ex-WHOOP PM, YC Founder Ben Katz: AI-Native Sports Training for Everyday Athletes & Why Coding Agents Are the New 10x Workforce
The following is a conversation between Alp Uguray and Ben Katz.
Summary
In this episode of Masters of Automation, Alp Uguray sits down with Ben Katz, co-founder of Hybrd and former growth product manager at Whoop, to explore what happens when a lifelong athlete turns a personal frustration into a Y Combinator-backed AI fitness platform — and why the rules of building a startup have fundamentally changed.
Ben's path is anything but linear. He started in sales, moved into analytics at Wayfair, spent years trying to break into product management, and eventually landed at Whoop — where he found the rare intersection of passion, craft, and career. After a stint at Apex, a mobile app portfolio company, he realized the founders he was evaluating weren't smarter than he was — they just had the agency to start. That was the push. Together with three co-founders — including a U.S. national rowing team member, a former Whoop analytics lead, and a senior AWS engineer who also happens to be a former U.S. national rugby player — Ben launched HYBRD on a plane to San Francisco, writing the first line of code en route to Y Combinator.
The conversation opens with Ben's unconventional career arc — what he calls "getting an MBA by doing" — and how sales became the most underrated founder skill. From there, Alp and Ben trace the development of HYBRD: from the initial hypothesis that serious athletes don't want an app telling them what to do (which turned out to be wrong), to the breakthrough of invite-only user acquisition, to the science-backed training plan engine built in partnership with the University of Utah and Complete Human Performance, the company founded by Alex Viada — the man who literally wrote the book on HYBRD athletic training.
They go deep on the economics of building in the AI era — how vibe coding with Cursor and Claude let a four-person team do in days what would have taken months, why the cost of building technology is approaching zero, and what that means for the things that actually differentiate a company: distribution, brand trust, user experience, and taste. Ben describes his AI coding agents as "feature bazookas" and shares how the team oscillated between vibe coding everything and pulling back when code review became the bottleneck — before the tools caught up again.
The episode also covers the science of Hybrd athletic training — the difference between cardiovascular and muscular load quantification, why the best training plan is one the athlete will actually follow, and how dynamic adaptation (weather, sleep, missed workouts, schedule conflicts) represents the next frontier. They discuss the wearable data revolution, preventive health, why your Whoop knows you're sick before you do, and the role of multimodal AI in eventually coaching form — once the social stigma of gym tripods goes away.
Ben closes with hard-won founder wisdom: most companies don't fail because they run out of money — they fail because the founders give up before the bank account hits zero. And you're a lot less likely to give up when you're building something you love, with people you love working with.
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Guest Bio
Ben Katz is the co-founder of Hybrd, a Y Combinator-backed AI-powered fitness platform built for serious multi-discipline athletes. Before founding HYBRD, Ben spent several years in the Boston tech ecosystem — starting in sales, moving into analytics at Wayfair, then serving as a growth product manager at Whoop, where he helped shape the product strategy for one of the most iconic wearable brands in fitness. He then joined Apex, a mobile app portfolio company, where evaluating founders gave him the final push to start his own company. The HYBRD founding team includes three former Whoop employees and one senior AWS engineer. Their training plan engine is built in partnership with Complete Human Performance and the University of Utah sports science program. Ben is a former college athlete, Ironman finisher, marathon runner, and self-described "washed-up college athlete" — exactly the user HYBRD is built for. He lives in Boston and is an active voice in the Massachusetts AI ecosystem.
Takeaways
People get hired for their technical skills but promoted for their soft skills: sales is the most underrated founder capability — your job as a founder is always selling to customers, investors, employees, and co-founders.
The best products solve personal problems: Ben's frustration with pulling training data from five different wearables into one coherent view became the core thesis for Hybrd — and every co-founder uses the product harder than any user.
Invite-only isn't just a growth hack — it's a data strategy: by requiring applications and filtering for serious athletes, HYBRD ensured every data point and every piece of user feedback came from their exact target market.
The initial hypothesis was wrong — and that was the breakthrough: users said they didn't want an app telling them what to do, but what they really meant was existing tools were too beginner-focused — when HYBRD launched advanced, science-backed training plans, demand exploded.
AI coding agents have rewritten the startup economics: a four-person team with Cursor, Claude, and agentic coding tools can ship in days what previously took months — and the worst AI will ever be again is how good it is today.
When the cost of building technology goes to zero, differentiation shifts: distribution, brand trust, user experience, and taste become the moats — not engineering velocity.
The best training plan is one the athlete will actually follow: physiological optimization means nothing if the plan doesn't respect the user's schedule, preferences, and real-world constraints like weather, sleep quality, and hockey on Tuesday nights.
Preventive health is here — your wearable knows you're sick before you do: with 34% of Americans wearing a device that measures heart rate, the data for early detection of pregnancies, arrhythmias, and illness already exists — the gap is interpretation and accessible context.
Most startups don't die from running out of money — they die from giving up: founders who work on problems they genuinely love, with people they love working with, are dramatically more likely to push through the hardest moments.
Aspiration pulls harder than features push: winning elite athletes first creates an aspirational gravity that pulls mainstream users toward the product — the reverse direction (beginner-first trying to go upmarket) faces resistance.
