How to Use AI to Research Stocks (With Brian Feroldi)

Transcript:

On this episode of the Personal Finance Podcast, how to use AI to research stocks with Brian Aldi.

Woo. What's up everybody, and welcome to the Personal Finance Podcast. I'm your host Andrew, founder of Master money.co, and today on the Personal Finance Podcast. We're gonna be diving into how to research stocks by using ai. If you guys have any questions, make sure you join the Master Money Newsletter by going to master money.co/newsletter.

And don't forget to follow us on Apple Podcast, Spotify, YouTube, or your favorite podcast player. And if you wanna help out the show, consider leaving a five star rating and review on Apple Podcasts, Spotify, or again, your favorite podcast player. Now, today we're diving into something that could completely change the way you research and analyze stocks without replacing your judgment or turning you into a robot investor.

Now, if you've ever stared at a 200 page 10 K and thought there has to be a better way, this episode is for you because AI won't make investment decisions for you, but it can make researching businesses dramatically faster, cleaner, and way more structured. And to break this down, I brought in the perfect person to talk about it.

Who is Brian Aldi, a long-term investor, author, and educator known for making fundamental analysis simple and approachable. Now, Brian has been on the show many times. I think this is his fifth time on the show. And anytime we're talking about stock investing, we're bringing Brian on the show. Now we're gonna unpack how to actually use AI like a junior analyst, how to give it structure so it finds the signal not noise, and how to demand citations so you know you can trust the data.

Plus we're gonna talk about how to break down companies into business models, moats, financials, and risks without getting lost in a black. Box, and we'll even walk through a live demo of prompts that Brian uses in real life to break down company step by step. The same kind of workflow that can help you analyze businesses faster and smarter.

Now, if you want to watch as we do the live prompts, you can watch us on YouTube. Again, search my name, androgen Cola on YouTube, and you will find us there and we're gonna be doing live prompts and sharing the screen on YouTube. So that's a great place to watch this if you want to, but you can also listen along if you are listening to the audio version.

So this is a great episode and action packed episodes. Without further ado, let's welcome Brian to the Personal Finance Podcast. So Brian, welcome back to the Personal Finance Podcast. Andrew, awesome to be here. Thanks for the invite. I am really excited to have you here. 'cause I think today we're gonna be going through a bunch of cool things that you can utilize AI when you are investing in stocks.

And we're gonna do a bunch of research here and we get a lot of questions about this. This is one thing I think most people are trying to figure out. A, you know, how do I utilize AI when it comes to my finances? B, how do I utilize it when it comes to investing? And I think it's gonna be a very powerful investing tool.

It is already today, but then it's gonna become even more powerful here in the near future. So we're gonna just get right into. Some of the tactics that we wanna dive into. And most of you have heard Brian on here before. Brian, this is probably your fifth time on here, I think. Uh, and every single time that we have you on here, you know, a lot of people come away with a lot of tactical things that they can do right away, which is why I absolutely love having you here.

So this is gonna be really, really fun. So first I want you to kind of talk through just your long term mindset. Uh, when it comes to investing that you learn from Buffett and Munger, 'cause you and I align on this a lot and we are long-term investors. We are investors who like to invest long-term. So can you kind of talk through your mindset and how you think about that?

So I tried every investing style that exists out there and I. Failed miserably on every other style. And it wasn't until I started to study the investing greats like Buffet, uh, like Munger, like Peter Lynch, that I really found a style that fit my personality well and actually generated positive returns for me.

So if you study the biggest investors, the best investors of all time, they all talk about the same fundamental. Principles. Think of stocks not as tickers, but as businesses only invest in businesses that you understand and you think have good long-term growth prospects use valuation techniques to buy those companies below their intrinsic value and let the companies do the hard work before doing any of that style, which is my current investing style.

