Revolutionizing Finance through AI and Data Science with Gabriela Gutierrez
In this episode of the Future Finance Show, hosts Paul Barnhurst and Glenn Hopper explore the transformative impact of artificial intelligence (AI) on financial planning and analysis (FP&A). The discussion revolves around how AI tools, from generative AI to sophisticated models like OpenAI's Project Strawberry, are reshaping the finance landscape. The episode features Gabriela Gutierrez, a finance professional turned AI enthusiast, who shares her journey of integrating AI into finance.
Gabriela Gutierrez is a dynamic finance leader and entrepreneur with a strong background in AI, Python programming, and machine learning. She has worked in various financial roles, including FP&A at eBay and as a local CFO for Teads in Germany. Gabriela’s passion for innovation led her to launch a stealth startup focused on AI-powered finance solutions. She shares her insights into the evolving role of finance professionals in the AI era and her experiences in building an AI-centric company.
In this episode, you will discover:
The potential of AI to automate and enhance FP&A processes.
Insights into OpenAI's Project Strawberry and its implications for the finance industry.
The skills finance professionals need to thrive in an AI-driven world.
How Gabriela Gutierrez transitioned from traditional finance roles to becoming an AI-driven entrepreneur.
Predictions on the future of finance jobs in the context of advancing AI technologies.
It's clear that the intersection of AI and finance offers both exciting opportunities and unique challenges. This episode provides a wealth of knowledge for finance professionals to embrace AI as a tool for innovation, efficiency, and strategic growth.
Follow Gabriela:
LinkedIn: https://www.linkedin.com/in/gabriela-gutierrez-15ba0571/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://qflow.ai/future-finance
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[00:59] - Introduction to the episode
[01:43] - Key takeaways from Carl Seidman’s FP&A insights
[07:02] - Exploring OpenAI’s project Strawberry
[11:58] - Gabriela Gutierrez background and career journey
[16:39] - Overcoming challenges in AI integration
[19:51] - Insights on Gabriela’s Stealth Startup
[26:29] - Fun segment: get to know Gabriela
[30:31] - Conclusion and episode wrap-up
Full Show Transcript
[00:00:59] Host 1: Paul Barnhurst: Welcome to the future finance show where we talk about.
[00:01:03] Robotic Voice: Forget copilot, let the robots take the wheel.
[00:01:14] Host 1: Paul Barnhurst: Future finance is brought to you by qflow.ai, the strategic finance platform solving the toughest part of planning and analysis. B2B revenue aligned sales, marketing and finance seamlessly speed up decision making and lock in accountability with qflow.ai.
[00:01:43] Host 1: Paul Barnhurst: This week on Future Finance, I want to share a post from Carl Seidman. Carl is an expert in FP&A and finance, a Microsoft MVP, and he has spent the last three months developing a course, and he lists seven things he's learned from his experience on what he calls his road to discovery. Few of these are very common sense. I won't go through all of them in detail, but there's a few I really like that he points out. First one, he just reminds us we're in the early stages, right? To be able to confidently and reliably utilize finance, which I agree with, we're still figuring out the use cases, still growing and developing. There definitely are use cases out there, but it's early. Two he talks about how he's currently underwhelmed by the true integration of AI within finance applications. And I think I share a sentiment that we have a lot of room to go in that area. But he talks about it's astounding how fast it's moving and how we're getting better every month. And I would agree with that. That goes with being very early. The third one is where I really want to spend a minute I really like. He goes, I question anyone who believes they're an expert in AI, especially for finance.
