Episode #57: Meet the Startup Revolutionizing A/B Testing With AI

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  Samantha Herrick:
  Are you tired of spending way too much money on A/B tests? Alison.ai can help you out.

  Asaf Yanai:
  Okay, so having two people with the same strength, the same weaknesses, the same mindset—it’s not always the best thing to do, right? You need some balance, and you need somebody to challenge you and somebody to complete you. The initial concept, the initial mission was: let’s replace A/B tests altogether from start to finish—let’s replace all of it by using AI.

  Brook Stroud:
  What is the statistical significance across all these videos? What’s working, what’s not working? And I think that is the hard problem that you have solved.

  Samantha Herrick:
  Hi everyone, welcome back to this episode of The Tech Optimist. We have a Meet the Startup episode for you today. We talked with Alison.ai. Now, the host for this episode is a newcomer to the show. Meet Brook Stroud. He’s a Senior Principal here at AV, and the person of the hour is Asaf Yanai, Co-Founder and CEO of Alison.ai. And then of course, you’ll hear my voice scattered throughout the episode. My name is Sam, and I’m the Guidance Narrative Writer for the show.

  Now, we have a long one for you today, but I urge you to listen to the whole thing because it is very, very fascinating. The more we get into it, the more stuff just comes up here and there.

  Brook and Asaf today have a really cool conversation about Alison.ai’s technology, how it’s innovative to the world of marketing, and how it might apply to your company. So, they talk about the issues in how expensive and how fast-paced the landscape is for A/B testing and for video marketing and everything else that they incorporate AI into their marketing suggestions with your branding.

  This company does this in a really fascinating way—we’re going to learn so much more about AI and machine learning today in this episode. But also, we’re going to get into some success stories with Alison.ai and how they truly have helped companies revolutionize their marketing. So, super excited to share it all with you. But yeah, hang tight, sit back, and enjoy this episode. Let’s get into it.

  Speaker 1:
  As a reminder, The Tech Optimist podcast is for informational purposes only. It’s not personalized advice and it’s not an offer to buy or sell securities. For additional important details, please see the text description accompanying this episode.

  Brook Stroud:
  Welcome to The Tech Optimist podcast. My guest today is Asaf Yanai. He is the CEO and Co-founder of Alison.ai, a company that is revolutionizing creative workflow through AI and data so that companies can stop spending and wasting so much money on A/B testing. Asaf, welcome to the show.

  Asaf Yanai:
  Thanks, Brook. Thanks for having me, and thanks for inviting me. It’s a pleasure.

  Brook Stroud:
  Well, let’s dive right in. When we met up and were discussing Alison.ai, in addition to being blown away by the technology and the product that you showed me—you opened up your laptop in the coffee shop and we just went right into it—one of the things I came away from that conversation with was such a deep admiration for the enthusiasm and level of just this can-do mindset. It was magnetic to some extent and also just so powerful to hear you speak about your vision for the company and get the sense of what you described as being like a boxer—taking punches and bouncing back.

  And so, I would love to hear, on a particular day or week where, for whatever reason, there’s a lot of punches being taken, are there certain things that you do or practices you have in place that over time have helped you cultivate and build this mindset of being so resilient and having this can-do energy? Because I imagine that’s not just something you can turn on and off; it’s probably something you work on and something you also learned as a child.

  Asaf Yanai:
  Absolutely. First things first, I’m not new to startups—this is not my first company, not my first startup. I think that really builds a lot of resilience because you’re, I wouldn’t say used to it, but you’ve done this before. Maybe not exactly the same, but you’ve done this—you know what you’re supposed to do, you know what the right path, the right trajectory looks like. You’ve experienced exactly the same punches, maybe in a different company, but you’ve experienced this already. I think this is a very good thing that builds resilience for me.

  I never get scared—seriously, never. Even my wife thinks I’m crazy, like, “How come you’re never scared of anything?” And I’m never scared because I automatically put myself into action. So, not scared, but acting: “How do I get out of it? What do I do now? What’s the best course of action? What’s step one—or even step zero? Where do I begin?” I’m not thinking about how difficult it is or “Oh my God, how challenging.” Not at all. I have a situation; I try to first solve it, handle it, tackle it, and then move on.

  I always say to my employees and my team, “Look, winning this marathon and having a good positive outcome for your company—we’re not conquering Rome within a day, and Rome wasn’t built in a day. It takes time. We need to accumulate consecutive small wins. Don’t aim for the big win because it’s too big, too far away. You can’t even see the path to it. Build the steps—small, consecutive wins. They will get you to this big win, the holy grail you’re looking for.”

