Jake Aaron Villarreal: I'm Jake Aaron Villarreal, born and raised in Silicon Valley, and here to take you behind the scenes and share what it's like to be a startup founder, the journey they're on, the problems they face, the products they build in an effort to make our lives better. I'm excited to have with us today Tyler Hochman, founder and CEO of Fore. Tyler, welcome to the show.
Tyler Hochman: Thanks. Uh, yeah, thank you for having me on, Jake.
Jake Aaron Villarreal: Well, I'm excited to have you on, Tyler. Your company, Fore, Fore Enterprise, whatever you want to say. It's um, it's in a space we're seeing a lot of growth and a lot of companies are in a, at a stage where they're implementing AI and trying to figure out how to do it, when to do it, and what outcomes they can get from doing it. So we'll dive in uh a little bit more on your background and then talk about your company. So just for the listeners, Tyler is the founder and the leader of the company, which is an AI solutions architect helping companies deploy AI to enhance efficiency, reduce costs, and unlock bold new solutions. He also founded multiple consumer and B2B apps, and was named to the 2025 Forbes 30 under 30 list for social impact. He's a graduate from Stanford University and excited to talk more about your business. So before we do that, um, where are you joining us from today, Tyler?
Tyler Hochman: Um, I'm based in Los Angeles.
Jake Aaron Villarreal: Really cool. Yeah, we talked a little bit about this a few weeks back. I'm currently in Laguna Beach, but always get up to seeing all the innovation happening in, in Los Angeles, and it's almost like the second, I don't know, tech industry in California. They think about all the big companies typically have offices down in LA and a lot of startups and Silicon Beach and that whole general area is really going, going off pretty well. Now, in terms of you, you've had multiple startups. So, I just want to touch a little bit on your previous experiences and really what shaped you to the path you're on today.
Tyler Hochman: Yeah, I started my first company when I was uh, roughly a junior in college. I think Stanford breeds an environment that is very conducive to being an entrepreneur. Um, so I definitely think...
[Stanford Origins and Early Entrepreneurial Successes]
...that that contributed to it pretty significantly. And that company was, at the time we called it Modern Memoir. What we did is, we were the first company to have the idea where, when you ask grandma, grandpa, mom, and dad, "Where are we from?" As a child, you know, our family heritage, no one necessarily knows the right questions to ask. And so we created an app that was like a Mad Lib almost, and it asks the questions that then the parent or the grandparent could speak into the app, records the answer, and then we sell it back to the families as books. Um, yeah, very cool companies. Actually continued on. I was, you know, I did not want to drop out of school, so the founding team continued to do it without me. Um, and was very exciting, but that was my first foray into entrepreneurship.
I loved it. Um, I got to see, it was through a program where we even got to, you know, we pitched to investors. We, we got to understand what term sheets are and how valuations work and you know, how does your business model... the whole, I mean, the whole thing and, and it just, it was so exciting. Um, and then it culminated with, you know, we actually did examples of this with families and then got to see like real-world impact. And that was kind of one of the first things that started my love of social impact because being able to deliver something that was a good business but at the same time had a profound effect on the family uh, was really cool.
Jake Aaron Villarreal: You know, it's interesting, hosted this podcast now and we've done 250 episodes and you know, we always look at ways we could impact the community. And you know, I always thought that there was so much value in the elder communities, right, where people are maybe, you know, kids are gone and they're now like just sitting around with lots of knowledge but no one to share it with. And like if there was a great way to take and capture their experience and knowledge and then share that with their family or maybe others would be interested as well. So the fact that you were doing that so long ago, I'm just curious, were there any challenges in that business in terms of connecting with families or showing the value or telling them it's going to cost a certain amount? I'm just curious.
Tyler Hochman: Honestly, it's, it's one of the few that I don't, I don't have very many regrets. And I wouldn't say it's necessarily a regret, but maybe I should have dropped out of school because it was a really good idea. Yeah. And the company, there's, the company continued on. There's been a lot of people that have, have now spun out the idea and doing the idea themselves and they're crushing it. Um, I think that was a, that was a very good idea at the right time. Um, not too many ch... the biggest one was I was young, you know, I was young. I, my parents, education was very important for them. Um, and so, and it was important for me as well, and I'm, you know, was at a premier institution and, and I wasn't ready to make that gamble yet, and ultimately I think that was the right decision overall just because it helped me then do what I ended up, you know, starting other businesses and, and having success there. Um, but yeah, it was, it was, it would have been a good business.
