Why Training Intelligence Needs First Principles

TriDot Podcast Episode 345

Why Training Intelligence Needs First Principles

Andrew Harley: Welcome to the show. If you listen to the TriDot Podcast or the RunDot Podcast, you are in the right place today. I'm going to tell you who is on the show today, and then we'll tell you what in the world we are talking about. It's me, Andrew, the host of the TriDot Podcast, and I'm here with Carrie Tollefson, host of the RunDot Podcast. How are you, Carrie?

Carrie Tollefson: I'm great, and you can see I have my big glasses on, so it's serious today. You know anytime I have my glasses on, it's podcast day. But I have my notes all ready. This is going to be a deep one, so I hope you all came ready to learn. Our guest today is Jeff Booher, the founder and CEO of Predictive Fitness, the company behind RunDot training, TriDot training and so much more. Jeff, thanks for joining us.

Jeff Booher: Oh, glad to be here. This is going to be fun. And I'm glad to see you have your glasses on, so I know I'll bring my serious talk.

Carrie Tollefson: Yes, get serious.

Jeff Booher: Maybe work in some big words with some little words.

Carrie Tollefson: I love it.

Andrew Harley: Isn't that just how language works, Jeff? Just a combination of big words and little words. Anyway, now I want to notate, I personally asked Jeff to come on the show, and in fact, we're going to be doing a whole series with Jeff rolling out in the coming weeks, all about the first principles of training intelligence. There are a lot of folks out there trying their hand at designing training plans for runners, triathletes, and other endurance athletes, and with over 20 years as a thought leader in this industry, I asked Jeff to just come on and take us deep into what has to happen for training design to actually be good for the athlete.

Carrie Tollefson: It's going to be fun. But as always, we're going to start with our warm-up question, move into our main set, learning from Jeff, and then we're going to ask Jeff an audience question on the cool down. Lots of good stuff today, so let's get after it.

Announcer: This is the TriDot Podcast, the triathlon show that brings you world-class coaching with every conversation. Let's get started with today's warm-up.

Andrew Harley: Whether it's a team sport, an individual event, or one of our beloved endurance sports, sports are exciting to watch.

Carrie Tollefson: And for today's warm-up question, what individual athlete across the sports -- doesn't matter if it's running, triathlon, basketball, whatever -- who do you think is the most exciting to watch right now, Jeff? So we're going to pass it over to you.

Jeff Booher: Well, it's a hard for me because I've been focused so much on running and endurance, triathlon, so I haven't been watching much else—

Andrew Harley: And Jeff, I do want to say, because this is so hard -- and maybe I'm cheating for myself here, just knowing what I want my answer to be -- but we will give you up to 3 people. It's hard to say just one across all the sports. So you can name drop one, you can name drop two, you can name drop three. Totally up to you. But we'll make it a little easier on the three of us by declaring that, today. Up to three.

Carrie Tollefson: Rules. Come on.

Jeff Booher: That narrows it. That's good. That goes – well, I'll do two then. I'd say first is Taylor Knibb. Think she's been fun. I got to see her in person on the Gold Coast and then the next weekend Oceanside. And back to back wins, so it's cool on different coasts and different continents, back to back. But then I have to also mention Kristian Blumenfeldt. Incredible. Oceanside, as well, with Taylor. But then Texas. He's unbelievable. So it's really fun. Real deal.

Carrie Tollefson: So Sebastian Sawe is the guy I have to mention. This week he ran 1:59:30 in London, which is unreal. We just saw John Korir break the Boston Marathon record. Jeff and I were there to see John do that. Sharon Lokedi, I think, is another big name. She didn't set the Boston Marathon record this year, but she won back-to-back titles. So she won last year, in a course record, and this year, and she's just such a competitor. And then I'm going to throw in an NBA guy. Some of you on C Tolly Run have heard me talk about Kon Knueppel. He was in the running for rookie of the year with his teammate at Duke. They're both now pros. Cooper Flagg. Cooper Flagg ended up getting the rookie of the year. But Kon Knueppel is my best friend from high school’s son, and she has four more boys. She has four more boys to come. The next one is 6’ 10”. So get to know the name Kon Knueppel and all the Knueppel boys. They are rock stars.

Andrew Harley: Yeah. And so that makes it extra exciting for you to watch them play, because there's a personal connection there. Absolutely get that. Some great answers there from the both of you. My answers here, Jeff, like I said, just representing my endurance sports pedigree, Kristian Blumenfeldt's on my short list. He's so exciting to watch. No matter what distances he's doing, he's in the running to win the race and do something crazy. My other two answers—go ahead. Absolutely, please do.

Carrie Tollefson: And wait, can I interrupt you? Has he run a straight marathon before?

Andrew Harley: Not to my knowledge.

Carrie Tollefson: So, I mean, okay. Kristian, there, I gave it to you. I think you're a sub 2:20 guy.

Andrew Harley: Not to my knowledge. Alex Yee is, I think, the big name in triathlon that just tried a standalone marathon, and that was in the news, there, for a little bit. I'm not sure if Kristian has.

Carrie Tollefson: Okay. Give us your 2nd or 3rd one.

Andrew Harley: My other ones -- Caitlin Clark on the basketball court, Carrie.

Carrie Tollefson: Love her.

Andrew Harley: Unreal. Unreal to watch. I'm more interested, in this phase of my life, in watching women's pro basketball and watching Caitlin Clark do her thing than watching any NBA matchup.

Carrie Tollefson: Girl dad. Girl dad.

Andrew Harley: Girl dad energy. Yep. And yeah, I love having women's sports on TV as Ellie walks through the room and sees women's tennis, or women's NBA, women's hockey. But the other one, as a tennis guy, Carlos Alcaraz, is unreal. What he can do on a tennis court with a tennis ball, and tennis racket, and his legs is un-freaking-real. Anytime he's playing, it is super exciting to watch. So those are our answers, from Jeff, Carrie, and Andrew. We're going to throw this question out to the I AM TRIDOT audience and the I AM RUNDOT audience. So join the podcast groups in your apps, guys. In the RunDot app and the TriDot app, you can now join a podcast listener group. We're going to throw these questions out there. If you're watching us on YouTube, watching us on Spotify, you can answer in the comment box. Or follow us on social media, where we'll post this question, because we want to hear from, you across all of sports, what athlete is your favorite to watch right now? And yes, you can also choose up to three.

Announcer: Let's go.

Carrie Tollefson: All right, are we ready for the main set? Because we are going to dig deep. We want to get all of Jeff's info in this little bit of time. So, Jeff, we are going to talk training intelligence today. Even right there, I’m like, oh my gosh, this is going to be exciting. So Jeff, you and I are at all these races right now, and we are seeing firsthand how these athletes on the start line, whether they're running marathons, or they're running triathlons, or different distances, this is something that we all hope that these athletes did something like big time to get there. We all hope they have that base and that background behind them. Whatever plan that they're on, or whatever coach they connect with, how different could each of their training really have been if maybe they were with us, here, at RunDot? Or TriDot?