I tried day trading. I tried penny stocks. I tried going for high dividend yield stocks if I was just starting investing today. I guarantee you I would be on Robinhood trading crypto. I would be trading options. I would be doing all the things that new investors are doing today simply because they don't have the right mindset for actually building wealth in the market.

So there's nothing wrong in my book with trying to create money in the short term through the market. I just don't know how to do it reliably. So my fallback is buy and hold great businesses for the long term, because that's the style that works. I went through the same exact cycle where I was trading penny stocks.

I day traded for six months full time. Literally did not make any money. I lost money while I was doing it, but for six months I thought I could figure it out and just never did. I started to invest in just a bunch of different things and then realized, okay, long term is the way to go. Because every single time I try to figure out, you know, how to time the market or anything like that, I'm wrong.

And so for now we're gonna go long term and that's when I started to kind of go into, you know, individual stocks, index funds and ETFs, those types of things. So that was a big, big difference maker for me. Now. AI helps a lot of people with a lot of different things right now, and specifically it can really help you when it comes to researching specific things.

So why do you believe AI can kind of help us accelerate our process when it comes to researching as an investor? Because this is gonna be a tool that a lot of us can really help utilize. We're digging through 10 Ks and all these different things, but instead, how does it actually help someone, like an investor out there, uh, accelerate their process when they are researching stocks?

So AI by its basic design is a wonderful resource for investors to use because at its core, AI is really fantastic about ingesting gargantuan sums of data and then using what it learned from the ingesting that data to help. Users come up with insights from huge amounts of information. When you boil it down, investing to its core, or investing the way that I do, which is individual company analysis, and then adding them to my portfolio, that's a perfect use case for what AI is built for.

So with ai. You can take a long multi hundred page annual report of a company, upload it to ai, and if you prompt AI the right way, it can parse through all that information very quickly and help you to glean some insights, uh, from it. It can also go through conference calls. It can go through earnings reports, it can go through proxy statements, and it can explain to you in plain understandable English, what you need to know about that company.

So it dramatically lowers the skills that you need to get started with analyzing, uh, individual businesses, and it can. Act as a tutor slash buddy for figuring out what the terms mean. So it is a godsend for new investors that are trying to learn how to analyze companies, I think, and that's the most powerful thing is for most people out there, this can tremendously help you, especially if you're a brand new investor.

This is gonna help you so much more than what we used to have to do, read, have to kind of read all these books and figure out what we need to do. I have to figure out, you know, how do I even look at this 10 K and how do I read through all this stuff? And so this is gonna help you tremendously when it comes to that research process for sure.

Now. Can you explain your junior analyst analogy and how that mindset helps investors utilize AI more effectively? Yeah. If you are going to be using AI to help you research companies and make decisions, it's really important that you approach AI with the appropriate mindset. And I stole this from somebody else, but they basically said, think of AI as a junior research assistant, or think of them as an intern.

If you were a fund manager and you have analysts working for you, you would not outsource all of the key critical. Information to that junior analyst, nor would you just did that. Junior analyst unlimited control over what it does. If you were gonna be using an intern or an analyst the right way, you would give them a specific task, give them a specific set of instructions to follow.

You would limit the number of resources that they could use, and you would have that analyst or junior analyst ask you questions to make sure you're getting the information that you want. As an example, if you've ever gone to chat CPT or Claude or Grok and say, Hey, is Amazon stock a buy? That is way too broad of a question to possibly ask an ai because it doesn't know what specific steps or instructions you wanna follow.

So it will do exactly what you tell it to do. So it might go out and look at Amazon's recent, uh, earnings report. It might look at sentiment, it might look at technicals. None of that might be, uh, useful to your specific investing process. So if you don't constrain that the AI into the instructions that you specifically want it to do, it's not gonna give you anywhere close to the right analyst.

