How a Founder Uses AI to Improve Hurricane Tracking and Risk Assessment

I sit down with Eduardo Siman, founder and CEO of Worldsphere.ai, a Miami-based company using computer vision to better understand hurricanes and improve risk assessments, insurance intelligence, and evacuation decisions.

Eddie shares how his path into AI started through enterprise software, angel investing, and an obsession with computer vision before eventually leading him into hurricanes and the mission behind Worldsphere. 

We talk about how Hurricane Andrew shaped his personal connection to this work, how AI can translate satellite imagery into hurricane wind fields, and why integrity, human oversight, and scientific rigor still matter in high-stakes AI. 

If you’re interested in AI beyond chatbots, this episode is a fascinating look at climate, risk, enterprise software, and the future of building with purpose. 

🔥 Topics We Cover:

• How Hurricane Andrew shaped Eddie’s obsession with hurricanes and weather 
• What computer vision actually is in plain English 
• How AI translates satellite imagery into hurricane wind field predictions 
• Building AI for weather tracking, insurance risk, and disaster decision-making 
• Why human oversight still matters in high-stakes AI systems 
• The rise of weather AI and what is accelerating in the field 
• Vibe coding, enterprise software, and the future of programming 
• Why resilience, mission, and deep industry understanding still matter 

Tools & Resources Mentioned in This Episode:

• Weather + Forecasting Data:
   ERA5, ECMWF, WeatherBench, satellite imagery, hurricane wind fields 
• AI + Development:
   Computer vision, Claude Code, Codex, Replit, Lovable, vibe coding 
• Business + Enterprise References:
   Flatlogic, SAP, NetSuite 
• Concepts + Examples:
   photogrammetry, 6D.ai, Pokémon Go, The Lean Startup 

Connect with Eddie:

• Worldsphere.ai 
• LinkedIn: Eduardo Siman 

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"How I AI" is a concept and podcast series created and produced by Brooke Gramer of EmpowerFlow Strategies LLC. All rights reserved.

Transcript

Eduardo Siman 0:00

When you are using AI, I think it's so important to be upfront. About what is being done with ai, what kind of AI, and what you're feeding into it. In other words, what are you training it with? And I think that AI ethics is just this huge area that many founders don't think about because they're just so focused on trying to grow revenue, grow users. But for us, with our main goal being we want to help people, we want to help anyone in the world have a, a better outcome when faced with severe weather, we have to think about it, right? We don't want to create a situation where our AI models are actually gonna make somebody call for an evacuation when there shouldn't be one.

Brooke 0:43

Welcome to How I AI the podcast featuring real people, real stories, and real AI in action. I'm Brooke Gramer, your host and guide on this journey into the real world impact of artificial intelligence. For over 15 years, I've worked in creative marketing, events and business strategy, wearing all the hats. I know the struggle of trying to scale and manage all things without burning out, but here's the game changer, AI. This isn't just a podcast. How I AI is a community, a space where curious minds like you come together, share ideas, because AI isn't just a trend, it's a shift, and the sooner we embrace it, the more freedom, creativity, and opportunities will unlock. Hello and welcome back. Today I'm joined by Eduardo Siman. He's the founder and CEO of WorldSphere AI. It's a Miami-based company using computer vision and AI to better understand things like hurricanes, improve risk assessment, and support smarter evacuation and disaster response decisions. Eduardo's path into AI is anything but typical from consulting with the US Navy and working in enterprise software to private wealth at Goldman Sachs to angel investing in some of the biggest names in tech. His story is really about curiosity and a deeper desire to build something that truly matters. In today's conversation, we talk about how Hurricane Andrew shaped his mission, what computer vision actually means in layman's terms, we get a bit into vibe coding and the changing future of software. He shares some of his hard lessons from building a self-funded AI company and why things like community matter now more than ever. If you've been curious about how AI is being integrated, especially in industries like climate, insurance and scientific research, this episode is for you. All right, let's dive in. Eduardo, thank you so much for being here on How I AI, it's such an honor to host you today, and where are you joining from?

Eduardo Siman 2:55

I am in Miami, Florida.

Brooke 2:57

Oh, okay. So we're both in Miami. Well, perfect place to be if you're in the industry of hurricanes, and this is what I love so much about my podcast. It's such a dynamic group of people that come on the show. I have not had the opportunity yet to speak about AI and weather or disaster prevention and how we're using AI for good in these industries. So let's go ahead and kick it off. If you could share a little bit about your background and how you found yourself at the intersection of AI today.

