SaaS Scaling Secrets
The SaaS Scaling Secrets podcast reveals the strategies and insights behind scaling B2B SaaS companies to new heights. Dan Balcauski, founder of Product Tranquility, leads conversations with successful SaaS CEOs, exploring their challenges, triumphs, and the secrets that propelled their businesses to the next level.
SaaS Scaling Secrets
The Surprising Downsides of Infinite AI Content with Yaniv Makover, CEO of Anyword
Dan Balcauski speaks with Yaniv Makover, CEO and co-founder of Anyword, about the evolution of AI in content creation and marketing. They discuss the impact of ChatGPT, the competitive landscape of SaaS AI copywriting tools, and the strategic differentiation Anyword employs. Yaniv shares insights on the necessity for enterprises to integrate AI, the challenges teams face in AI adoption, and the role of data integration for improved marketing performance. They explore how AI is transforming marketing workflows, the importance of testing AI-generated content, and ensuring high-quality outputs. Yaniv also emphasizes the need for companies to quickly demonstrate AI's ROI while investing in future AI infrastructure.
01:06 Meet Yaniv Makover and Anyword
02:01 The Competitive Landscape of AI Copywriting
02:32 The Evolution of AI and Anyword's Strategy
03:42 Challenges and Solutions in AI Content Generation
06:39 The Impact of AI on Marketing Teams
08:45 Navigating the AI Boom and Market Shifts
13:40 Enterprise Focus and Product Strategy
17:51 Future of AI in Marketing and Business Processes
21:51 Challenges in Business Processes and AI Integration
23:25 Adoption Struggles and Successes with AI
24:32 Decentralization and Oversight in AI Content Creation
25:53 Balancing Quick Wins and Long-Term AI Investments
27:47 Navigating AI Development and Validation
33:09 Private LLM Infrastructure for Enterprises
39:24 Rapid Fire Closeout Questions
Guest Links
Then chat g PT happened, and then this is what you're talking about, everybody's yeah, like you're, you have a nuanced kind solution. What do I care? Just give me AI to write my blog posts in which I, now I can write like infinite amount of blog posts. okay, like AI is not gonna be as good as me. I'm always gonna be better than ai. What this is just not, this is GPT-3, whatever. That's not the conversation anymore. So it's pretty clear to everyone this is the future. Call to action is that if a company doesn't adopt AI in the next five years, they're gonna be at such a disadvantage. They might die, right? if you spent, three hours prompting the ai, why don't you just write the thing yourself? It's a cliche, but really like, it's like, there's so many bad things are gonna happen to the company and to you and you really need to shrug it off
Dan Balcauski:Welcome to SaaS Scaling Secrets, the podcast that brings you the inside stores and leaders of the best scale up. B2B SaaS companies. I'm your host, Dan Balcauski, founder of Product Tranquility. Today I'm excited to welcome Yaniv Makover CEO and co-founder of Anyword, an AI copyrighted platform that helps performance marketers write higher converting content. Yaniv has scaled Anyword to over a million users and he brings a unique technical background to the AI space with a master's in information systems and data mining from Ben Gurion University Yaniv. Welcome to the show.
Yaniv Makover:Thank you for having me, Dan. Excited to be here.
Dan Balcauski:I am excited for our conversation today. Before we dive into your scaling journey, can you give us the elevator pitch? What does Anyword do? Who do you serve?
Yaniv Makover:Yeah. We serve enterprise marketing
Dan Balcauski:I.
Yaniv Makover:We help them get better results from the AI that they're using. So whatever AI they're using, open ai, Gemini if they use it with Anyword, then Anyword to plug in lots of data. IB tested data into lms. Make sure that the content, they're generating social posts, ads, emails. better for their target audience. And the promise is basically a 15, 20% lift in results.
Dan Balcauski:Well, Anyword it operates in what might be one of the most competitive spaces in all of SaaS right now, AI copywriting. You've got, the foundational models themselves Jasper, copy, ai, dozens of others all launching around the same time all realizing that, AI powered writing was going to be a big market. I guess when you looked at that landscape, like how did you think about the. Sort of strategic challenge of competing in the space where everyone had access to these really powerful underlying technologies.
Yaniv Makover:Yeah. When we launched Anyword, this is like before, before chat, GPT and
Dan Balcauski:after chatgpt
Yaniv Makover:launched Like my mom phoned me was like, oh, you heard about this thing? I was like, mom, I've been working on this for like six years about LLMs and ai. Um, so it was annoying. But but really thinking about where Anyword belongs in the ecosystem. Like it's something we started way, way early.
Dan Balcauski:Hmm.