The same way if an intern fresh outta college, you said, Hey, is Amazon a buy? If it knew nothing about your investing philosophy or your investing process, it's gonna go out, find information, and present you with information that is basically. Utterly useless to what you want. So if you have the right mindset of thinking of the AI as a junior analyst, that's really gonna help you to come up with a prompt to give that analyst instructions to follow, to make sure you get exactly the information that you want and none of the information that you don't.

I think that's one of the most powerful mindset shifts that people need to have is you can't just take every single thing that it says, kind of utilize that information to take action. You have to utilize it as someone who is an advisor or a junior analyst in your life that can kind of help you make decisions as time goes on.

Now, when do you think investors should kind of think through and utilize their own judgment? 'cause your judgment still matters in today's day and age. So when should they utilize their own judgment rather than ai? Yeah, you have to be the ultimate decision maker. After all, if you're gonna make any investment, this is your money that's on the line.

And if the AI makes a mistake in the analysis, it's going to cost them nothing. And if anybody here has been using ai, you've probably noticed that the AI gives you. The wrong information or the analysis back, nothing related to investing. And then when you push back or correct the ai, at least for me, it always says you're right.

I totally overlook that way to go. And it has a very positive bias to it. So I would never outsource all of my decision making and thinking to ai. That would be a bad use of ai, especially with investing. Again, this is my money that's on the line. Um, so I wanna make sure what AI is doing is the task that it's most well suited for.

Go out. Find information, ingest information, analyzing it, using the rules that I said, and then present that information back to me so that I can make a more informed decision. That to me is using AI the right way. It is, and I think there's all these memes that are coming out now where I saw this commercial the other day where a kid was walking into a gas station and the, the gas station attendant, you know, asked him, Hey, how's your day going?

And he looked at his AI and said. My gas station attendant just said, how's your day going? What should I do next? And like, that's how some people use ai. They think of it and they use it as their only thoughts, especially with stuff like this. And you gotta make sure that you're still utilizing your own judgment because it's really, really important, especially as we start to go.

And AI keeps advancing, and this is gonna be really, really important for a lot of people to be able to do that. So a lot of people also worry about AI hallucinations. And so how do you personally make sure that the information you're seeing is trustworthy and kind of vet that information? Yeah, this is a huge, huge problem.

Again, if you've used AI for any decision making or research in any other aspect of your life, you've probably see it bring back information and you just intuitively know that's wrong or that's not right. And AI tools that are out there are unbelievably complex and and sophisticated, but. They can provide you with wrong information or they can make up facts and then present them to you.

So that is a huge problem. In fact, when I was doing research on how to use AI the right way, I was talking with lots of investors that are in my community, and this was the number one concern that they had before they would bring AI into the research process. They were basically, how can I possibly trust the information that's coming back from ai given the.

Hallucinations that happened. I do think there are steps that you can take to minimize the number of hallucinations that exist and to make sure that you can actually trust the information that comes back. But again, this is why you can't ask it broad unbounded question like is a B, C stock a buy? You have to get much more granular than that, and you have to provide it with very detailed, highly specific prompts to make sure the information that you get back is trustworthy.

That's, I think one of the most important things is kind of breaking it down and understanding how to prompt it properly. I know for a lot of times we do a lot of really deep research with AI when it comes to just even looking at historic market data, or we'll look at statistics and kind of break down some really advanced statistics when it comes to people's finances and when we do that, if I try to do one big, large prompt, it is something where I don't get the information back that I want, and so I have to.

To break down the prompts and the research into much smaller chunks. So why for you is breaking, you know, research into smaller modular prompts so important? Yep. So, uh, with any complex task, again, researching a stock from scratch, at least from my process, there's lots of little micro tasks that are embedded, uh, in that question, such as what does the company do?

What does its business and business model? Another task is what is the company's moat and competitive advantage? Another task is what are the company's financial statements? Another one is, what are the company's growth prospects? Another one is, what are the. Company's risks. What are the company's management teams?