Eduardo Siman 3:31

That's a great question. I mean, my, my background is quite unusual in terms of people, who've become AI founders, but, essentially you have this kind of overriding arc of becoming obsessed with AI and then also becoming obsessed with, with hurricanes and weather and trying to figure out how to bring that together. But in general, you know, I, I had a the traditional kind of trying to move up the corporate ladder background. I, I did my, under my undergrad, at University of Miami. focused a lot on mathematics. Um, and then right after that, I spent quite a bit of time as a, as a consultant doing software implementations. And I was really lucky because I, I, I had an opportunity to work with what's called SAP, which is a, like, very, very well known and use software all over the world. So that gave me the chance to get a job consulting with the US Navy and working out of Washington, DC. And that was an incredible experience, kind of implementing a enterprise system for them. and that kind of led to where it usually leads, right? You're at Deloitte Consulting, you've been there a few years, and so you go to business school. So I went to Columbia Business School and then. typical thing happened, which is, everything there is about finance. So I kind of got caught up into the whole finance world. It's very mathematical, so I, love the math part of it. Got a job at Goldman Sachs and worked there for a few years doing private wealth which was really, didn't match kind of like my mathematical inclinations, but it was just such an amazing opportunity right. To, to work at Goldman. And then kind of realized, you know what? I should probably go back to the world of IT and software implementations. And so for about nine years I, I was leading IT teams, so, as a CTO, as an IT director. And then eventually, kind of got to the point where I was like, burnt out in the corporate world and just said like, I don't wanna do this anymore. And I had been angel investing in ai. I had been, I had been learning so much about AI and I felt like I'm just not gonna be able to do this at work. I'm not gonna be able to do what I wanna do at work. And so that's when I in 2022, I was like, okay, I'm just gonna take the leap and do something different. Like actually try to do something that makes an impact. And that's where the idea of, of using AI for for hurricanes came from.

Brooke 5:43

What an incredible background to get you where you are here today. So let's tap in deeper to that moment where you ended up back in IT. When did ai really start to get its hooks on you and you feel like this is something you could actually start to build with?

Eduardo Siman 6:02

That's such a great question. I think, I think of the year 2016 really as being like the inflection point because like when I came back to the world of IT, corporate enterprise IT, which was around 2013, I. AI wasn't really on my radar. But around 2014 and 15 I was invited to these meetings which were the Walmart CIO council. So, one of my heroes her name is Karen and Terrell. She was the CIO of Walmart at the time, the Chief Information Officer, and she invited the head of it. various different companies that were big Walmart suppliers, and my, my company was a big apparel, suppliers, a big, apparel manufacturing supplier. And so I got to go and learn from her, four times a year and she started talking back in, in 2013, in 2014, about, Hey guys. machine learning thing is a big deal and at Walmart we're starting to think about how we can use it and you should be thinking about it too. And she also started talking a lot about data visualization and she was like, being able to visualize data and like beautiful dashboards and understanding, how all of this data that Walmart has in one place like this is gonna be really important. And she was a force behind creating like these really cool rooms that they have at Walmart, which are like these decision rooms where they have these, all these TVs with all these different type of visualizations. And I thought to myself like, I really, I really have to do this right? Like this, this has to be part of what I'm doing. And that's really what started making me think like. I need to figure out a way to be part of the AI world. And I, I knew that in the apparel industry, in the clothing and fashion industry, there was only so much that I could do. So that's when I went out and said, well, maybe I can find some companies that would let me be a very small angel investor. And that way I could learn. by, being in the meetings and, and understanding how things work. And yeah, that's where the kind of the let's research AI part started. Let's try to figure things out and see what this whole AI thing is about. And that led to, to computer vision. So somehow, that kind of inspiration from from uh, Karen led me to just get really into the world of, of computer vision, which is like this one very specific area of ai.

Brooke 8:28

Computer vision, so fascinating. First of all, wow, what an incredible space to be in Walmart on the early side of ai. I, I remember first reading. About how these big grocery department stores would use AI to have like drones or something just fly over and that's how they would do inventory. They would just electronically scan and be able to do it quickly. That was one of the first use cases I remember reading about AI and I was like, whoa, that's incredible.

Eduardo Siman 9:00

It's absolutely true. Um, One of the things that, that we used to talk about was like how, Walmart had the ability to look at a parking lot and figure out how many people were at the store based on satellite imagery of parking lots. Um, They also were using, they were using all the cameras that they had in the stores to try to understand why certain products were not were not being replenished. So, yeah, this kind of stuff, they were doing four or five years before. I remember one of my favorite conversations with Karen was like they were testing out an emotion testing product, right? So they would, Karen was great at having all like, startups come in and, and show her like, okay, this is what we have. One of them would tell you like, customer is happy, customer is not happy based, just based on, a, a, a video capture. And I told her like. This is so creepy. Like I can't imagine like finding out that I went to Walmart and, and, and someone at Walmart corporate is trying to find out, am I happy? Am I not happy? And and I remember she told me, she was like, 15 years, you're not gonna think this is creepy And I was like. Wow. And that was, and that was back in 2000, I don't know, 2015 or so, and, and now, I don't think it's creepy. It sounds pretty normal now to, to do like, emotion detection and for that to be kind of part of the way that, that you would expect shopping to happen. So, yeah, you're absolutely right. I mean, Walmart is the biggest company in the world by revenue. So the things that they do are pretty amazing and and I was really lucky that I had a chance to learn from the leaders there.