Chapters
00:00 Introduction — Ironman finisher, Whoop alum, and YC founder
01:58 Getting an MBA by doing: sales, analytics, and the winding path to product management
04:55 From Wayfair to Whoop: finding the intersection of passion and career
06:12 Apex and the push: evaluating founders and realizing it was time
08:07 The founding insight: five wearables, one fragmented picture
09:03 Writing the first line of code on the plane to YC
10:32 The founding team: athletes who happen to be engineers
11:05 Git worktrees, cookies, and the early days of vibe coding
12:32 The evolution of AI coding tools: from explaining files to feature bazookas
13:37 When vibe coding backfired — and when the tools caught up
16:50 Cardiovascular vs. muscular load: the science HYBRD had to build from scratch
18:04 The UX trap: why users said they didn't want recommendations (and were wrong)
19:47 Dynamic training plans: weather, sleep, missed workouts, and hockey nights
23:40 Context is everything: why AI coaching needs to understand your full life
25:46 Invite-only as a data strategy: filtering for your exact target market
28:34 The Ironman: collecting fitness badges and training on a desk bike during user calls
31:45 The aspirational pull: why winning elite athletes first changes everything
33:11 The insight users actually wanted: advanced, not just data-rich
36:18 Preventive health and the wearable revolution: your Whoop knows you're sick first
40:12 Multimodal AI and coaching form: the technology exists, the gym tripod stigma doesn't
41:09 Agency in the AI era: you have a ticket to play the game
44:00 When the cost of building hits zero: distribution, brand, taste, and trust win
47:22 What AI should never touch: the founder's personal phone number for support
50:08 The future of Hybrd: never question what you should be doing today
54:57 The genetic ceiling and the mental edge: what separates athletes when data is equal
56:34 Why Boston: the most underrated tech city in America
01:00:49 The ultimate vision: don't make the user think — just help them hit their goals
01:02:37 Founder advice: most companies fail because people give up, not because they run out of money
Quotes:
"People get the job for their technical skills, but get promoted for their soft skills. Especially when you're a founder — your job is literally always sales." — Ben Katz, Co-founder of Hybrd
"The worst that AI will ever be again is how good it is today. We call some of the new frameworks for AI agent coding 'feature bazookas.' It's actually crazy that you can have these tools working overnight for you while you sleep." — Ben Katz, Co-founder of Hybrd
"Most companies don't fail because they run out of money. Most companies fail because it gets really hard and then they give up before their bank account hits zero. You're a lot less likely to give up if you're doing something you love." — Ben Katz, Co-founder of Hybrd
Listen Now
“People get the job for their technical skills, but get promoted for their soft skills. Especially when you’re a founder — your job is literally always sales.”
“The worst that AI will ever be again is how good it is today. We call some of the new frameworks for AI agent coding ‘feature bazookas.’ It’s actually crazy that you can have these tools working overnight for you while you sleep.”
“Most companies don’t fail because they run out of money. Most companies fail because it gets really hard and then they give up before their bank account hits zero. You’re a lot less likely to give up if you’re doing something you love.””
Transcript
Alp Uguray (00:00): Well, thanks for joining. Yeah, I'm excited for this conversation. Like, what do you guys are, I think your personal story, right? Like three times Iron Man.
Ben Katz (00:12): Oh god no, once. Once? Once. I've done a half, you count that. Yeah. I've done a half and then did my first full this past November. So, not three yet.
Alp Uguray (00:23): And then, I mean, you've been in Mass, like MA, and then you've been part of this community and then to YC, came back, right? Like you're here. Went through Whoop, which is an amazing story, but now the wearables and the biomarkers, and you're building your own company now. But let's like imagine, go back, right? Like you are in the beginning of your career and you are just getting started. Like just walk me through how that life looked like and then that shaped the next thing.
Ben Katz (00:45): So I've known for a long time that I've wanted to start my own company, but I didn't necessarily have the confidence to go and do it. I never felt like I was really ready. And so in college, I studied entrepreneurship and finance and I'd always been one of those kids that was doing a landscaping job and hiring underclassmen and like doing social media marketing for local small businesses and did a bunch of that through high school and college. Didn't want to do any of those things full time as a real job. I was like, I want to do something that's like bigger than that.
And so I kind of was figuring out what do I actually want to do from a learning how to operate a business perspective. Spent a lot of time evaluating investment banking and consulting. At the end of the day, ultimately didn't feel like either of those were actually going to help me become an effective operator for the type of business that I wanted to build. And so I took a —
Ben Katz (01:58): I took my career that I called getting an MBA by doing, which worked out pretty well. But on paper, you're like, ah, what's this guy do? Like, does he know what he — that didn't make any sense. But I started my career in sales, which I am very thankful for, for a lot of different reasons. I think one of my favorite like career anecdotes is I think people get the job for their technical skills, but get promoted for their soft skills. And especially when you're a founder, your job is literally always sales. It is my job to sell the company to customers, to investors, to employees, to get my other co-founders who are making good chunks of money to quit their jobs to come do this little fitness app with me. Like, it's hard. And so that skillset was a really strong foundation. Went from that into analytics because I was looking to get a better understanding of how do I actually use data to inform business decisions in the real world.
Three months into my time at Wayfair, which is where I was an analyst, I discovered what product management actually was. I'd never heard of it before, which my own fault. Yeah, I wish I had heard of it sooner. But so I then spent the next like two and a half years or so trying to get into product. So was like, that's the thing that will actually help me understand how to build a product in a company. Talked to like half of the product managers in Boston and trying to network.
Ben Katz (03:20): And ultimately ended up finding my way into Whoop, which was this very serendipitous opportunity because I both — college athlete, love training, fitness, et cetera, part of my life forever, never really thought I'd be lucky enough to be able to build a career in that space. And it happened to be a perfect combination of the exact type of role I was looking for at one of the best tech companies in Boston in an industry that I am genuinely passionate about and never really looked back from there.
Alp Uguray (03:50): And it's a big inflection point too, because like, you learn how to sell the distribution channel works. And then you learned in the — like Wayfair especially is like an e-commerce business, like large amounts of data, like the transactions nonstop. But then when you joined Whoop, it's more like your passion and your actual work meeting together.
Ben Katz (04:10): It was a lot of fun. It was a really good experience.