  Every founder is looking for something different. All of us are looking for a successful company, I think, but some focus more on financials, some on scale, some on going international or solving big problems. In any case, small consecutive wins will get you there and build resilience.

  But for me personally, it’s also my personal background and childhood. I grew up in a highly achievable, highly professional family. Both of my parents are entrepreneurs—they founded a company 35 years ago, bootstrapped. I’m the first-born, the oldest. I experienced firsthand how this evolves. Imagine having a bootstrap company 35 years ago and growing it to international standards, international brand standards. It’s a rollercoaster with wins, challenges, punches.

  I saw my parents overcoming challenge after challenge, not giving up, keeping their minds positive and mission-oriented: “What do I do now? I’m not afraid, I’m not scared. This is not a challenge; I need to do something. What do I do now?” When you put your mind into it and train your mind into it, that builds resilience.

  This really built me. Having role models like my parents was phenomenal. I even joke that I’m 37 and have 37 years of managerial experience—almost—because I absorbed everything from my family. When you’re a couple, mom and dad, with one child or a small family, and you also run a startup, a bootstrap company, you bring everything home—the challenges, the wins, the punches, the misalignments. You bring it home, talk about it, fight about it, celebrate it together. Year after year, challenge after challenge, win after win—it really builds resilience and shows you that the bigger picture, the marathon, is more important than every single mile in it.

  Brook Stroud:
  Wow, very different from a marriage and being co-founders with your wife. It reminds me of thinking through who to work with as a co-founder, because that’s a different type of marriage through business. It’s amazing that both your parents co-founded and built a highly successful company over three decades.

  I wanted to ask, when you were thinking about who to build Alison with, were there particular things you learned from your parents or from your previous successful startups that helped you narrow in on traits you wanted to see in your co-founders—whether that be complementary skill sets or extreme alignment on work ethic and vision?

  Asaf Yanai:
  One main thing I took from my parents is to see how your partner or co-founder complements you. Having two people with the same strength, the same weaknesses, the same mindset—it’s not always the best thing to do. You need balance. You need somebody to challenge you and complete you. That’s a big thing I took from them and how I was brought up.

  From my personal experience, I also considered other factors. You just mentioned a few. Work ethics, absolutely. Big dreams and aspirations—because if your co-founder doesn’t have those, they’ll always come from you and there’s a lack of that complementary aspect.

  Experience is another one, especially in the environment we’re running in—a high-paced, startup environment, organized chaos. You hire, fire, grow, open offices, attract customers, face challenges. Having someone who is experienced, calm, thoughtful, and responsible is highly important for a company’s sustainability and growth. These were the main things I was looking for.

  I call it dating—I dated a lot of potential co-founders before I met Koby and before we decided to “get married” professionally. I interviewed and met many ex-founders, new founders, entrepreneurs at heart. What triggered me with Koby was that he said, “Look, Asaf, my professional passion is AI, machine learning, tech—that’s what I love doing at work. In my personal time, my passion is video. I want to merge those two things.”

  When he said that, it was an aha moment. I felt like, “Okay, that’s the perfect match”—someone with deep tech experience who also has fire and big aspirations around video. That was highly exciting for me.

  Asaf Yanai:
  Plus, again, Koby is a phenomenal, phenomenal tech expert. I call him a superstar on many occasions. He has taken different paths and, by the way, most of the startup companies that he founded or joined became unicorns—most of them. So obviously he was successful, obviously he faced a lot of challenges. I looked at the bigger picture. I looked at this person as a whole and thought that we complement each other, and he also brings a lot of things that are important to me when choosing a founder.

  Brook Stroud:
  Well, let’s dive in and talk a little bit about Alison.ai. One of the very over-quoted—but I think relevant—things I think about when looking at your technology is the quote that I think David Ogilvy probably popularized, but John Wanamaker famously said: “Half my advertising spend is wasted; the trouble is I don’t know which half.”

  When I think about the opportunities you’re providing to customers and the ability to deliver this very granular level of marketing intelligence, particularly in the world of digital marketing and video, talk to our listeners about that aha moment—what you saw as the opportunity then and today. Give us a little bit of background on what Alison.ai is solving in terms of pain points for your many customers, including many large multinational customers.