[00:02:52] Host 1: Paul Barnhurst: There are just too many different applications of technology and not universal best practices to follow. There are also so many FP&A tools that incorporate AI and it's impossible to master them all. And so I thought that was interesting. I agree with the concept that there's probably not a lot of experts. It's been out for a few years. Generative AI I do think there are experts in AI. Ai has been around for 20 years, so I challenge that part. Machine learning there are experts there, predictive algorithms, different areas of using it within finance. But when it comes to generative AI, we're still all learning. There's people who've invested a lot more time in this. And that, I would say on the scale of things, are more toward an expert than not. But I think it's a reminder that, look, we're all learning together. It's still very early and there are so many things out there. For he mentions he thinks copilot will change the game entirely since it's on every PC running windows he talks about. It's still not a mature product, and I agree. So far I've been underwhelmed with copilot, but I do think it will change the game. I think we'll see huge advancements in the next 12, 24, 48 months with copilot. That will be astounding how different it will be in a few years. Number five, he goes the integration of files, PDFs and code with an offer.
[00:04:11] Host 1: Paul Barnhurst: AI offers some of the most promising applications for finance professionals. Right. Upload your data, models and reports and have AI augment them in seconds. I agree with this, I use it, I'll load files, PDFs, have them review them, summarize them. I think there's a lot more. I have them create files and the code interpreter. It's an amazing area that I think people should lean into. And they can really do some cool, cool stuff. I thought this was very interesting. I speculate that chatbots will eliminate hundreds of thousands of clerical finance jobs. Basic and routine administrative duties should be replaceable with the technology. This is especially true for account reconciliations payable and receivable. Follow up. I'm not sure I'm that bullish, but I think the tasks, many of the tasks will go away. We'll definitely be able to reduce. I guess that's similar to the clerical, so that'll be interesting to watch. But those people who are using technology, who are doing more than just process tasks for their job, will be much safer. And then number seven, his boldest prediction, and this is one that I do think we'll see a real shakeup in, is he goes, my boldest prediction is that investment banks will be able to eliminate 80% of their analysts and associates within the next 10 to 5 years, as I will be able to build robust financial models.
[00:05:38] Host 1: Paul Barnhurst: Managing and interpreting them may still be left to humans, but DC's valuation models and pitch deck due to largely be done by AI, and I'm not sure I'm there yet to say it's that big of a shake up. But I do think it's definitely going to play a role. I will start being able to build models. I'm not sure they'll be able to fully build them, but we'll be able to reduce people. I think there's no question there and it will change that industry among others. So I thought these were eight. Interesting. Not eight. Sorry. Seven interesting observations from Carl. As we are all figuring this out together. Remember, the key here is to make sure you're learning from AI and that you're sharpening that saw and experimenting and pushing the limits so that you're not left behind because things are changing. You know, it's still a new product. There's still not a lot of experts out there. We definitely don't know all the applications. And we're in the early days. So let's continue to figure this out together. And no matter what, one thing he says at the end of his post that I agree with, I guarantee you that it will disrupt rolls against our will. So the most important thing is we need to be proactive and learn and grow. All right. That's what I have for this week. Hope you enjoy the rest of the episode.
[00:07:02] Host 2: Glenn Hopper: Welcome back to Your Job Is Safe for Now, where we dive into the latest AI developments and figure out just how worried you should be about your job. This week we're going to talk about OpenAI's project Strawberry. So what exactly is Project Strawberry? I mean, it sounds innocuous enough, right? Strawberry is a highly advanced AI model that falls into what OpenAI calls the reasoner stage of AI development. In OpenAI's grand scheme, they see AI evolving in five stages, where we are now in stage one with chatbots, the next step will be the reasoners, which will evolve into AI agents and then innovators and organizations. We covered this a couple weeks ago, so we won't go into a lot of detail with that this week. But just strawberry is important because it moves us beyond chatbots to the reasoners. It's not released yet, but it should be coming in the next few weeks or months. I guess on Reasoners just think of them as they can tackle complex problems and then come up with solutions on their own, which is an important step toward developing true AI agents. So let's talk about what makes strawberry so special. For starters, it's been crushing it on math tests. It scored over 90% on a collection of championship level math problems, which is a huge leap up from GPT four, which got a 53%.
