S12 E3: Overhauling Enterprise Deal-Making via Real-Time Dynamic Deal Rooms and Autonomous Investor Analytics with Arsham Ghahramani, Founder & CEO of Ribbon

Speaker A: This episode is sponsored by Mesmo. If your team is collecting large volumes of logs, metrics and traces, but still struggling to get timely answers, Mesmo Can Help Me is an active telemetry platform that processes and enriches observability data in real time before it's stored or analyzed. That means lower data volume, lower cost, and faster root cause analysis across your existing observability tools. To see how it works, get a demo@mesmo.com CodeStory that's Mezmo M.com CodeStory this episode is sponsored by Brain Grid. If you are building with AI coding tools but your features keep breaking, you need to check out Brain Grid. It is the product management agent for AI builders. Brain Grid turns messy ideas into clear specs, tasks and prompts that coding agents like Cursor and Claude can actually build the right way ship real software, not fragile prototypes. Start free at ah Braingrid AI Today's episode is brought to you by Tech Domains and this one hits close to home. Back in 2016 I was building my startup and went hunting for that perfect.com and found next to nothing. So I did what every founder does. Settled. Here's what I wish someone had told me. You're building a tech startup. Just get a tech domain. It instantly tells investors and customers what you're about. Don't overthink it. Secure your tech domain today from any registrar of your choice. This episode is sponsored by Unblocked. Unblocked is the context layer your agents are missing. It synthesizes your PRs, docs, Slack and tickets into organizational context that agents actually understand so they make better plans, write higher quality code, use fewer tokens and require fewer correction loops. If you're running Claude, code, Cursor or any agentic workflow, Unblocked is worth a look. Learn more@getunblocked.com Codestory

Speaker B: we started off just by looking at like what's out there? Is there other voice models that we can use? So one of the things we looked at was Elevenlabs, which is a text to speech model. They have a few other things now but back then that was the main thing that they were there's a voice model you can use off the shelf. We started exploring that and that was our MVP actually. So we had like an end to end working thing there and one of the interesting things that we found was that oh uh, this we use off the shelf models. It doesn't work that well and we started seeing some weird things. One of the examples that we saw was that if you use 11 labs off the shelf every now and then in let's say 1 in 20 interviews, the AI would scream. My name is Ashem Gharamani and um, I'm the CEO at Ribbon.

Speaker A: This is Code Story, a podcast. Ah bringing you interviews with tech visionaries. Six months moonlighting.

Speaker B: There's nothing on the back end who

Speaker A: share what it takes to change an industry.

Speaker B: I don't exactly know what to do next. So many goes to get right.

Speaker A: Who built the teams that have their back.

Speaker B: The company is its people. The teams m help each other achieve. Most proud of our team.

Speaker A: Keeping scalability top of mind.

Speaker B: All that infrastructure was we've been fighting it as we grow. Total waste of time.

Speaker A: The stories you don't read in the headlines.

Speaker B: It's not an easy thing to achieve.

Speaker A: Took it off the shelf and dusted

Speaker B: it off and tried it again.

Speaker A: To ride the ups and downs of the startup live, you need to really

Speaker C: want it not just about technology.

Speaker A: All this and more on um Code Story. I'm, um, your host Noah Laupark and today how Arsham Goramani is helping you stay afloat in your hiring. Talking to candidates, scoring them and helping you hire faster. Arsham Gharamani lives in Toronto but grew up in the rural countryside of the uk. Out in the middle of nowhere on a farm, he was surrounded by tractors, chickens and other animals. Over time he moved to bigger and bigger cities. Until six years ago he jumped across the pond to Canada. Outside of tech, he loves team sports, playing a lot of soccer and starting to get into hockey. When he transitioned to hockey, he immediately enjoyed how fast paced it was and how many tactics carried over from soccer in the past. Arsham was the head of machine learning at a prior company. His now co founder and he worked closely together and they were both pressured to hire good people. Quickly they started to notice some patterns in the hiring market, including the regular submission of AI generated resumes. This is the creation story of Ribbon.