What's the company's valuations? Et cetera, et cetera, et cetera. Again, if you ask a very broad question to analyze a business, and it's gonna have to go through each of those steps. Um, if you don't break those steps down into miniature steps for the AI to tackle on, um, it could be reasoning up from faulty information.

So if it gets the business and the business model wrong, how could it possibly have the moat and the growth potential, right? If it's literally looking at the wrong company or the wrong competitive advantage. So breaking. Complex task down, like researching a stock into smaller steps and you can verify the information of those steps as you go, allows you to use building blocks that you can trust and building on top of that as you research up.

So yeah, just an overall great thing to do with AI in general is to take big tasks. Break them down into smaller micro tasks. Verify the information of each of those tasks as you go, and that will give you much more confidence in the final output. And for those of you out there listening who just have not been prompting properly, or you feel like you've been using AI and you use just, you know, a couple of lines and all of a sudden.

You know, you're getting all this information back and it's not as detailed as you'd like it to be. If you break it into smaller chunks, like what Brian's talking about here, this is gonna make a big, big difference and just get way more granular and specific. That's gonna help you tremendously. And if you develop processes like we're talking about today, that is gonna change the way in the information that gets spit out to you every single time you have these conversations with ai.

So when it comes to this, so we're talking about ways to research. We're getting this information back. How do we kind of gauge the difference between our own judgment and what AI is telling us? How do we make decisions based on having two sides of the coin? Because we could have previously believed one thing, AI spits out some different information.

So how do you kind of utilize your judgment and balance that with AI's information? Yeah, so there's a couple of core prompting techniques that I think are just good AI hygiene that investors can use to make sure that the information that they're getting back is as trustworthy as possible. And again, if you don't have full confidence and full trust in the process and the information that you're getting back doing this analysis will be.

Utterly useless to you. It won't give you any advantage at all. Having conviction in the information and making decisions, making on solid information is absolutely foundational. So there are three techniques that I think listeners should know, and if you do all three of these techniques, I think your confidence in the information that you're getting back would just be sky high.

So one technique that I think everybody should do when using AI is to assign AI to be a specific role as a really simple example. Let's say you're a value investor and Warren Buffett is your investing curio hero. One of the things you should put into the prompt is act as Warren Buffett. Mm-hmm. Act as Warren Buffett.

If you give it that simple one sentence upfront, what AI will do is go through its vast database of information that's using to making decisions, and it will. Prioritize information that is pulled from analysis on Warren Buffett. So it might use Berkshire Hathaway's annual meetings, transcripts. It might use interviews that Warren Buffett has done.

And immediately the AI will know that you're looking at value investments. It might look at Warren Buffett's portfolio to pull information from. It'll know what the terms like moat and management mean. It'll know to emphasize factors like return on equity and competitive advantage, and that simple one word phrase.

Act as Warren Buffet will dramatically improve the quality of information that comes after that. And of course you don't have to live in it to Warren Buffett. You can say act as a value investor. You can say act as Joel Greenblatt. Uh, my favorite investor to follow is a guy named Terry Smith of a fun Smith.

So I would say act as Terry Smith. If you're a short seller, you could say act as a forensic accounting. So assigning the AI a role upfront, wow, that one step alone will dramatically increase the quality of information you get back. A hundred percent. And I think that is one big thing that you can use in everyday life.

You know, across the board, I've even done this where I have utilized AI and assigned it a role based on, you know, specific athletes. So one big thing I did over the course of the last year was trying to get in the best shape I possibly could. And so I looked across the board and I was looking at, you know, different athletes out there and what they are actually doing.

And so if you can assign that role to a specific person, it will act in that way and it'll help you kind of make decisions. And I think that's really, really important. So you also have. Some other core prompting techniques. So the first one is assigning a role, making sure we have that role in place, but what other techniques can you talk through in terms of how people can actually prompt AI to make sure they get the right answers back?