Jake Aaron Villarreal: Yeah, I like that social impact. And at the end of the day, you know, it's all about relationships and memories and, and what you've learned and what you can share and hopefully someone else learns from it, which is one of the reasons we're even doing a podcast ourselves. So, but really, really cool. It sounds like your heart was in it for the right reasons. And you know, now as you're looking at new businesses, my assumption is your heart's still in the right space, but just, you know, what problem are you solving today? Um, you know, how did you, I guess, get started in, in the field of AI? I mean everyone's thinking about it, talking about it. And you went to school and were you an engineer to start with, and that's kind of where it started, or kind of walk us through, because I know you've had multiple startups, so give us a little bit more of that path before we talk about your company Fore.
Tyler Hochman: Yeah, absolutely. So I was, I studied engineering at Stanford. A variety of different types of engineering, uh, and then ultimately graduated as an industrial, what we call it Management Science and Engineering, but it's most similar to like an industrial engineering major. At school, I started a company that was kind of an analytic style firm. We were looking at doing like quantitative analytics for, you [snorts] know, trading platforms and different, you know, different things associated with trading algorithms. That was more just pure tech, not, didn't take it very far. Created interesting algorithms that could parse earnings reports and so on and so forth. Um, so that was one interesting angle.
The... I started a company, actually another very interesting one that was a, we call it was Safe, its name was Safe Stop. It still continues to this day. Um, and it was, we started it during the kind of George Floyd riots when there was all these tension and, and, and there continues to be tension between law enforcement and you know, obviously with everything that's happened recently, law enforcement and communities. And so our idea was the culmination of this tension appears in one, in one significant way during traffic stops. Um, and so we thought, what if we could facilitate initial communication at a traffic stop via video chat instead of having an officer approach your vehicle? Because that's oftentimes when the situation boils over, is they don't know where your hands are. It's dangerous for the officer. It's dangerous for you. Or like, what if you can just video chat them ahead of time and they can say like, "Hey, my name is so and so. I'm going to be coming to your..." if they have to. Sometimes they can just issue a warning over video chat, but if they have to, "I'm going to approach your vehicle now. Let's keep your hands at 10 and 2. I'm going to get there and then let's reach for your license and registration." Um...
[Building Safe Stop and the Trap of Accidental Virality]
...and we had a lot of, a lot of success with that company. And actually almost too much success because in order to make the, to make it work, we had to partner with individual sheriff uh, you know, stations and police stations. And they have certain jurisdictions. And so when we launched the app, we actually got too much interest outside of the jurisdictions that we were partnered with because everyone wanted to use it, and, and that, that was very problematic for us. So that, that was another interesting learning experience of like going viral, virality before you're ready. We had just partnered in West Hollywood. Um, that was our first, that was our first station we were working with, but like we had people from different states. I mean everyone was downloading this and the app wouldn't work because we had no partner law enforcement organization in, in that area. So that was another cool one.
Jake Aaron Villarreal: When you talk about virality, you know, a lot of companies are trying to figure out how to get their word out. You figured it out really sounds like in reverse, where the product wasn't quite ready. What was it that you learned from that, and have you been able to replicate that?
Tyler Hochman: Yeah, I think as a learning experience from that, it, it, it definitely answered that question you just described, right? Like, I always assumed virality was the end goal, and very quickly I determined that that is not, that you can actually have the reverse problem, and I had never experienced that problem before. And so it very much was a problem like, your product has to be ready. Your messaging, the biggest one was messaging for us. Like, we I think could have done a clearer job of making sure people, or even a way to, to manage excitement in areas we weren't yet deployed in, so we had engagement and we had community building without actually having to be able to use the application. Um, and that way you keep the user base engaged, but you don't have them actually using the app because we had no partner organizations there.
So, it definitely taught me a lot about engagement and community building. Uh, and, and something we've paralleled now, like kind of taken it full circle to our AI business because interestingly enough, we started Fore as a workplace turnover solution. And so, it was pretty cool. This is almost 5 years ago now. We were using uh, AI to predict whether or not employees were going to stay or leave a business, and then if they were going to leave, then the employer or the business could have targeted intervention strategies tailor-made to the reason the employee was going to leave. So if they're going to leave because of like burnout as an example, like we would actually, the AI would generate a schedule adherence algorithm that would plot the optimal schedule for the employee, which maybe involves like coming in an hour later, leaving an hour earlier, so on and so forth. And then that would ideally reduce the reason they want to leave, and then they won't leave. And so we, we often knew people were going to leave before they even knew themselves they were going to leave. Um...
Jake Aaron Villarreal: Wow.
Tyler Hochman: Yeah. It was a very interesting business and a cool application of AI.