Alp Uguray (04:15): So walk me through because after Whoop, you got into YC and walk me through before YC.
Ben Katz (04:25): Yeah. So there was actually — so after we actually went to one other company in Boston called Apex and they were a mobile app portfolio company. And their thesis is essentially, you know, there are a bunch of mobile applications doing a couple million dollars a year in revenue that are one to three people that are not actually like business experts. They're more specialized on the development side. They just built something that they thought would be cool or that they really liked or solved a problem for them. And it blew up. And so their thesis was if you buy some of these businesses and then implement standard paywalls, pricing, paid advertisements, et cetera, onboarding funnels that you could then like cashflow them at 20, 40% more per year and pay back the debt and grow. Went there for about a year and it was a great experience for a lot of reasons, but I think that the thing that was the best for me was I got to talk to a lot of founders because when I'm — part of my job was evaluating which companies should we consider buying. And one of my biggest takeaways was that when I'm talking to these founders, I was like, you know, that guy's making a lot of money. And I actually don't think he's that much smarter than I am. Whereas at Whoop in particular, you're surrounded by some of the world's smartest people that are like best in class at the one thing that they do. And so it's like, okay, I'm never as good at marketing as that person, or I'm not even close to as good at strategic finance as this person.
And it's really hard to compare yourself to those people in these disciplines that they have spent their whole lives and are some of the best in the world. Yeah, exactly. And so Apex really gave me the push to go out and really be like, all right, it's time. I have enough experience under my belt. So incredibly thankful for it.
Alp Uguray (06:12): And was it like a search fund accelerator style, like buying businesses and then like —
Ben Katz (06:18): Yeah, so was more like — it was very much like they just went, they had a lot of credit and they would go out and find businesses that fit their buy box. They would acquire them and then they would bring them into the fold and we would implement all those standard levers and grow them. Which was a great — which is like a pretty good business model in a lot of ways. It just wasn't super exciting to me from like a product development really going after like specific user problems type of thing. And so for that reason, ultimately ended up being like, okay, you know, like this is good experience. I feel very motivated. I love the people I work with, but it's not the long-term thing for me. And so started thinking about what would that long-term thing be and nice, like intersection point from a career perspective, along with kind of realizing that I had this personal problem that I actually might be pretty well positioned to go and solve. And so we — I basically spent nine months talking to people and researching and being like, am I crazy or is this actually something that other people are experiencing too? And it turned out that it was. And so the core observation that we kind of initially made was I was a triathlete, I use very loosely, that likes to lift. And I was doing runs, rides, swims, lifts, CrossFit, all these different things. And I have every gadget and gizmo under the sun because like, it's part of my job and B, I just love it. And it was impossible to actually take all that data and pull it together in a way that was actually useful to me in my training and helping me figure out, should I actually be doing something differently? Was this leg day and this ride the next day good or was it bad? Like, should I have switched out specific exercises? It was really impossible to take that data and make sense of it.
Alp Uguray (08:07): It's such a personal thing, right? Like for every person it's different. It's custom. It's unique.
Ben Katz (08:12): 100%. And so we essentially set out to solve that problem. And we applied to YC not thinking that we would stand a chance of getting in and got in. And it's been a lot of fun.
Alp Uguray (08:25): I like earlier when you said if it is on Excel, it can be an app.
Ben Katz (08:30): Yeah. If it's a spreadsheet, it could probably be a business.
Alp Uguray (08:35): It can probably be a business. So like that approach is so true because like it's so modular. It's as complicated. It's hard to deal with it. So like — won't be true right now because your co-founders are also like similar mindset athletes like love the space. But again, like you guys are very business focused founder group. And of course the AI wave coming up — like the coding agents, vibe coding, Claude Code, especially once you enter a device, that must be — that's the story of like —
Ben Katz (09:03): A hundred percent. And it's amazing actually even just to look back and think about where it was then versus now. It's only been like a year and four months and I'm like, whoa, like that's crazy. But so for context, we are a founding team of four, three out of four of us used to work at Whoop. I was specific to the growth product management team. My co-founder Motts, who was on the U.S. national rowing team and is a freak cardio athlete, was a core product manager at Whoop. And then Shoe who smoked me in the Ironman we did together in November, was one of the first analytics hires at Whoop. And then our fourth team member, Ruiters, who — he was at a senior technical lead at AWS. He is like, I say this with love. It is really hard to find an engineer in the center of the Venn diagram of really good athlete and really good engineer, because the middle is like not that big. And he happens to be like the gold mine. And so the four of us were both complementary skill sets, lifelong athletes. Ruiters was on the U.S. national rugby team for under 18 and has gone on to do half Ironmans and many other things as well.
Alp Uguray (10:25): I mean you guys like you — you do your like all hands and like Ironman.
Ben Katz (10:32): Yeah, it's a passion problem. I think it's hard to — I think part of the motivation was like we actually wanted to fix this for ourselves. 100%. But when we first got into YC, I'll never forget, Ruiters in the kitchen, we're all sitting there at like 9 PM. We had just baked a batch of cookies and he's trying to explain Git worktrees to us and using cookies as like the — he's like sitting there with a spatula and he's like giving us a thing. And we have this great photo from that night. It's really good. But we went from that where like I had worked with engineers in a product capacity for a lot of, for a long time and understood at a high level how systems work, but could not read a lick of code. And when we first started using tools like Cursor in YC, it was very specific to like, you had to know what file the logic was in, then you could go to that file. And then I would be asking Cursor, explain to me what this file does. And then it would explain what the file did. And then I would be like, okay, this is how I want to change this logic. And then it would do the thing.
And then I would submit the PR and Ruiters would be like, really? Yeah, so it was like, you know, a lot of feedback loops on that side. But fast forward now, you can whip up anything in a second. You put Claude in plan mode or whip up Claude Code. I mean, it's night and day. It's insane.