Yep. So prompting technique number one, force it to assign a role. Technique number two is to force it to use. Citations and force it to look up information from resources that you trust, that you personally trust. Because if you're restricting the resources that it can use to create that information, you're building on a solid foundation.

Now, for me, when researching companies resources that I trust are very, very few and far between. I trust the. SEC filings. I trust information that comes directly from the company, and then I trust a few other sources such as Morningstar's Moat Scale. So when it's presenting with me with information, I force it to put a citation with a link to where it got that information from.

So as I'm reading the results of the prompt, if I'm like, where did it get that information from? Or why is it saying this? If you click over to the citation. It will take you directly to the SEC filing or the company filing that that information came from, so that you can confirm easily that the information that it's building on top of is correct.

So again, if you limit the places that the AI can pull sources from, and you force it to give you a citation of where it came up with that source from, with a direct link, that dramatically increases the trust that I have and the information it's giving me. For sure, and I think these direct links have have gotten so much better.

I remember early on prompting ai, like, where'd you get that information? It would try to pull sources from just these random places, but once it kind of understands the core sources that you want to have in place, it'll remember that information. It'll make sure to pull from these places that you actually wanted to pull.

For example, one of the places we always wanted to pull is all the Federal Reserve data that comes out when it comes to consumer spending and finances and those types of things. And so if you can kind of set it up in a way where it knows what sources you like, then that's gonna be really. Really important.

And then you also talk about when we're prompting, we need to make sure we have the proper order and the proper steps in place. Can you kind of talk through how we set that up as well? Yep. So step three here is to give the AI a step-by-step instruction to follow, to make sure that it is following the process that you would use to analyze a company its own way.

Uh, so for example, when I'm analyzing a business for the. First time, the very first thing I wanna know is, what does this company do? What are its key products and services? Who are its customers? And how frequently do they buy? What geographies does the company operate in? What are the revenue segments by product line or category?

And then I also wanna just know about the company. Do I think this company can raise prices in the future? What happens to this company, uh, during a recession? Are its product and services highly cyclical or are they recession proof? So a prompt that I built. It helps me do exactly that. Uh, so the prompt goes step by step through answering the specific questions that I wanna know about any business in the order, and then it presents it back to me answering it in the exact stepwise function that I have.

So not only are the prompts that I've built, but. Built out to answer steps in a stepwise function. But I use the prompts to analyze one part of a business, and I give it specific instructions for how to do so. So if you have an investing checklist that you use, or again, if you want it to analyze a stock like Warren Buffet, you can literally say to the AI follow Warren Buffet's investing checklist, and it will know the process that it should use to research information.

So if you tell it, do A do B, do C, do D in that order and use citations. The trust that you'll have and the information that comes back to you will be sky high. And this is why it's so important, I think, for a lot of people out there to start to build out a bank of prompts that they're gonna be utilizing, you know, more frequently.

'cause a lot of people, what they do is they throw it into chat, GPT or whatever AI they're utilizing, and then all of a sudden these prompts just kind of disappear. They forget where they left them and you gotta build out the whole prompt again. But you gotta make sure that you have this database in place that kind of helps you when it comes to getting started and utilizing those prompts on a regular basis.

So. Brian, if we can, can we jump into a live demo to kind of show what this looks like and maybe we can analyze a company like, you know, IREN or IRE and kind of go through step-by-step using your prompting system. And for people listening here, if you want to watch us visually do this, you can join us on YouTube.

If you're only listening to the audio side, you can join us on YouTube and we will share the screen so that you can see exactly how this works and what goes on here. Yep. So let's do that. So IREN is a company that you know well and honestly I do not know it well. So I'm gonna copy and paste over, uh, a prompt that I have here.

Let me just walk through this prompt so that people can understand. And by the way, we'll give you free access to this prompt. So this prompt that I call is called the business analysis prompt. And right up top, I tell the prompt I'm gonna zoom in here so you can see this a little bit easier. For anybody that's watching at home.