[00:08:23] Host 2: Glenn Hopper: And I think GPT four, the latest model from OpenAI, got 76.6% on the same exam. And, you know, that's some serious number crunching skills, especially compared to humans who participate in these math contests. But it's not just math. Strawberry can also solve intricate word puzzles like, I think the one they mentioned in the last article I read was connections from the New York Times, and it does things like comes up with creative marketing strategies and all the other applications that go beyond what our current level of chatbots are doing. And that's kind of the beauty of a reasoner, is that it can apply these problem solving skills to a wide range of tasks, looking under the hood, you know, without getting into the technical. Obviously, I don't know exactly how it works, but looking under the hood without focusing on the technical details, strawberry can plan ahead. It can break down complex problems into smaller steps, and then it'll work through them systematically. This is a big step up from the chatbots that we use today, which are great at generating that one shot, human-like responses. But when you ask it to do a bigger problem, it struggles with more complex reasoning. So, you know, what does this mean for the future of AI? And really, strawberry is just the beginning.
[00:09:37] Host 2: Glenn Hopper: As AI moves from chatbots to reasoners and beyond, we can expect to see more and more systems that can think, plan, problem solve in ways that were once, until very recently, unique to humans. It's a glimpse of a future where AI can tackle increasingly complex tasks across various industries. Think finance, healthcare, scientific research. And for listeners to this show, you know, the finance side is what's important. So what does that mean for us? And it's kind of a mixed bag. On one hand, having an AI that can crunch numbers and analyze data like a pro, like a financial analyst, that can help streamline processes, reduce errors and make the whole industry more efficient. But on the flip side, it's hard not to look at things like Project Strawberry and wonder if it's coming for our jobs. For some, AI tools like strawberry can automate certain tasks and change the nature of their work, but for others, the same tools could augment their skills and decision making, allowing them to take on higher level and more strategic roles. The key is to approach AI not as a threat, but as a potential partner. And as the world of finance evolves, the most successful professionals will be those who can adapt, learn and find ways to work alongside AI.
[00:10:48] Host 2: Glenn Hopper: This might mean developing new skills like data analysis or programming, or learning to interpret and communicate the insights generated by these AI tools. It could also mean taking on new roles that emerge as AI transforms the industry, such as AI, ethics consultants or machine learning specialists. Ultimately, the future of finance jobs in the age of AI will be shaped by how we choose to respond to these changes by staying curious, embracing lifelong learning, and being open to new possibilities. We can navigate this transition and find our place in the future of finance. Because Project Strawberry is just one example of the incredible progress that's happening in AI, and it certainly won't be the last. And as the technology continues to evolve, so too will the world of finance. And it's up to each of us to decide how we want to be part of that story. Remember, your job might be safe for now, but the key to long term success is to stay adaptable, keep learning, and be ready to embrace change. Because whether we want it or not, it's coming like a freight train. Anyway, that's it for this week. Thanks for tuning in. Until next time, stay curious, stay nimble, and don't be afraid to think outside the box. The future is coming and it's going to be an exciting ride.
[00:11:58] Host 1: Paul Barnhurst: This week we're thrilled to welcome to the show, Gabriela Gutierrez. Gabriela is somebody I interviewed for FP&A today and was fascinated with her story, so I'm thrilled to bring her as a guest on Future Finance as Glenn and I talk with her. She is the founder of a Stealth Startup, which will hope to hear more about in the coming months. It is an AI powered finance company. She's living in the Bay area. Prior to that, she lived in Germany and she's also lived in other countries around the world. While in Germany, she worked for eBay and FP&A and she was a local CFO for Teads. She's also very knowledgeable when it comes to Python, machine learning and AI. She learned it out of necessity to be a better finance leader and to better forecast for business. So we're thrilled to jump into the conversation with her and talk to her. Welcome to the show, Gabriela.
[00:12:54] Host 2: Glenn Hopper: So Gabriela, your educational background started on the standard business track with a bachelor's in Business Administration and a master's in International business. But along the way, you got interested in statistics and probability. Tell me what you saw that made you realize that you needed to shift? Or maybe a better way to say it is to refine your focus and really lean into the math side of things.