Speaker B: So high level on Ribbon is that we are an AI interviewer. So what that means is it's primarily a voice AI and it can interview people. Essentially in place of an initial screening interview, you would speak with our AI. Usually when people think of a voice AI, they're thinking Alexa or Siri, but we're a next generation of that. So our AI can show emotion, it can ask really complex questions, it can research you on the Internet. Has read the last blog post you wrote, has read your LinkedIn, has read your resume, able to ask really complex follow up questions and so you essentially go through one of these interviews with Ribbon. And then after the interview we help the recruitment team understand how did the interview go. We helped them score it, we helped them analyze it, and ultimately help them hire someone. We essentially operate on, I'd say, both sides of the spectrum and we help the candidate. And ultimately what we're trying to help candidates do is to get a job faster. And then on the other side, we also help companies by helping them hire faster and ultimately hire better as well. So we have been around for two years, only actually had a product out for the last year or so because we spent the first year just focused on extremely deep tech to make the voice AI work and helping extremely large enterprises out there now. So the origin of Ribbon actually starts at another company called Ezra, where I was the head of machine learning there. And that was where I met my co founder, Dave. So he was the head of people and talent, I was the head of AI or head of machine learning. And we worked really closely together there because we were hiring a lot of people. We were under a lot of pressure, to be honest, just to hire extremely quickly because one of the fastest growing teams in the company was mine. We were an AI health tech company that was focused on using MRI to find cancer in people. As the head of AI there, that was like one of the key roles because we would heavily use AI in looking through these MRI scans. And so there was a lot of pressure from like our investors, the rest of the team, to make sure that my team was operating really well. And one of the key parts of that was just hiring. So I met this guy called Dave. Um, he actually hired me into the company. We initially honestly just worked together really well as friends. We would go to bars together. We were under the same kind of stress together. After a couple of years of working together, we started noticing some interesting things about the way that we were hiring and what was happening in the hiring market as well. And one of them was that, ah, a lot of the applicants that were applying into this company were starting to apply using AI generated resumes. And so this was a few years back where this was more of a rare thing. Now if you speak to any, let's say head of people in town or any kind of recruiter, they will tell you basically all the resumes they receive by AI generator. And we started getting intrigued by that. We were a little bit confused at first, you know, why are we getting a lot more applicants than before? And then why the applicants are great, are actually getting like, why do they look so fake? We started to Just think deeper about that. This seems like a big thing. What's the implication of that? And we started speaking to other hiring managers, other recruiters and what we were hearing on the ground was that ah, essentially most recruitment teams weren't looking at resumes anymore because they decided there's not a lot of value in that because they're all just fake now. And it's so easy to create any resume that's really highly tailored. You have these apps that will let you send a thousand applications in an hour. So it's just really easy to send an application now. And we started asking them like, okay, if you're not using the resume, what is the signal that you're using? Like how are you filtering people? And they said, we're honestly having to resort to the old fashioned way, which is we're just interviewing and we're interviewing more people than ever. And that's the only signal left that was interesting to us because uh, we all know that intuitively, right? If you interview someone usually within five or 10 minutes, you feel like you have a fairly good read on like, how does this person operate? What are they interested in? What are the tools and skills they've used? There was this point at which we sat down, I remember we, we were thinking and said to each other, there's going to be more interviews in the future. That's the only signal that is left because all this application stuff is just fluff now. And we started asking each other, what's the tech that allows companies to interview more people? And we explored a few different ideas. One of them was like, can you help people schedule more efficiently? So you know, instead of doing eight interviews in a day, is there a way that we can pack 10 in or something like that? And that was okay. We actually had a prototype of that, but then realized if you execute that really well, maybe you get a 40% increase in the number of interviews you do in a day. I had a stint where I was working at Amazon and so I was lucky that to have seen some of that frontier voice tech and was pretty excited by it. And so the kind of next step from that was essentially like a natural progression where we thought, okay, we are, uh, trying to find ways to make interviews more efficient, but what if we just built an interviewer? What if there was an AI that could interview these people and then that would unlock two things. It would mean that as a candidate you don't have to just interview from 9 to 5 anymore, right? Like on the terms of when a recruiter is available like uh, you can interview whenever you want and then on the company side they can interview everyone that they want. That was the story of like how we started getting obsessed with the space. And we set out on this so far two year journey to create the voice AI that will interview millions of people.

Speaker A: Let's dive into what you would consider the MVP for ribbon then. It's that first version of the product you built. How long did it take to build and what sort of tools were you using to bring it to life?