Yaniv Makover:we was like always big believers in the fact that LMS are gonna be commoditized. Everybody's going to be using them and it's gonna be everywhere. then how do you build a product in a mode? We, like my first company that I started was all around performance ads and content and publishers, and just basically learned the power of creative. And the data aspect that goes into creative. So like, there are just better ways of saying the same thing. And there's and in marketing 10% better is really what's gonna either make or break your campaign. The dependent on what your goals are. And so, we can build Anyword with the notion that AI is gonna be free and everywhere. But addressing the main. I would say gap that AI has and the ga, the main gap is that even though AI's been trained on the entire internet, or some teams have their own first party data it's not enough for it to know actually what works. So, and a LM will rate 10 amazing emails. One of them is gonna work better. It just doesn't know. It can hallucinate and it doesn't know because it hasn't seen enough data. It doesn't know. And so we've actually tested this out. Like if you take, gPD four oh. And give it a pair of texts and ask it to guess which one work better. It won't know, it will know like 60% of the time. It's not because it doesn't it's not smart enough. It's just that hasn't seen enough data. And so we thought, okay, that's something that if we solve, we just, it won't be affected by the evolution of ai. So, saying that then building a product around it and a strategy is something different. But that's how we thought about this problem.
Dan Balcauski:I, I'm curious'cause you say like these LLMs. I, I've had very similar experience, right? Either trying to write emails or, I do my own sort of social marketing organic, LinkedIn posts or, or anything else, right? So I've gone through some of this as well, right? And you can ask any of the mo models for, Hey, improve this and it'll give you completely different directions of feedback, so, it could be, a little bit scattershot. You're like, wait, did, but I do have. I, I am surprised to say like they haven't seen enough data, because as I understand it, right, these things are trained on like the corpus of all human knowledge. So I assume that you're using data in a very specific way when you say that they haven't seen enough data. Mm-hmm.
Yaniv Makover:Yeah the, that's exactly the problem. They've seen the corpus of the human knowledge to, let's say you have two versions of the same social post on LinkedIn. You want to post about, I don't know. This podcast, like this recording or having me. the only way you can actually tell a model what works better is you have to show it comparisons, like actual ab tests because say you compare a post that you did about another recording with me and with another guest, and that guest is way more interesting than me. It has more, brand just, just a better looking person. Then the model can't actually generalize because that data, it wasn't really set up in where it can learn what actually works better. Now when you're, they're telling the LM to generate like 15 different ations for a post about our recording or our session you can only tweak like three words. It's still gonna be me, so there's like 15 variations of the same post, and then those words really matter. And so to train a model you really need to have. specific and very high quality comparison data. And without it, still knows, like it knows like, like I said, 64% of the time, which is like 14% of random, but it's not gonna get to high accuracy or high enough. And in marketing, like I said, even 10% lift is everything. So,
Dan Balcauski:hmm.
Yaniv Makover:not that LMS don't know they know, they just could know better.
Dan Balcauski:I'm curious going back to, I'm sure your mom is a very smart woman, obviously you, you share the same bud line. But I imagine that experience was not unique to her. Right? Where, everyone had this sort of chat pt moment where they realized like, oh, this is a thing. And I'm sure that, definitely jumped that, gap between a very specific group of technical insiders, mostly s centered around silicon valley or, the type of work that you were doing previously. And all of a sudden everyone's just like, oh, well there's this tool out there. So I'm curious, like, it sounded like you were prepared from the beginning of a company that this was gonna be, widespread. What, I guess what went through your mind when, you realized this sea change of like, okay, now my mom understands what this is, but I, I'm sure a lot of teams be, there's like, well, the, the base bottles are, are well enough. Like how did you think about, okay, how do we carve out our space? Within this world to really tell this story because I, I could imagine that's a very, it could be a very nuanced story to, to tell, and maybe people are just like, oh yeah, the underlying models are just good enough and now I can just create content and mass and maybe I just, create a hundred campaigns and I run my own tests. It's like, how did you think about that challenge?
Yaniv Makover:Actually, it's a good question, I think I divided into like three phases
Dan Balcauski:Hmm.
Yaniv Makover:GPT, first year, like during Chad, GPD, and then what I would call this phase right now. Before Chad G like, before Chad, GBTI had to go to like 50 different investors and convince them that AI is gonna be writing stuff. And I had like, back where models weren't that big. There was like a model called Berts and then GT two and then GT three. And then GT two would like spit out like five different answers. Some of them nonsense one was good. So if you do two plus two equals and you ask GT two what it was, it would go four. It was like, that's amazing. Okay. It learned math from words and then four plus four, and it would go like nine equals nine. So, so, just had to get like one investor out 50 to believe that this is the future. Forget about
Dan Balcauski:Mm.
Yaniv Makover:for a second. That was like everybody was talking about blockchain for the past 10 years and then convinced'em about this problem. So that, that was the pre, I would say pre-chat, GPT, just getting marketing teams to actually use ai. Then chat g PT happened, and then this is what you're talking about, like everybody's like. Yeah, like you're, you have a nuanced kind solution. What do I care? Just give me AI to write my blog posts in which I, now I can write like infinite amount of blog posts. And this was like, I would say like a big part of the AI explosion, but for actually for us we're still small, so it kinda like helped us because now we couldn't, we didn't need no need to convince any marketing team that AI works. They all knew that it works now, it
Dan Balcauski:Mm.