Jake Aaron Villarreal: Yeah. I mean, just a signal of that would be tremendous for a founder if you knew that, you know, out of my hundred employees, these 13 are going to leave and here's why. And you can get in front of that and have the conversations and maybe look at, 'Oh, wow, you know, I didn't realize I haven't been paying them as well as I should have,' or, 'You know, they're in a problem scenario where, you know, they're not getting the support they need.' So, really interesting. Is that a company you still have or where, kind of I'm seeing the patterns of you starting these great companies and they're doing really well, and then you're starting another company, which is like the serial entrepreneur mindset.
Tyler Hochman: Yeah. Yeah. And so, well, this is kind of taken us full circle, which is that company had so, very similar to Safe Stop...
[Predicting Employee Turnover Before It Happens]
...picked up the virality based off that idea you just described, right? Like it's an easy concept that people can gra- get behind. Um, that you know, you can, you can picture a lot of benefits from, and we, we did derive a lot of benefits from there. Where it gets complicated is, you need, in order to make the AI work well, you need very well structured input data uh, from a variety of different sources and structured in a very unique way. And so ultimately, and this was kind of my learnings from Safe Stop, taking those quickly, is that we realized we actually had to swim the product downstream, because most companies we worked with did not have the necessary infrastructure or architecture to actually ingest and structure that input data to then make the AI work.
And that problem was ubiquitous across all different types of AI. There's, you know, predictive algorithms for workplace turnover. There's chatbots. There's all different kinds of things that they wanted to do with AI. None of it worked because their input data wasn't structured and streamlined correctly. So, that's how it took us to our current thing, which is honestly kind of me trying to avoid building a product that was going to get picked up, but then, but then at the same time, no one was actually going to be able to use it or generate ROI from it. So, now we, now we swim even earlier in that ingestion pipeline.
Jake Aaron Villarreal: Wow. Really cool. So, let's talk about Fore. Where, where is the focus for the company? There's every company is looking at AI and trying to figure out what products or tools or systems they should use, implement. They've got a lot of data. They're trying to figure out, what do I do with my data? Do I clean it? Do I label it? How do I structure it? How do I use it? Who needs to access it? Like all these fundamental, I guess maybe first principle questions. But then when it comes to actually doing something, no one really quite knows what to do, when to do it, how to do it, how much it's going to cost, and then what's the right team to do it, and have they done it before, and what outcomes can I see? And so there's a lot of different questions in the market. Take us from the beginning of this, where, where, where is your focus, and what have you seen in terms of delivering what you're bringing to market so far?
Tyler Hochman: Yeah, absolutely. So, I think there's two primary things that, that we, three primary things we focus on. Um, the first one is obviously ROI. It's a big, big...
[Why Fore Shifted Focus to Data Ingestion Architecture]
...point for us. You know, we want to make sure that what we deliver, you're getting, you're being able to recoup that investment in a reasonable timeframe. I mean, we're not talking years, we're talking months. So that's something we always consider when we approach a problem.
The second thing is output, like the actual quality of the output. And AI is very good, and we, we can get into it of what things it's actually good, you know, in terms of like research as an example, like there are industries and sectors it is very good at researching. There's also ones it hallucinates and just gets it completely wrong. I mean, it's not even close. And so like the quality of the output and what you actually, the in terms of the problem you're solving, is very interesting as well.
And then the third one, which I think is, is very often overlooked in today's world, is accessibility. It's, you know, yes, could you make this work if you had a degree from Stanford and a team of computer scientists at your business? Probably. But, but, you [laughter] know, ultimately, in order to get the first two, in order to get the ROI and the quality of output, you need the product to be super accessible. Because most likely IT and AI tech really, and the use of AI tech is not your bread and butter. And so, I think, you know, we got, we got um, we got dessert first with chat, like examples like ChatGPT. It's very simple to use. You know, you can, they really solved that accessibility piece. And as a result of that, everyone thought, "Okay, now if I apply this to my business, it's going to continue to be that easy." And, and it, and it's not. And so those are the three things we look for when we're designing our solutions.
Jake Aaron Villarreal: Yeah. Got it. So give an example, like what type of company would be a good fit for what you provide? And when you come to the company and start talking about what they're trying to solve, like what's the reality? What are they trying to solve? And, and what do you do different than other companies aren't doing? Because you're talking about in a few months solving something and providing an outcome, which is music to the ears for a lot of companies. They're just trying to figure out, 'Okay, I want to fail fast or get value fast. And I want to know what I'm going to pay to get there. And then if I can do that, then I know that there's a path forward and I also know there's maybe a company forward to go, you know, continue further.'
Tyler Hochman: Yeah. Yeah. Exactly. I think, you know, we're asking ourselves, first off, we don't necessarily take on all like projects that, that come our way. We like to use an acronym, and it's not a great acronym. I wish I thought of something better, which is like, just because I was frustrated at, at people asking me for, to do all these services. Sounds like 'grr,' but it ultimately became just 'ERR,' which is like, is your problem expensive? Is it repetitive? And is it repeatable?