Jeff Booher: Yep. TriDot or RunDot, either one. But, massively different. So that was one of the dilemmas when I first got into sport. They could be either massively similar, in the sense that they're all following a generic template plan, but if they're customized, they're massively different. And that, sometimes you think that's true. However, if you got 10 credentialed coaches and have similar athletes, almost the same athletes, or you proposed, “Here's the same athlete,” you would get back 10 meaningfully different training plans. The same athlete for the same race. All of the coaches are experienced. They're all credentialed. They're all claiming to be data driven, but they can't all be right. So I'm like, how can that be? That's what I found when I first got into sports. How can all these -- they're data driven, but they're all different. And this plays out across the entire industry. I mean, that's, it's everywhere. This is not -- and so basically, the problem is not a debate, it's a foundation problem. So I know personally, and I've been through many certification courses -- USATF, USA Cycling, USA Triathlon -- and the amount of time that they actually spend on how to design training plans and how to go about that is minuscule. We had -- and I know Andrew's been through certification as well. I think in his two days, they spent an hour, maybe 45 minutes, on how to put it together. And it's just theory. It's subjective. There's so much subjectivity, it's incredible. And that subjectivity is passed down generation to generation. So what you see is coaches training on how they learned, how they came up as an athlete, or what they’ve learned, or the philosophy that they subscribe to, but there's no standardized framework of how to determine if that's true or if it's right. It's just adopted and passed down. So our approach is very different than that. We have that first principles approach. And so it's not a question of how has it always been done, but instead, what is fundamentally and incontrovertibly true. It's a big word. I can't even say it.

Carrie Tollefson: That is a big word!

Andrew Harley: Big words and little words, today.

Jeff Booher: Yeah. You can't argue with it. It just is true. So that's what we tried to find, rather than just leaning on how it's always been, our mission and goal, and what we've been doing for 25 years, is seeking out how can we know what's true? How can we discern how each individual should train -- by age, by physiology, by all of these different things? And so when you have triathlon coaches or running coaches, they didn't build their training plan -- when they build training plans, they're not building them from scratch, actually. They're building them from an inherited philosophy that they adjust just enough to be their own. So they inherited some philosophy from somewhere, that was derived then from somewhere else, and they're passing down theory, philosophy, or what worked for them.

Andrew Harley: A really bad game of fitness telephone.

Jeff Booher: Yes.

Carrie Tollefson: Yeah, it sort of is. And it's definitely give-and-take and trying to figure things out on the fly, which I think is interesting. I mean, and I've done that. That's how I was coached. And there's a lot of trial and error along the way.

Andrew Harley: Yeah. And so imagine if you can take that trial and error out of the situation and actually feel confident in your training. But that's a lot of what we're talking about today. Jeff, when I was doing my USA Triathlon coaching certification, like you alluded to, it was wild to me that even in the one hour where we were talking about designing training -- we literally had an assignment where we broke up into small groups, and each small group was given an athlete and given a scenario, and we had to write a training session and a training block for that athlete. And my group of 6 people, our ideas of what we should do with this athlete were so different. And so you have to make a final decision. Each group presented what they decided to do, and the instructor didn't even give any sort of, “Oh, yeah, okay, so you tried this. Maybe think about this, this, and this next time.” He didn't even poke holes in anybody's answers. It was just kind of like, “Okay, yeah, great job. Onto the next group.” It was just like, “Yep, you guys, as adults came up with this, and regardless of if it's good or not, we're going to roll with it.” It was just wild how little thought and feedback went into that part of it. And, Jeff, we started the TriDot Podcast, and we started the RunDot Podcast, largely because we looked across the podcasting landscape when I was listening to triathlon podcast as a new triathlete -- this show was telling me to do this, and this show was telling me to do that, and this show was saying, well, don't do this or that, because this is what an athlete should do to have success. And I was like, woah, woah, woah, what is going on? And I got plugged in with TriDot, we had some conversations, and we launched some podcasts. So definitely excited to learn today. And I'm curious, Jeff, because you've seen this over the years, as all these different groups, parties, coaches, training programs are debating, oh, training should be this way, training should be that way, training should be like this – I think there's some instances where healthy debate and disagreeing ideas can help an industry, but sometimes it can hurt an industry. What do you think is happening here, in the endurance industry, as all these different parties have different training philosophies, training ideas?

Jeff Booher: I think healthy debate is wonderful. I always want healthy debate. The problem here is there is no debate. It's not a debate at all. Because a healthy debate assumes that there's shared, measurable truths, and you're talking from the same starting point. So experts disagree about interpretation, or validation, or different things, and that's wonderful, and that's very productive. However, what we find here is a foundation. It's a foundation problem, and that's very different because you have different inputs, and the inputs are inconsistent. There's no standards. All the standards, even, are subjective on what your training zones are, how you quantify training stress, or do you even quantify training stress? A lot of people use recovery, and your heart rate, and activity metrics for stress, and it's not stress. And we'll get to that. But the outcomes are also unverifiable. So you train with one philosophy. How do you know if that's the best one? What if you'd done it differently? Can you measure the delta between the effectiveness of two different approaches? No one's doing that. No one can do that. So there is no ground truth for a debate to even begin, healthy or not. So if you think of -- other programs and platforms have done the same thing as the coaches, and they've looked at inherited theory, and then they automate it, or philosophy, and then they automate it. And there's the subjectivity in that -- didn't disappear, it just gets baked into the technology. And that has real consequences for the athlete. So they're beginning with a base that is of inherited subjectivity and it just trickles all the way down. So did the actual—

Andrew Harley: And it has consequences for the athlete, and the athlete might not even know. You go do your event, and you do okay. I don't know. Could it have been better if I had done my training another way? You never know.

Jeff Booher: A lot of times they'll get some improvement, and they're happy with that. They're content. My training worked. Well, did it work or was it a limiter? Could you have done so much better? And so the platform prescribing their workouts may just be automating it, and it is automating a version of training that's never been examined, never been proven, never been validated itself, other than, hey, you're doing consistent activity, that's going to lead to some improvement. But that just efficacy, and that testing of what's actually true, to be able to have the debate as a starting point is not even there.

Carrie Tollefson: But I think what I find interesting about RunDot, and I'm not a triathlete, so I'm not on TriDot, but being able to trust the process, trust the app. And knowing there's a lot of different training platforms out there, but how can we trust this, and know when we open up our app, we're going to get the right training?