[00:13:16] Guest: Gabriela Gutierrez: Actually, that was coming from a problem that I was facing back then at my job. So it wasn't that I decided to go and study math and statistics, but rather like I was dealing with a lot of data, and I needed to create an accurate forecast on that data and everything that we have tried before that was not really working. So then I needed to kind of get out of the normal box of tools that I would have and then like, really dig up and try to find creative solutions, how we would be able to increase the accuracy of those forecasts. And one of them, it was really like going back to statistics. I would say like back to basics and really like, yeah, iterate from there. And then obviously at some point we were able to really increase that accuracy. But it wasn't like my first choice that I would say like, yeah, let's go for that. But rather it was kind of trying to find a solution to the problem that I had back then.
[00:14:19] Host 2: Glenn Hopper: I love that because it's very similar to my own sort of moving into heavier math and into machine learning from statistics. And that's kind of the gateway drug to get you into Python and all that. You know, I think Paul and I talk about this a lot in FP&A, when you're building out these complex models and you're trying to solve for things, you kind of run up against limitations of what you can do using, you know, just standard linear regression or whatever. And then you might start playing around with polynomial regression and stuff like that. And it just you start once you start going down that road and see the power of it, it really opens a lot of doors. But I love hearing stories like that, and I think that for FP&A folks, if you really go back to statistics and statistical models, even if you're not using machine learning, you have a lot of power to really make better models. I love it. I love that story.
[00:15:08] Guest: Gabriela Gutierrez: Yeah, definitely. I also believe that how we have been building models in the spreadsheets, there is a lot of room to improve. So like, yeah, it was also a nice challenge to have.
[00:15:22] Host 1: Paul Barnhurst: How was the outcome when you got all done with the project and really kind of going down this route? How big of a difference did it make for you?
[00:15:29] Guest: Gabriela Gutierrez: It did quite a big difference because first of all, what we also figured out is that we didn't have a proper data pipeline. So that also made us really think like, hey, how are we going to design that? All the data would come to our central place and how we could make it as simple as possible and, and less manual as we were doing before, because it will take us like, for example, to make them a C-suite update. Every week, we would have to spend one day or one and a half days just collecting data. So like really thinking through how we could improve it, establish this data pipeline, and then achieving almost like 100% accuracy in long term planning. That was a huge difference as well for strategic planning and to really know, like where should we invest our resources, where should we stop, and so on.
[00:16:24] Host 1: Paul Barnhurst: So it made a big difference to the business as well, and the whole data side of thing, because I'm sure you realize there are different data points you needed than what you may have easily had for your traditional model, versus you started getting into statistics and machine learning. So I'm curious, as you started focusing on this, was there a certain path you took, like did you start with statistics and start learning Python, you know, getting into some algorithms or how did it kind of progress? What was the kind of journey as you started focusing on this.
[00:16:56] Guest: Gabriela Gutierrez: I kind of jumped into the cold water. So like I said, okay, the mod like the forecasting models that I was using and the spreadsheets weren't working, what is kind of the best way to improve that? And I figured it out quite quickly that the best one was, for example, to run time series predictions and the best way to do it was with Python. So then I kind of stopped anything else and really went directly into Python, because that's where I figured out that I would have the best results as well. So like quickly doing some tests, like I, I noticed that that would make a big difference. So I took that and but then obviously the learning curve is quite steep. Like you need to learn programming, then you need to understand the basics of data science, and then you need to test like different models and so on. And I also like to figure it out wide as well. Many different finance Professionals. They are not really going into this route, and it is because of the knowledge gap that we will see that you need to acquire so many different skills even before to be able to do that. So I think that's also something that it could be like, I would like to have some tools that could be the bridge between data science and finance, because it's like a very thin line of what do we do and what they are doing as data science.
[00:18:22] Host 2: Glenn Hopper: I think going down that road. And I talked to obviously in the podcast business that we're in, we talked to a whole lot of people doing FP&A and I think there's a bit of a debate around it. But what are your thoughts on for FP&A folks? What level of understanding do you think they need to have about data science or even coding for your typical FP&A person?