Jake Aaron Villarreal: I like that.
Tyler Hochman: Yeah. Yeah. And so you know, you're frustrated, you're like... and those I think are the best problems that, that AI can solve. And so is it expensive, right? Is the either the actual cost associated with it, both in terms of time cost, in terms of you know, maybe there are real dollars associated with the problem from lost, you know, sales, you know, leads, or from you know, lost upselling opportunities or, you know, errors in outputs. Um, there's, there's dollars associated with that. So is it an expensive problem? Is it a repetitive problem? Meaning is it happening very often? You know, is, is the bulk of that work something that you see on a daily basis? And then is it repeatable? Is it the same problem every time, or does the problem look different? And I think if it hits those three criteria, it's a pretty good, it's a...
[The ERR Framework: Expensive, Repetitive, and Repeatable]
...pretty good solve for AI, or AI could potentially be a good solve for it.
So, a couple examples of that. Like, one of the really interesting ones we've been working with is in the real estate industry. Uh, a lot of large real estate firms that have a lot of tenants have to do lease comparisons all the time. Essentially meaning that they check that the lease that the tenant has is compliant with the regulatory, with the regulation necessary for that style of tenant. And it's super laborious. It's, the time cost is very high. Fines are associated if you don't get it right. So it's expensive. They do it every day. They do it all the time. It's repetitive, and it's repeatable. It's the exact same format in a, in a range of formats of leases every time. A perfect solution for AI.
So we can create an ingestion engine that can input the leases, they can analyze them, take out the information relevant for the regulatory body, you know, compare it against the necessary regulation, and then be able to say, "This lease is in or out of compliance, then these are the steps you should take to put it in compliance." Um, so that's just kind of an example of, of what, you know, we, we do these problems across financial houses, fashion houses, real estate, uh, you know, industrial. I mean, the, the problems are ubiquitous, which is actually something, it's kind of cool we found. Like, the, the, we don't, we're not experts in a bunch of different industries, but the problem is the same almost every time.
Jake Aaron Villarreal: Yeah. What's been the one you've been most shocked by or interested in that you guys have worked on and you're solving today?
Tyler Hochman: You know, we work with sports teams. Um, so that, that's a pretty interesting one. Uh you know, doing some really cool video, AI video recognition stuff. Um, yeah, I think it, it gets interesting and it's... I actually, I'm, I'm really, I'm happy about it because there's two big things that people look at when they talk about sports. There's descriptive and prescriptive AI, right? So descriptive AI is, can you use AI image recognition or video recognition to essentially describe a whole bunch of different scenes and then output that as, as functionally like stats, you know? And then theoretically you can...
And the reason I like it is because it's an efficiency play. It makes so much sense, right? Because you have all, taking let's say like football as the example or basketball is an even better one. Let's use basketball. You have basketball footage coming in from the entire world because everyone's playing basketball now. And so if you're a scout for an NBA team, you are potentially missing a lot of foreign players because you just don't have the time to look at all that footage. So if an AI could analyze that footage for you in the way that you would analyze it, now theoretically you're, you're democratizing the access to the NBA across the entire world. I mean, it's, it could have such cool ramifications and doesn't require the AI to be that smart. I mean, it requires it to be able to know like, you know, what does a three-point shot look like? You...
[Democratizing NBA Scouting with Video Recognition]
...know, what does a jump shot look like? You know, all the different types of plays. But it doesn't require it to have an understanding of what makes someone better or what makes a play better, what makes a team better. So, that's descriptive AI, and we've done that for, for a number of teams. Um, and it's super cool. Where I get scared is people now are leaning to like prescriptive AI, which is like, like, 'Be my, be my coach, you know. Be my coach of the football team and like tell me what plays we should run, tell me how to make this person better.' And, and I don't know if it's there yet. I mean, it's a little scary. I, I don't think it's that good yet.
Jake Aaron Villarreal: Yeah, I think that's a great spot. I mean, you know, and sports is so global that, you know, every, every sport industry could take advantage of AI if it actually works. You know, you see commercials, you know, on the NFL, you're watching the playoff game and you see AWS talking about, you know, capturing all the stats and then being able to give, you know, coaches like, you know, a ranking of like, what players do you think are going to go in the draft and who should we think about because of, you know, their body type or their speed or, you know, their skill level. And that's helpful to make a decision. I think a lot of it's about making better decisions, and you know, if the data can help you do that in a better way, that's great. So, I mean that space I'm fascinated by. I, I, I knew at some level it was going to really support and help it, and I'm sure there's a lot more that's going to come in the future. I guess for, for... let's just stick with that topic then. For a sports team that wants to capture this video, like what can they expect? Is that like a couple months and they're able to use something like a ChatGPT with the same kind of UI that they can then put prompts in to see their data in a different way or like what's, what's the user experience like?