Brooke 10:37

I can only imagine what it would be like to be in a Walmart decision room as you described it. So let's fast forward to your work now as the founder and CEO of world sphere. Ai. How do you use AI for your work now? What does your research look like day to day? Both as a founder and someone who has studied meteorology, if you could just share all about how you're using it now day to day.

Eduardo Siman 11:03

So I mean our, our number one goal is to try to make an impact, right? So, and, and that's why the company was started. And, and so in order to have sort of an impact based company, you have to think about, well, how do you, do you get to a point where you're going to be able to raise enough money either by investing or by revenue that you can continue making an impact over a long period of time. But also, like you, you really do need to, to think about what exact work or what exact research could lead to better decisions, right? So, um, have been focusing a lot. two different kind of work streams. One is creating software that can help insurance companies to provide their customers with a better risk intelligence. So basically being able to say, there's gonna be a tornado. There's gonna be a hurricane, there's gonna be a wildfire. Here are all the different properties that are gonna be affected by hail and all that kind of stuff. And then our other focus, which is kind of like the real heavy AI focus is getting satellite imagery and churning that into some kind of wind field. So, and this is something that was missing the National Hurricane Center. And in general, hurricane researchers were missing the ability to have a really accurate windfield of a hurricane when it was out in the ocean and there was no way of measuring it from an airplane or, any other kind of measuring equipment. So this was a, a gap that they had that they told us that we talk to folks at the National Hurricane Center and they said, we have this gap. We need to be able to figure out what the windfield is, not just the maximum wind, but the full windfield just from a satellite image. And that's when, we really started thinking, well, how do we use computer vision to figure that out? And, and it's been a few years now and, and we've been getting better and better and better. Our focus has been again making an impact. And because of that, we've been really, really serious about the science and we don't make any claims that we can't, prove scientifically. And I think that it's probably hurt us on the business side because like a lot of other people are willing to say like, Hey, we have the best model in the world, or We're gonna, we're gonna give you the best prediction of this, and without any kind of evidence. And we just. We haven't been willing to do that because we feel like if we do that, then the end goal, which is to have an impact, will suffer because somebody will adopt our product and then they won't actually be able to have the impact that they wanna have because it's not gonna be right or they're not gonna know how to use it, they're not gonna know, or the air might be. So we just, we wanna do it right. And that's why we've been self-funded up to now. Because I haven't wanted to have, a VCs kind of telling me, no, you have to do it this way. So,

Brooke 13:58

You touched on something important here, which is integrity in the AI space and hype marketing. So maybe we can dive deeper into that in a minute. But first I wanna touch on what you mentioned, computer vision. So for people who are not technical, how would you explain like what your AI is actually seeing and what it looks like and and satellite imagery and how that turns into real insights for you?

Eduardo Siman 14:25

Yeah. I love that question because computer vision, obviously I'm very passionate about it, and when you first hear it, you, you might think like, oh, this like. A robot's ability to like get around town and like deliver food or something, which is actually part of computer vision. But I'll, I'll start at a very high level. Like you break down the things that like a robot or an AI can do, it can see, it could talk, it could hear, right. And the seeing part both looking at things and creating things. Sort of the picture part, the video part, that's what traditionally was called computer vision. Now there's a lot of AI kind of hype terminology that you might hear, like generative AI or like, image to video or whatever it might be. But for many, many years before the hype, that was just, what it was referred to as as opposed to audio or language, it was computer vision. So does, what does it do? Right, so essentially it's a pattern recognition system. So when we're training our ai, right, so in the, in the, in the AI world, training means like teaching the AI how to understand patterns, right? What we do is we say, okay. You have two sets of images. On the left. You have satellite images, so this is what anybody would be able to see by going to like a weather website and or go or turning on the news and be like, this is what the hurricane looks like from a satellite perspective. Then on the other side is. The wind, right? So what we know the wind was for that hurricane.'cause this, this is historical, right? This is something that has already happened. In other cases we might say, here's a simulation. Of what the satellite image looks like, and then here's a simulation of what the wind should look like. And what we do is that we put that into, into the model and essentially it just looks at like thousands and thousands and thousands of combinations. And little by little it starts learning and saying, well, when I see these colors in the satellite image, when I see them in this particular arrangement, when I see the, this type of circular kind of look or concentric circles or rain bands, then maybe that means that the wind is higher or lower. And it might seem a little confusing because you're thinking like, well, is the AI really thinking through it? It's like it's not. Kind of thinking through it, like the way that like an agent would now, that where, where we have agents that can kind of go and do multiple steps, it learns as it goes along by kind of refining its understanding of do you translate this set of colors, which is satellite to this set of colors, which is wind. And at the end of the day, it's like. It's really like two paintings, right? And I describe it a lot as trying to get a Matisse and turning it into a Van Gogh like, but keeping the same underlying structure. That's the type of AI that we use. That's what it was originally designed to do. And we use it for scientific visualization.