Yaniv Makover:like some enterprise solution. as the, I would say the, the market is progressing. It's actually easier and easier for us to say, Hey guys, you can ab test 500 emails here I just wrote for free. And this is just, it's impossible. How do you know you're using the right version? There's no like person standing in the way. And also I think marketing teams are feeling like the, actually the degradation of performance when they're using ai. So they used to have an expert and now they're using like everybody's using AI and they're not experts. So they can't even edit or vet the outputs. And so I think it's easier for us our story resonates more. It's like, just like, okay, AI works. Got it. We don't have to spend anything about that. You're already creating content with ai. Now let me tell you how it will work better, and and you'll know this before you publish.
Dan Balcauski:So I think the one thing I heard, and you can correct me if this is a misinterpretation, is this move from it's, there's a step that is producing the content, but what I heard you outline there is like, it's not just producing it but taking it through the entire workflow.'cause you might be able to produce 500 pieces of content, but then do you have the. Capability to then actually test all that versus thinking through the production into the validation workflow. And so the, the base level LLMs really only give you that first step and leave you without a way forward on that. On that follow on parts of the, of the, completing that whole task.
Yaniv Makover:Yeah.
Dan Balcauski:is that correct?
Yaniv Makover:you're right about what you were saying, but I actually
Dan Balcauski:Mm-hmm.
Yaniv Makover:else. I meant the fact that AI, you would have a person maybe writing three variations of the email you wanted to send out for a campaign. And you test those emails and you send those emails to 10 people a time. And then the best email you would actually send it to to everyone. So I'll give you an example. For my first company, one of the customers was in New York Times, New York Times would. eight variations of a tweet. those variations out. Send the best version out as the actual tweet to everybody. That's what they do. So the AB tested those eight versions. Now you have ai. So you can tell AI had to write a thousand variations of the same tweet and they'd be slightly different. It is impossible to test a thousand variations. It costs a lot of money and it takes a lot of time. So now everybody has the power of ai. But it can actually hurt you because testing costs, time and money, lots of time, lots of money. You can't test everything, and you really need to know what the best variation out, out there is, or one of the best variations to use that. And when the more you use ai, you can like get a feeling for that. Hey, I could use this version, I could use that version. They're all great. Marketers care about performance, like they care about stuff like this is gonna work the
Dan Balcauski:Hmm.
Yaniv Makover:And so I feel like it's an easier and easier story to tell.
Dan Balcauski:Got it. Got it. So, so this shift from how they were doing things as well was part of that story. So, so you, you talked about these kind of three eras of this sort of pre-chat gt where you're trying to convince, one out of 50 investors, like, Hey, these underlying technology's gonna progress, it's gonna get better. And then you, there's this sort of chat GBT era or like around, when, when that launched. So was that, was that late? 2022, I think the times all collapsed post COVID. I, it is hard to separate the years. So I'm curious, like when that happened and you and people realized that AI was a thing, like I guess was your plan that you kind of had, had been pursuing. Did you realize then like that was going to be enough to differentiate you or did that require a retooling of your strategy in order to separate yourself from the rest of the pack? Who realized like, oh, like AI written copywriting is gonna be a thing all over and all of a sudden you have. 10 new competitors, probably not net new. I think right before, probably six months before the chat GBT launched, actually the Jasper's based here at Austin, and I randomly had was meeting some friends for drinks and like ran into those guys like six months before and they were like, hyping this up. And I, I, I didn't know any better. And so they're like, oh yeah, we'll give you a free account. And I test it out. But it was like, it was. It was GPT-3 and it was like exactly the experience you just denoted, which it was like, yeah, you generate 10 versions and like nine of them were gibberish and like one was good and I was like, yeah, I can't really use this to write a blog post or anything, but like all of a sudden, three, five and then four, comes after, after now you're like, okay. People realize the thing. There's a bunch of attention, bunch of VC money in the space. Did that require any sort of retooling of your overall approach or, or were you guys set up with. The strategy you had in place before that to navigate that landscape.
Yaniv Makover:I think for me and for us, it's like, okay, you have conviction about what AI is going to be and what are gonna be the problems that you need to solve for it to
Dan Balcauski:Mm-hmm.
Yaniv Makover:us it was always like, okay, we need to close the feedback loop for AI to make it better and make it like work, work better. And that's gonna be a challenge. It doesn't matter if it's AI or lms, orent, lms. The model wasn't trained on AB tests. You need to tell it what AB tests work. And so I always knew that, or like we always had, and I still know that that's my conviction. That's our conviction about this is what we're trying to solve, this is how we're gonna make AI better. Tooling and product strategy and differentiation are different, right? So before chat, GPTI would go and do this and Jasper did an amazing job just like creating all these templates. This thing writes an email. These just making ai easy to use for everybody, for SMBs and enterprise and stuff like that. And then, so what happened to us was after Chat GPT this actually make everything better for us because we could just focus now on enterprise. Who cares about performance Enterprise? SMPs just care about, like, this thing writes really well, so this saves me a bunch of time. That's not
Dan Balcauski:Mm.