Jeff Booher: I think it's important, some of it is to think about what is your app being -- what was it designed to do? Most other apps, they'll start, or they did start with what's easy to measure. They're not starting with what drives adaptation. So they're putting their training on top of a platform that was prescribed or designed for something else. So whatever their platform is, it's prescribing training on top of a layer of something else, and it's not their foundation. So think about maybe a navigation app if you're going somewhere. And that navigation app is guiding you somewhere, but it's only tracking your speed, and that's it. So you're seeing this dashboard, your speed, you're measuring your activity, but it's ignoring the terrain, and the weather, and the traffic, and the construction, and maybe whether you're headed in the right direction. So it's useful; it's created to be a dashboard of a speedometer or whatever, but it's not actually navigating you anywhere. So that's close to what most training apps are doing. It's giving you maybe even a direction, you don't know if it's the right direction, and it's checking your speed -- are you doing some activities? And so listeners, they have a workout this morning, and that's most likely the type of training that their system generated. It was something that was designed never even to ask, “What is that workout actually going to do to their body?” So neither activity metrics nor response metrics are training intelligence.

Andrew Harley: So, Jeff, those two terms, activity metrics and response metrics, what are those for listeners? Because those are things we see in our apps. They're not labeled as that in these different apps. So when you say that, just define those two terms for us.

Jeff Booher: Yeah. So activity metrics, think of that as miles, minutes, steps, anything that's just an accumulation. So you're doing an activity, a time, sessions, anything like that. They say nothing about the physiological cost of what that activity entailed. Response metrics are similar. So that's how your body reacted. So heart rate -- my heart rate climbed after the workout, during the workout, the next morning. My resting heart rate is high. My HRV was low. Again, those say nothing about the cause. There's a correlation, but it's not the cause. So you can have two people -- one person runs, I don’t know, 10 miles in 80 minutes at an identical pace. Both these runners, one of them is endurance- dominant, well-recovered, cool morning. The other one is coming off a very stressful week, they're power-dominant, they're running in great heat. So the metrics that you're seeing, the activities are identical, and the responses look similar; however, the physiological stress that they went through is drastically different. So neither runner's platform sees that in the actual app, because none of them are actually measuring the physiological cost of the activity, and neither one of the response metrics are actually identifying a cause, because there's so many different causes that are there. Think about waking up even the next morning after a workout, and you see your heart rate is elevated or your HRV is low. Was that because your workout yesterday was hard? Was it because the workout from three days ago was really hard, and you're not recovered from it? Is it because it was a hot day yesterday? Is it because your chronic training load, your last 6 weeks or so, was heavy, and you're carrying this high load to begin with? Or are you getting sick? Or were you dehydrated? There's so many different things that that could be symptomatic of, so it's not a causal, it's a correlative. And you don't, you can't even isolate. There's so many different signals and things happening. You can't anchor those metrics to any one thing. So heart rate and HRV are shadows of what actually happened. They're not the actual thing itself. And they don't show you. They don't show you a consequence of why and what to do next. So if inputs are just shadows of what you're using for your training -- heart rate, HRV activity metrics -- then if those are just shadows, then nothing downstream from that, that you're making decisions based on it, can be accurate.

Andrew Harley: Yeah. I've been a longtime Strava user, well before I was a TriDot user. And they do, to their credit, a great job of showing you nice, shiny graphs before and after, or after workouts. I used to look. They had one in particular, there was this really cool graph they have that is like your fitness rating. Here's how fit I am according to Strava. And I used to think it was so cool. It blew my mind. Like, “Oh my god, look at this thing climbing. I'm getting fitter, I'm getting fitter.” And this is even pre TriDot, it finally dawned on me, my score goes up when I'm doing more, and my score goes down when I stop doing stuff. That's all it is, is an activity metric, is the term you're using here. And, Jeff, this might be a slight aside, and changing topics a little bit, but I'm like this close to killing my Strava account.

Carrie Tollefson: Ooh.

Andrew Harley: And it has nothing to do with that. It has nothing to do with that. One, it's because in the TriDot app now, and in the RunDot app, we now have added some social features. So now I have a social profile there.

Carrie Tollefson: It’s all about the socials, Andrew. Let's go.

Andrew Harley: And honestly, Strava, that's the only reason I've kept it, is because I can fire it up and I can tell all my TriDot friends, “Hey, good job on your workout. Good job. Kudos, kudos, kudos, kudos.” And I can see what people are up to. Well, now I can see that in my TriDot app. And that's the only reason I was hanging onto Strava in the first place. So my profile is set up on the TriDot social app. I am @podcastandrew, so go find me and follow me on TriDot. Interact with me. Join the podcast group. But, Jeff, we now have these social features. There are other apps in the fitness industry that have different social features, badges, engagement features of some sort. Where is the line between these being helpful things in our fitness journey, or just a distraction to have these in the app, whatsoever, if that makes sense?

Jeff Booher: I would say maybe it's not a line, but a priority, and maybe a foundation. So engagement layers on top of training intelligence is wonderful. It's fun, it's engaging. All those things, that's great. And they make the platform, they make the training program stickier. You keep doing. That's wonderful. But when engagement layers replace, or are substituted for training intelligence, that's the problem. So there's a lot of social, like Strava and others, that they were designed to be that. The training is just the context of the social. So the training is not the point. You can't gamify your way to a marathon PB. No badges, all of that stuff is not going to lead you to a PB there. So when you're thinking about athletes that are training for a marathon, they're training for 18 weeks, 20 weeks, and their app is giving them kudos, and leaderboards, and all this stuff. But what matters most when you hit mile 22, is whether that training was actually preparing you the best possible way so that your body can do what it needs to do to get to the finish line and reach your potential. So the engagement was the draw in those situations, but the training was not the point. Whereas we've taken a different approach with TriDot and RunDot, we have community features because people want them, athletes want them, for sure, but we were built first, and built on top of FitLogic, which is our intelligence engine, and we're not built the other way around.

Carrie Tollefson: So, Jeff, did you just debunk my social is everything. Did you just debunk that?

Jeff Booher: I don't know.

Andrew Harley: And I can tell you guys, something I found in getting neck deep in the TriDot ecosystem, is actually, and we joke about it on the podcast sometimes, having your Strava account there in the back of your head during your workout, sometimes has a negative effect, because it's, “Oh, if I go too easy today, people are going to see, my average pace for my workout today is going to look slow.” Well, it should be. It's an easy run. Let it be slow. But if you're thinking about your Strava followers seeing your work, or, you know, heaven forbid, your Strava followers see that you did a 3.97 mile run instead of a 4 mile run. Knowing there's people looking at it and thinking of the social before the training effect, I think leads a lot of people to train too hard, too fast, too often, too much, as opposed to the way you're pitching, Jeff, that we do here.

Jeff Booher: There's a saying that you get more of what you measure. So if that's what you're measuring, you're going to get more of it. You will. And that's what gets celebrated, and so that's what get chased. If that's what gets you recognition, celebration, kudos, and all those dopamine hits and praise from others, then that's what you chase. And so we try to flip -- our gamification and those things are different. We have a TrainX score which rewards your training execution. How well did you do what was prescribed for you? That's it. If it's 3 hours a week or 10 hours, whatever it is, do that well and you're going to max your points. So it's rewarding for people for doing the right training, not just more, or not just faster every time.