Speaker B: So that's where it gets technically interesting. Is the MVP there? We started off um, just by looking at like what's out there. Is there other voice models that we can use? This was a while back, but actually a lot of the tools are pretty similar. So one of the things we looked at was 11 labs, which is a text to speech model. They have a few other things now, but back then that was the main thing that they were there. It's a voice model you can use off the shelf. We started exploring that and that was our MVP actually. So we had like an end to end working thing there. And one of the interesting things that we found was that, oh, uh, this we use off the shelf models, it doesn't work that well. And we started seeing some weird things. One of the examples that we saw was that if you use 11 labs off the shelf every now and then in let's say one in 20 interviews, the AI would scream and that sounds like a funny thing. And we, I remember the first time I saw this, I was laughing like, oh, the AI just screamed. But then we were thinking like, if we deploy this to like some of our customers and one of our customers for example is one of the largest automotive manufacturers on earth, right? If we sell this to them and they interview 10,000 people and then some number of them there, there's like screaming that will get in the news. That's going to get us in trouble. It'll get them in trouble. So we had this moment where we're like, okay, the MVP where we just use off the shelf stuff, this is not going to work. Like where do we go from here?

Speaker A: That actually answers my next question on maybe one of those, the hard decisions you had to make, um, around the product and how you built it and it sounds like you went forward, you know, in MVP kind of mindset. Chose 11 labs and then realized that wasn't going to get the job done.

Speaker B: Yeah, exactly. And it was, look, we had something that looked like it worked, that was demo able initially. But then we super quickly realized, like, we need to improve this. The screaming thing, that's not okay. And the other thing that we found was that, uh, if you use off the shelf stuff, it just had latency that was too high and that wasn't acceptable because you would just, it would lead to an experience that felt like you were turn taking. So like you would say something, there'd be a pause, then the AI would respond and it just didn't feel like a fluid conversation. But we had something that we could demo, right? And we, we started demoing that out there.

Speaker A: Today's episode is brought to you by Tech Domains and this one hits close to home. Back in 2016, when I was building my own tech start up, I went on the hunt for that elusive dot com, looked high, looked low, and guess what I found?

Speaker B: Nothing.

Speaker A: What I did find cost me an arm and a leg. So I did what every founder does under pressure, Threw in extra letters, settled for the less than optimal name. And here's what I wish someone had said to me back then. Noah, you're building a tech startup. Just get a tech domain. Tech M startup, Tech Domain. It could not be more obvious. It tells investors, customers, and anyone who looks at your website, really, that tech is at the core of your build. And I've kicked myself plenty since, especially when I see the clean and sharp names. Tech companies have landed on Tech nothing. Tech1x Tech, Aurora Tech, CES Tech, Ultra Tech, Alice Tech, Neon Tech, Blaze Tech, PI Tech. You get the idea. So take it from someone who learned it the hard way. If you're building a tech startup, don't overthink it. Secure your tech domain today from any registrar of your choice. This episode is sponsored by Brain Grid. Building with AI coding tools is exciting until the moment things start breaking. You ask for a small change and suddenly three other features stop working. AI gets confused, Mrs. Edge cases and loses track of your intent. The problem is not code generation. The problem is planning. That is why Brain Grid exists. Brain Grid acts as your product management agent. It writes clear specification maps, UX flows, asks the clarifying questions you forgot to ask, and breaks big ideas into engineering grade tasks that AI coding tools can build reliably. It guides Cursor, Claude, Code Replit, Windsurf and others so they deliver features that work and keep working. Founders use BrainGrid to build real AI native SaaS products without a, uh, technical background. If you want reliable features instead of fragile prototypes, try Brain Grid for free at braingrid AI. That's braingrid AI. Let's Go from that point, you, you made that decision, you started demoing the thing, you calling that your mvp, but you know, you're going to have to build something yourself. How did you progress and mature the product from that point? And I think to wrap in a box a little bit, what I'm looking for is how you built your roadmap, how you went about deciding that, okay, this is the next most important thing to build or to address with ribbon.