Yaniv Makover:This last 10%. Of the conversion of the landing page, right? So,
Dan Balcauski:Mm-hmm.
Yaniv Makover:Really care. So it actually allowed us to focus on that segment and also focus on our part. Like you don't have to create thousand different templates, like you can do anything with a prompt, right? So that thing just basically the playing field for any that creates content like copywriting. And then a, people use Anyword when you prompt with Anyword it will tell you what talking points to add to the prompt. So the AI knows what works for your target audience, for your channel and for your goal. So whatever you're trying to do, and then when you generate a bunch of results from the LLM, doesn't matter what element you're using, you're gonna get like 10 versions that we'll rank them for you and you can test it out. So our biggest customers, I won't say which, like are the LLM Foundation Model Marketing teams. use Anyword with their models to market their own thing, just to rank and stack that, that thing. So the conviction never changed. Definitely the product kind the way the product worked. The emphasis on how needed to work had to change with with the market. Now it's all about agent. So like, it's not like that changes our vision, but we'll definitely have to be, a server in the agent work and playing that, that role.
Dan Balcauski:You, you mentioned in that. Arc that you realize, the enterprise, really cared about this delta in performance of one, set of copy versus another. I guess was this did that shift in the broader market? Were you guys already purely focused on enterprise? Did that make that more crisp and clear for you or was that something that was already, you already knew from the before that happened?
Yaniv Makover:So ai, it's a good, AI is such a unique market. We already, we always knew that enterprise would be like, what where we needed to go, but just couldn't do it. Like people didn't have ai, so you had to like sell them ai, like they didn't have Chad GPT. You had to like
Dan Balcauski:Mm
Yaniv Makover:G PT three or GPT three four. That had to be part of your product. And then the early adopters are SMBs and you're talking about
Dan Balcauski:mm.
Yaniv Makover:sold to, dunno, millions of SMBs. They did such a great job. And so there was no getting around, PLG and like no getting around like, SMBs to get the enterprises the market is mature enough where if you are talking to an enterprise marketing team, they're already using JGPT or something, or whatever, some version of that. they just need to step up their game, right? So they're, they already know Doing. It's easier for us to show them, Hey, this is AI and this is Anyword. And and yeah, we always knew that that's where we're going, but we couldn't do it. We just had to like service SMBs. In the beginning, we had to do this whole, like, everything that you needed to get because marketing teams for enterprise weren't using it. They weren't gonna buy it.
Dan Balcauski:That's that's interesting. That's counterintuitive from what I would've expected in that, like the enablement of this broad based, technology actually sort of made it, made it easier to, for you to penetrate those markets because they now had, were familiar with the underlying technology when they were not necessarily the early adopters upfront. I'm curious like you've been so. You've hit at this a couple times where like, AI is fundamentally changing how marketing teams, operate and you've had a front row seat to watch this happen, probably across your customers in your own organization. I just, what have you observed changing and how, marketing teams are structured in terms of how they're, creating content, how work actually is getting done?
Yaniv Makover:So, first of all I think we're like really early. I think it's work in progress. I think everybody's I think it's being mandated by boards and management teams to like adopt AI. Every that uses Anyword uses it differently. Workflows are still different. It's clear that they, people trust ai. We're. Like we were talking about the fact that enterprises were like, weren't the early adopters of ai. There would usually be some person there that is like the chief content person, whatever their title was, and they would be reluctant this thing is like, not gonna be as good as me. And that would be a valid conversation. Like, okay, like AI is not gonna be as good as me. I'm always gonna be better than ai. What, like, this is just not, this is GPT-3, whatever. That's not the conversation anymore. So it's pretty clear to everyone this is the future. to get there is not clear, right? So
Dan Balcauski:Hmm.
Yaniv Makover:whole conversation around agents, like what an agent can and can do. if you cannot define the business process easily and accurately, then how can the agent do it, right? So I could see much faster than agent managing your website or your social page. Then managing your kind of customer life cycle, marketing whole thing. That's much more complicated, the more channels. The more kinda like, dependencies there are, it's much harder. But I definitely do see like, there's easy things to do, like creating change, like Anyword now has an agent that changes your copy on your website all the time. Just tests. It changes it and it evolves and makes better decisions shows different copy, different people. That's super easy. Like it doesn't even break. Your CMS Deja knows what, there's a goal. And it knows how to optimize for the goal. It doesn't have to like get approval from 50 different teams. But that's a whole different thing than like, affecting your, your branding your positioning, your strategy. And and I feel like I'll give you an example like where AI has been trying to solve this problem for. I dunno, 15 years, even before generative ai it's not gonna be solved. Even with generative ai, like people would think it's super easy. Why doesn't AI manage your budget between your search ads and your social ads? Right? You have a budget for paid advertising there's no AI that will take money away from one channel to the other. There are like been plenty of attempts, plenty of tools, but. You'll never see a paid ads team actually
Dan Balcauski:Yeah,
Yaniv Makover:that. Why? That should be a simple enough problem,
Dan Balcauski:I I, my, my pet theory would be, it would be because attribution is so difficult at attribution channel, attribution, like there's any number of ways to calculate it, and no one way captures every sort of nuance.