  Samantha Herrick:
  Okay, really quick before we dive into Alison.ai’s technology, I want to provide a little bit more context on the company’s values and morals and the main lessons and vibes, you could say, that they run on every day.

  On their website at Alison.ai/aboutus, they have a great page that really summarizes this. I’m going to read that and share it before we get into the technology because I think it’s powerful to understand the people behind the technology first before we start unpacking the really cool technology they’ve developed.

  So:

  “We are redefining creativity. Alison.ai empowers every step of your creative process. Our platform’s transformative AI technology revolutionizes advertising strategies, guaranteeing your brand stands out in today’s challenging landscape.”

  Here’s their mission and principles statement:

  “At Alison.ai, we believe that exceptional creatives come from data-driven decisions, not gut feelings. Our mission is to empower professionals with AI-driven insights and data science, enhancing brand performance globally. By replacing intuition with precise data analysis, we provide tools for teams to create impactful video content confidently. We are revolutionizing the creative workflow with evidence-based decisions and cutting-edge technology.”

  The first time I read this, I really liked how they’re replacing gut feelings and intuition with precise analysis. That’s something a lot of companies don’t do, and I think this is awesome.

  As far as principles:

  “We are guided by values that inspire our team, shape our strategy, and ensure we deliver exceptional results. These principles form the foundation of our business and foster a culture of innovation and excellence.”

  Those principles are:

  • Customer-obsessed

  • Pioneering change

  • Collaboration and teamwork

  • Excellence and quality

  “We place our customers at the heart of everything we do, prioritizing their success and earning their trust through our actions. We strive to break boundaries, exploring new ideas and innovations to make our solutions more efficient and powerful. We foster a culture of collaboration, valuing diverse perspectives and working together to achieve common goals. We are committed to delivering the highest quality user experience, striving for excellence and continuous improvement in our products and services.”

  Here are some awards they also highlight on their website.

  So from here, we’re going to do our first ad to get that out of the way. Then we’ll hop right back into the interview where Asaf really starts to break down their technology and how it’s innovative, as well as how he and Koby developed it. We’ve already heard a bit about both of their passions for entrepreneurship, video, AI, and machine learning—now we’ll figure out how all of these tie together. You know the drill—hang tight.

  Sophia Zhao:
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  Asaf Yanai:
  Absolutely. As I said earlier, my background is specifically within online marketing in different companies—big, small, different sub-verticals or sub-markets within online marketing. I’ve worked with the biggest names in the industry in my career—from big CPG companies to huge banks around the world, to online-first companies that have grown massively: Tinder, Waze, OkCupid, Uber, HelloFresh, and others.

  What I’ve learned is exactly the phrase you mentioned earlier: 50% of our marketing budgets are going to waste. That’s a fact. Now, let’s try to see what we’re lacking in terms of visibility toward that 50%. We’re spending it on hunches, guesswork, tests, and things that aren’t yet proven—we’re just trying to figure them out.

  What we’ve learned is that A/B tests and the operation around them in online marketing specifically take huge amounts of budget, overhead, and headcount. To fuel this A/B test “monster machine,” you need a lot of people, you need to create a lot of different creatives, and you need dedicated testing budgets. It’s heavy—it takes a lot of time to get a concrete insight, if any. Sometimes you’re just shooting in the dark.

  We realized that A/B tests and creatives around online marketing are the only black box in today’s marketing. Everywhere else—media placements, audiences—we have enough data and visibility to help us optimize. But with creatives, there’s absolutely zero visibility.

  What happens is that all the social platforms and media platforms take advantage of this. They know it, and they take advantage of it to increase their own revenues by masking or not showing you what’s working behind the scenes. So, you can’t really optimize on your own—you 100% need an external tool to help you do it.

  Asaf Yanai:
  And this is how we came up with Alison. The initial concept, the initial mission was: let’s replace A/B tests altogether from start to finish—let’s replace all of it by using AI. And we’ve done it. I mean, we’ve done it even before we completed our first year. We’ve successfully replaced A/B tests, and our customers—multinational, huge brands—report back to us that around 50% of their overall marketing budget was saved. Fifty percent. It’s crazy, it’s madness, it’s huge.

  Nowadays, the marketing cost line within your P&L is the second-largest cost line. Now imagine you cut it by half, just by integrating, optimizing, and augmenting your current workflows and replacing some of them. You can create such a big efficiency boost for the company—and that’s just about cutting costs.