[00:18:46] Guest: Gabriela Gutierrez: I think right now, like the minimum that I would expect is someone to at least know how to code or like to manage SQL like and to be able to run queries in SQL. That would be like the minimum like requirement, because companies are acquiring more data like most of the time, like the fastest way that you are able to manage and create reports is using SQL. So that would be like a very necessary skill I think is still a nice to have, is having a bit of an understanding of data science and how you could test and even for Barron's analysis that we do every month, that having the understanding it will help a lot.
[00:19:30] Host 1: Paul Barnhurst: All right. Well at least I passed the minimum I got SQL down.
[00:19:34] Host 2: Glenn Hopper: Yeah but you can run circles. You can run circles around all of us in Excel though.
[00:19:38] Host 1: Paul Barnhurst: So details, details. No pythons now in Excel. So you guys will be able to run circles around me. Although I guess it's still in beta, so you probably don't have it, but I do. Alrighty. So you know, a question for you around your startup. Would love to just learn a little bit more about what you're doing. Maybe. Is there anything you can share with your audience about how that's going, what the experience has been like, kind of what you're building?
[00:20:05] Guest: Gabriela Gutierrez: Sure. So I'm not able to share yet what exactly we are building because we haven't launched, but I'm happy to share about the experience. So as well, it actually comes from a story that I'd have, or I'm a person that cannot really sit still, and I really want to improve things and how to do things better in finance. And that's one of the things that I noticed again, like why we are still doing so many manual processes as well related to data and the willingness to change that. And that's where I started with the idea and how to really make an impact for financial professionals and to really kind of remove all the, like, not nice tasks that people are doing and let an AI take over and pretty much do all the boring tasks.
[00:20:57] Host 1: Paul Barnhurst: Ever feel like your go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet qflow.ai, the strategic finance platform purpose built to solve the toughest part of planning and analysis B2B revenue. Qflow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stats, make segmented scenario planning a breeze, and closes the planning loop, create air tight alignment, improve decision latency, and ensure accountability across the teams.
[00:22:05] Guest: Gabriela Gutierrez: On the journey per se. It has been quite challenging. Like I'm not going to lie. Like knowing a bit of Python to really build into an AI, companies are quite different. So it had put even a higher bar in, like the amount of knowledge that I need to have and also like execution and implementation.
[00:22:26] Host 1: Paul Barnhurst: So drinking from a fire hose, it sounds like.
[00:22:29] Guest: Gabriela Gutierrez: Yeah. But I'm up for the challenge.
[00:22:34] Host 1: Paul Barnhurst: So you jumped in the deep end again is what you did. Remember you said you jumped in the cold pool earlier. So I figured you just did it again.
[00:22:40] Guest: Gabriela Gutierrez: Yeah, I think so too. Maybe I really just like the shock of new things.
[00:22:47] Host 2: Glenn Hopper: So you're still fully in stealth mode. You're not releasing anything out about what you're doing or any of your funding or anything like that. You're saying you're keeping it under wraps right now? All of it.
[00:22:56] Guest: Gabriela Gutierrez: Yeah. So we have a couple of beta customers where we are still developing the technology with them. So really finding what really brings value and really iterating with them. And once we are ready, then we are able to launch as well.
[00:23:15] Host 2: Glenn Hopper: This isn't your first startup, right? I mean, it feels like you've definitely got this entrepreneurial bug. And I guess you hit on it a little bit in Paul's question. What do you think it is that drives you to create new businesses? Because it's funny. I always think about that story time, Paul. I always think about something that happened. I think it was after Sarbanes-Oxley where the CFO, the finance people kind of got elevated and became the adult in the room. And the CEO could be like, what was the WeWork guy that went around barefoot and just saying crazy stuff. Meanwhile, the CFO. Yeah. Meanwhile, the CFO has to kind of come in and clean up the mess and talk like this is a business. This is what we're doing and all that. So it's. And also I don't I'd like to see stats on it but the number of you know like CFO going to CEO or finance person going to CEO. I think a lot of people would say, oh no, it's two completely different personality types. You can't do both. I don't know, I mean, so I guess thinking about it from your perspective.