Carrie Tollefson: Okay, you ready to get techy?

Jeff Booher: Okay. Always ready.

Carrie Tollefson: All right. So you've categorized the trading platform landscape into five groups. Walk us through the four that are not training intelligence, even though it may seem like they are on the surface. So let's dig into it a little bit.

Jeff Booher: Yes. So I mentioned a couple of them before, kind of activity trackers. So they’re measuring activities, so they're built to observe and report. So it's Garmin, Strava, Apple Watch, Training Mode, Coros. There's no prescription in these. It just brings -- the athlete brings the thinking, and they just bring, “Here's what you did.” And it's a measurement tool. It's like a dashboard. So the platform holds the data, puts it all in one place, and shows you what you did in the past. But what you do in the future, it's up to you. That's category 1. Category 2 is a lot of template platforms. So it's a platform, it may be automated, maybe not, but is basically built to automate a training philosophy. It's someone's methodology applied to everybody. And the anchor phrase here, if you think about -- automation, is not intelligence. So even if the AI is AI-automated, it’s still not intelligence because it wasn't built to do that. It's built to just apply a philosophy, a methodology, or what someone believed. And so that's category 2, just the template platforms. Reactive platforms are the next. They're actually making decisions in there, but they're reacting. So it's built, basically, to prescribe activity based on the activity and the response metrics. And we talked about those a second ago. It's the actual activity, not this training stress, not what it does to your body, and then your response, how your body reacted, but it doesn't show the cause of the response. So there's so many things going on it can't identify, but it's just reacting to signals without the ability to diagnose the cause of the signal. So those are built to adapt—

Carrie Tollefson: Having diagnosed. Diagnose is the big word, there.

Jeff Booher: Yeah, so they can't diagnose, they weren't built to diagnose, they were just built to adapt. They were not built to understand, but just to adapt. So it's these predetermined things that, when this happens, then do this. But it's not knowing why. The next category is more of recent category, last several years. It's the general purpose AI, the LLMs, the ChatGPT, and all the others. Those are built to recognize patterns and language, not physiology. So they can -- they're trained on words, not workouts. So you can even give it your workout, your data, and it's looking for patterns in there, so it can read your training log, but it can't understand what it did to you. It can't understand the physiology, has no framework. It's applying theory broadly and giving you something that looks like a plausible plan. Like you can't look at it and say this is wrong or that's wrong, so it looks logical, but it's still, it's missing the mark. That's not what it was designed to do. It’s train on language, patterns and language. The last category is the training intelligence. And so that's us. So that's built from the ground up to understand what training actually does to a specific athlete. Fitlogic is the intelligence engine. It's the only platform in this category in the world. It's the only one built from first principles, from the ground up, to do this. So none of the apps I mentioned in the first 4 categories were designed to understand you or your training.

Carrie Tollefson: But I want to go back, because I still need to learn a little bit about ChatGPT, AI, all that stuff, and the difference between training intelligence. So, let's go back to General Purpose AI, Category 4. Tell me a little bit more about that.

Jeff Booher: So it's an astonishing tool, ChatGPT. I don't use ChatGPT. I use Claude and other things for other things – writing and reasoning—

Carrie Tollefson: See, and this, everyone, we need to learn this from Jeff Booher too, because he studies all of this. So we don't want to use ChatGPT, we want to use Claude.

Jeff Booher: Claude code, there's all kinds of stuff. There's a reason why I do that. But they're different based on what you use them for. But they're all amazing. They're all incredible, and they're great at what they do. They were trained on words about training, not training itself. When applied to a training plan, they're applying theory, and not the actual understanding of the physiological impact. There's no framework for understanding how you apply that to men, women, old people, high performance, low performance. So it can read your training log, and it can generate a plausible sounding plan. So you look at it, and you can't look at it say, “Oh, this is wrong.” I mentioned that a second ago. It looks right. But I've gone in there, just jokingly, and said, “Hey, give me a plan for XYZ,” and it gives you a plan. I said, “Well, what about this?” and it'll adjust it some way. And I said, “Oh, I really meant that,” and it'll adjust it the other way. And you know, it's just making up stuff, and the details matter, the little things matter. If you're running, have interval workouts, and it's just 15 seconds off per 400, or something like that, it's just a huge differences. And the amount of training stress, it's not even managing the training stress. That's getting into some of the first principles. But it has no way to measure training stress. That input's not even coming into it. And so what does that, you know, the workout impose the training stress. And there's no ways to individualize the physiology either. So there's no input from that. Talking about how does a man, or a woman, or an older person, or a high performing person, or younger tolerate stress differently? How does that residual training stress dissipate? There's all these things that it has no visibility to whatsoever, much less an understanding of it. So there's no way for it to even validate its own prescriptions. And there's no way for it to learn from the outcomes. If you gave it a problem -- oftentimes you do this, working with it on many different problems -- you give it some problems, it'll come back with different responses, different solutions every time you use it. So there's not even that consistency among the same exact prompts. It's giving you different things different times, and that's just amplified over time with training data that it doesn't understand.

Carrie Tollefson: And that's what I think is so cool about TriDot and RunDot, is, yes, we have live coaches that can go along with the app, but the app, as you said, can read what's going on with us just by sleeping at night or by watching our everyday activities. And Google, when you go and Google a couch to 5K, yeah, it probably gives you some good guidance as to how to do that, but--

Andrew Harley: It's going to get you there.

Carrie Tollefson: It's going to get you there, but it's not the same. So is AI. AI is just taking Google, basically, right, and grabbing some of these couch to 5k plans. So I do love that, that your platform, Jeff, really looks at the athlete inside and out, even without a coach. But then when we also use our coaches, we have an extra tool on our tool belt. You know?

Jeff Booher: Absolutely. And it's our app, so it's not just my app. A lot of brilliant people, including you guys. But you think, it can't understand. So it can read our training data, but it can't understand it. And it's a sophisticated autocomplete. It's not a substitute for training intelligence. So the real risk, though, for athletes, and you're talking about, “It'll get you there,” but at what cost? So we talk about better results in less time with fewer injuries. So is the cost the injuries? Is the cost that you just trained an hour, 2 hours, 3 hours more per week for the last 6 months than you had to? Or is it, this is your bucket list marathon, or IRONMAN, or whatever, and you went an hour and a half to 2 hours slower than you could have? So for the rest of your life, your bucket list time, you always have this regret or doubt or wonder. Like, could I have broken 12 hours? Or whatever your goal was. And so that's the real risk.

Carrie Tollefson: And I just have to say, I got to interrupt you one more time, because I watch you speak about this, and we do know that you are very much into the technical side of things, but you come from the athlete background, you come from the coach background. You've studied the body. You know all the physiological things that are happening with the body before you even decided to have an app like RunDot or TriDot. That, I think, is what's so cool, Jeff. And I don't know if everyone really understands that you can do all the old school ways of coaching, because you know what's going on, but you've implemented now the technology to go behind it. So bravo! I love it.