  The other side of the equation is uplifting performance and increasing actual revenues. That’s why I call it the dual effect: you reduce costs and increase revenues at the same time. You increase the delta, the gap between what you’re spending and what you’re generating. In my world and in my mind, that’s extreme efficiency. Understanding that AI could generate and onboard this extreme efficiency for many different companies—that was my aha moment.

  It’s not just an A/B test tool replacement; it’s augmenting and revolutionizing your company. It changes everything: mindset, headcount, costs, production. I think this is the big premise around Alison. I’m very happy we have many customers already seeing those huge uplifts and this extreme efficiency I’m talking about. Honestly, it just fuels me and gives me more passion to see that it works—to see that with a simple SaaS tool, you can revolutionize your marketing organization altogether. It’s crazy.

  This was part of what I was looking at when I started building Alison—and that’s just one part of the equation. Because introducing efficiency, cutting costs, and increasing revenue is nice and dandy. But when you speak to many customers who are new to AI or haven’t yet adopted AI tools, it looks like witchcraft. They say:

  • “Should I use my studio or not when I use Alison?”

  • “Should I use my analyst or not?”

  • “Should I fire 50% of my marketing organization because this AI can replace it?”

  Typically, we say no—that’s not our aim. We’re not trying to replace everyone and leave a one-person team with a sophisticated tool.

  What we aim for—and what we actually do—is show that with the same headcount, the same people, capabilities, expertise, and skill sets, you can do 300 times more. And I’m not joking—it’s 30 times more, easily.

  At Alison, internally within our company and externally with our partners and customers, it’s all about extreme efficiency. I believe efficiency is the new age. In 5 or 10 years, I don’t think we’ll see companies with thousands of people in different departments. It’s going to change—and the only way it changes is through sophisticated, intelligent tools like AI or generative AI and their adoption.

  Education and adoption are also very important for us at Alison. We want to show the market, our customers, everyone, that this big dream—the holy grail of online marketing—is achievable. Not in 10 years, not in 5 years—it’s achievable now. The only thing you need to do is change your mindset and adopt it. Instead of trying new creatives with A/B tests or relying on new metrics and methods, try a different tool that might help you gain a different mindset, different insights, and a better understanding of how you should run your marketing organization.

  Brook Stroud:
  What really jumped out to me when you described the opportunity is that, when we think about startups and companies that can become massive, there’s clearly a huge market and an interesting technology wave to ride. But also, getting back to really simple ideas:

  • How does this software save people time and money?

  • How does it unlock new opportunities that weren’t there before?

  • Is it delightful to use?

  When I say these elements, I’m not talking about 10% time savings or 5% money savings. I mean, like you said—10x time savings.

  I’d love for you to provide a specific example. Because the fact that this is currently video-focused is really important. Video is the future of advertising—but not just the future, it’s already the current preferred method.

  What about video? Can you describe a way a company uses your technology to assess their portfolio of marketing videos, change what they do in the future, or gain learnings from their own or competitors’ videos? I know I just asked about 10 questions at once.

  Asaf Yanai:
  Yeah, absolutely. Let me explain how it works, and then we’ll jump into the example—it’ll be straightforward.

  Nowadays—and you said it—90% of the creatives we see online are videos. Platforms like Google, Facebook, TikTok—they prioritize video over other types of content. Users prefer engaging with video content rather than static content. Advertisers worldwide focus on video ads because they can deliver multiple messages, hyper-personalize them, test constantly, and resonate better with audiences.

  So the big question is: how do we know the right video content or composition to produce and engage with? This is where Alison comes in.

  The first thing we do is scan every single video our customers have ever run, tested, or launched—it could be a few tens or tens of thousands. Then we use a stack of AI models to run a process called feature extraction. We identify 25,000 features from every 30-second video we scan. Then we correlate those features with the actual performance of the videos.

  Features can include sound, voiceover, backgrounds, colors, buttons, logos, facial expressions, product attributes, and more. Once those features are correlated with marketing metrics, the magic happens. You understand which elements in your creatives actually trigger ROI or revenue—versus the ones you assumed were generating results but aren’t. Without high-scale, sophisticated AI, you cannot achieve this level of granularity.

  But we also realized it’s not enough to only look at our customers’ creatives. None of our customers operate in a vacuum—we’re all in highly competitive markets. If we only look internally, we miss the market, trends, and unknowns. We constantly have to run competitive intelligence and analysis.