Brooke 17:36

So you shared a few other instances that computer vision translates. What made you wanna go into weather and, and hurricanes instead.

Eduardo Siman 17:44

Yeah, so there's, there's so many cool computer vision applications. Like I was so. I was really obsessed with something called photogrammetry, which is essentially taking pictures of like your environment and turning them into a 3D model. Right? And I, I invested in a few companies related to that and, and my favorite was six d.ai, which they sold their photogrammetry software to, to Pokemon Go to help them to kind of map the Pokemon Go spaces. And I used to go walk around with my. With my iPhone scanning everything using their technology. And people thought I was crazy but I was like trying to build all these 3D models, using my phone, but. So I had a lot of different areas of computer vision that I could have gotten into. But hurricanes was something that, outside of the AI world was a huge deal to me.'cause when I was 10, so I'm, I'm born in 82. Not, not to date myself too much, but you could see a little bit of the, of, of the white hair. So it's like, know, but I was born in 82 and here in Miami and in 92 we had a really bad hurricane called Hurricane Andrew and for anybody from Florida it was like a, such a defining moment in our lives. And many of my friends lived in the area that was hit the hardest. So my family lived in West Miami and we were hit by probably category two level winds, and we only had minor damage, but many of my friends lived in this area called Cutler Ridge and Homestead and Pinecrest. areas got. Category four, category five winds. And so there were many stories of people losing their roofs and kind of having to go into the bathroom during the hurricane and just watch the roof just like fly out. And it was a really traumatic event because here in Miami, we, we hadn't had a hurricane in 25 years. was a once in a generation thing. School was out for three months. It was like complete madness, like, a whole month of no electricity. So obviously I was very much affected by that. And since then I was just very obsessed with hurricanes.

Brooke 19:49

Yes, I've lived through my fair share of hurricanes in Florida. You grow accustomed to them, but at the same time, it still is very impactful and very sad and devastating when it hits closer to home and, and, and impacts you and your community directly. So thank you for sharing a little bit more on your story. Let's switch back to that topic of integrity, because I wanna hear more from you about how a AI has really changed the way that you have become a leader or make decisions as a founder when working on something that is so high stakes, like disaster prevention.

Eduardo Siman 20:28

Yeah, I mean, this is a great question and I think people don't think enough about it. When you are using ai, I think it's so important to be upfront. About what is being done with ai, what kind of AI and what you're feeding into it. In other words, what are you training it with? And I think that. AI ethics is a, just this huge area that, that, that many founders don't think about because they're just so focused on trying to grow revenue, grow users. But for us, with our main goal being we want to help people, we want to help anyone in the world have a, a better outcome when faced with severe weather, we have to think about it, right? Because we don't want, it's kind of like the Hippocratic Oath, like, do no harm, right? Like, we don't want to create a situation where our AI models are actually gonna make somebody call for an evacuation when there shouldn't be one, right? So I think that I. It's humbling in a way because you have to understand that you can't totally understand why an AI model might give the result that it's giving. you have to work very, very hard to try to understand um, is happening. Like when I give a slightly perturbed or a slightly different input, why am I getting this different output? Right. You can't just look at the results that you get and say, okay. done a hundred hours of training on this AI model, and now I can tell you National Hurricane Center, or I can tell you, you know, uh, city of Miami, that when you look at this particular windfield, this is going to be a hundred percent the windfield that's gonna hit like this particular part of Miami. You can't do that. What you can say is like, in the testing that we've done, we found that 95% of the time, this is within 5 meters per second, or five knots, or five miles per hour of what we know historically. Right. we also have to be honest and say, look, AI models. can hallucinate. In other words, we cannot 100% control or predict every single little thing that they're gonna do. So it is possible that result that you get going to give you what's called an artifact or basically some kind of prediction that makes no sense physically. and that's why you need to have a human who can say, Hey, look, that looks like it's, it looks like there's another eye in the hurricane, but that's impossible. That's not what's happening. It's, it's just a bad result. And we're here to help you to, and say, look, the model's giving a bad result. We need to go back and make sure that it understands these rare cases and you don't get this bad result. So there's a lot of responsibility involved. You just can't just throw a model out there and be like, here you go. Good luck. Like. very much like handholding, as if you were doing like an MRI prediction type of model.

Brooke 23:46

Of course.

Eduardo Siman 23:46

be really, really careful with it.

Brooke 23:49

Of course. If you could next share, I'd love to hear what have been some of the biggest breakthroughs and advancements in this, this weathered AI technology. Catch us up. What's happening in the industry? What's exciting you?

Eduardo Siman 24:03

It's pretty amazing. Like when, when, when I started thinking about this in July of 2022, my hypothesis was in general, like AI weather's gonna be a thing. Right.