Yaniv Makover:but why is a person better at it than the ai? Like they don't know that you're
Dan Balcauski:That's, that's, yeah. Fair enough. Yeah. I, I, yeah, I, I guess nobody has any idea I what do you think is going on there?
Yaniv Makover:I've thought about this a lot, right? This is like before a lens. Okay, so if you could sell your product a hundred times for$20 profit, or 50 times for$30 profit, sometimes it, it's like I can calculate the ROI and I can do that. There's lot, so many dependencies on where you can end up like that And those tendencies change. Like there's seasonal dependencies and there is like, also sometimes you, you, you wanna be more aggressive with the strategy'cause you're paid ads, support your branding. There's so many things that in a perfect world you could define for the ai and it could do it. It's just very, very hard to define. Like, and it's like, unstructured. It's not really well articulated. There's just a lot of that stuff going on in every, and the work that we all do in in, in the business place. And it's even in a simple problem about how to spend your budget between and Google, which should be super simple. Doesn't happen because there's so many contractual things that you need to take a into consideration, cash flow issues, whatever, ROI, attribution, all that stuff. That you definitely have a person there and like, okay, I'll just make the decision every week, or something like that. So, and I think that is a good anecdote for almost a lot of the business processes in the company. So people are worried they're gonna lose their job and the guy's taking over. It's really hard to define even my job, like your job, like I wake up in the morning, I don't even know, like every day, what was my. My objective, my function objective that I like, okay, I only need to do this. I have like seven competing ones and sometimes I don't feel like doing this one, and like maybe I do the other ones and it's not really well defined. So I think it's hard.
Dan Balcauski:Well, it is definitely challenging and I think, so, within there, I think you, you. Something I'd pull out for the listeners just is that it's definitely something where like the closer you are to an observable metric, the ais are, are better able to take over that task, right? But much like anything in, in, in business of any complexity, like how much is our brand worth and how much should we invest in brand or, where should we put our positioning? Those don't necessarily have very clear next touch goals that we can, we can measure against as we, as we tweak things. Because you're, you end up in a, a very complex landscape that's hard to, navigate or boil down to any, any particular set of numbers. I'm curious'cause you, you talked about, it's still early days and, and I, completely agree with that. In terms of how teams are adopting ai, I'm curious, what are you seeing teams like most struggle with as they try to, build work ais into their workflows? Right.'Cause yeah, just as, as with with where Anyword is playing.
Yaniv Makover:So I think who gets to use the AI to create content? Like the, if you were like the email person in the marketing team or the, even the ads person, you did not create your content. You would get it from another team that created a content for you. So there would be like a centralized team copy team, content team, whatever you wanna call them. would be like the service providers for the rest of the teams. I think now, like, with running that, dude, I have this ai, I can just like. Spit it out, and should I do it? And then what kind of oversight do I need? And if I have this oversight, am I leveraging the ai? So I feel like there's this like re reconnect changing of the and I'm talking about it like very specific, like from a content perspective, like how Anyword sees the world. So I, I know people have like a much bigger kind of view of how AI is changing the workspace. But from a
Dan Balcauski:Yeah.
Yaniv Makover:there, there's a team responsible for every channel and, now they're creating a ai, so it's distributed, not centralized. And then how do you make sure that they all work in sync? And now they're also wanting to have agents do that. And so how does agent, how do agents work with people? So it's pretty pretty early days about how do you approve the content how do you make sure that everybody uses it
Dan Balcauski:A shift so a shifted the roles and responsibilities even among sort of team members is yeah, I can imagine that's tough to navigate, right?'cause overnight. It's like you have capabilities at hand, right? Where, there's definitely a trade off as you involve multiple groups or multiple people in any workflow, there's handoffs and coordination that has to happen and then delays. And you're like, well, if I could, I could just turn around and chat GPT and ask for 10 versions of copy and just pick one and, and get on with my life. Or I can, submit this request to this other team and then wait a couple days to get the. Something back and then realize it's not exactly what I wanted and then go through that process. So I can imagine just yeah, there's, there's not necessarily a clear path on how you resolve those type of tensions in a business as, those responsibility shift around. I'm curious, like, as and maybe this is related to that point or, or something else, I guess the, what are you seeing still being early days, but I'm guessing you're, you're, you have customers where, you know, some of those customers are definitely, able to adopt better and faster and, and have more success than others. I guess in the companies that you see where they are able to, really adopt these tools and workflows successfully. Like what did you see them doing that maybe other companies aren't like these the leaders, the pioneers, the folks who are, who are able to really make these systems work in their organizations.