Speaker B: So we started breaking down into what's, what is the end to end of, uh, a good AI recruiter look like. And then what are the pieces that we don't have today that are missing or that we have that are just not good experience? And so with that initial mvp, one of them was, for example, the latency I just mentioned, right? So you say something, the AI will take too long to respond. So we thought, okay, let's improve that. The other thing was, okay, in some percentage of these calls, the AI will scream or show the wrong emotion. How do we control that better? Another one was we need really deep analysis of the candidate after the interview, and it needs to be available roughly a couple of minutes after the interview's over. How do we achieve that? And so one, one by one, we took those and then we essentially ranked. So what's the top issue right now that if we solve, will essentially differentiate us over anything else that's out there. And we landed on the actual control of the voice AI experience of the screaming issue that I mentioned, that was our top thing. And so we spent actually roughly the next nine months just focused on that. And we started building out a few different aspects of the stack all internally. Right. So if you think about our AI recruiter, uh, when you actually do one of these interviews, what's happening in the background is that there's around eight, eight different AI models that are all running in real time. Some examples like you have the text to speech. You also have speech to text. Where we are. We have another model that's running in real time that we call our compliance engine. It's making sure that you don't say anything that's like off limit. And the AI doesn't say anything that's off limits. We have another model that's looking at predicting when you're actually finished with a sentence so it's the other person's turn. All these things are running in real time. And so we essentially took each thing and thought, is this something that we should build in house or is this something that we can use off the shelf? And for each of those, it was like a bit of a different trade off. If we just take something off the shelf, sure, that's, that'll mean that we can get up and running in a couple of weeks, but then if we use the off the shelf thing, is it good enough? And so we essentially landed on. There were three internal pieces where we were like, these are things we have to build. No matter how hard it is, if we don't build them, it seems like no one else is actually solving this for an interview use case. And so we just came to that conclusion, like, you know, this is going to be tough, but if we don't solve it, then we don't have a genuine innovation here. And spent the next nine months really just grinding on those three ML models.

Speaker A: So then I'm curious about team, right? To do something as, you know, as big as this, this is a huge thing you're building and a big problem you're solving, you got to have the right people. How did you build that team and what you look for in those people to indicate that they're the winning horses

Speaker B: to join you, the team is the most important thing. We're actually still a very lean team. Like even today we're actually only 10 people and for some rough context, we're doing around a million interviews a year now, so still very lean. The core team actually created, like the initial version of Ribbon was actually only four people, including myself and Dave. And so we've always believed in the idea that small teams can achieve outsized wins. If you're structured in the right way, you have the right skills and the right motivations and all that. For us, the most important thing is that we hire fairly senior people who are still very hands on, right? So they're actively in whatever their last role is. For example, on the product team, you should be shipping code every day, you should be actively involved in designs every day, should be on, on, on the ground, actively making decisions all the time. And you should have been involved in shipping something at least at some scale, right? And that was something that I like learned the hard way, especially at previous companies, is that if you hire people that haven't seen things at scale, you end up kind of building things that just don't work as you scale. And we always had it in our minds that one day we will be doing what we are today, a million interviews a year. Next year we think we can get to around 100 million interviews a year. So when you're on that kind of trajectory, you have to really think about what happens if we if we 10x the number of interviews we're doing, what is, what's the next bottleneck, all that kind of stuff. So we've always hired for extremely agentic people who can take something and run with it, have experience shipping things at scale, and have really good product sense. So that's something that is really hard to interview for honestly, and you only really get a sense for that when you actually work work with someone. But having good product sense to us means that there's each day, even if you get handy, the perfect set of plans for this is what you need to build next. Each day you still have to make a hundred small decisions about where does this go, where does that go, what's the size of this, how do I implement this, how do I engineer this, those hundred little decisions, like you can't have someone hovering over you and helping you with each of those. And so we've always lent towards people who just have a good sense of what is a good product and how do you solve a problem for a user?

Speaker A: This episode is sponsored by Unblocked. Your coding agents have access to your code base. Maybe you even connected other tools via mcps. But access doesn't mean context. Agents m can't reason across mcps. They don't know your architectural decisions, your team's patterns, or why the API was shaped the way it is. So agents look in the wrong place and deliver bad outputs. Then you spend time correcting more loops, more tokens. Unblocked is the context layer your agents are missing. It synthesizes your PRs, docs, Slack, and tickets into organizational context that agents actually understand. So they make better plans, write higher quality code, use fewer tokens, and require fewer correction loops. If you're running Claude, code, cursor or any agentic workflow, Unblocked is worth a look. Learn more@, uh.getunblocked.com Codestory this episode is sponsored by Mesmo. If you're responsible for reliability, performance or platform architecture, you already know the problem. Telemetry volume is growing faster than teams can manage it. Mesmo addresses this by moving observability upstream instead of storing everything and asking questions later. Mezmo processes telemetry in motion, filtering, transforming and enriching logs, metrics and traces before they reach your observability backend. The result is cleaner data, reduced ingestion costs, and faster root cause analysis. Using the tools you already rely on, Mesmo integrates with platforms like Datadog, Dynatrace, and open source stacks, giving teams more control without adding operational overhead. This is especially useful for platform engineers and SREs supporting complex distributed systems where context and speed matter. To see how active telemetry works in practice, get a demo@, uh, mesmo.com codestory that's M Mezmo M.com codestory M. My next question is around scalability. It'll be interesting to hear what you have to say on this piece because scale is obviously foundational. Scalable technology is obviously foundational, what you're building. I'm curious how you approached it in the beginning and if there's been interesting areas where you've had to fight it as you've grown.