Jeff Booher: Thank you. Thank you.

Andrew Harley: I don't know, Jeff, if you've heard this story -- and not that we need to talk about ChatGPT for too, too long -- but we've, you and I have both been at conferences and we've been at events where ChatGPT comes up as this ‘gotcha’. And AI, like, a coach who's not with us will get on stage and kind of like, “Oh, I asked ChatGPT to design me a training plan, and it was horrible.” And everybody kind of laughs at it, like, “Oh, of course it is.” And it's like, okay, so first of all, is that technically AI? Yes. Is it, like when we talk about what we do as AI, is it in the same category? Absolutely not. That's what you're talking about. And Jeff, I always heard that example kind of get thrown around and scoffed at. And in my head, I was always like, “Okay, I get they're joking about this, but who would actually do that?” Fast forward to the other day -- have you heard Corey's story? So one of our software developers, Corey, has a guy that lives in his neighborhood -- so this is somebody who works on our team, has a buddy who has used TriDot in the past for a couple IRONMAN events, and got it in his head, hey, I've been using ChatGPT for other things. I'm driving with it. Let me see if I can use this for my next event. Drops off of TriDot, fires up ChatGPT training for his next event, and has a bad experience. Womp, womp, womp. And is now back on TriDot. And Corey was telling me the story, and I was like, “Oh my gosh, it actually happened.” Somebody actually tried it. I didn't know people were actually trying that. I just thought it was a joke.

Jeff Booher: I think it's happening a lot, a lot more than we think. And because people use the word AI like they use the word vehicle. Like if you say, “I'm going to use a vehicle. I'm going to use a vehicle to get here, across the state to this other city.” And you're going, “Crazy!” And you're thinking vehicle -- my scooter or my a skateboard, that's a vehicle. A vehicle can mean a whole lot of different things, and there's orders of magnitude difference between them. So AI in the sense of a ChatGPT, or anything like that, is not what our AI is. They're completely different purposes. Built -- they're architected differently, completely differently. So the real risk to the athlete is just like Corey's friend, or neighbor – I’m assuming they’re friends. But the real risk is ChatGPT can sound confident, and you look at it, and you don't have a way to discern that, but it has no way to verify itself. And so an athlete trusting that is trusting something that was built to recognize patterns of languages, or language patterns, rather than health and performance.

Andrew Harley: Yeah. Jeff, I want to ask a question about reactive platforms. Carrie was just poking at ChatGPT is a general-purpose AI, category 4 on our list today. But you talked a little bit about reactive programs, programs that see what your workout is and then immediately react to how this workout today went for you. Because TriDot and RunDot training is adaptive. It does see what you're doing and update what it's prescribing for you. And I've seen some athletes, Jeff, that they hear that, and they know that, and I think they're expecting, “Oh, okay, I just did this workout today. Here's how it went. I'm expecting to see tomorrow, and the next day, and the next day update immediately.” And it doesn't. They don't seem like major updates. They don't see major changes, and they think, “Oh, it's not reacting on my behalf like other apps do.” So we're not a reactive AI app. Are those apps overreacting, and we are reacting, but properly and in a measured way? What would you say to athletes that see TriDot/RunDot and think that way?

Jeff Booher: A wonderful question. So ours is adaptive and reactive. It does those things also, but that's not what it relies on only. So you're looking at those reactive apps, that are looking at those HRV, and there are predetermined decision points. So there's two things. We're not only looking at that, we're looking at so many other things. So let me back up before I continue that answer. We're prescribing it to what it should be to begin with. So if you're seeing a program and you're having these drastic changes between I did the workout today and then what's prescribed the next day and the next day, they're not prescribing the thing correctly. They're going blind, and waiting on the thing to happen, and then they react to it. We're prescriptively saying, here's how much training stress that we want to induce tomorrow. Here's the residual training stress that's going to happen from that, because of your age, and your gender, and the temperature you did it at, and the time of day, and the hilly of the course. So on Friday, you're going to be ready to do this workout. We know, if you do this workout right, you're going to be ready, and we're not going to have to change Friday's workout. Only if you deviate from that, not super substantially, but quite a bit, do things need to be changed. Your intensity, the distribution, your volume for the week, your long sessions, all of that can be changed. However, if you went in there and just take a workout that you're going to work in the morning -- if you want to see reactive, proactively reactive -- and you're going to do that workout, and it's 55 degrees, and it's 6 o' clock in the morning, say, “I want to do this in the afternoon, in 85 degree heat,” you'll see the program change dramatically. You're going to see your intensities change by 20 seconds per mile, or however many watts on the bike, depending on where you're at. So that's a proactive. We know your environment changes, and so we're going to change the intensity, because we know that's a different level of effort in a different environment. None of those other apps do that. To do it only after the fact, and it’s a surprise, and then it's this predetermined reaction that they say, if your heart rate is this, or moves by more than that, then they just have this predetermined: “Here's what's going to change.” But they don't know why it changed. They just know it changed. Again, back to the example I did earlier, was it because that workout was hard, or you didn't recover from the one 3 days ago and you put another workout on top of that, or your chronic training load, or your lack of sleep, or all of these different things? Or was it the hard workout that you did in the afternoon, and it was hot? That's why you're elevated, because you kept doing the same pace that you're supposed to do at 55 degrees, but you did it in 85 degrees. That's what it was. The workout wasn't too hard. The environment changed, and you didn't change your plan. So there's so much about it. One, is it's predictive and prescriptive ahead of time, so we're not getting shocked. “Well, let's see what happens.” The trial and error, “Oh wow, that wasn't so great.” And then the responses are not arbitrary or predetermined. We're actually separating the signal from the noise so we know what happened and what needs to change for the next workout. It's not just an automated template.

Carrie Tollefson: Do you ever find athletes get pissed at the app? I used to get really mad at my heart rate monitor. This was years and years ago. I'd be like, I know I can go harder. And then it'd be like 2 days later, I was throwing up because I had the flu coming on or something. Sometimes you get a little tick because the technology doesn't lie. It tells you.

Andrew Harley: Stop being so honest with me, heart rate monitor.

Carrie Tollefson: Yeah, I'm fine. I can push through this workout. Well, maybe not.

Jeff Booher: Yep. But when you're able to track so you can get that, that needs to be complimented, though. So even back to your training stress, if you're quantifying the training stress, is that just a moderate load of aerobic stress that you're going to be recovered from in 2 days, or is it neural stress or muscular stress that's much going to take much longer? And if you know that ahead of time, do we need to adapt it or not? Or are you going to be fine or no? Versus having, with your Garmin -- there's all kinds of different devices -- you do hard workout, “You need to recover for 3 days.” Well, how do you know that? It depends on the type, because it's using these arbitrary rule, one size fits all, algorithms.