Tyler Hochman: Yeah, I think that, that is definitely something, and, and obviously depends on, you know, everyone's going to caveat with saying like, how many uses you want, you want it for. Um, and how specific you want it to be able to understand your style of looking at, at play, your video as an example. So, like if you're a basketball scout um, and you're looking at, you know, footage from all over the world, it's going to do a good job of capturing the generic categories almost immediately. And so then you can prompt it in a similar way you would, you know, Gemini or ChatGPT and ask it questions, and based on the input video footage, you know, what was the, the natural distance of the three-point shots, three-point shot to the basket or, you know, how many two-point shots were in this range and so on and so forth. That's going to do well.
Where it gets interesting is, can you... the feedback loop, right? How is it learning based on the way you as a scout like... what is your IP and how you look at this? How is it learning well? And so that may take longer and that's based on how much you're willing to use it and also how much, how much training data you can give it. Like essentially, and we've, we've, you know, I'm not going to name the team, but what we, what we did is if you could describe what good-looking plays are. You know, you can say like, 'These are not good-looking from the sense that the player performed well, but it has all this, these videos have all the things I like to see in a, in a video for a scout.' You know, it has a three-point shot, it has a two-point shot, it has layups, it has, you know, these types of exercises. Then we can train the model really well to identify those things.
And then when you're qu- when you're asking it, it may be different than the scout to the right of you because they may not need to see all that. They need to see a different set. So the model almost becomes tuned to the way you look at it as opposed to the way someone else would. And so that, that can take more time. So like immediately you can get off the ground in a couple months with a base level model that'll give you the, the core of it. But then to make it tuned to, to your preferences, you know, I would say you're looking at probably six months of, of interaction.
Jake Aaron Villarreal: Yeah. Got it. Really cool. I love it. That's so fascinating to me. You know, when you look at AI in general, a lot of these organizations that are trying it are starting out with like these proof of concepts where it's like a short window of time, take a small little project with the company and see if it actually is going to work. We're hearing that a lot of the proof of concept projects are failing. From your perspective, sounds like you're not failing. What's missing? What are other providers missing, or what are customers asking for that isn't able to be successful at?
Tyler Hochman: Yeah, most of our... it's, it's, it's a really good point because most of our clients, we are not the first time they've tried to use AI. In fact, we're not even generally like the third or fourth time, we're probably the fifth or sixth time. I mean, they've gone through a number of different solutions. And I think the big disconnect that we've seen with these clients is that you need to spend a lot of time at the very beginning estab-... like we, we before we take on any client, we do a full, in addition to just understanding their work processes and their workflow, we do a full demo day where we'll bring in at least you know, five to seven people, some of them professors from different universities that, that we advise with. And like analyze their workflow and their problem set by sitting over them and actually seeing exactly what they are doing. Because a lot gets like, and that's why I started, I said one of the tenants was accessibility. A lot gets lost in the interim of like, 'This is a good idea in concept, but in actuality it is not delivering any type of ROI and from a POC perspective.'
And so that's what I would say the number one thing is, is like level setting and spending a lot of time at the beginning because most of our clients, they have, there's someone in the vice president or president level that's been tasked with a job to AI my business, cut costs, improve efficiency, increase output and use AI to do it. And they're like, 'Fantastic, here are 10 different ways that you're now going to go and accomplish that.' And they see there are buzzwords everywhere. I mean, it's, you know, you can do it from sales, you can do it from, you know, inbound outbound emails, you can do it from an engineering perspective, you can do it from a coding perspective. There's so many different ways to do it. So, like, 'Great.' And then they try them all and they're like, 'Wait a second, that, that has nothing to do with our actual work processes.' The solutions are great for a consumer. They're not, they're not great for a business. So we spend like a month. I mean, before we onboard a client, we'll spend almost a whole month...
[The Real Reason Most Proof of Concepts Fail]
...understanding the problem. And then target a tiny piece of that problem, like a very, very small piece. And that's another thing I think a lot of people bite off more than they can chew.
Jake Aaron Villarreal: Yeah. Yeah. It's fascinating. We work with companies as well. We do a lot of hiring for companies and a lot of AI startups actually. And we have a very thorough process. We call it clarify and verify. And it's you tell me what you need, let me clarify that. And then we repeat it back to you and we verify exactly what it is you need. And we do that over and over until there's no missing gaps in alignment of truly how do we solve your problem. And the companies that are more open to share their knowledge and their challenges and just their transparency, those are the ones that truly want to partner.