Yaniv Makover:Okay. The main comment. Call to action is that if a company doesn't adopt AI in the next five years, they're gonna be at such a disadvantage. They might die, right? They might
Dan Balcauski:Mm-hmm.
Yaniv Makover:have to do it. Everybody knows it.
Dan Balcauski:Mm-hmm.
Yaniv Makover:that I think I see that they should be doing one, you need like quick wins. Like somebody needs a reason to use ai. If you just, like most people just have a hard problem, like just challenge adopting it, like, how do I use it? let's say it gives you some lift in your ad spend or your ad performance, that's great. That's like you need quick wins or your blogs work better, whatever. Like, that's something. But the other thing is you should invest in your AI infrastructure. So enterprises need to connect AI to their data sources. They need to understand how the, I wouldn't say the five years from now, but two years from now, how their ecosystem, their vendors will look like. So you need to spend some time and budget on that, but you also need to show quick wins. So you can't just now get into a project where it'll take you two years to map out all your data sets, all your workflows, all your use cases define who does what. You just need to start using it. So the main thing is like most people on your team don't know how to use AI there. Maybe they use Chad GT if they're younger or if they're kind of like, if they, if but maybe they don't know how to do that. And it's a pain for them. It's not doing what it needs to do. So I would find the use cases. Usually it's in, customer support and it's marketing. It's like there's easy market content stuff that you can do. Social posts, ads refinements of existing copy show immediate ROI to people, so they like believe in it. That'll make them better at their job. And think about the infrastructure you need to start building and start building it. So I feel like
Dan Balcauski:Hmm.
Yaniv Makover:the more advanced, successful teams
Dan Balcauski:So there's a Go ahead.
Yaniv Makover:a balance. I see. I see some companies investing in infrastructure and they'll just, again, like a two year project and nobody's using ai and it's, I don't I, I, I think they're gonna struggle and it's not moving.
Dan Balcauski:Yeah, I, so I, because I've heard of a couple different patterns. I have a friend who works at one of these, one of the major tech firms. And so he's become a, he's somewhat been anointed, but also he, he had a proclivity for it of a, one of these AI front runners. And so he's tapped with running around the organization giving talks, giving assistance of like, Hey, here's the surfacing the use cases. Here's the, here's the way to organize around it. Here's, how. Prompt, better, et cetera. And so, I, I, it's, in any large organization, talking about, difficult to find metrics to run an AI against, it's difficult to understand if that, is that the way or is it, you carve out, Hey, we. We have a, we have a team and we're, going to run them, in a very different way for a type, to make sure that they can iron out all the processes before we try to push this organization wide. I'm curious if you've seen any patterns like that as, as marketing teams have tried to adopt at least the kind of workflows that Anyword is involved in.
Yaniv Makover:Yeah. I think the patterns I've seen is like, let's try it out. Let's like use something And, perfect our prompts or make can like, just start doing that content for marketing is actually easy for you. Like that. Maybe coding and support. So, so I feel like there's a lot of low hanging fruit in marketing for creative, generation. It's gonna be much harder to get an agent to manage your kind of like life cycle and more complicated things like that. So I've seen that. I also seen struggle, like people are like using AI and they, it's not giving them what they want, so they don't know how to prompt and they know they should be. So it's not that I, I don't think adoption. going as smoothly as people would think for an enterprise. It's like
Dan Balcauski:Yeah. Yeah.
Yaniv Makover:for content, which is not that difficult, just write me an email about the next thing. You have formatting and you have style, and you have tone of voice, and you have brand vocabulary, and you have all that. And then you, and this thing needs to work. if you spent, three hours prompting the ai, why don't you just write the thing yourself? And and then. so that gap has to be, is still not solved yet, I feel like,
Dan Balcauski:I, so, I, I curious the, the challenges that you mentioned before with, shifts in roles and responsibilities. I'm curious how if at all you've experienced that internally in Anyword. Like, like as these technologies have gotten, very powerful, like how are you as a leader managing, right? Like all of a sudden everyone has a, a. A peer or an assistant that they could ask, right, where they may have used a shared services organization. How are you as the leader of, of your own organization, like helping the team navigate those situations.
Yaniv Makover:Yeah, so, so first of all I would say the really easy things to do is like any. Analysis, an analyst type of jobs in the org, which there's a ton of these stuff. Like, Hey, let's just like
Dan Balcauski:I.