So I'm saying you are executing a business analysis prompt, and I'm telling the AI follow each instruction precisely in order. The next thing I'm saying is your identity is you are an expert. You are an expert in financial analysts specializing in business model analysis from SEC filing. So I'm giving it a roll right up top.

And then I'm saying your mission is to request the company name from the user. That's us. Analyze the most recent 10 k, answer the questions that I have down below about the company's business model. Output them in a clean markdown filing and provide information, answers, and being not too brief, but not overly detailed.

And then there's some execution data in here. There's a specific sequence that I want it to go through. A, B, C. There's a verification process that goes here, and then there's the output method that I want. I also insist on original sources. So this is a bit of a meaty prompt. Again, people that are listening will get free access to this in just a little bit, but I've put that in there.

And then if it does this correctly, chatt, PT should then say, okay, I understand. What company do you want me to do this analysis on? And that's the thing that I've learned. Have the AI in just the information, and then ask you afterwards what company you wanna do on, rather than having to go into the prompt itself and put the company name on here.

So I'm gonna skip over and just say what company? Ticker simple. Do you want me to analyze and I'll retrieve the most recent information as of today's update? So this is IREN and I'm gonna push, um, enter, what's the name of the company, do you know? Yeah. The full name is, uh, ire, IREN. Limited. Is the full name.

Yeah, so it's, I find it's best if you do the company name and the ticker symbol. So Iran Limited and the ticker is IREN. So let's go ahead and put that out there. And here's what we got back. So let's read right at the top. So it says, using iRead Unlimited's 10 K. So it's annual report and. As of June 30th, 2025, and it's 10 K from June 30th, 2024.

And right next to it here, Andrew, where it says SEC filings. So there's the link, and if I click that link, it should take me right to the annual report from 2025. This is where. This prompt is pulling the information from, so we have confirmation that this is the right company and this is the most recent 10 K.

So that gives me a heck of a lot of confidence in what I'm about to read. Exactly. I think that's one of the most important things people need to understand is A, we're pulling from the correct location, the SEC filings, somebody lies on this, they're going to jail. So this is a huge, huge deal, for sure.

Yeah. Okay, so, right. I have this prompt program to say, what is this company? Do so let me read back and you can tell me if this is correct. I Ran, which used to be called Iris Energy, is a company that operates in the digital infrastructure slash blockchain slash AI cloud space. IT activities are bitcoin mining, AI cloud data center services where repurposes or augmented infrastructure, and it also generates other income from.

Electricity resales and participation in programs. Now, each of these are point by points and there are links next to each of those bullet points, so I can double check if I want to click where it found that information. So it says, overall I ran is a hybrid between a crypto miner and a cloud GPU infrastructure provider.

Is that accurate? That is accurate. And the thing that big one for sure is the data center services, but that is a hundred percent accurate. Okay, great. So it got that one correct. Next one. Next question I have is, how does IREN make money? This is a key question for me. So based on its 2024 and 2025 disclosures, revenue sources include Bitcoin mining activity slash proceeds from the sale of mind, Bitcoin, which is its largest and foundational revenue driver.

AI Cloud services revenues, although, although small, this is a growing vector and it brought in 3.1 million in sales in 2024. And then other income include electricity resales, demand response program gains of financial assets. So in last year, AI cloud was negligible, just 3.1 million compared to mining income.

So mining dominates and compare it to 501 million in total revenue from its Bitcoin mining business. And again, there are SEC filings. Links next to all this information is, is that accurate? It is for sure. Accurate. And then for 2024 and then 2025, which is what's gonna be kind of interesting is we'll see a, a drastic difference in some of the data center stuff, which will be interesting.

So if you've never heard of this company before, I know it's a Bitcoin miner with GPU Cloud services businesses, and Bitcoin mining is the vast majority of sales right now. But this AI cloud services business is growing, uh, rapidly. Great. So next question, who are this company's customers? So it says, for Bitcoin mining, the customer is just the open market, right?