Brooke 24:14

Hmm.

Eduardo Siman 24:15

just wish that I could have invested in something, in a stock called AI Weather's gonna be a thing, and that, and I could have just made money from that, right? Unfortunately, that's, there was no stock called AI Weather's gonna be a thing because it turns out that it became a

Brooke 24:27

Okay.

Eduardo Siman 24:28

thing. But there's a reason for that. There's, there's certain people in the field who really focused on saying, okay, a meteorologist. am a atmospheric scientist, or, or I am an AI expert, and I can see how these two things are gonna come together, and I'm gonna do the hard part, which is like actually trying to build these large scale, like world scale models that are gonna predict various aspects of the weather. People at large companies did a really good job of that. So at Google, at Hui in China at Nvidia and at Microsoft, there were teams that were essentially. Getting all this really good data that we have. A lot of it is from what's called the ERA five ERA five is a gigantic repository of data that's maintained by the European Union in, in their, like, main weather center, which is a European center for medium range weather forecasting or E-C-M-W-F, and think of it as like a replay of all the weather from the last 40 years, but using the best technology we have. So it's like imagine you go back and film like all of your childhood memories, but with like iPhones, right? So it's like your whole life, but with the best possible way of filming it. And that's really good data. And a lot of people started realizing, wait, we have 40 years of good data, could probably train with this and get very good results. And so all of these different labs at these large companies started doing that. But they used different techniques, and different different neural network structures. In other words, knew they wanted to use deep learning. They knew they wanted to use ai. But they had different approaches to it, but essentially the data was the same. So that's why we ended up having this, this like, thriving ecosystem. And we could e even have things like weather bench, there's been two, iterations of weather bench and weather bench is sort of this open source kind of scoring mechanism where you can, you can say, here's my prediction for the year 2020 based on training from, I don't know, 1990 to 2018. And it can tell you your model has this level of error, right? And, and that was created kind of in an open source community way. It wasn't an official thing, but it was a huge catalyst to helping people figure out where am I, how good is my model? So, it's been just an incredible evolution over time, which has, it's led to a much faster adoption than I expected in overall AI for weather.

Brooke 27:27

You know, You're right. I can't think of a better industry that would have just a ton of data that they're sitting on, ready and loaded to, to add into ai.

Eduardo Siman 27:37

The film industry.

Brooke 27:39

Yeah, so I love to always ask my guests, those learning moments and you mentioned you're self-funded, and I'd love to touch on maybe some of the harder lessons you've learned in building AI as a technical founder.

Eduardo Siman 27:54

I, I really think to myself, like, I hope that my experience being a founder can help other people when they do it. Right. And that's a, that's a really important part of, of answering these questions because I think someone's out there and they're trying to decide, should I be a founder and what is it gonna be like? Right. Especially if you're gonna, if you're gonna try to do it self-funded. And I think the FA lot of people will just say like, it's so hard. Oh my freaking Lord. It's so hard. don't think that's particularly helpful. I think anything is hard. That is meaningful, right? Like. what you need to think about is what will help you. And I think the number one thing is resilience. So much rejection that you face as a founder, because think about it, like you're going from something that doesn't exist at all to something that kind of exists to and then it kind of exists in a different way, but nobody really kind of knows what it is. Nobody knows who you are, especially if you're going into a brand new field. And so you have to kind of start with this idea of like. I am going to be resilient. Like no matter what happens, I am going to keep going. And then I think the second thing, and, and this is, know, that I, I think in terms of reading about being a founder, like the best book that you could, that you could read is. It is the, is kind of the classic classic playbook that tells you how, how to think about pivots, right? And I, I think Steven Reese is the author if I didn't mess up his name. But, but the book explains to you what, and he, he invented the pivot. He invented the idea of the pivot and about every single thing that you do. As a pivot is extremely important because you think of one action as being, either you succeed or you fail, then you're just gonna rack up a whole bunch of failures. But if you think of everything as an experiment that can allow you to pivot and change your path. Even in that small little thing, it might be a sales meeting, it might be code that you write, it might be the way in which you approach an employee or a possible hire. I think that, thinking of everything as like a pivotable moment is really, really critical. So, so yeah, that's, that's how I've tried to deal with, with the difficult moments, but I think that. It's important to be honest and say, yeah, of course it's not easy to go and start something from scratch. Absolutely. Like, you're gonna be competing against incumbent companies that have been selling to who you wanna sell to for five years, for 10 years. But that doesn't mean that you shouldn't do it. If you have an idea, if you think you can create something, if you're passionate about it, if you want to. Get to some kind of end goal. And that, and that's something that's really important to you. I think you can kind of build the psychological infrastructure to help you deal with with the, with the downsides. I really do. So. I'm not a big fan of this. It's, oh my God, it's so hard. Don't even do it. Like, I hear a lot of founders saying like, oh, I shouldn't have done it. It's so hard. But they're like the most successful founder ever. And it kind of sounds like they're just saying, I am good enough, but you're not. Right. And I think the message that I like to send is like, anybody can do it. You can learn it. You can learn how to be an entrepreneur. You can learn how to pivot and, I, and I have to learn every day based on different things that I face right.