Alp Uguray (11:55): Yeah, 95% accuracy right there. Maybe small tweaks, but sometimes not even reading the code.
Ben Katz (12:00): It's actually interesting for us because we went from a point of — in the very beginning of the company we were all vibe coding all day and all night because we just needed like code velocity. We wrote the first line of code for Hybrd on the plane to San Francisco. And so we're like, right, well we —
Alp Uguray (12:20): We are ten weeks.
Ben Katz (12:22): And so it was like all the time. And then it actually, after YC and like probably like early spring of last year, we realized that it ended up being more time consuming for Ruiters to review our AI slop agent code than it was for them to actually use the agents themselves and do things. And so we actually stopped vibe coding as much. And like there was some like —
Alp Uguray (12:45): Did he take the ownership of that and then just went all in or how did you manage that?
Ben Katz (12:50): Yeah, so he and Shoe would write a lot of code. Shoe had some back-end experience and is now basically like a full-fledged developer that actually can read the code, unlike yours truly. Basically, myself and Motts took a step back on the coding side, because we couldn't read the code at all, and we're kind of just wild-westing it. We're now back at a point where the agents are so good that we can touch the code again. And granted, we're not always — Like, I mean, at this point, you've got like Gastown and Ralph Wiggum and like all these other things that are just like feature bazookas in reality. And Motts and I don't touch those. But for little things of like, I can go in and put in a PR to like update Amplitude events, no problem. Or like, I want to go and like change this small thing on the front end, no problem. Or like small screens, no problem. Like little stuff that isn't tied into deep logic, really easy. Like I just redesigned our entire website myself. Which is great.
Alp Uguray (13:55): And you understand the sales, you understand the distribution, so you understand the user behavior along with your co-founders. Just having that ability to translate that into just code, that exactly how you want it to be, it's 100% eliminates the middleman there.
Ben Katz (14:10): Doing so without distracting Ruiters. Like I think about Ruiters and we also have one other engineer, Michael, who was a very competitive cyclist for a long time and also — He also worked at road with Motts in college. So we're kind of like keeping the circle small for now. But like their time is gold. Like it's the most valuable asset that the company has. And so it's like, let them stay in their island with all of their AI agent colonies and armies while you're like — You guys stay over there and do your thing. I don't know what's going on over there, like it's working. And then like now I'm like, okay, well I can go and do this website. That's no problem. I don't have to be like, hey, Ruiters stop what you're doing and like implement this design. And then actually I want this button to be a little bit over here, like move this color. No longer a problem.
Alp Uguray (14:55): They're just huge. And so like you're in the airplane and then you guys are vibe coding and then you're about to land. And then the next thing is getting users. So like, what was that? Like maybe the first problem that you guys solved.
Ben Katz (15:10): So the very first problem was just a data aggregation problem. It was, I use five different wearables and I track my lifts on a Hevy or a Strong. How do I pull this into one place? And so we built out a bunch of those integrations. We partnered with a YC company called Terra to help us do a lot of those. And then we built the first AI capture workout logging experience where you could just freehand text like 2x10, bench press, 225, 3x10 squat — like whatever, or you can take a picture of the whiteboard if you're at a CrossFit gym or like you can voice to text, etc. Make it really easy to take unstructured data and actually categorize it, structure it. And then the next piece was trying to understand what is the impact to your body? Because every workout has three technically impacts. There's the cardiovascular impact, there's the muscular skeletal impact, and then there's the neurological impact. Neurological is much smaller technically than the other two.
Cardiovascular has essentially been solved. I mean, you've got strain, you've got training load for Strava and TrainingPeaks, etc. All of these are basically just a function of what was your heart rate for how long and how does that compare to your maximum heart rate? It's very, very simple. It's actually all operating off of a similar data science model that was published in like 1989. The muscular side does not have a standardized golden data science algorithm that everybody uses because nobody has the data and it's really hard to track and capture the data. And so we had to actually partner with, you know, sports scientists to develop that. Cause I mean, we're obviously pretty adept at — we know a lot about sports, but we're not — like I am not a PhD sports scientist. And so we partner with folks that help us actually go in and kind of like tweak these algorithms a little bit. But we built out the equivalent of that for muscular. And now we quantify every single workout into the cardio component, the strength component, and can tell you what does that actually do to your body, and then use that as part of the feedback loop for other things like recommendations in the product.
Alp Uguray (17:25): And then you were like, while you were in YC, you mentioned to me earlier, like you guys were getting all the other founders onto the app and then like getting athletes onto the app on run clubs and then like collecting the data and then trying to see their experiences.
Ben Katz (17:40): Yeah, we forced everybody to participate in a YC workout club every Sunday morning. It was fun. But it was also a great way to get user feedback. And also a great way for us to feel embarrassed every Sunday morning when we're like, all right, guys, now log in our app. And they'd be like, well, this doesn't really work right. And we're like, all well, we're gonna go fix that.
Alp Uguray (18:04): But from working out, you guys probably smoked everyone on the runs and stuff. So from the design side, it's very interesting because I think a lot of the data collection perspective, like understanding cardio versus strength is very valuable for athletes, professional athletes, but for also like enthusiasts, like learning about its structure day to day. So how did you guys find the scientist part, for example, to look into it?
Ben Katz (18:35): Yeah, so we're very lucky. Motts, through his U.S. national rowing experience, has connection to some very high level coaches and connections in that front. And we actually connected with this woman, Anna Cruz, who is the director of sports science at the University of Utah. And she agreed very early on to be an advisor. And was very, very, very instrumental. Could not have done it without her to help shape out some of how we do that.
Alp Uguray (19:00): That science then is — so it's fully science-backed training plan, custom based on the biomarkers that are collected.