The ones that are more like, "Hey, here's a job. Good luck. Go try and fill it. We'll let you know if we like, you know, the people you're sending to us," it's just, that's really not the type of collaboration that gets you to the end zone to keep with the metaphor of sports. So the fact that you're spending a month, that's a lot of time and a lot of investment on both sides. And if you can capture that, you know, and talk their language, I think that's fantastic. That's really... in some ways it's like you have a product and cons-, you're also a consulting service. Is that correct?
Tyler Hochman: Yeah, combination. Yeah, it's a great, it's a great way... to product and we're like implementation specialists to make sure that the product actually works for you.
Jake Aaron Villarreal: Yeah. What usually breaks between a proof of concept and production?
Tyler Hochman: The cl-... I mean the, the, it's, it's, it's what I think, I don't think I'm saying anything revolutionary here. It's scale, right? It's, it's when you know, edge cases will break the product so quickly. And so in my, in, in my experience at least, ironically, the edge cases are what take all the time. Like someone is very good at their job 80% of the time. And 80% of the time because they do the exact same job every day, they can actually do it really quickly. But the question is what happens in that 20% of the time when one thing is out of place and then they have to go and talk to their manager and then the manager has to go talk to this person and like now you've wasted a whole day. And that's what we were trying to solve for. But instead the AI just solved for that bulk 80%.
So they made, imagine you're you know, you're at a, a 70% efficiency, now you're at an 80% efficiency. Great. You got a 10% efficiency boost, but you spent a million dollars to do it. But the real inefficiency came from those edge cases and that was causing you know, 90% inefficiency, and the AI doesn't even solve that. In fact, it breaks because of the edge cases. And so that's actually where I think a lot of the POCs, when they, when they're trying to take into actual products, fail is they should start with the edge cases. It's, can you solve the super annoying thing that the person has to do that derails them for an entire day? Don't try to solve the bulk of their work. That's easy, right? And that's like why you know, that, that's kind of the way we look at it. Like that's the easy, that's the dessert.
Jake Aaron Villarreal: Yeah. Yeah. We've heard a lot that, you know, when you look at a company and how it operates, if you can optimize so they operate, they're going to be, you know, better for it. They might be more profitable or generate more sales or whatever it is. You know, AI versus workflow optimization is different. Is it how you operate, your value chain is, you know, you go from point A to point B to point C and then you get your outcome. Uh, that might not need AI. That might just be you have to tweak how you operate internally. But AI is actually helping really automate stuff. Is that correct? And getting to the data differently and providing access to that information in a more streamlined fashion.
Tyler Hochman: That's exactly correct. And I, and, and I, I love the way you said it because a lot of businesses should not invest in AI as...
[Why Edge Cases Will Destroy Your Tech Integration]
...it stands right now. It's, it's, you know, I'm, I sell AI products and I'll be the first one to tell you like it just, it does not make sense. It's expensive to, like, and I'll give you a great example. So paralegals, if you're familiar with like the legal industry, um, they do, you know, they do the bulk of lawyers' research and one of it was like, one of the first use cases from a business perspective that people were like, 'Okay, we can take the deep research capabilities of AI, apply them to the paralegal position, and then you know, be able to essentially hire way less paralegals because you're going to reduce the efficiency of all these paralegals.' Complete bust. I mean, there have been numerous studies that have come out that have said that the AI either, in some, in some cases actually made the paralegal more inefficient for a number of reasons, both from the quality of output to the accessibility.
And it's not to say that, you know, I'm, I'm not a, you know, there are obviously advantages to AI, but I just think it's a very interesting use case where because of the things I described earlier, the ROI, the accessibility, and the quality of output, the paralegal position, it, it hasn't worked. They haven't been able to do it, and they did not define... it's too broad of a position to make AI work well for uh, and to generate ROI. Now, if, if we were going to approach the paralegal position, the way we would do it is we would want to look at a hyper-specific firm, let's say tax litigation. Then we'd want to take into account a time period, let's say the last five years, you know, the precedent that, that we'll be looking at. And then we'll design an AI that will be specifically targeted to tax memos, you know, a very something that everyone does all the time for the last five years. And like that's what we're going to give you. That I think could work. But like if you're telling me you're going to build an AI that's going to look at all of you know legality for the last 100 years and produce quality outputs, I'm going to take the paralegal.
Jake Aaron Villarreal: Yeah. No, it makes total sense. Which leads me to my next question. What should leaders understand, whether it's for paralegal work or anything else? What should they understand before budgeting or hiring around AI?
Tyler Hochman: Very, very good question. They, you know, I...