Brooke 31:38

You touched on something that is very near and dear to me, and it's pivoting and we're, as a society coming up to such incredible amounts of change not only in the job industry, but how we're all working with one another. And a lot of people are questioning where they are, where they're going, what's gonna be relevant and pivoting either ahead of the game, choosing to take it into their own hands or eventually we'll need to pivot. So this is actually a space I love speaking on and supporting people through because I myself,

Eduardo Siman 32:18

to why, how did, how did this become something so important to you? Because

Brooke 32:22

yeah.

Eduardo Siman 32:22

that you've talked about this in previous episodes,

Brooke 32:25

Yeah, the quote that was coming up to me, that famous quote, failure is feedback. And fail. Fail Faster is another really good book as well. And. The quicker that we can come up against the hero's journey of starting over again and, and turning those learning lessons into new paths and new directions, rather being a victim of our circumstances. I have myself come up against a lot of moments where I was forced out of industries, or it just was the wrong space for me, or just difficult moments in my life. Of moving across the country multiple times and trying to really find my flow and my groove because, you know, I wasn't on this one track mind of I'm gonna be a lawyer, I'm gonna be a doctor, I'm gonna be an accountant or anything like that. It was really just, me getting information and collecting information and then making the next right step. So I've had to pivot quite a lot. Is this.

Eduardo Siman 33:22

from put points of success, I mean, it's not

Brooke 33:24

Yes.

Eduardo Siman 33:25

points of failure only,

Brooke 33:27

Right. Exactly. Exactly.

Eduardo Siman 33:29

people don't understand that pivoting from points of success is hard too.

Brooke 33:33

Hmm. Yes. And so, so many of my friends are coming to me now that, are esteemed doctors and surgeons, and they're like in their forties now and considering what is their next pivot and move, which I think is so fascinating that we are collectively all coming up against the pivot. If you're in the middle of a career pivot or thinking about building something of your own, I made something for you. I created the free product framework quiz that helps you connect your skills, life experiences, and the problems you care about solving to uncover a product or a project that you could build with AI as the spark. If you're curious what your next step or pivot could look like, head to my Stan store linked in the show notes below, and take the quiz today.

Eduardo Siman 34:23

I really like the way that you put it. And so many of my friends and my acquaintances have kind of dealt with with this situation, especially now that coding is becoming something that humans are no longer going to do.

Brooke 34:39

Mm-hmm.

Eduardo Siman 34:40

and I think this is the part that for those of us in the computer science world for, or even who've been adjacent to the computer science world, who know programmers, who have friends that are programmers shocking. I will tell you. first, I thought that AI would be able to be like a coding assistant. and in the early days you would say, Hey, can you help me with this function? And it would say, sure, you might want to try this. And then you would copy it, you would put it into your code, and now you basically write one instruction such as. Can you build me a really great web app that can track all of my family's activities for the next two years? And you go get a coffee and you come back and you have 14,000 lines of code and a working product. and I think that many of the people that I know and that I talk to have been shocked by this. And when I say shocked, I mean like not a little shocked. Extremely shocked. and the reason why is because like, think about everything that we were told the last 15 years about coding. If you know how to code, you will have a job. If you're a great coder, you are gonna get a job at Meta, you're gonna get a job at Google, you're gonna get a job at Amazon, I mean. I'm sure you heard as many times as I did, like, why would you study art? Why would you study music? Why would you study? Like me, like, in my MBA all I did was study mathematical finance. Like why would you do mathematical finance when you could just learn how to code? Like coding is what actually makes people money, right? It's like, and now. irony of all ironies, the first profession that is going to be destroyed and completely like, taken over by AI is coding. Which is why like if you go on LinkedIn, you'll see a bunch of founders being like, I just created this with Claude code. I just created this with Codec. I just created this with, fill in the blank because everybody's still fricking scared that they don't wanna fall behind, and they know that their previous skills no longer matter. So they're trying to figure out this new skill and they don't want them to be the last person, like the person who figures it out a month late because it feels like being a month late is like being 20 years late now.

Brooke 37:15

Yes, this is why I've been doing a lot of workshops and education around topics like vibe coding and explaining how people can just build product very easily now.'cause a lot of people don't even understand that this is a tool and a resource to them. I think this is a really great segue to ask you. One of my favorite questions I ask every episode is. If you could wave a magic wand and invent one thing missing in the AI field right now, what would you create?