Speaker B: One thing about scalability is you will never know all of your bottlenecks, right? So, like, you can't lose sleep over that. There's two ways to like, scale, right? One is that you just decide we're going to have extremely fast velocity on how fast we build stuff, and as we hit scale limits, we will fix them, right? That's a bit of a gamble on, like, your team, essentially, that you, you are faster than you will scale. The other is that you just regularly test things for an order of magnitude greater scale than what you're doing right now. And we've essentially done a bit of both. One of the things that we've developed in house is this, like, system that allows us to test and scale interviews at scale. So if we're doing a million interviews a year right now, we've actually tested all the way up to 10 million. So we can easily have the scale to that if we needed to tomorrow. The other aspect for us is that we actually use a lot of serverless technologies wherever we can. And that's just like, we're just huge beneficiaries of, of the modern tech stack there. So wherever we can. And it's not an issue. We've used serverless, were huge uses of AWS Lambda, for example, and that's, uh, helped us a lot.

Speaker A: So as you step out on the balcony and look across all that you've built thus far with ribbon, what are you most proud of?

Speaker B: What I'm most proud of is the actual AI interview experience itself and how it's helping candidates at the moment. I think when a lot of people look at us, uh, and what we do, they see us essentially as a recruitment tool, right? They see us as like another HR tool. And often, honestly, I think a lot of people see us as shifting the balance of power towards recruiters. If you speak to candidates, you see a, um, very different story. And I'LL explain like why unintuitively. Um, we help a lot of candidates. If you speak to the average candidate who is in the market, they're looking for a job for on average around 17 to 90 days. What you hear from a lot of them is the most frustrating part of the experience is that they're just getting ghosted all the time. Like they'll apply for a role, they don't hear back or they have an interview or two, then they get ghosted again. There's no feedback on why they were rejected or they don't even know actually concretely if they were rejected or not. One of the things that we built is that after each and every interview we will actually give automatic feedback to the candidate. So we don't wait for the recruiter to give it. We will give automatic feedback on, um, this is how much of a match we think you are. So this is like independent feedback outside of the company who's ultimately hiring. It actually comes directly from ribbon and it's just one little step towards you not always getting good. One of the reasons why that's what I'm most proud about is because ultimately we want to help both sides, right? I think we can get to a future where you can hire anyone for any role in around 48 hours. And as a candidate you don't have to jump through a million hoops and go through all these interviews and then eventually get rejected and ghost. And I think there's a lot that we can do to improve that experience and I'm pleased with this kind of little part, ah, of that whole ecosystem that we're doing so far.

Speaker A: Let's flip the script a little bit. Arsham, tell me about a mistake you made and how you and your team responded to it.

Speaker B: One of the early mistakes we made, we were trying to over affect some of the things that we were building without really getting validation from the market. So I remember that some of the early things that we did. One of the features that we built, for example, was this feature that would help you as a recruiter understand what are the top questions to ask this candidate in their next interview. Right. So they would like typically do a ribbon interview first and then after that they would do kind of like a regular interview with someone on the team. We spent way too long perfecting this feature of like, how do we help recruiters understand like what were the questions that weren't covered in this first interview? What are the ideal next questions? What's other context that we can pull in? And it's actually an amazing feature. It's loved by a lot of people now, but we just spent a bit too long on it and I'd say all the areas where we've ever made mistakes on the product side, the common theme tends to be you just take too long to ship something. And what I always think about is the action results in data often. And if you're not sure whether a feature is going to work or if you're not sure how people will react to it, the best thing, honestly is just to get it out there and you just ship the absolute simplest thing. It should be an MVP or even simpler than that. You can fake it if you want, even. And it should just give you some initial data on what's the reaction, is this useful or not? And so I'd say that, yeah, the common theme across all of our biggest mistakes have been not realizing that and taking too long to get that initial data.

Speaker A: Okay, Arsham, let's move forward. So this will be fun to hear the passion in your voice as the, uh, as the creator and the founder. Tell me what the future looks like for Ribbon, for the product and for your team.