Ben Katz (19:08): Exactly. And then the training plans themselves and even the sports science now, we've actually partnered really closely with Complete Human Performance, which is Alex Viada's company. And if you're not familiar with Alex, he is the guy that literally wrote the book on Hybrd athletic training in 2014 that then kind of kickstarted the movement. And there's nobody in the world that is as good at coaching and knows as much about all the different physiological adaptations that happen while concurrent training than Alex and his team. And so we work very closely with them, like weekly calls, to make sure that we're shaping things in a way that is actually grounded in sports science.
Alp Uguray (19:47): And it's just that the key insight there was that it's tailored for them, it's advanced instead of just being a beginner plan and giving back high level metrics to them.
Ben Katz (19:55): Exactly. Like, I mean this in zero way negative because it's a great product. But if you ever use like a Zing Coach or something like that, like if you're new to the gym and getting into fitness, it's awesome. It's great. But if you are an experienced person that has been lifting for 15 years and, you know, competed at a D1 level, it's not like — you know everything it's going to tell you.
Alp Uguray (20:15): How much of it is like do you think driven by habits? Because as an athlete, you already have the habit of like being disciplined, disciplined on making sure workouts are hit.
Ben Katz (20:25): Yeah, yeah, a lot of it. I mean, I always when I think about the fitness product landscape, I think about this distribution curve. And on the left side, you have the entry level, be more novice athletes. And on the right side, you have the very serious experienced athletes. And there's obviously way more people on the left, right? It's where the vast majority of the populace is. But the problem is that you have to get those users to both adopt a new consumer app, which is hard, and adopt working out, which is harder. And so there's a lot of companies that make their bread as like a paid user acquisition arbitrage business in that cohort where they're like, I can just make 40 to 150% on every users that we acquire. They'll stay for three months. Great. Like that's fine. You can do that, but that's not the problem we were passionate about solving.
But you come over to the other side of this and you have these people that have really high willingness to pay. You have really high retention because they have very high intent and the working out is already solved for them. Like they're gonna do it every day no matter what. They just have a really high bar for value. And so if you're able to meet that bar, you can win in an audience that is willing to pay a good chunk of change for something that is genuinely delivering value and they'll stick around too. I also think that there — if you are able to win in that market, you have this advantage of what I call the aspirational pull. And I think about this actually — I think Whoop is a great product example here, where they won first in a very elite athlete setting. And because of that, as they have traveled down this distribution curve to the more average consumer, the people that are like just before where they're really targeting want to wear and use the product because it makes them feel like they're making progress.
Alp Uguray (22:00): If [a top athlete] is using it, I should use it.
Ben Katz (22:05): And so that is like this pull where like these people want to use the product because it's for people that are just where they want to be. Whereas the opposite is true if you start really niche and like — or like really early on. If this product is a beginner product and you start trying to go to the advanced people, they're like, I don't want to use that. That is like not meant for people like me. And so it's a push. And so as we grow in scale, I think ultimately we will travel down that distribution curve and it's a matter of how, when.
Alp Uguray (22:30): Yeah, and it makes sense. Like I got my Whoop and I am definitely at the end of that curve.
Ben Katz (22:38): You're doing a half Ironman. You're not giving yourself enough credit. That stuff's hard.
Alp Uguray (22:45): Yeah, I'm already in pain. And I used Hybrd to like do some of the analysis on how I should balance out my — because I don't know, right? Like, as a user, and then in a way there's that loop of like learning from the best and then train where I'm not as knowledgeable. And then that creates a good product feedback loop as well.
Alp Uguray (23:05): Like when you look at the broader market, there is also like big shift right now, I think, is preventive care. Like preventative health, like identifying something before you go to the doctor and then making sure that like everyone's healthy.
Ben Katz (23:16): Huge, I'm a huge fan of that. I think that's really important.
Alp Uguray (23:20): What do you think about it? Tell me more about some of your — what do you see? Because you were at Whoop, you were in — like you saw the early days of that coming up as well as the market.
Ben Katz (23:30): For sure. I think that we have, at this point, something like 34% of Americans have a wearable, some form of wearable. Almost every form of wearable is able to at least measure your heart rate. There's a lot of data you can get out of just heart rate data. And a lot of that data, I mean, you've seen it with things like pregnancies, you've seen it with things like COVID, you've seen it with things like heart arrhythmias, like all sorts of different health concerns that you can catch early because of just wearing a device. And like that's amazing. And it can get even more granular than that at an accessible price point now with companies like Whoop and Superpower, etc., doing blood work and analysis and helping you understand like you are more likely to have this problem long term because of X, Y, Z. Here are the things that you can do to change it. It's really powerful stuff. I'm personally a huge data nerd and so you give me more inputs and I'm going to try and like micro optimize everything and not everybody is like that and that is totally okay and maybe better. But it feels like one of those things where if you have the password hint, why are you just slamming the keyboard with random letters?
Alp Uguray (24:45): Yes. Yeah. And in a way it's just like interpreting that hint as well. Like I think like if you don't have the knowledge of interpreting it, it may mean nothing. And they're just like trying to find their way. So there's that education is missing in the market.
Ben Katz (24:58): I think we're starting to cross that chasm as companies like — I think companies like Whoop and Superpower are particularly good at — yes like, we have all this data, like you do your blood work, we give you all the actual readings, but like the most important thing is the takeaways. Like I don't actually know what this obscure protein is and like why it impacts this thing. Like it gives me this diagnosis that I read and I'm like, I have to drop that in ChatGPT in order to understand what it means. Like that's no good. User experience needs to be, I can have all of these measurements that I can give to my doctor if I want to. I can see whether I'm in optimal range or not. And if I'm not, it gives me some recommendation on what to change or whether I need to be concerned because otherwise I have no clue.