Eduardo Siman 37:45

For me, it's so clear it would be the ability to take a vibe coded app and make it work in a production environment at a big company. So. This is, spending 20 years in, kind of in the enterprise space. I dealt with usually the day-to-day problems of working with big software like SAP, Oracle, NetSuite or even like, warehouse software. the software that runs big companies. And typically what you have to worry about is security, right? Like, is, can someone hack it? Two is is it gonna work every day? So dependability is, is it gonna be fast? gonna be able to use it and it's fast enough to do their job? And four is like, can you add new functionality without breaking the thing? Right? And I think that. That within the world of vibe coding, right? It just doesn't exist at this point. Because what we can get to is we can get to this amazing prototype, incredible demo kind of demo creation that is like so close to the real thing. And it is a real thing. It can do exactly what the real thing can do, but I think that companies, big companies and small companies are still concerned about saying, Hey, let me bring this vibe coded app into production because they don't know about those kind of day-to-day issues and how it's gonna be able to handle them. So if there was a way that I can just say. All right. I've been prototyping on Claude cowork or on Codex or, or Repli, or lovable, whatever it might be. And now I need to turn this into something that is usable by a lot of people, either at big companies or with a large number of consumers. I feel like that would be such a huge game changer, right? Because you know who could do vibe coding, which is anybody now I think. What you were saying is so true, like people need to understand, like anybody can develop software. Now, I'm not saying that you're gonna, that you know what software, exactly how software works. I'm not saying you have some, computer science degree from Harvard. But how to actually turn that into something that works with many people or in a big company, that's really, really tough. That would be my magic wand and we will get there. And I think that when we get there, crazy things are gonna happen in terms of the economics of software.

Brooke 40:22

It's gonna be really cool when that moment is surfacing. I'm just thinking back to all my prior roles and what it would've been like to have that at my fingertips.

Eduardo Siman 40:32

Actually, let me ask you,'cause I'm really curious.

Brooke 40:34

Yeah.

Eduardo Siman 40:34

If you can make any software based on your career, and I know you've been mostly like on the marketing side

Brooke 40:39

Yeah.

Eduardo Siman 40:40

and production and things like that, the software that you would make?

Brooke 40:45

Yes. I have some software in mind that I'm creating and without giving too much away'cause I've just had a few initial conversations on, is this possible it has to do with home. The home, home design and something I'm very passionate about because there's just really cool technology coming out with 3D scanning, interior design and architecture. And so that's something that is just practical for me in my own use. So yeah, some creative pursuits on the side for that, but pretty much every single day I think about things like this of what I'd want to do and put together, but there still are so many steps between that and something I'm leaning into more. You know, very fortunate to live in a city like Miami. Very tech forward. There's a couple hackathons coming up within this city so I'm going to attend the Google one coming up. I think is this Friday, just to get more of a taste of it and also maybe try to sit down and, and build a bit deeper in a room with, with support

Eduardo Siman 41:47

That's awesome. I mean, I, I think this is a great example of how like someone who would be considered like a creative executive is now gonna be building applications. How cool is that?

Brooke 41:58

Yes.

Eduardo Siman 41:58

and so what does that mean for the economy of software? What does that mean for what, what it costs to pay for software? Right.

Brooke 42:06

Mm-hmm. So I want to just have a couple wrap up questions for you. If you can please share what's next for you, what's coming up for you and, and Worlds Sphere, ai.

Eduardo Siman 42:20

Well, what I've been working on lately is basically trying to create like a second version of, of our World Tier platform using Claude Cowork or Claude Code. And by the way, like I am not the only founder doing this like a lot of founders are basically looking at their tech stack and what they've built and said, can I rebuild this without the help of any developers on my own as a founder using something like Claude Code? It's, it makes sense because what are founders, people who like to do things on their own people to try to see what is the most that I can do with my brain. Right. So it's like a lot of people are doing this. Partially it's an experiment because, you know, I, I've been lucky to have an amazing development partner. They're, they're called Flat Logic and they're a development team out of out of the Czech Republic, Poland, and Lithuania. And they did an incredible job for us in building our risk management platform. But now I wanted to see, can I do the next version of this on my own? Which even six months ago would've been complete madness. But it's kind of happening. And I've had some moments in which will, for example, say. As I'm going through this, I guess you could call it vibe, coding experience I would like to include like the realtime satellite images from this satellite onto, onto the globe and do a heat map that shows like, maximum wind uh, per city. And I wanna do it in a way that has this font, that has this color, that has this look. And five minutes later it's there. And. These are things that would've taken even with an amazing team, like the Flat Logic team, at least two weeks with, a quality assurance person, two, two full-time developers and a development lead, right? And I did it in five minutes and it works. And I don't even know what to say. I literally don't even know what to say. Now I, I will admit, like, when you work with the team, like Flat Logic. You're starting from an enterprise ready platform, right? Because they are building software and they know how to build software for enterprise. When you start now with Vibe coding, it'll create something that's that is. facing, and then if you want to make that work. For the kinds of prospects that we have, which are large insurance brokers like Gallagher and Alliant and these the kind of billion plus level insurance brokers, we have to turn that into something that they can rely on, right? So it's gotta actually have a database. It has to actually remember what one user says versus what the other user says. This kind of experiment that I'm working on is very exciting because every, every day I find that at least the model that I'm working with, which is the latest Claude model, is able to do things that I, I thought were impossible. Like I said, it's scary. If you're not in the space and you haven't done by putting, you've never tried this before, you might think that it's like, oh my God, it's so easy. You just do what you've ever want Ev the thing that you've always wanted to build. You just build it and then that's it. You have a business, everyone's gonna buy it. No. Everybody is able to do this now.