Speaker B: What we're really excited about is compressing the hiring cycle, right? The average hiring cycle right now is like 60 to 90 days for most companies. We think it can happen in 48 hours. And we think a lot of recruiting is information exchange. So you have a candidate who knows things about their skills and tools that they've used and how they work. You have a company who knows nothing. And the hiring process is exchanging that information to the point where they're both happy to hire. Now, we think what the future looks like is that you will eventually actually do one single interview with Ribbon. It'll be likely the hardest interview you've ever done. It'll be a very deep interview. It could be 60 to 90 minutes. And it'll cover almost everything, but not everything that the company would be interested in about you. And so if, and that that will cut out a lot of the hiring cycles. So if you look at like a typical, they're like software engineering hire, there's around six or more rounds if you include all the rounds. We think that the future with Ribbon is that we can reduce that down to two rounds. One extremely deep interview with Ribbon and a second interview, they'll be directly with your hiring manager. And that's it. And so instead of having all these rounds where you know, in each interview, the starting questions are always the same, right? It's things like, hey, tell me more about yourself. And you're always giving a recap of the same things over and over.

Speaker A: Right.

Speaker B: And there's just so much wasted time in that. We think we can get to a point where essentially for almost any role it's two interviews. One that's with ribbon, extremely hard, extremely deep, but can be reused across many companies. And a second interview just with the hiring manager. Whole thing happens in 48 hours and everyone's much happier. Right. Ah, as a candidate that means that you're not on the market for that long. And as a company it means when you need to hire someone, you can find them extremely quick. I think that's the future that we're really excited about because I think if we're going to unlock that, and I think we're about two years from that, I think the gains across the economy will be huge because there are so many people today that in roles they don't enjoy or who are in roles that are not using the best of their ability. And a lot of the reason why they're they'll end that role is because there's this activation energy where they are just not sure if they should leave because they're not sure how long it'll take to find that next role. Now I think we can make it really easy to actually find the next role. A lot of people will move out of the roles that they're in right now that are suboptimal and they'll be in a role that's a much better fit. I think economically we'll see huge gains from that.

Speaker A: Let's switch to you Arsham, who influences the way that you work. Name a person or many persons or something you look up to and why.

Speaker B: One of my main influences honestly is some of the greatest scientists who are out there, right. Someone who I think of, uh, for example, Francis Crick, one of the co discoverers of DNA. He also did it with a team including Rosalind Franklin. I have always been scientifically minded, right? That's the world that I started in. I started off as a scientist and I think a lot of approaches out there, especially in the product world, in the uh, entrepreneurship world as well, could actually benefit from a more scientific mindset. I don't mean that necessarily in like a data way because even as a data guy I think sometimes data can mislead you. But more in the sense of the pursuit of knowledge and discovery and using those things to like improve yourself, improve the way that you work, uh, and improve the way that we understand things as well. That's just one Example. But I would say, honestly, in general, I take a lot of inspiration from scientists, whether they're famous or not, and how most scientists out there today are really doing it for the love of the pursuit of knowledge. Something that's always fascinated me.

Speaker A: Last question. So you're getting on a plane, you're sitting next to a young entrepreneur who's

Speaker B: built the next big thing.

Speaker A: They're jazzed about it. They can't wait to show it off to the world and can't wait to show it off to you right there on the plane. What advice do you give that person? Having gone down this road a bit,

Speaker B: the key advice that I would tell any entrepreneur is this is a long game, right? In almost any area that you enter into, you'll see competitors enter and then exit. You'll see investors enter with a lot of interest and then they might exit that eventually. Like we saw that with like crypto, for example, in the past. I'm m sure they'll happen with AI at some point. The sub parts of AI, uh, as well. I'd say that the top advice that I'd give is really play the long game. Know where you want to get to in 10 years or so and then just look towards that and ignore a lot of the noise in the meantime. A lot of people, I think along the way will try to encourage you to make shorter term decisions in some sense. But I think if you're always playing the long game, it's hard to lose because people, other people will just naturally lose interest in your area if they're competitors. And you can just have an advantage in playing the long game and being the patient person who has a longer term vision.

Speaker A: I think that's fantastic advice. Well, Arsham, thank you for being on the show today. Thank you for telling the creation story of Ribbon.

Speaker B: Thanks a lot.

Speaker A: M. And this concludes another chapter of Code Story. Code Story is hosted and produced by Noah Laphard. Be sure to subscribe on Apple Podcasts, Spotify or the podcasting app of your choice. And when you get a chance, leave us a review. Both things help us out tremendously and thanks again for listening.

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Speaker B: of $45 for three months, $90 for six months or $80 for a 12 month plan required $15 per month equivalent taxes and fees Extra initial plan term only greater than 50 gigabytes me slow when network is busy See Terms Most

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