Alp Uguray (25:40): And then context is a big thing. And I feel like AI is going to address that very well, right? Like, because especially when we look into like my ankle hurts — like you alluded to that earlier — because I sat all day and should have known better. But then again, like that understanding of context of the user, the healthcare data, like biomarkers — like we had in the podcast, from Duke University, AI researcher, and she's a professor, and she was wearing Oura and was able to detect that she was pregnant like about two, three weeks before. Which is insane, right? So the technology tells that that preventative care is actually here today. It's just a matter of how do we act on it, what type of things that it matters.
Ben Katz (26:20): Totally. My wearables know I'm sick before I know I'm sick. Half the time.
Alp Uguray (26:25): That part is awesome. Like when you think about it. And so what is like what you're building, right? Passion. You're passionate about it. And of course, like AI — it's built on top of AI. It is bringing it up, right? Like it's helping you guys to vibe code. It's right over product. You have the users. So for any other entrepreneur, for example, watching, like what would be your advice? Like as someone who went through YC and you guys were pre-product and pre-revenue as well.
Ben Katz (27:00): Well, first of all, it helps to not be those things to get into YC. We got very lucky. Biggest piece of advice, find a way to get free credits. No, I'm just kidding. But in all seriousness, I think that now is a really unique time to build a company for a lot of different reasons. But one of the questions — if you think about five years ago, what a company like ours would have done after YC, we raised a little bit of money and like that's great. But we would have had to go and hire a handful of engineers in order to build out the product so that we could actually grow the company. And we would be on a really, really high burn rate and it would be tough. One of the things we talk about internally is that the worst that AI will ever be again is how good it is today. And so we're already so much more efficient because of AI and AI coding tools and even things like market research, being able to send your Claude bot to go scrape — like look at Reddit and be like, hey, like, I'm thinking about this other company. Can you go and do research on their subreddit and tell me what people are talking about there? And it can just give you a report of bullets in like seconds. Used to be an intern's whole job.
It's really amazing. And it impacts how much you're going to hire. It impacts how you think about what you need to hire for. You are doing yourself a disservice if you were a small company and you're not leaning into the cutting edge of all of these tools because you are one of the few companies that are able to move fast enough and actually bringing these tools into their systems and into their folds to benefit from them at all. And it's — I mean, we call some of the new frameworks for AI agent coding like feature bazookas. Like it's actually crazy that you can have these tools working overnight for you while you sleep.
And obviously not everything is perfect and it's not just like magic, snap your fingers, the product is done. But we're doing in days what would take months. It's really truly an incredible time to be a founder in this environment. I think one of the things my co-founder and I joke about is — right now, if you are a founder and you are embracing the AI tools, you have a ticket to play the game. It may not be a winning ticket, but 99% of people don't have a ticket at all. And so we're playing. We'll see how it goes. But we're playing, and we're playing hard.
Alp Uguray (29:20): You are in the game.
Ben Katz (29:22): Yeah, we're in the game, and we're playing hard. We're definitely not losing, which is good. We haven't won yet.
Alp Uguray (29:30): Yes. And I feel like the rules of the game changed because now understanding the user and then distribution, the sales is actually more important than how to design a button.
Ben Katz (29:40): And I mean, truthfully, I think that's always been true. But I think that when the cost of building technology goes to zero, the things that are going to matter are going to be distribution, price, brand and trust, user experience. And so how do you build a business around those things? And thankfully we've kind of taken that — we've had that thought process since day one.
Alp Uguray (30:00): And I liked like, for example, Whoop early on had very like good athletes to come and then try it and then be part of the product. And that got them to start. And I feel like right now, because like somewhere, someone is vibe coding an app and then like, this will be cool. And then they started and then it's something — I think Claude bot was essentially that. Like now he joined OpenAI. He was very smart. He already did this many years. He knew what he was doing. But again, it gave him an opportunity not to increase the team size. For sure. Like be more lean and then target on the user's main problem he wants to solve.
Ben Katz (30:30): I think we are in the very first time in human history where genuinely in my mind, like the only thing that really is going to make a difference is like whether you have agency or not. Anybody can build the things that I'm building. I still don't know how to code. It's just a matter of I am staying up on nights and weekends. I'm going to go home after this. I'm going to play with my Claude bot and try and get him to figure out how do I create more automations that will help drive value to Hybrd? Because the more things that he can do automatically without me having to think about it that are genuine value additions, like that's great.
Alp Uguray (31:05): And let's talk about that because that is, I think, it's a very important point in the industry itself, like the agency. Because every time we give the agency to the agent, it does make a decision out there and then executes tasks for us. And then brings back, of course, 99% accurate, we hope. And then there are sometimes hallucinations happening as well. Like from a company building standpoint, like if you were to rank the task of agency that you would want to give to AI, what would live on that as like, there's no way I'm gonna let AI do it too — it can definitely do it.
Ben Katz (31:40): That's a really good question. I think it depends on a lot of different things, I guess, which is a cop out answer. And so I would say that — back to the point of the things that are going to help us win are going to be things like distribution, user experience, brand trust, etc. Brand trust and user experience are not vibe codable. And so I would say that those are the types of things that we want to actually have like real human connection and real human thought behind, especially on the brand trust side. Like you open — if you have a problem with Hybrdand you open the app and you go to contact, like to go to the like, write a support ticket or like make a support email — there is no support email in the app. It's literally a "contact the founder" button and it texts my iPhone. It's not even a Google Voice number. It's literally my personal cell phone number. And like you probably should be a Google Voice number. That's okay. But like that's — we would never want that to be an AI agent.
Alp Uguray (32:40): Be close to the user, and be accessible.