It sells bitcoins to the open market, uh, for cloud services. The customers are third party compute clients, like AI firms are enterprising that need GPU capacity and for the electricity, such resale, demand response, counterparties could be grids or local utilities. What do you think of that response? That response I think is good.

And I think the order of operations at the grid and local utilities thing is, I think is a huge portion of what's gonna be coming forward, which is interesting. Okay. Next, where does the company operate? So this company is incorporated in Australia and uses US operations for its mining and data center assets.

It's revenue is not broken out by region, but its major operational assets are in the us. Such as Childress, Texas for mining and data center expansion. And the geographic exposure is mostly US operating assets with its corporate parent in Australia. Is that accurate? That's accurate, yep. Great. Next business dynamics.

So these are some custom questions that I wanna know about every company. The question I have is, so how often do customers buy? For me, this is a key question I wanna know, do customers buy from the company continually, or is an occasional purchase every couple of years? I prefer customers that buy continually.

So what's it say? It says in mining, revenue is continuous and operational. Bitcoin is mined every day and sold every day regularly. Um, in ai, cloud services, contracts might be recurring like a multi-year lease, but as of now, they are a small part and likely early adoption with multi-year commitments. And then retention is nascent in the AI business, but in mining, retention is not applicable because it's not a contract business, it's selling to the open market.

Is that accurate? That's accurate. Yep. If we wanna look up where to get information, we can just look at the SEC filings. Looks like this one came directly from the company's most recent earnings report. So the information that's pulling from everything that we're talking about here is sourced. Is sourced directly.

Yep. Next key question for me is, can the company raise prices? Warren Buffett says that the number one most important factor of any business is pricing power, the ability to raise prices. So let's see what it says. Okay. In mining pricing power is weak. Bitcoin is a commodity. The price is set by the market, so the company's ability to make profit depends heavily on electricity costs and mining efficiency, not markup in AI cloud services.

There may be more pricing power, especially if demand outstrip supply, but competition is intense and customers may negotiate. And the 10 K mentions risk factors about. Exposure to volatility in crypto prices and electricity. Cost pressure and their margins will depend on operational efficiency, discounting, and capacity utilization more than pure price marketing.

Is that accurate? That is accurate. So far, so good. Then. Okay, next question I wanna know is what happens in a recession? I like to invest in companies that are recession resistant and have demand for their products no matter what's happening. So what's it say? It says Bitcoin demand or price may drop sharply during economic stress hurting its mining revenue significantly.

Cryptocurrencies tend to be volatile and correlate with risk appetite. Operating leverage and fixed cost structure such as electricity and depreciation means margins, pressure if revenue collapses and the company's annual report notes uncertainty about its ability to raise capital and sustain operations under stress.

So last year, they flagged significant uncertainties exist about the company's ability to generate positive free cash flow and raise capital. And that suggests that the business may be fragile in a downturn if macro shocks reduce crypto valuations or raise borrowing costs. Would you agree with that? I would agree with that.

And here's one thing I wanna point out to people listening right now is this is a great example of how this prompt is gonna help you when it comes to becoming a long-term investor. So if you're a long-term investor and you read something like this and you go through here and this research comes back, this means you wanna dive deeper into this area.

Because if you think that this is not a recession resistant business, that's a huge problem for long-term investors. 'cause we wanna hold these companies for long-term. And so this can help you make decisions based on that. This is just one example of many that we're going through here. Yep. And then the final thing here are sources.

So it has links to all the sources that are used to create the document. So it has SEC filings from 2025, the annual report, and 2024. It's got the most recent earnings and public failure from stock analysis.com. And then it's got recent results commentary directly from the company's investor, relat. And I'm using chatt, PT here and Chatt PT allows you to click directly into any of these links and it'll take you directly to the source.