[00:24:15] Host 1: Paul Barnhurst: Real quick. Before you finish the question, I'll give you some stats.
[00:24:17] Host 2: Glenn Hopper: Okay. Yes, do that.
[00:24:19] Host 1: Paul Barnhurst: We released last year, 2023, one third of the fortune 500 CFOs that left their job. So a third of those that left to become CEO, highest number they've ever had. We're seeing more and more CFOs become CEOs because the role is becoming more and more strategic.
[00:24:38] Host 2: Glenn Hopper: I could see that that makes sense in the modern era.
[00:24:41] Host 1: Paul Barnhurst: Sorry, I only blew up your question. You can now ask it again.
[00:24:47] Guest: Gabriela Gutierrez: But I really love the sand. I think it really shows. And like, as you said, Paul, a huge trend of becoming more strategic. The role and also like the CFO is in like has the perfect skills to really lead a company. And I think also like maybe 30 or 40 years ago, many CFOs at some point were becoming CEOs, especially when, like the companies, it was more about reporting and controlling and everything about accounting. And then we saw a change that maybe COOs or CPOs would become CEOs. But I like that CFOs are back.
[00:25:29] Host 2: Glenn Hopper: So, all right. Well, we're I guess one last question on the startup, and I know you gotta have discipline, messaging, discipline around this, but you have a timeline of when we might hear more about it. Or is that under wraps, too?
[00:25:42] Guest: Gabriela Gutierrez: No, I think by the end of the year, beginning of next year, like, then we would have some news and then we would make the official lounge.
[00:25:52] Host 1: Paul Barnhurst: All right. Can we get an invite to the lounge?
[00:25:55] Guest: Gabriela Gutierrez: Yes. Of course.
[00:25:56] Host 1: Paul Barnhurst: We won't be there for the launch in the office there. And so you're in San Francisco, right?
[00:26:02] Guest: Gabriela Gutierrez: Yes.
[00:26:05] Host 1: Paul Barnhurst: How fun. We need a good excuse to go to San Francisco. Right, Glenn? Yeah.
[00:26:08] Host 2: Glenn Hopper: That's right. Always. Always. Yeah.
[00:26:10] Host 1: Paul Barnhurst: All right.
[00:26:12] Guest: Gabriela Gutierrez: Happy to have you here.
[00:26:13] Host 1: Paul Barnhurst: Cool. We'll have to figure that out. So we know you love entrepreneurship, starting companies. You've done more than one of those. You love technology. So we want to ask this question before we jump into our get to know you better section, where we have a little surprise for you. So what's the coolest thing you've done with generative AI?
[00:26:33] Host 1: Paul Barnhurst: So far? What's maybe your favorite or unique thing you've done?
[00:26:37] Guest: Gabriela Gutierrez: Yeah, I think I can give you an example of what I was working last week. So it was really about thinking about the branding of the company, thinking about the logo, thinking about how we would want to design the website and so on. And I went into many different platforms to get some inspiration, like Event Rebol and so on. And what I noticed is that, like I could just write a prompt, like with generative AI with flux, and which is a competitor to Midjourney. And like the inspiration, like there was no limit. I could just like really create an entire brand based off of like taste colors that I would be choosing. So I think like, yeah, like just creating the entire branding and the design of the company using generative AI, that was mind blowing, like a very nice exercise. And it took maybe like one hour, one hour and a half and I would already have a very clear direction.
[00:27:43] Host 1: Paul Barnhurst: Nice. That's a really good example. When we did our podcast, Glenn used generative AI to create our logo. And then we tweak the rest of it in Canva.
[00:27:53] Guest: Gabriela Gutierrez: Okay. Yeah, that's super cool.
[00:27:56] Host 2: Glenn Hopper: Until I finally get my EA, I'm offloading everything I can to generative AI.