Yaniv Makover:this Excel. Any question you have to ask. Like, it's a huge time saver. So, just getting, and because we're like an AI product company, like it's pretty easy for people to use that. So just like that happens in sales, that happens in, in, in in marketing. It happens in in, like that's the easiest thing you can do, just like, and then from, I say product RD perspective, I think AI has changed the development cycle. And some, and for me there is a big emphasis inside the company to how to develop in this new ai development cycle, which is different. Like you, there's tons of things you can do with AI that would, took just really easy to do that would've taken you, I don't know, months. From an RD perspective, but it also changes the way you validate the products for your customers. like how fast you can get something to them. Does it work? Sometimes does, and AI is unpredictable, so you have to be really good at, mapping out use cases as opposed to like, standard software where. This thing does three things and that's it. I need to test those three things. How do you test ai? That's really, really hard
Dan Balcauski:Hmm.
Yaniv Makover:Like you don't know what input is gonna be in there. And then, so I feel like that is a big focus for us. On the marketing side, obviously we use Anyword to create content. It's like we, we, we don't dog food and to measure results. Our website is managed by Anyword. Our website content. And we publish content with Edward all the time. So that's kinda like low hanging fruit for us on the coding side. We're using some ai. I wouldn't say that we are like ready to basically write all of our code with with with ai. I don't push like, I don't know. Some teams, companies say, Hey, you just need to do that. I think coming from development side, myself. You really hate somebody else's code. It's like living in somebody's, a developer's worst thing is like to fix somebody else's code. It's like living in somebody else's house. It's like, it's not comfortable. So I'm very aware of that. So it doesn't matter if it's ai, another person not pushing like wants to use ai fine, if not, also fine.
Dan Balcauski:I, I wanna pivot a little bit because you've, you've mentioned the, obviously there's the underlying infrastructure and AI first companies are. AI first and any AI enabled companies are making some pretty significant infrastructure decisions now correct me if I'm wrong, it looked like from, at least what I could glean from your website, that you have private LLM infrastructure that's isolated from external providers. Is that correct?
Yaniv Makover:Yeah. We provide that to our enterprise customers. Yeah.
Dan Balcauski:So, so that seems like a pretty massive strategic and technical investment. I guess what sort of led to that decision?
Yaniv Makover:Yeah, the market, right? So if if comic a customer is looking for a private model, then they don't wanna share it with the foundation models, then we'll, we will do that for'em. So it was mandated actually.
Dan Balcauski:So totally market driven. There was never a thought in your mind to have it another way.
Yaniv Makover:It's not that we it, it, it's like it's table sticks. It wasn't that something that differentiates the riches us Anyword at any point, or it doesn't give us any value. It's just like, no, like the customer saying, Hey. I need to know that you're hosting this and I'm not sharing my data with anybody else. And so we went ahead and did that.
Dan Balcauski:So I, I'm curious'cause like when you're, when you're building a product that depends on AI models I think the AI models are a bit different than maybe, like nobody necessarily cares that your backend of your SaaS databases, Microsoft SQL Server or Oracle or any other database system. But, potentially that may, be relevant to customers, and you have to make a decision of, how much of that to expose to customers versus abstract away, do you let customers see or choose which models are powering their content, and how do you think about that balance? Mm-hmm.
Yaniv Makover:Yeah, they can choose either their own internal model or like external model. Like it's a bit complicated because there's like generating the content, which we can completely decouple from. You can give us your, just use GPT or whatever, or, and then there's like ranking and sorting. So there's like different aspects of the product that we. Can plug different models into, so for instance, if chat GPT creates five versions of an email, you can send it to Anyword that Anyword will rank them. And then the, Anyword model will be, it's also an LM but it's hosted, it's perfectly hosted, but you can use whatever model to generate those variations and prompted. And so, we were capable, careful to decouple from foundation models'cause a lot of companies just use those. Maybe enterprise GPT, but enterprise or Gemini, but still, financial models and some would prefer a private model. But for us, because we don't want to compete with an lm, we're not in the LM business we need to be orthogonal to that. And so we do that. I think the way I see it really, I think in five years. If you're gonna be a vendor selling to an enterprise company or any company, they're gonna have their LLM, they're gonna have two or three LMS plugged into their data with
Dan Balcauski:Mm-hmm.