  Before Alison—or without Alison—running competitive analysis on creatives at scale is nearly impossible. A common use case is a company with at least five competitors, each with 200 videos. That’s 1,000 videos—across Facebook, TikTok, influencers, real people, animation. How on earth can you analyze that without high-scale, sophisticated AI tools? It’s impossible.

  Asaf Yanai:
  So we’re analyzing both creative sets—the customer’s set of creatives and their competitors’—and then we correlate the features with performance, as I mentioned. We come up with insights or recommendations. Look at it as a kind of recipe for video success.

  This recipe tells you: “Okay, your next video for this product, on this platform, should be a 15-second video with four scenes. This is the script for the first scene, and these are the features and elements it should include.”

  That’s the first part of this analysis—this aha moment when customers realize what’s actually moving the needle versus what they only thought was moving the needle.

  When we ask customers, “Are you using these recipes, recommendations, or insights?” they say: “This is phenomenal—we’ve never seen this level of data. There’s a lot of data here that could be utilized. But even though it’s impactful, we still have a long workflow ahead of us. After gathering the insights, we need to create a marketing brief, iterate on it, make different versions, develop a storyboard to visualize the video, and ultimately produce the video. There’s a long way to go from insight to execution.”

  This was another aha moment for us at Alison—specifically for me. I thought: “Okay, we’ve successfully created impactful insights and recommendations, but how do we take it to the next level? How do we augment the entire workflow, not just analysis and insights?”

  What we did was build a dedicated AI model that takes raw insights and engineers a prompt. This is a sophisticated prompt—more like code. It’s machine-to-machine communication. When you just type a prompt manually, a lot of data is missed or not utilized. So we completely changed how we think about and engineer prompts.

  We use these engineered prompts to generate marketing briefs, storyboards, and commercially ready videos.

  That’s another challenge: I’m sure you and our listeners have heard of generative AI models—from Sora by OpenAI to MidGen and Stable Diffusion—where you type in a prompt and get a video. But these outputs aren’t commercials or ads; they’re raw videos or footage. There’s still a long way from raw footage to a finished ad.

  We wanted to go the extra mile for our customers. We didn’t want to just give them another raw asset that still required design work, studio time, and agency involvement. We wanted to give them the holy grail of marketing: a self-fitting loop where you run campaigns, generate insights, create another video within 30 seconds, and push it back to Facebook, Google, TikTok—completing the cycle in 30 seconds. And this is a ready-made commercial.

  In a way, we’re bringing power back to marketing teams. If you want to create different videos or storyboard versions, you don’t need to be a designer or video editor. You don’t need any design experience—just a basic understanding of data. Because the way we built it, it’s not about design; it’s about translating, generating, and transforming data into design.

  Brook Stroud:
  That’s what really jumped out to me during the demo in that coffee shop. I’m not a data scientist, yet on screen I could see a high-level overview and also drill down into individual video elements. That was really exciting—thinking back to A/B testing and its challenges: expensive, time-consuming, and even when you pick video B over A, there might be elements from the other video that could make it perform even better. I think you’ve hinted at that here.

  Samantha Herrick:
  Okay, so Brook and Asaf have talked a few times about this first meeting at a coffee shop, where Asaf opened his laptop and showed Brook a demo of Alison’s technology and software.

  This is my MO—you can probably guess what I’m about to do. On my YouTube page, Alison.ai produced a really cool video with great animations, an awesome color palette, and solid sound design—a demo of their technology. It visually explains how Alison can help your business and media assets become the most efficient and high-performing they can be.

  I’m going to play that video now because I think it’s really cool. It’s about a minute long, and then we’ll hop back into the conversation. Enjoy.

  Speaker 6:
  Would you drive a car with only half the parts? Would you bake a pie with only half the ingredients? So why would you run a performance campaign using only 50% of the data you need for success?

  Imagine expanding your analytical horizons beyond just media metrics. Introducing Alison—a creative analysis technology that fills in the gaps and transforms every creative element into a multidimensional world of measurable data.

  Alison can recommend which creative component, piece, or ingredient of each campaign will work best for which audience, on which platform, in which country. Detect ad fatigue and maximize your creative’s potential to achieve your ad goals and KPIs.

  Alison’s unique competitive analysis solution drills down into the performance of each element of your competitors’ leading creatives, giving you actionable insights for your next creative masterpiece.