[Tech Automation vs. Workflow Optimization]
...think hiring around AI is very interesting. And I do think there is going to be a, a massive amount the ed-... it's going to start from an education perspective first. And we're seeing the transition now even in younger education and high school all the way to college to graduate school, where every position, every degree has an AI attachment to it. If you are a writer, you are learning to write with AI. If you are a coder, you're learning to code in, in adjacency to AI or using AI. So I think there will be from a hiring perspective a very interesting shift in no matter what position you are looking at, AI competency will be important.
And like we even, we, we changed the way we do our performance evaluations where we directly reward people for using AI. Even if they use it incorrectly, like even if it produces bad outputs, we like the fact that they're using it because the efficiency gains is so much from... and this is a software engineering perspective, which I actually ironically think AI works the best. We, AI essentially software engineers designed a tool that can mostly help software engineers, which is what Claude is right now.
Jake Aaron Villarreal: Yeah. Yeah. Is it Claude you use or Cursor or I mean there's a number of tools out there, but like we're in the space of helping find engineers for companies a lot and they ask us you know, "We don't even want to hire an engineer right now if 80% of their code is not done by a, a coding agent." And so, you know, it's okay. The question is, you have to know how to engineer and you have to understand the tooling, but is there one better than the other? And I'm just curious to get your thoughts because we get asked this from our customers a lot of which tools should we be thinking about they should have and you know, they haven't been out that long. So, what's been your experience with that?
Tyler Hochman: You know, it depends. I mean, listen, I would, from a hiring perspective, I would take anyone that uses any tool 80% of the time. I think all the tools are pretty, pretty equal. Now, if you're talking about good to great, there are differences in the tool and it's going to depend on the problem set you're trying to solve for. So, if they can delineate between which tools are better for which problem sets, that's, that kind of takes me from good to great. Um, but I would, I would be okay. I mean, I, I like 80%. I think that's a great, that's a great bar to set because yes, if, if someone is not using an AI agent that much time at least, then they're not taking advantage of, of what exists right now.
Jake Aaron Villarreal: Yeah. Well, talk about hiring a little bit on your side. So, what has worked for you in terms of building teams over the years and specifically now in the AI space?
Tyler Hochman: It's, it's such a good question. And I think there's, there's been a big component of education that, that we adopt from a team building perspective, which is you know, we've been around now call it half a decade or almost half a decade where we've kind of seen the ability for AI to help the workflows of our engineers. And as a result of that, but they didn't necessarily know that at the beginning, and so one of the things we're really, we're really large proponents of is like every Monday our President of Engineering teaches a class. Like it's literally like a Monday class for all the engineers. But like what's going on in the world of AI, different ways you can use new tools. He, he comes, comes up with funny names every single Monday. Like we're, we like anime uh at Fore, and so there's like anime arcs and the, the last week was 'The Rise of the Machines' and it was a specific... so it goes a funny thing, but, but education is...
[Hiring Software Engineers in the Age of Coding Agents]
...a big part of it. And so I actually think that like at least in, as we're in this transition period, employers that can take... and it doesn't have to be you know, if you're not in software engineering but you're in a different field, you know, there's still room for education. And, and both from, from a, a top down but also a bottom up level. Like you may be in a specific industry that employs engineers, but, but you are not an engineer yourself, and I would actually take the time to let your engineers teach you. Because AI has allowed people with an engineering background to, to learn it really well.
I mean we, a really cool example, we work with a fashion brand um, and that you know, the, the highest level of the brand I would say has, has no engineering background. I mean, they're very creative and good at fashion, but we've worked them for a number of years now. And I'm listening to the, the founder talk about, you know, the 'Are you using this, you know, type of AI technique here and here?' And I'm like, 'How does she even know this?' I mean, she has no background. And it just because she's allowed herself to learn for so many years, or not so many years, like the last two years. So, education, I would say that as an employer, top down or bottom up, is, is super important these days.
Jake Aaron Villarreal: I love that. Yeah, it's something that we actually do as a company as well. But you know, we've got AI trainings you know every week about you know different tools and different products and you know, across the business. It's not just you know, one aspect, I mean go-to-market tools like what are you doing for sales, how are you optimizing it? Are your, do your teams understand what they, what they do, are they using them? I think you know maximizing the use of it is probably half of the battle too. You can...
[How Fore Educates Its Team on Evolving Tech Trends]
...tell someone what to do and how to do it, but if they don't do it, it's not going to matter. That's, you know, and then marketing and, you know, recruiting and all across, all across the board. So, uh, yeah, I think education is so critical right now, not just for the companies that are looking to buy and use, but also the companies that are building it. Yeah, pretty incredible. What role are you still playing today that you would most like to hand off as a founder? We're all wearing multiple hats and you've built the team. I know you've got people that are operating in the business, but what would you like to take off and say, "I need someone here to go and run this for me."