Andrew Harley: So, Jeff, here's an interesting one. You just mentioned Garmin, and before I started training with TriDot as an athlete, personally, I was just going on vibes for my swim. I was going to the pool and doing what I felt like doing. I was going on vibes for my running. I was going out the door and running how I felt like running. The only type of quote/unquote “training plan” I was trying, I was using Zwift for my indoor cycling, and Zwift has a library of workouts. And I would choose a workout, and I would do it. And again, vibes, choosing what workouts sounded interesting for today. But that was my first taste--

Carrie Tollefson: Which one has the best playlist? You know, or whatever. Best course.

Andrew Harley: And so I'm wondering, Jeff, for athletes that are using some sort of library, or are using some sort of training plan -- and maybe they're using Garmin workouts. These days, your Garmin watch will tell you, “Hey, here's a workout you want to do today. You want to try it?” There are athletes that are seeing improvement, having a good experience. They're getting faster, they might be PR-ing their races, and they are doing that kind of training -- just going off of Garmin or doing what Strava tells them might be a good workout for the day. So they're doing some sort of training, that's prescribed for them by something that sounds intelligent, and they're having a good result. What would you say to them? Is there anything wrong with that?

Jeff Booher: Well, it depends on how you define wrong. I think increasing my injury risk unnecessarily is wrong. I think not realizing my potential is wrong. I think spending time away from my career, my family, my wife, and not getting the results I could is wrong. I think those are wrong. If you're out there just to spend your time, and to have activity, and some results, and you don't care, then maybe not. And I'm not being flippant here, there is real results that those athletes are seeing. If they're improving from effort, consistency, their genetics, they're recovering well, they're eating good. Those are great, and those are real results. So the question is not, are they running well because of the training, because of the app, because of the workout library? Or the question is, are they running well despite that? That could be a limiter, and they could be running much better. And there's also not an acute consequence for doing those things. You can run for a while, and you get injured in a year and a half, and it's just cumulative over-training over time. And the other aspect is you don't know what your potential was. A lot of them say, “I did well.” ‘Well’ compared to what? What's your reference point? So if they never even had an idea that they could have done so much more, better result. Or they didn't realize, I could have trained an hour and a half, 2 hours less per week, with my family or whatever that is. That's a lot accumulated over time. So if they don't have that reference point, it's -- their judgment, I guess, just needs an anchor point. And that's what the first principles does. It creates that reference point and that anchor point. So the ceiling for someone's athletic performance may not be their potential at all. It's their platform. They never even get visibility to it. They don't know what's possible. So I know that -- Andrew Hall is just a classic example, and he's kind of a high-end athlete. He's an amateur.

Andrew Harley: The faster Andrew. The faster than me Andrew.

Jeff Booher: The Faster Andrew Hall.

Andrew Harley: I wish I was as fast as Andrew Hall.

Carrie Tollefson: You just came from Boston. That's how you say ‘Harley.’ Andrew ‘Hally.’

Jeff Booher: Hally. So just Andrew Hall. So he raced IRONMAN Texas. I've seen him race several times in person. He's a great athlete. He finished IRONMAN Texas, ran about 8:30, which is incredible time. Amateur. So he's an amateur champion, two years in a row. Won it, and he went 8:30. He beat 40% of the pros, as an amateur. 40 years old, okay, so he's not a young pup. So a lot of the pros are much younger than him. So he's 40 years old. And I even had, I've told the story before, “Oh, he ran in college.” I said no, he didn't start running until he was 33 years old and got into triathlon. So he was late coming, had no run background, beating 40% of the pros, whipping everyone else in his age group, all the other amateurs. And the elites at that level are generally training 25 to 30 hours a week. So here's the kicker: he was training an average of 15 hours a week. So when you look at the cost and doing well, there's other athletes that performed great, but the amount of time less that he trained, that other athletes at that same level did, he's trained enough time to have 8-hour days for 3 months. 3 months of 8-hour work days is how much he didn't train that they did train. Think about that when you quantify.  That's time away from work, or your job, or your family. Or wear and tear -- all of that time is wear and tear on your body. So you want to keep doing the sport? Maybe the cost to you is not performance, it is time saving, but I just want to keep doing it until I'm as old as I can, and I don't want to have to get out. So think of all that, much less wear and tear on your engine. So the platform, when the athlete is doing the thinking, working with a tracker app, or any of these other things, the platform can become the ceiling. And the athlete has no, even, idea of what their potential could be, they have no idea of the risk that they're incurring, and they have no idea that they could be achieving better results. And it's very counterintuitive that they could achieve better results for training less. So in that situation of athletes doing better and getting great results, kudos to them, but that's because of their consistency, their hard work, their ethic. Some of that, they're going to get some of those benefits from training, but they've been supplying all that. It's not their platform. It's coming from their training, their consistency, their behavior to the extent that they do get results from those platform.

Andrew Harley: Yeah, and we need to have the faster Andrew, Andrew Hall, and his coach, Matt Bach, on the show sometime, and just hear about his experience. Because he didn't start off training with TriDot. He adopted it a few years ago and just saw his results skyrocket. So, Jeff, getting back to the topic, you've said very plainly, TriDot/RunDot, at their core, at their foundation, they are built upon training intelligence. And we've added a shiny app onto that. We've bolted social features, engagement features onto that. A lot of these other platforms start with those kinds of things. Could they then bolt training intelligence onto their app and make it better than it currently is? Why or why not? What would it actually take for someone else to attempt what we're doing through what they're doing?

Jeff Booher: I would have to equate that. So you're looking at platform, the way the software is architected, what it's designed to do. I think an easy-to-understand analogy is could you take a warehouse, a physical warehouse, and upgrade that, retrofit it, into a hospital? And obviously, no. The warehouse is not defective, it just was not designed to be a hospital. The warehouse is serving the function, whether it's gamification, or tracking, or any of those different things. It was never built with what a hospital requires in mind, with the structural, the electrical, the whatever, the how they clear out the air to make it sanitary. I mean, there's so many different things that a hospital requires from day one. You can't just retrofit that into a warehouse. You cannot renovate. You have to scrap and start over. Scrape it and start again. And so that's what other training platforms that were not designed to training intelligence, they can't do all of the things that need to be done. It's not a bolt on, it's not possible, it's not training intelligence. You can't just add features. If other platforms had every feature that we list, features wise, that's not training intelligence. The training intelligence comes from the foundation, the engine that it takes to produce it. So that gap is not features. It's the actual foundation of what we were built upon and what we've been doing for the last 20, 25 years. So you can't bolt on training intelligence onto a foundation that it was not built for. So training--

Andrew Harley: Jeff, have you ever seen a gas station that's a gas station for a long time, and goes out of business, and then it becomes like a Waffle House?

Jeff Booher: Yeah. Still awful. Yeah, still a gas station. Not just a structural thing.