Ben Katz (32:45): Exactly. And then on the user experience side, if you think about, okay, it's really easy to make a million features. Do you enjoy working in Photoshop? Like, no, it has a million features, but it's like hard to use in a lot of ways. The user experience becomes more important, taste becomes more and more important. Deeply understanding your user in their workflow and how they intend to use something and how they think about things is like more and more important. And so it becomes much more about craft than it is about labor.
Alp Uguray (33:10): Yes. And then there are a lot of tools coming out on that cycle. Like if you can — what the distribution is a graph, right? Like there's the — like startups coming out addressing sales side as well. Like AI SDRs that scan and book meetings.
Ben Katz (33:25): If I have one more AI SDR in my DMs being like, do you want to book more B2B sales with your perfect persona? I'm like, dog, I'm B2C. Like, your AI clearly doesn't work. Like, I am not your perfect persona. I swear to God I get like five of these a day. It's ridiculous.
Alp Uguray (33:45): It is frustrating and they send like these emails like also sounds fishy too. Like it's like, is this a phishing attempt or is that a real — so in a way it gives a — maybe does it two years ago, right? ChatGPT. What were we at GPT-3.5?
Ben Katz (34:00): I have no idea. I mean, it's actually — the first public basic version came out in November of '22. So that's three years ago. Two and a half years ago.
Alp Uguray (34:10): So at that time, the AI wasn't great, right? Like to your point, it's only going to get better. And again, crazy to imagine. How much of it do you think would also translate into the physical world? Right? Like I'm not talking more of the robotic side, but like, if you think about more of the workout part, right? Like especially the — like in the past, like earlier we're texting LLM and it was giving just like a bunch of emojis and paragraphs that's so not personable. But there's also the part in workouts that rather you can collect all my biomarkers and data, but there's also the form, like the way maybe like I am swimming, the way I'm lifting or like the way every everyday life that I may have scoliosis, like the things going on. How do you see that maybe multimodal AI also influenced the space in general?
Ben Katz (35:00): I mean, it's 100% going to happen. I think there's a couple hurdles that they'll have to overcome, or we collectively will have to overcome. There's obviously the technology capability of can we actually do this thing and can we measure it, yes or no? That's one problem. The next problem is can we measure it in a way that is not going to be outrageously expensive, which is the next. And then the third is — can we measure it in a way that is not outrageously expensive and is also socially acceptable? Like, people still don't like to have a phone on a tripod in the gym. There are vision models now that can monitor your form with no problem. But they're not going to be consumer mass adopted until the stigma around being the dude with the tripod in the gym goes away. And so that's a problem.
Alp Uguray (35:40): The initial thinking is like, this is going on TikTok or like —
Ben Katz (35:45): And it's like kind of like nobody wants to be in the background of it. And like, I don't know, it's very — as somebody that has to film in the gym for their marketing job, I can tell you that I hate it. Very. So like, this is literally my livelihood. So I'm like, I'm sorry, everybody around me, please bear with me. But I can totally understand why your average consumer would not want to do that, because I would not want to do that. Yes. There are companies like Perch, which is actually based here in Cambridge. They actually recently sold to Catapult Sports, but they built something for serious teams. They sold B2B for a long time that would — it was a camera that would monitor athletes and their form and their like, you know, what weights, sets, reps, exercises, etc., were they doing. The technology exists. It's just going to be a matter of form factor, trackability, being able to actually build the models out and having mass consumer adoption for it.
Alp Uguray (36:40): So like for example, thinking about — I'm not a dystopia person.
Ben Katz (36:45): Well, I think that — already has me questioning whether you're a dystopian person or not.
Alp Uguray (36:50): And that follow up will be a dystopian question. So I was listening to like Michael Phelps and he — like was a big fan of collecting the data, making sure his form is correct. Like he's perfect. Like he trained for like two, three years nonstop every day until he goes to the Olympics, like obsessed about it. Also data driven, also fixing the form and everything. So let's say I have five to six athletes and then they go through exact same thing. Like data is collected, like everyone has like this fine tuned, well-trained model to guide them through. Like what would be one differentiator to actually win? Genetics. Genetics or like — not a lot of — mentality or maybe like —
Ben Katz (37:35): The mental difference is huge. Like mentality, genetics are gonna be huge. There's gonna be all sorts of wearables that you may be able to try and control for. We can't really control for sleep, recovery, muscle tightness, things along those lines. You can put everybody in the same exact environment. You could lock both of us in this room for a month. Every single day our data will be different. All sorts of different things.
Alp Uguray (38:00): So there's like the winning recipe is actually very changing. Like it's not even equal, right? Like from person to person or even if it's applied custom in certain ways, right?
Ben Katz (38:10): Yeah, I mean, it's an interesting question. There is absolutely a mathematical optimal form for certain sports, but every human can only be so good based on their — like there's a genetic potential. And then there's like, how much of the other things are you doing that will help you reach that? Mentality? Are you mentally tough enough? Are you doing the training that you need to do? Are you doing the training that you need to do at the level and degree that you need to do it? All those things are going to matter. I am five foot eight, I will never be an NBA player. It's just like not in my genetic potential. And that's okay, even if I had perfect form. And so, there's always gonna be some nuance.
Alp Uguray (38:50): Yes, it varies in a way then based on — there's variability.
Ben Katz (38:55): 100% and then even on game day even more so than the mental toughness to kind of like grind through pain and like do the hard training and do the hard work etc. On game day one thing that rattles you mentally or like you doubt yourself for a split second can change everything.
Alp Uguray (39:10): So I want to ask about — this is a lot of the founders who when they go to Y Combinator, they typically stay in San Francisco or move to New York, but you guys moved back to Boston. So how was that decision like to come back to Boston, stay, be part of the community and you guys are active on the Massachusetts AI Coalition.