Tyler Hochman: You know, when, as a, as a founder with a vision, for a very, very, very long time, the best person to sell that vision typically is yourself.
Jake Aaron Villarreal: Yeah.
Tyler Hochman: It's I mean, it's your vision, right? As a result of that though, that means that you are spending most of your time selling your vision. You're not necessarily even getting anymore to develop the vision or grow the vision. You just have to sell the vision. And so, I would love to be able to, you know, and we're actively hiring and promoting for, for the ability for the team to be able to sell that vision with me and, and the way we work and, and the reasons to use us and to use our products. Um, so that's something that, you know, from a, from a sales perspective, I would be very excited to have se-...
Jake Aaron Villarreal: Yeah, that makes sense. I heard something a long... someone told me a long time ago that you'd be shocked at the vision the founder knows, but to get employees to repeat it, almost no one can do it. And it's because it isn't presented enough, it's not anywhere they can read it, and if it's really where the company's going or headed or what they believe in and no one really understands what it is or even where you're headed, it's tough to continue to, to build your culture. So I like the fact you brought up vision. I think it's very important.
I mean, we have an off-site meeting here in a couple, in next week, two weeks, actually. And all of my employees are saying, "Excited to hear your vision for the next, you know, 5, 10 years," and I'm like, "Uh, really 12 to 24 months is really, like, things are changing so quickly. I could tell you anything for 5 years, but the reality is what's going to happen in the next 6, 12, 18 months." And you know, I heard a podcast. It was Cook over at Apple talking about, you know, I think his first vision for a company that he was going to start out of college or he was in college and he was writing a five-year plan for, for himself and then I think maybe longer 10-year plan. And I think the first six or 12 months actually made sense and anything after that was irrelevant. And so it's like, things are changing so quickly, you have to, you know, take action today. But also, yeah, where are you headed? Where is your north star? So, I think it's important to have that and to be able to totally talk about that a lot as a company. Um, you know, we're just starting, kicking off 2026. How big are you today? Did you get funding and what are you excited about as we continue to head into this new year?
Tyler Hochman: Yeah. Yeah. So, we receive, I mean, we received funding, the last round was maybe two, two and a half years ago. We've since then, we've been cash flow positive. Um so our goal is not to...
[Why Founders Must Constantly Sell Their Vision]
...receive funding again, um, to continue to grow our product suite, um, and then you know potentially in the next call it three to four years look for you know, some type of exit um, if it's possible. I think uh, you just end up in a position where it's, it's... I mean I'm sure, I mean people use zero to one all the time and they use it in such hilarious environments. Everyone zeros to one everything, so I'm not going to use it. [laughter] Um, but you end up in a position where you know, I think uh, exits become, become more attractive just through your ability to generate reach.
Um, but uh, but yeah, we are, we're well, you know, we're cash flow positive. We, the team actually, we are about to open up our second office. Our first office is in Los Angeles in Beverly Hills. Our second office will be in the Bay Area, probably in Palo Alto or Menlo Park. Um, establish...
Jake Aaron Villarreal: Yeah.
Tyler Hochman: Establish a presence there. We have a small presence in New York. Uh, but yeah, it's, it's you know, we're, we're growing.
Jake Aaron Villarreal: Yeah. Really cool. Well, profitability is music to the ears for a lot of founders. So, congratulations on getting there and continuing to grow. I know there's a lot of demand for it. If you're turning companies away because they don't, you know, meet your you know, methodology. Uh you know, you're in a good spot. I mean, it's not like you're out there trying to chase down business. It's, it's coming to you. You're deciding what you want, what you don't, what you can be successful with, and you know, what else do you want, right?
Tyler Hochman: So, don't get me wrong, I'm still sad when I turn down that business. I would like [laughter] the business, but uh I try to make it work.
Jake Aaron Villarreal: Yeah. No, that's, that's, that's great. Well, I'm really excited to see where things go for you, Tyler. And, uh, you know, I'm happy that we had some time to chat about this today. And to all of our listeners for listening, I'm excited you spent your time with us as well. If anybody wants to find you, Tyler, or find your company Fore, where do they go?
Tyler Hochman: We're at foreenterprise.com. No, I think if you Google us, there's, we, we have a decent amount of media on us. So, it's click any of the links and, and you'll find us, and we, you know, look forward to talking with anyone.
Jake Aaron Villarreal: Really cool. Well, as I said, I'm your host, Jake Villarreal, signing out for now. I can't wait to catch up with you all on the next episode. Tyler, the world, take care. If you like what we're doing, don't forget to subscribe. Leave a review on Apple Podcast or wherever you listen and follow us on YouTube where we go behind the scenes to learn what it takes to be a startup founder.
.jpg)
.webp)
.webp)
.webp)
.webp)