  Fully customizable to your needs, Alison simplifies complex analysis and lets you make decisions based on your specific data—so you can produce stunning creatives that perform. Eliminate guesswork and create successful, profitable ads by transforming your art into data.

  Join the creative revolution with Alison. Contact us for a full demo.

  Brook Stroud:
  One other question I’m thinking about: now that you’ve been in the market and have very large customers, are you seeing strong pull from the market on certain features? Has it informed your roadmap—maybe causing you to focus more on some areas than you initially expected? Any big insights from having the product in customers’ hands and seeing how they use it?

  Asaf Yanai:
  Absolutely. First, the advancement of generative AI has definitely catapulted and sped up our video generation capabilities internally. Now we have sustainable, scalable, robust tools available off the shelf that we can use, layering our technology on top.

  We no longer have to build or engineer entire models ourselves—which is time-consuming, expensive, and requires significant capital and staffing. The advancements in generative AI have undoubtedly propelled our product and its video capabilities forward.

  Asaf Yanai:
  But two, I’ll tell you the opposite—I think there’s a lot of excessive hype. Maybe I’m wrong, but this is my humble opinion, and if I’m hurting anybody, I’m sorry. But I think there’s excessive hype around agents—AI agents. Every company I encounter has some AI agent that does X or Y, or multiple agents collaborating with each other.

  I think this is a silly way of handing the technology to people, honestly, because agents are inherently limited. The use of an agent means saying: “Okay, don’t do everything. Just do this one thing. That’s all I want you to do.” Like a designer, a travel agent, a simple analyst, or a copywriter, for example.

  So I think these agents are just a means to an end—a step along the way. I’ve heard from many investors: “Are you going to incorporate agents within your AI or your tools? Are you thinking of using agents?” And I don’t think this is where we’re going. This isn’t where AI or generative AI is heading.

  I believe AI is moving toward robust, comprehensive, intelligent, faster models that can truly react and interact. Agents don’t interact at that same level. What I’m saying is that the advancement and widespread use of AI agents in many companies has done nothing to influence us or push me toward adopting that mentality or usage within Alison.

  Brook Stroud:
  Yeah, I appreciate you saying that. Another thing I’ve noticed—and heard from both investors and large companies—is this idea that AI and large language models are going to make software as a service (SaaS) less important. Some even say, “SaaS is dead because we have AI now.”

  I really struggle with that, because yes, teams have data scientists and engineers, but this idea that a small internal project could build a specialized niche product—and, within the limits of that team and time, achieve the same level of insights as a full-time company that has been solving the problem for years, talking to many customers, waking up and going to bed thinking about the problem—it just doesn’t make sense.

  This dynamic of “let’s vertically integrate and build everything internally” is misguided, especially when dealing with hard problems. There’s enormous value in outsourcing to a company focused on solving it full-time. Your engineers and data scientists should focus on your competitive advantage—where their time, energy, and resources add the most value.

  What I’m hearing in the market seems misguided—particularly for the next several years—because we’re just not there yet. Companies risk spinning their wheels building internally when awesome, focused solutions already exist.

  Asaf Yanai:
  I absolutely agree with you. But going back to AI agents—I was just thinking of an example I used to talk about.

  Do you remember—you’re almost my age, I think, more or less, without revealing our real ages—

  Brook Stroud:
  Without revealing how much less accomplished.

  Asaf Yanai:
  [Laughs] But you remember the MiniDisc, right?

  Brook Stroud:
  I used to have one, yeah.

  Asaf Yanai:
  Me too—it was the MiniDisc.

  Brook Stroud:
  Sony MiniDisc.

  Asaf Yanai:
  And back then, we all thought it was phenomenal. We thought it would change the world, that it was the future. Those small discs—

  Brook Stroud:
  Doesn’t scratch.

  Asaf Yanai:
  Doesn’t scratch.

  Brook Stroud:
  Don’t skip.

  Asaf Yanai:
  Yeah, and you could rewrite them and do a lot with them. But just a few years later, they completely vanished—gone. Because they were never the end goal. They were never the final product or the ultimate technology that companies or users were looking for.

  What we’re trying to do at Alison is take 100% of the workload—100% of the workflow—and transform it.

  Asaf Yanai:
  And going back to the last question—it’s exactly like the MiniDisc. Building internal capabilities or trying to do everything on your own is often just a waste of time and money. Some people succeed—you mentioned vertical integration. It’s not necessarily about acquisitions within the supply chain. Sometimes it’s just trying to vertically integrate internally and build everything yourself.