Yaniv Makover:and you're gonna have to work with that. They're not gonna use 50 lms they're gonna, they're gonna have three like data
Dan Balcauski:I. Are you are you familiar with the company Clay, the like, revenue account enablement software. Anyway, so, so, I, I used Clay before and so they have their, like AI agent tools. So you could say like, Hey, go scrape the website of this company and find out if they have a pricing page or if they're B2B or B2C stuff that. Necessarily wasn't stored in clean schema data that you might buy from dun and Bradstreet or, or LinkedIn before, right, that you can now use this, these LLM powered agents. And so, one of the things is that in their. In their user interface, you can select, they have their own homegrown models. And then you can also select, you wanna use Claude or Gemini, or which version of chat GBT you wanna use four oh or, or five or, or, any, any selection. And so I guess, there's, there's a power to that, but it also could be a little bit overwhelming in that, when I'm, I'm like, okay, I need an, I need this thing to go scrape this company page and see if they have a pri public pricing page. I don't know which one of these models is gonna be the best at it. And they have credit prices for each one. And so I'm curious, like how do you think about that decision in terms of what to expose to customers and how to guide them in, in that choice? Because I can imagine, most people are not. Technical don't really under, I think one of the big, I, I dunno sure when this will release exactly, one of the big ahas out of the, recent ch CHATT five oh release is that, most people were who using chatt were using the four oh models. Most, most people had never used these reasoning models. And so one of the big probably unlocks the majority of people think five is amazing is'cause they'll finally get routed to a thinking model and be like, oh my God, five is so much better than what I was using before.'cause they were just using. Four. But not knowing any difference.'cause it was really a power user who like understood that, because of open AI's terrible model naming that 3.0 was actually better than 4.0 or, or oh three was better than 4.0. So I'm curious, how do you think about like, educating and making that simple for customers to choose what's most best for them given all that technical complexity?
Yaniv Makover:Yeah I totally understand your point. I think there's a difference between B2C and B2B vendor. Right, so you're
Dan Balcauski:Mm-hmm.
Yaniv Makover:like need to choose a model. You don't really care. Someone is better than the other. we partner with a big enterprise marketing team, they have like CTO and they have like some, they have an understanding of what they want to use. And so we need to provide either a private model the foundation model of their choice. They probably have a working relationship with with open ai, with or with Google, whoever. And so Don't concern ourselves. Like, like use the model you want. It's not, we don't have the B2C issue. So I totally get your point. I just I haven't had spent enough time in that space where I have to, like, from my perspective, they're basically the same. Like there's no for for creating content for marketing. There's not a, there's not a difference between those models. It's not something that you can see. I think for more complex tasks, yes, for sure you should use the reasoning models. But so, so the, it's just my experience that all the models are the same. You probably have some sort of business concern or strategy kind, like input going from some team, and we'll just provide whatever you want.
Dan Balcauski:Well, there's a whole bunch we didn't get a chance to get to, but I wanna be respectful of your time and the audience time. I can talk to you all day, but I wanna wrap it up with a couple of rapid fire closeout questions. Is that okay?
Yaniv Makover:Yep, go ahead.
Dan Balcauski:Awesome. Well, so, when you think about all the spectacular people you've had a chance to work with, is there anyone that just pops to mind who's had a disproportionate effect about the way you think about building companies now?
Yaniv Makover:One of my first investors, our first investor is a guy that, his name is Gil. He's like a, he runs a startup accelerator. and this is like my first company and he just basically taught me like everything around, founding company. Like, I spent six months in, in Menlo Park, in Palo Alto everything I knew, and I was a software engineer, like, and I did a master's degree before that. Before that, my whole world changed. Like everything I knew about how to build a company. A business, how to bring advisors, how to sell to customers. You have to have your own like energy and drive, but if, if, if you don't know the people that will just, you how, what's the blueprint.
Dan Balcauski:Hmm.
Yaniv Makover:like very, like maybe in Austin and where you are at and in, in Palo Alto, it was like everybody knows this stuff. But it's not common knowledge everywhere else, like the rest of the world is like, how do you do this? So yeah he I can, I credit a lot of like what we, we've achieved to him.
Dan Balcauski:Was there any one thing that sticks with you all these years later? The of that he like was, maybe it's cliche now, but it was surprising at the time and the way you thought about building companies.
Yaniv Makover:I think, people always ask, should I quit my job and start this company? Right. Are you familiar with this question? Like people
Dan Balcauski:Yes. Yeah. Yeah.
Yaniv Makover:as a founder, and I kinda the answer is, I don't know. I barely know enough about my own business. But what I do think is a cliche maybe is. Do you wanna work on this problem?'cause you're gonna spend like 10 years on this. Like, is this like something you wanna work on? Because this is not gonna be like a, and you're gonna probably change your solution, your product, like 20 times. You're gonna pivot. Do you really want to to work on it? And because stamina is everything. It's a cliche, but really like, it's like, there's so many bad things are gonna happen to the company and to you and you really need to like, shrug it off or it's gonna. You're just gonna go you're, it's easier just getting a job, right? It's like, it's just much easier. So, So you, you
Dan Balcauski:yeah.
Yaniv Makover:that a lot.
Dan Balcauski:Well, y this has been fantastic. If listeners want to connect with you, learn more about Anyword, how can they do that?
Yaniv Makover:So I need Mark over at you can find my LinkedIn Anyword.com yeah. So pretty easy.
Dan Balcauski:Yeah. Well, I will put those links in the show notes for our listeners everyone that wraps up this episode of Sask Galy Secrets. Thank you Nadi for sharing his journey insights For our listeners, if you found any insights valuable, please leave a review and share this episode with your network. It really helps the podcast grow.