[00:28:01] Host 1: Paul Barnhurst: So when are you getting that EA? Glenn? Come on. We need more of you.
[00:28:06] Host 2: Glenn Hopper: I don't know. Well, I'm waiting on OpenAI, you know. Strawberry. I don't know if you guys saw, but the new reasoning agent that they've been building at OpenAI, it looks like that's going to drop this fall. I'm hoping that it can be reason enough to schedule meetings for me and do all that kind of stuff, but I think we're going to get the reasoning improvements this fall. And then at some point, maybe late next year, we're going to get GPT five. And if I can hold out and not have the expense until then, I'm just going to turn it all over to the bots. And then I'm even going to do AI generated videos of myself for that. I'm not going to do any of my work. I'm going to be phoning it in from the beach.
[00:28:42] Host 1: Paul Barnhurst: Let me know how that works for you. What do you think? Do you think you'll be able to get to that point? I'm not convinced.
[00:28:49] Host 2: Glenn Hopper: Yeah. So I don't know. I figure I've got probably ten years to retirement, and I'm hoping maybe I can decrease that glide path a little bit by leaning more and more into AI. We'll see.
[00:28:59] Host 1: Paul Barnhurst: All right. Well. I'm sure you can hopefully help a little bit. We'll see how that goes. Once it takes over for all of us, we'll just all retire. And then we'll all go to San Francisco and hang out together.
[00:29:10] Guest: Gabriela Gutierrez: Yeah. Why not.
[00:29:12] Host 1: Paul Barnhurst: Yeah. Why not.
[00:29:13] Guest: Gabriela Gutierrez: So our sun is shining here almost every single day.
[00:29:16] Host 1: Paul Barnhurst: I don't know, I've lived in the Bay area. There's plenty of cloudiness. That fog that rolls in.
[00:29:23] Guest: Gabriela Gutierrez: Yeah, and it's true in the early morning or late afternoon. But midday, the sun is there.
[00:29:29] Host 1: Paul Barnhurst: Arizona had plenty of sun. The problem was it was too much.
[00:29:32] Guest: Gabriela Gutierrez: Oh, yeah.
[00:29:34] Host 1: Paul Barnhurst: All right. So this section is a get to know you section. These questions I have are from Gemini. I have 19 questions here.
[00:29:43] Guest: Gabriela Gutierrez: Oh wow. Okay.
[00:29:44] Host 1: Paul Barnhurst: What I'm going to do is you got two options. These were randomly generated. One, you could pick a number between 1 and 19. And we'll get to ask you that question. Or I can use the random number generator to pick a number between 1 and 19.
[00:30:00] Guest: Gabriela Gutierrez: Okay. Let's choose a random generator. Yeah.
[00:30:04] Host 1: Paul Barnhurst: So far I think everybody's chosen the random number generator. Like, what's the difference? I might as well just go all random. All right, number 16. This is a pretty good one. If you could have dinner with any historical figure, who would it be and why?
[00:30:23] Guest: Gabriela Gutierrez: That's a really good question. Okay, I need to think about that one.
[00:30:28] Host 2: Glenn Hopper: Doo doo doo. Doo doo doo doo.
[00:30:31] Host 1: Paul Barnhurst: We hope you enjoyed that interview with Gabriella. Unfortunately, we weren't able to ask her. A couple of our AI generated get to know you questions due to some technical difficulties, but we enjoyed having her on the show and loved her sharing her experiences and just talking about how learning Python, learning about AI has helped her and helped her in her role, and how it can help all of us. So we hope you enjoy this interview and we will bring you in the next interview and future interviews. Those AI questions because we love to ask our guests and get to know them a little bit better. So I just wanted to explain if it felt like a little bit of an abrupt ending this week, there were some technical issues that we had, and unfortunately we weren't able to get through all the questions we normally would. But thanks again for joining us on Future Finance. Thanks for listening to the Future Finance Show, and thanks to our sponsor, qflow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.