  But we have a specific vision, roadmap, and trajectory. It’s important to stay focused. Other companies have their own vision, trajectory, and path. By using each other’s strengths, it can be one plus one equals three.

  For example, we don’t need to internally develop a huge generative AI model. We do develop AI models internally, but for generative AI we use off-the-shelf solutions. That makes things quick, easy, and seamless. Replacing it, trying different tools, vendors, or versions—it’s all a low barrier. The cost is just a little money or time to learn the product and platform. That’s it.

  So, there’s a huge benefit in outsourcing or leveraging other companies’ strengths.

  Here’s another example: we could have built an internal ticketing platform to solve tech problems and communicate between teams. But there are excellent solutions available for very little money. We could have built our own CRM tool, but there are fantastic vendors and software out there. This logic applies to many things we use at Alison. We stay focused, mission-oriented, and outsource everything we can.

  I mentioned earlier about advisors and consultants—it’s similar. It’s like outsourcing your thought process or strategy. You’re acquiring knowledge. Sure, I could read books or listen to podcasts, but interacting and speaking with someone who knows you, understands you, and helps solve your problems—someone who can take on heavy lifting and help you see a clear path for your company’s vision—is priceless.

  Lastly, I think a lot of companies end up pivoting because they tried too hard to do everything themselves.

  Samantha Herrick:
  Now, I thought this was a great opportunity to hop in and share something. During this whole conversation about Alison.ai, I’ve been wondering: What do their AI-generated videos actually look like compared to what internal teams produce themselves?

  They have success stories on their website, and one I want to show you today is from the company Laffy Taffy. Ferrara, the American candy manufacturer, produces Laffy Taffy. Their new campaign aimed to increase viewership KPIs across all platforms.

  The marketing team had already identified many top-performing creative elements over the years but wanted the right combination of tags to engage audiences across networks.

  On their website, Alison shows a comparison of Ferrara’s original creative recommendations versus Alison’s:

  • Brand Tenants: Original – Family connection. Alison – Dad joke energy (which is great).

  • Sound: Original – Music and sound effects. Alison – Music, sound effects, and voiceover.

  • Font Type: Original – Not specified. Alison – Stylized font.

  In the article, they also provide video examples: the original Ferrara asset and the new asset after using Alison.ai. I’m going to play those now. On the visual version of this podcast on YouTube, LinkedIn, or wherever you watch, you’ll see them side-by-side.

  Here is the original video asset from Ferrara. And here is the new asset after Ferrara used Alison.ai.

  Okay, we’re going to finish off with our last ad, and then hop right back into the rest of the show. Hang tight.

  Speaker 7:
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  Asaf Yanai:
  I really think that’s one of the main reasons. It’s rarely the case that the market is failing, locked, or too crowded. I don’t believe that’s usually the problem. The main issue is focus—companies trying to solve all their problems internally instead of leveraging smart people who’ve solved similar problems for many companies.

  With a simple SaaS login and password, a simple integration, you can access that expertise. Once we discovered and embraced that mindset—that we don’t need to build everything internally—we focused on building Alison’s core capabilities only.

  That’s why we now have 15 different proprietary AI models internally. We spent our time on what’s truly important: analysis, insights, feature extraction, and prompt engineering. These are the things that matter most to our business, and that’s where the vast majority of our resources go.

  Brook Stroud:
  I think that’s a deep insight into how both founders and large companies can think about the build-versus-buy approach—really maximizing competitive advantage, their teams, their time, and focusing on what they’re good at.

  I was also thinking about the MiniDisc example, which I loved. If I’m a large company that’s always done A/B testing—that’s just how we do things—what’s the MiniDisc equivalent? It’s: “We’ll save you time and money by doing A/B testing faster.”

  If I’m hearing you right, it sounds like that’s been the state of play for some adjacent competitors: “We’ll make your A/B testing better, we’ll make it faster.” It’s like saying, “We’ll make the CD smaller and skip less.”

  Asaf Yanai:
  Exactly. It’s going to be smaller, it’s going to skip less, but it’s still the same MiniDisc. It’s the same process—you put it in the machine and that’s it.

  I think we’re focusing too much on hype and on “How can I do more of today?” instead of looking at the future and understanding what’s coming next year.

  Yes, there are companies in our space saying: “You’re running heavy A/B test operations. You need to create 30–40 different video versions. Let us do it for you—not with 40, but with 4,000 versions.”