Ben Katz (39:30): Yeah. Well. Boston's home. Like I grew up here. I went to school here. I love Boston. I also think that frankly, its tech scene is underrated. I think that it's hard. San Francisco — like there are obvious reasons to be in San Francisco. Like there's access to capital. And if you are a B2B sales company, you have like your customers are there and it's best to be where customers are. We're a consumer company. We can be anywhere in the world. Our athletes and users are going to be all over the place. And so the question becomes, where are we best positioned to win? And where do we want to be? And we want to be in Boston. And candidly, I think Boston is one of the best places to be for our business, too. It's the number one sports city in the world. Although my co-founder, who was originally from Philadelphia, will fight me on that one. Boston is up and coming, for sure. And I'm really happy that we're able to be part of the, you know, the cohort of voices that are trying to help drive that change a little bit.
Alp Uguray (40:20): And then talent too, like there's a lot of talent from the top schools.
Ben Katz (40:25): We have some of the smartest people in the world in this city, and we do a pretty bad job of keeping them here. And so I'm optimistic that a lot of that is going to be changing over the next couple of years.
Alp Uguray (40:35): Yeah, I think so too. Then I think now especially — even I think Y Combinator's founders are in Boston. Other than that — yeah, Cambridge. And do you guys like go to — they have users in Europe or other parts of the world too? So you can actually go see them, see what's going on, their training routines, different landscape, different diets, right?
Ben Katz (40:55): All over the place. We gotta make a little more money before we can justify the international flight for a user interview. But it is fun to come talk to different users and learn a little bit about different training styles and things like that.
Alp Uguray (41:10): Are there any things that stood out to you in terms of like how people work out differently in different cultures?
Ben Katz (41:20): No, but we got a lot more early traction in Europe than we expected. I think part of that is because Hyrox is originally, you know, a European company and grew really, really fast over there and is now doing the same in the United States. But we originally were like, we're only going to focus on the U.S. market for a while and found our way into having a lot more European users than we expected to. Good problem to have.
Alp Uguray (41:45): It's a good problem to have. How do users find you guys right now — word of mouth?
Ben Katz (41:50): It's a lot of word of mouth. We do mostly organic. We run a little bit of paid advertisement spend, but very, very minimal. A lot of organic social and a lot of lead magnets — we'll build little things with vibe coding that we can give away for free and capture an email as part of. So for example, one of the ones that has been particularly successful for us is we created a Hyrox race analysis tool that tells you — you drop in your bib number after a race and it'll tell you what percentile were you in for the burpee broad jumps, for the sled pushes, for the sled pulls, etc., help you understand where you were strong and where you were weak. And to get your analysis, it's free, but you have to go with your email. And then after you get your analysis — like, hey, we can help you get better at that. And we upsell you with training, etc. And so we go to these races all the time and we put up flyers and all of the convention centers with a QR code. It's like, get your free race analysis and kind of go through that.
Alp Uguray (42:55): That's awesome. And yeah, I mean, it is a space that is changing a lot. And if you were to automate the parts of the training plan and everything, and let's say like the end product, right? The last product, like the ultimate vision. And I know it's organic, like it changes based on user feedback and things of that sort, but like what would be on your mind right now if you could tell Claude to code it right now and it just does it 100% accurate?
Ben Katz (43:20): I mean, my goal is that if you are an athlete, you never have to question again what you should be doing today. We should be able to fully understand every single piece of context around all of your training history, all of the things that would potentially impact your ability to train, like weather, like your calendar, etc. And we should know what your goal is. We should be able to take all of that. And then every single day be like, here's what we recommend. And if you're like, I don't really want to do that today, we should be able to give you the next best thing too. And so, you know, I want to really lean into the don't make the user think but help them actually hit their goals.
Alp Uguray (44:00): And then it helps define those goals too, like based on how they are. I'm thinking about your journey a little bit, right? Born and raised in Mass, you've seen the distribution, you understand the tech now and then the true Whoop experience and then the search fund accelerator company. And out of the many problems that stood out to you, you chose something that you were passionate about. If you were to tell other entrepreneurs, right, that are looking for problem statements that they care about — when did you know that this was the problem?
Ben Katz (44:35): Yeah, so that's a good question. I spent a really long time debating between do I go and try to build something that is a B2B SaaS where my statistical likelihood of having a hit is higher, but I may not enjoy working 14 hours a day, or do I go and work on something that I literally love to work on, but that has a statistically lower likelihood of hitting? And I think I'm ultimately glad that I did what I did because for a lot of reasons obviously. A, it's working, which is great. And then B, I'm having more fun than I've ever had in any job ever. And I had a lot of fun at Whoop. And so it's like saying something.
You can have a fantastic life with either of those choices. I am building a life that I am very excited to wake up to every single day. And I don't know that I would feel that way as much if I was working on AI B2B tax software. And there are people out there that love that problem. And like, that is awesome. And there are people out there that don't care and are going to make a bunch of money solving it and like that's also awesome. It just wasn't really what excited me. And that's okay.
Alp Uguray (45:40): And I think in a way that ties to giving up and not giving up too.
Ben Katz (45:45): Totally. I think that people don't respect enough that most companies actually don't fail because they run out of money. Most companies fail because it gets really hard and then they give up before their bank account hits zero. And you're a lot less likely to give up if you A, are doing something you love, B, doing it with people that you love working with, and C, having fun every single day and giving yourself the space to do that.
Alp Uguray (46:10): Yeah, and then honestly, like you — your co-founders are athletes. You guys have been in this space — the bottom of the barrel here, man.
Ben Katz (46:20): Very much so. There's a reason I'm the talking head. Because they're actually training right now.
Alp Uguray (46:25): Yeah. I mean running 50k over a marathon is — that is still awesome. Well, thank you very much for joining me today.
Ben Katz (46:35): This is a lot of fun.