Andrew Harley: And it's the most goofy looking Waffle House, ever.

Carrie Tollefson: And you can't get over that there were gas and things outside of it. What are those called?

Jeff Booher: But think about it the other way. Think about it the other way -- if you had a Waffle House, and then you want to turn into a gas station. Well, there's pumps that need to be underground, and there's tanks, and there's all of this stuff. You got to start over. You can't just insert that plumbing, and the hardware, and all the things for environmental controls that need to be done. So the only pathway for any of those circumstances, whether it's a Waffle House and a gas station, warehouse and hospital, or training app that was not designed for training intelligence. The only way is to start over. You have to measure differently, think differently, architect differently.

Carrie Tollefson: Well, you’d mentioned 20 years, and that made me think of FitLogic, because you guys have been around for a long time and I am newer to this group, to this trio, here. Andrew has talked about being the podcast host for a few years at TriDot, and I just started at RunDot, so I'm learning all the time. So let's rewind and talk about FitLogic a little bit.

Jeff Booher: So every other app, I'd say they took a -- they didn't really take a shortcut that's not the best way, but they just were not intending to do training intelligence. They did other things, and they're trying to bolt on, or add, or give something that's plausible for training program, based on what's the easy to measure metrics, activity metrics, response metrics. But we went the other way. And before a single workout was prescribed, there had to be a way to accurately quantify training stress that was imposed on your body by separating it from type in the environment. And before you could individualize a plan, you had to have a framework built around age, and biology, and genetics, and training history. It’s not just your preferences, but it's how does that actually affect people on those multiple degrees of freedom and spectrums. And then before that, the system had to actually learn and it had to be able to validate, have a closed loop validation, to where we could validate what's being prescribed so that then we could learn from it, and improve at the physiological level, and see what the actual training result was. And you mentioned earlier that I started with a problem to understand all this. We didn't come at this -- like from day one, for me, I didn't say, “Okay, how can I go about designing training intelligence?” It started with me and a problem. I was an athlete with young kids, and a wife, and a family, a career, and all this stuff. And I didn't want to train as much as everyone else was saying, 25 hours a week. So I started, well, everyone's saying all these different things. I saw the same thing that Andrew mentioned. They're saying this, someone else saying that, they can all be true. And so just trying to research and find out what is true, and gradually, incrementally, over time, building and discovering the algorithms, and how do you quantify training stress, and what is the scale, and what should this framework be, and what should that framework be, and building it incrementally. I researched for 7 years, from 2003 to 2010, before we launched TriDot. So there's 7 years of data on hundreds of people throughout that time before we launched it in the first -- and we continuously improved after that. Learning more. Learning more—

Andrew Harley: Interesting, Jeff, for people to know you're not just like a salesman, insurance guy. Your background is in data analytics to begin with. And so you're not just a dad training, trying his hand at developing this. There was some -- there was a fit there.

Jeff Booher: Yeah, so that's, my degree’s in management information systems. So I was building neural networks and doing AI stuff, simplistic at that time, but 35 years ago, more than that.

Carrie Tollefson: Don't age yourself, Jeff. Don’t age yourself.

Jeff Booher: I mean, 25 years ago.

Carrie Tollefson: You were 10.

Jeff Booher: So, I've had the athletic background. I’ve always loved athletics, always loved data and technology. And what I love most of all is solving problems, that are real problems for me. And then if I learn it, from me, I want to share it with people, I want to bring that to others. So that's what I started seeing other people experiencing the same thing, and really responding to, “Hey, this is really cool. Y'all are on the podium, and you're present with your family, and you're--” and so that’s a rewarding part for me. And so it is, I guess it's the patience born out of necessity, just my station in life at the time, and work, and career. I was working on growing another company, doing the research. So I had that patience and the conviction that I wanted to do it right, but it started with doing it right for me, for my family, so that I could fit my competitive goals, and striving to reach different goals, and my potential, but in a way that respects my family and the other priorities of my life, as well. So TriDot and RunDot came out of that. So they're a direct athlete application, but they're built on the FitLogic engine that I've been building for more than 25 years. So it's not just a feature advantage over other apps. It's, we have a 20-year head start on the only foundation built for this, that actually works and understands training.

Carrie Tollefson: Yeah, I think I would like to dig into that just a little bit, just to humanize this a little bit more. And you've already alluded to it, but there are so many people that have given up going to kids soccer games in the morning on Saturdays to get their long runs, or their long rides, or long swims in. They've skipped date nights. I am one of them. I'm like, “Charlie, I’ve got to get up at 5am. I’m sorry, I'm going to bed early.” There's part of us that I think feels bad, maybe, about what we've done in the past now that there's these new ways to train. So do you think that it's actually cost us time in our lives by training the other way?

Jeff Booher: I absolutely think so. I think you can't go back in time with information that you have now that you didn't have then. So you only have as much regret as you had information to make a different decision. So I think all of those things are not the athlete's fault. They did what they could. They optimized their schedule, their sleep, their nutrition. You did all the things that you knew to do and you're aware to do. But the one thing they didn't, and I don't think they had a chance really, is to audit the foundation of what is my training actually designed to do? Where did this actually come from? You didn't have this list of choices, and say, “I'm going to pick the ineffective one,” or the one that didn't have the -- they didn't know. So you went among the options that were available to you, but the consequences of that are no less real. And I think there's really three. The first one is the limited performance, that your progress is going to stall out well below your potential. When you do that, you're wasting training time. So you're putting in hours that you don't have to, that are unnecessary, that could lead to more injury. But they're not as efficient. You wouldn't invest in stuff if you didn't get the same kind of return. You wouldn't keep making the same decision. Make investments, and you have interest rates, and so you know, hey, here's how much I'm going to make back. If you have that with a training program, here's how much I'm going to make back with this option, here's how much I'm going to make. You determine how much you're going to invest, and so as you invest your time in that, you're making wise decisions. But when you don't have the opportunity to choose that, or an alternative that's even measuring, or giving you a quantifiable number, or can track it itself, then that's a real cost. And I think a big one is the injury risk. That over-training, most of the time you don't see it coming. It's a gradual thing. It's not the high intensity stuff that leads to injury. It's the chronic, the lower intensity, that leads to injury. And Andrew had a great experience of his dad training, and his own, too, work, and coming to the platform, and having some ‘A-ha’ moments. Like, “Wow, I'm not getting injured anymore, this is incredible.” And to be able to have a platform that's actually having you do the right amount of intensity, and the right kind of intensity in the right quantities to make sure that you can recover from it. And it's not just using heart rate, or HRV, and it’s not measuring a shadow of a bunch of stuff that happened, but we're isolating what actually happened. So your performance is limited, your training time is wasted, and your recovery risk does go up.

Carrie Tollefson: No brainer.