  It sounds exciting, almost like witchcraft: “I’ll run 4,000 A/B tests, so I’ll definitely find the right formula.”

  Not really. The trick is understanding how the backbone and infrastructure truly work. Even if you take the entire production workload off your shoulders and a company hands you 4,000 videos, you still need to run an A/B test with 4,000 live versions on Facebook, Google, TikTok, etc.

  That means:

  • Each of those 4,000 videos needs a testing budget.

  • Collectively, they must accumulate enough impressions, clicks, or actions to produce meaningful results.

  Let me ask you: how long do you think the biggest platforms—Google, Facebook, TikTok—would take to run a 4,000-version A/B test and produce meaningful insights? And how big would the testing budget need to be?

  Brook Stroud:
  Yeah, the quick answer is I don’t know. But to make it statistically relevant, each of those 4,000 needs enough impressions to confidently say, “This one performs better.”

  Even then, after running all 4,000, you could rank them by performance—if there were truly enough impressions. But we’re not even touching on the deeper question: what about the variables in each individual video?

  What’s statistically significant across all these videos? What’s working, what’s not? That’s the real, hard problem you’ve solved.

  And just the number—4,000—makes me think about how many data scientists, how much time, energy, and money would be needed to handle that.

  Asaf Yanai:
  Exactly. Before trying Alison, we had customers who used heavy A/B testing with hundreds of versions—not thousands, but hundreds for each test. It took them months to gain insights.

  Now imagine you run a test for months—by the time you get results, the market has already shifted. Trends, seasonality, everything has changed. You’d need a different test entirely.

  Secondly, because each version requires its own budget, you’d need at least $50,000 to $500,000 just for a single test. And out of that $500,000, $400,000 would likely go to waste.

  I can’t imagine a single company—or a CFO or CMO—hearing those numbers and saying, “Yes, let’s invest more in A/B testing.”

  Instead, as you said, we should be understanding billions of variables simultaneously. You’ve already tried many of those variations—maybe not in one ad, but collectively. And even if you haven’t, your competitors have.

  Asaf Yanai:
  So, by looking at every single creative you’ve run and analyzing your competitors, we cover about 95% of the variables that exist. If you cover all those variables and analyze them using AI—it takes just a second—you have far more power and a more powerful tool at your fingertips with Alison than running 4,000 videos in an A/B test.

  Brook Stroud:
  Having seen it firsthand—not just talking about it—it’s really, really impressive. I can’t wait to see the maturation of this product over the next few years and watch your company continue to expand and grow its customer base.

  I think this is a good point to wrap up, but before we do, I just wanted to ask: is there an ask to our audience, to our community of listeners and network? Is there anything they can do—or a way they can reach out to Alumni Ventures—that would be helpful to you?

  Asaf Yanai:
  First, just a general statement: don’t be AI-shy. The same way you spend thousands of dollars on A/B tests, try spending some time, money, effort, and resources on using AI, adopting it, seeing its true value, and exploring how it could potentially transform your business. That’s one.

  Second, what really excites me is talking to fellow founders or marketers who are hands-on, experiencing the challenges we’ve discussed. I’d love to hear their perspectives—how they’re overcoming these challenges, solving problems related to lack of visibility and analysis, and what tricks, tips, or tools they use to succeed.

  That’s interesting to me because that’s how Alison started: by questioning marketers, experiencing their challenges firsthand, and learning from them.

  Even as experienced as I might be, there’s still a lot I don’t know. Knowledge makes you stronger—not just as a person, but as a founder and CEO. The more you know, the better you can react and adapt.

  So, I’d love to hear from founders or marketers about their specific marketing challenges, how they’re overcoming them today, and how they expect to overcome them in the coming years.

  Brook Stroud:
  On that note, Asaf, thank you so much for joining The Tech Optimist today. It’s been an absolute pleasure having you on the show, and I’m looking forward to doing this again in the future.

  Asaf Yanai:
  Likewise, Brook. Thanks for having me. It’s been a pleasure, and I’m looking forward to the next one.

  Speaker 1:
  Thanks again for tuning into The Tech Optimist. If you enjoyed this episode, we’d really appreciate it if you gave us a rating on whichever podcast app you’re using, and remember to subscribe to keep up with each episode.

  The Tech Optimist welcomes any questions, comments, or segment suggestions, so please email us at [email protected] with any of those. Be sure to visit our website at av.vc.

  As always, keep building.