very strong fully loaded payback periods and then starting to look at even just basic stuff. Okay, what is the IRR on your CAC stuff where we are spending a lot of energy which is rather mechanical or you are, you are searching for nuggets. If I can go back and figure out a way to dump all my raw data, I'll come back with some very interesting insights that we are generally trying to solve every single day, but it's taking us long.

When you get the internal data and you have those internal trends and you can anonymize it because you're, I'm going to say use deep research here and you might not be able to use that locally, I don't know. But you can anonymize it to take out to make you feel better. Just give it the trends, pair it with external trends in those geographies.

This is exactly what I was going to say.

Yeah, okay. Okay.

It's deep research plus the similar web API.

Yes.

What happens is you get all this data internally in any kind of company of scale. If you're a 10 person or 100 person or a thousand person company, you get some data and you're Trying to contextualize it. Right, but you're trying to contextualize it largely on anecdotal information. And once AI helps you synthesize it and find the key trends and timing on those trends, you then just like, you can just basically bring in deep research, similar web API at a couple external sources and like, it'll correlate very perfectly for you guys, I promise you.

You guys have totally changed my mindset in 30 minutes.

I am.

I mean, look, obviously I've always had this hurdle to think about. How am I going to apply it? It's always in the back of the mind. We are spending so much energy right now. We know exactly what questions we're trying to answer. We know where we are, we know how we're going to drive growth. But the latency in getting those insights is just insane right now. And, and what I want to go back and do is, is is pick somebody from my team to figure out how we're going to. And then maybe you're right. You know, there is elements of anonymized data which we can actually upload and put patterns. An interesting thing is in crypto, market behavior is easily capturable because the biggest market driver is a global bitcoin price and we have a lot of price indexes. So that can eliminate that variable and really understand what else is happening in a particular geography different from another one.

Yeah, I would say you have actually incredible external trends as well. Like, I would say there's some like really interesting patterns across geographies in your external trends because, you know, it's such a vibrant and popular space.

Yeah, yeah.

So like the other one that's really cool is I have all these different project assistants and so I have one. So like I did one for growth. Now a project assistant is basically you have, I'm sure you have an ea, we have an ea. She's incredible. But like to be across every single thing you do that is hard to scale. Yeah. I started to build these project systems for different projects and there is one, a good one for growth, which is if you have a Google Jam, OpenAI, custom ChatGPT or cloud projects, you can just literally not care about the structure of anything that people are giving you, as long as it's specific. This project, just say like, hey, put all the things that are related to this project, your updates, your experiments, whatever.

They may be, it doesn't matter.

Everything in a single folder. Right named project, which is usually a goal, like increase activation rate by 50 in 2025. Everything goes in a folder, then everything that folder goes into Project Assistant. It is pretty incredible. You can, you can get everything you need. If you're saying like, tell me about the three experiments in January that we did, the one that basically led to the biggest impact and why the other two failed. You never have to go on this like conundrum of slacks and emails to try to figure out stuff and that. And again, these mundane use cases are the ones where there's huge upside because it takes us. If you're managing hundreds of people, all of that stuff takes hours and hours of your day. And that one I, that one I have found to be incredibly valuable. And the other thing, the last thing I was showing KIP earlier is like it can build out interfaces dynamically for you in the, in the actual console. So example would be anytime someone does, anytime I do a meeting, I get the meeting transcript. And I said add all the follow ups into a table. And so it will keep updating the table in the, in the AI instance. And I say remove this up now at these ones. I've done those. It's just for me, you can't share it with people easily because it doesn't write back into a file. But it's unbelievable. Like I can't describe how much of a game changer these assistants are if you just attack the. The key is to attach them to a singular project so they, and that's, that's your repository for the data for them. And then they are your assistant for a specific project. And then you don't have to care about all of this crazy structure. You just say, please put all of your updates into this folder.

It kind of, you're like, why do I need project management software?

Yeah. I don't think you do in the future. You definitely don't in the future.

It's kind of wild.

Yeah. Yeah. And actually, even if you do, even if you're using it, it's not effective because as a human brain, you're not actually mapping out all the dependencies.

Yeah.

You're not thinking through. And also the worst thing, the point you made about, okay, what are the last three experiments you did and which one worked and why? Why the one that didn't work. The challenge we have today in the mechanical operations is we are not going back. We are not looking at what we learned from those experiments. Because.

Because it's too hard.

It's too hard.

It's too hard. Exactly.

Yes. So it's not only that we are inefficient because it's so manual, but Then we become ineffective because we are not technically applying the learning from experiments that were done. And imagine if you were not the one who did it, then it's even harder.

Right?

Literally one of the best things Kieran, you and I did in the last six months was like we were going through planning at the end of last year and we just took some of the raw decks and data from just planning and we were just put them in chat GPT and we were just like, what are all the dependencies we're not accounting for? What's like, what's the stuff that's going to go wrong that we're not thinking about? And you just like, historically that's just been really, really hard. And we got like a great readout. We changed a couple things. It was awesome. And you're like, it took like 20 minutes. You moved on. It was great.