Jeff Booher: So those things are factual. So you have an athlete that's training for their first marathon. They're running, they're dedicated, they're saying, “Hey, I'm going to commit my time to this. Even though I'm going to spend some time away from my kids, but I'm going to inspire them, too. You can go after hard things, do challenging things.” So there's a good piece of that that's good, and it's not like these athletic endeavors come without sacrifice. They always do. But it's the unnecessary sacrifice, that's the part that you don't want. There is a necessary part of that. You do have to put in the work, and you do have to give up some things. They give up their Saturday mornings for their long runs. They give up some sleep. They have to go to bed early, so they don't go on a date night. They do all of these different things, and maybe they do finish the race, but maybe they get hurt. Maybe they don't, haven’t performed their potential. Maybe all these other things, those Saturday mornings given up, those date nights, they don't come back. They're gone. And your kids don't get any younger. Like, opportunities. So our time is the most valuable thing. So you want to go through and have a great experience, achieve your best, your potential, but you want to enjoy that experience, but you don't want to sacrifice things that you just don't have to sacrifice, when you can actually perform better doing it a different way. So whether that's running a marathon or training for your first IRONMAN -- there's a joke out there that I've heard forever. They don't let you finish and your family be there at the finish line anymore. But it used to be your family would be at the finish line. And I always used to joke, and say, you know why they do that? You know why the family's at the finish line? Because they haven't seen him in 6 months.

Carrie Tollefson: True, yeah.

Jeff Booher: So it's that, you've sacrificed, but it doesn't have to be that way. And that started with my desire. I said, “It's not going to be this way.” So I drew a line in the sand, and I said, “I need to solve for how that I can train more efficiently so that that's not me.” And so those are not outcomes, the getting injured, the lack of potential, the spending too much time. It's not just bad luck. Those are outcomes because of training built on assumptions that no one ever questioned, and people don't see the alternatives.

Carrie Tollefson: And how I think of efficiency also brings longevity. People will ask me all the time, you don't train very much anymore. I train, and I get in 25 to 35 miles a week. It's not a 100 like I used to train when I was a paid professional, but I still train pretty hard. But I want to be able to do this till I'm 85. A woman just ran the 85-year-old 10k record here, in Minnesota, and that's what I want to do. So I think with that efficiency comes longevity. So I think that's a big thing, too.

Andrew Harley: As we wrap up today's main set, with everything we've learned today, everything we've talked about, what is the one takeaway you want people to have in their heads as we call it a day and move into the cool down.

Jeff Booher: So I think that athletes should ask themselves, was my training platform even designed to understand me or training? I think most people training have never asked that question. They just assume that if something has training on it -- their app, their device, whatever -- that that's what it was designed for. But as we talked about before, the answer is no. They're designed to track, to gamify, to make you consistent, whatever, have fun, kudos, to accumulate metrics. But the answer is no, they don't understand you or your training. So everything downstream from that is only a shadow of what training intelligence actually looks like. And so the four principles of training intelligence themselves, they make a closed loop system that makes it possible for you to understand what training actually does. What does it actually mean for an athlete's training when you have that kind of a platform? So the first principles are not options, they're the inviolable laws that determine whether the training actually works.

Carrie Tollefson: I like that. That last sentence was powerful. And we get to hear more from you, Jeff. So I can't wait for this series to start, Andrew. This is going to be fun.

[Transition Sound Effect]

Carrie Tollefson: We are getting ready for the cool down. This is the last and final question of this pod, today. So, I'm excited about this one. If you can believe it--

Andrew Harley: I picked a good question. I picked a good question for this one.

Carrie Tollefson: --Andrew picked a very good question, and it's from Coach Matt Sommer, who I guess just ran the Boston Marathon!

Andrew Harley: Sure did.

Carrie Tollefson: Awesome!

Andrew Harley: Shout out to Matt.

Carrie Tollefson: Yay. Okay. Jeff, want--

Jeff Booher: He did great. He did 3:06, something like that. I can’t remember the exact time.

Carrie Tollefson: Oh my gosh. Did you get to see him out there, or no?

Andrew Harley: Yeah. Low 3’s.

Carrie Tollefson: Did you get to see him?

Jeff Booher: I did, yeah, a couple times. Did that shakeout run, 5k. I didn't do it. He did it. I talked to him afterwards, with Brady and a couple other folks, RunDot/TriDot.

Carrie Tollefson: I got to see Brady, obviously. We saw Meb, and Howie, and, Andrew, I got to see Jeff. Sorry, you got to come to Boston. Anyway, okay. Back to the cool down.

Andrew Harley: I lived it on the socials. I lived it through social media. I was basically there.

Carrie Tollefson: Matt Sommer wants to know, “When is Jeff going to do his next IRONMAN?”

Andrew Harley: Oh, got him.

Jeff Booher: Oh man. So I've been not racing for quite a while, so I'm not sure. It's been a while. It's been more than a decade, almost 15 years. In 2010, I started a junior team, and I did my last IRONMAN a little bit before that, and I spent 15 to 20 hours a week doing high-performance draft-legal racing with the kids, and coaching them 15, 20 hours a week, all over the country. Andrew knows this very well. We spent a lot of time on pool deck together with our kids and training. And I just put my training on the back burner for a long time. And working, growing the company, building the technology, building the team. So now my sons are -- they were on the team. They're out. They’re in college. They're beyond college, and so they're starting to race. So they've done a couple 70.3s. And so now they're, “Come on, dad, you can come back and race.” And so I want to race, so I'm thinking about it. I think they're trying to recruit me, because they think if we all do it together, then it's a family thing, and I'm going to pay, and we're all going to go together. So I think there's an ulterior motive there. But I'm excited to. I am starting to train a little bit more now, more consistently. And so I look forward to maybe a 70.3 next year, in ‘27, and then we'll see where we go from there.

Carrie Tollefson: I like that. 70.3, I don't know why, but 2027, it feels like the numbers sort of jive.

Jeff Booher: Yeah, maybe. But see, I’m also think about a T100. So I was just in Singapore on the Gold Coast, watching a couple of those races. Those are super exciting. It's a little bit more of attainable distance. Vvery fun. And so that's kind of there, too. So looking at the destination races, maybe Alcatraz could be a great one. So there's a bunch. But I mean, I'm thinking, even this year, I considered doing a Supertri in Austin or Chicago. So that's fairly close to us, and I was doing, thinking about that. So I'm probably going to ramp it up and do one of each. And so I make it up—

Carrie Tollefson: Well, now that your social media is blowing up and it's getting going, now you--

Andrew Harley: Go follow Jeff Booher on social media. He’s posting.

Carrie Tollefson: He's posting and then you can post your training.

Jeff Booher: @_jeffbooher. So it's very uncomfortable for me. It's very uncomfortable. So please follow me. I need a couple more people. I'll get over that dozen mark, somewhere in the -- appreciate it.

Carrie Tollefson: Keep getting after it, Jeff Booher and Andrew Harley. I will too.

Jeff Booher: I will do so. Thank you for the encouragement.

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