Brooke 45:54

Yes.

Eduardo Siman 45:55

That's what's so hard about it, like, so what's your differentiator? Like.

Brooke 45:59

Mm-hmm.

Eduardo Siman 46:00

Why should your business be the one that has their vibe coded app, make it into like the enterprise mainstream. And that's where I'm hoping that my experience and the experience in the, personal qualities of my colleagues and my co-founders are going to help us to stand out.

Brooke 46:21

Yes, we were just discussing this during my live event that I had here in Miami we were discussing how everyone is building in public and really that moat that's gonna identify you between everybody else in the industry is your community. I mean, I've seen it in real time happen to even past guests that have built a really cool product, and then the copycats come out, but they haven't lived the experience, they haven't been impacted by their direct community as a result for these founders to create these products. So it, they really stand out in their own unique way from people that are just copycats or others trying to get into the market of a market they don't really understand. They're just seeing an opportunity and wanting to grab it. But yes, I'm willing to believe that people like you are going to be able to stand out.

Eduardo Siman 47:15

I really hope so. And I think it's about being obsessed with the industry that you wanna serve.

Brooke 47:20

Mm-hmm.

Eduardo Siman 47:21

having a very, very clear mission as to what your company is about.

Brooke 47:24

Mm-hmm.

Eduardo Siman 47:24

I don't think things are changing in that way. I mean, if you're trying to sell to insurance brokers like, like we are, and you don't understand how they operate, I don't care how amazing of a vibe coder you are, nobody's gonna buy your product. Right. So same thing if your, if your mission is like, make money, who's gonna be inspired by that?

Brooke 47:45

Right.

Eduardo Siman 47:46

Right. So I think the way you said it is, the community around you and the purpose around what you're doing is still In fact, maybe even more important than it used to be.

Brooke 47:58

Yes. Beautiful. I think that's a, a perfect final key takeaway for this episode, and I want to leave this space for you to share what's the best way for listeners to contact you and reach out. How would you like them to connect?

Eduardo Siman 48:16

Yeah, so, you can contact me via email, so it's. Very simple. It's the first letter of my first name E. The first letter of my second of my last name, which is S at my company name Worlds sphere.ai. So e s@worldssphere.ai. Also I'm very responsive on LinkedIn. Not as much as on email, but on LinkedIn. If you send me a message, I, respond unless you're one of those, founder spam people and you really just want to talk to me or you wanna learn something from me. I will respond. I think LinkedIn is fantastic for that, but there's nothing like email, right? So email me es@worldshare.ai. I love talking to people. I love collaborating. I love, thinking about things and sharing ideas, living through this moment of this kind of vibe coding revolution, living through it with other people. Please reach out to me. I'm very much someone who loves having as many people around me as possible.

Brooke 49:15

Well, thank you so much, Eduardo. I really appreciated your thoughtful questions back to me, giving me the space to share a little bit more about myself, which I don't always typically get the opportunity and save those for my solo episodes, which have gotten behind because I have such amazing people coming my way, like yourself. And these stories just need to get out there. So thank you so much for your time.

Eduardo Siman 49:37

Oh my pleasure, and I'm so happy that you were willing to answer those questions because we have a lot of, in common, in terms of how we've navigated the corporate world and then trying to find our niche in this new world, right? Where you have to be constantly pivoting and figuring out, like how do you apply your, your skills in this new world of ai? So, so yeah. Thank you so much for having me. It's, it's been a pleasure and I really hope that we get to talk more.

Brooke 50:00

Absolutely, especially since we're both in Miami, I'm sure we will run into each other very soon.

Eduardo Siman 50:05

Awesome.

Brooke 50:06

Wow, I hope today's episode opened your mind to what's possible with AI. Do you have a cool use case on how you're using AI and want to share it? DM me. I'd love to hear more and feature you on my next podcast. Until next time, here's to working smarter, not harder. See you on the next episode of How I AI. Have you just started exploring AI and feel a bit overwhelmed? Don't worry, I've got you. Jump on a quick start call with me so you can walk away with a clear and personalized plan to move forward with more confidence and ease. Join my community of AI adopters like yourself. Plus, grab my free resources, including the AI Get Started Guide. Or try my How I AI companion GPT. It pulls insights from my guest interviews along with global reports, so you can stay ahead of the curve. Follow the link in the description below to get started.