You know, with, with all the content that you guys are sharing, like what's the best way to get the portfolio of the platforms for different type of use cases? It's like, how do you feel if the team's now going into the second gear? For example, the gear one is okay. Hey, we've tasted blood, we've seen it works now it's about expanding the horizons and really pushing the limits. Now are you guys, do you guys have a bit of a Gear 2 playbook? Like exactly what we just discussed and there are a lot of names. I was trying to capture some of them and I'll dig in. But any thoughts on. On that? Is that something that you guys are helping guiding the industry with like going.

Going from AI foundational use cases to then really scale the AI use cases?

Is that what model to use for what thing? Is that what you're asking?

Yes, yes.

What model to use for what thing? And that's what's tricky.

I think, I think Claude for. Right.

If you're a marketing. Just, just.

Yeah, Claude. So anything, any write in is like Claude. Even internal stuff for execs and stuff. I still run through Claude. Claude is a better creative model than OpenAI and Google Google Coder still, by the way, from what everyone tells me, because I can't delineate between all three. They all seem pretty great at coding. Claude is like the preferred use case or the preferred model for coding and people don't know why. I don't know if you've seen that. People are like, I don't know why it's so good, but it's just like it's way better. Right. Claude is also this weird Thing where we had a Scott on who leads product for Entropic and he was asking like, why do you love Claude so much? And I was like, well it's like, why do I like one friend better than the other? And I don't know, I just like gel better with cloth. Right. It just, yeah, it gets me, it understands me, it just knows me. Open the O3 OpenAI models are definitely better for strategy. And so if you take a growth that example, I showed a growth dashboard and took the data and then asked for a strategic doc. I still find OpenAI in particular the recent models and you can see on the benchmarks it's just better at strategy. Google Gemini is not far behind and I need to actually get deeper into the Gemini 2.0 releases. The beauty of Gemini is just the integration with Google's platform.

It's huge.

That is their key advantage. And I think the thing that could still mean that they win because just the integration into G Drive is you have all the context. Right.

I got a rough draft of everybody's performance review just from all the decks in G Drive and Gemini is so much faster, so much better.

Oh man. Yeah, this is, this is so amazing, man. I mean obviously we've been using AI at different places, but I swear I'll have so much more to share in four weeks because I'm taking a lot of energy away from here.

Yeah, I think this is a great episode because we got some real insights into where a company like Kraken and you are how you think about AI and then just riffing on these things of like what are things you're thinking about in the future and it'll be interesting for you to come back on and tell us like, did you find it good? Did you not find it good? Is it, has it been impactful or not? Because everyone's at. Everyone's just trying to figure out like, because AI is such a broad thing like you can use it anywhere. Everyone's really trying to figure out like where and it's, it's really missed more than it's hit. And so I think everyone's trying to figure out like, well, where, where is this actually worth my time? Because everyone is busy, right? We don't have time to be spending iterate on AI and having a bunch of misses when we actually have real goals and results to drive.

Yeah, I think that's the, that's the biggest part that comes back to that operational question. But I think this is where leaders have to just take it on upon themselves and, and Just get the feet in hand. And I think the key is, is. Is what strategy you use to. To filter out from the clutter because it's so much. It is very easy to get distracted. So that's why your point at the end is important. Look, we've shared a lot of names, a lot of platforms, but just focus yourself on two or three. That's it, you know, and those are the big ones. And start there. Then you can start to become niche. Oh, here is this one thing. But otherwise it can get really noisy and then you get overburdened. Then you go back to your old habits.

Yeah, yeah, yeah. I actually think this is the most interesting test of how quickly users change behavior of all time.

Yeah.

Because to your point, the natural inclination will always be just, you know, it's a bit hard. I've tried seven times. It's missed six times. I'm going to go back to doing the thing I used to do.

Well, the other thing Kieran, you and I have talked about a lot is that, like, I truly believe that, like, the what model is best for what thing question is probably not the right question. I think the right thing to say is all of these things are transformatively powerful and we are underusing them. And so I just took one and just obsessed about becoming deeper in my adoption fluency expertise in just one. I'm probably far better than everybody else. Right. Because it's like there's so much of all of these models that we're under using because we are flipping back and forth between all of them.

Yes.

Yeah. I think this is a great episode. We really appreciate you coming on. Meyer is everywhere. As always. You're an incredible guest and I think your insights here are great for. For our audience. So I appreciate it.

And I think we just. We're trying to go through the process everybody has been going through. Right. And like, everybody is kind of living in the same world over the last six to 12 months. And I. Hopefully we shared some. Some perspective that's helpful. And then Meyer's going to come back and give us a round two, which will be awesome.

Cool.

Awesome.

Awesome.

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