Now, as we showed, this was a prompt that followed the analysis to say we, we gave it a specific role. We gave it specific step-by-step instructions to answer the questions in order, and I insisted on direct sourcing of information with links. So while. I've never researched this company deeply before.

Based on this, I really trust what AI put back to me because it ed the key principles of prompting. Exactly, and I think that the huge key here is, again, remember these are pulling from the proper sources, the sources that you actually wanna be looking into. It's not any hype or any extra, you know, fluff that's out there.

Instead, this is pulling from these sources where you want the real information, the information that you know, actual investors will be looking at before they invest it in something like this. Alright, well we have plenty of time. Do you want me to do another prompt on this company or do you wanna do a different company?

Sure. Let's do a different company that maybe we'll do one that more people know and we can try to do one that makes sense that goes forward there. So we can do a blue chip if you want, or we can do something else. Whatever you'd like, man. I'm, I'm happy to go whatever you want. Let's do, um, yeah, let's do, let's do Apple.

That's perfect. We'll go with Apple. Sure. Alright, so we're gonna do Apple next. We're gonna do a prompt for Apple. Since a lot of people probably know what Apple does currently. This is gonna help you see what the prompt actually spits out when we stop chat. GPT. Let, let me put the prompt in. Um, same prompt, copy and paste.

We're gonna go through that same process there. If this prompt is working correctly, what I should get back is what company do you wanna research now, which is exactly what it says, and I'm gonna say Apple, A-P-P-L-E, and I'm also gonna do the ticker symbol A A PL. I find it's good to do both the company's name and the ticker symbol.

All right, so here we have a. Same prompt, and it says right at the top, I'm using apple's 20 24, 10 K, which ends in fiscal year, September 28th, 2024, and the Q3 of 2025, the most recent quarterly report, which is from business ending June 28th, 2025. So it's using the most recent annual report and the most recent 10 Q or quarterly report, so we can at least have confidence that it's pulling from the most recent information.

All right. What does Apple do? And Chatt says, apple designs, manufactures and markets consumer technology products, hardware, and delivers a suite of software services and digital content. Its product line includes iPhone, iMac, iPad, wearables, home accessories, and its services include the app store, iCloud, apple music, advertising, cloud services, and licensing.

That sounds pretty accurate to me. Yep. Great. Question two, how does Apple make money? It says, Apple's revenue is divided into two categories, products and services. As of the six months ending March 29th, 2025, here's an illustrative breakdown. So a. Products, which are iPhone, iMac, iPad, and wearables were $167 billion out of the 220 billion total.

So 76% of total revenue was products base, and then services were $53 billion outta the total, so 24%. Of the company's revenue was services based, and it says within products, iPhone is typically the largest single component with iPhone accounting for $47 billion as of the quarter ending March 29th, 2020, 2025.

So again, if I click into one of these here. Where did it get this information from? Actually, I'm gonna go back and I'm, I'll click into the most recent 10 Q to see where it got that information from and it should pull up the 10 Q. So this is the company's most recent 10 Q, and if I scroll down, I can see the, the split between revenue and products and services.

And this says, let's see. 219 billion in total revenue. Is that what it said? So there's the two on 19. Yep. 6, 5, 9. I have the direct source of information from there. 166 billion in products, 166 billion in products, and then 53 billion in services, 53 billion in services. So I can click and see where did it come up with this information from?

I got the direct link and we verified manually that that information was 100% accurate. So good job chat chi pt. Love that, and I think that's why it's so important to prompt this properly, 'cause you have those sources linked up and so you can see, and you can double check. This is also a very important thing to do is when chat GPT or whatever you use, spits this back out, make sure you're double checking some of these numbers and kind of going back and forth to ensure that it's actually giving you the right information.

Yep. If you're gonna be making investing decisions, make sure you're making them based on solid information, and we've done everything we can to make sure the information we're getting is solid, but it doesn't hurt to double check. Okay. Exactly. The next question is, who are the customers? Well, according to Chate and customers such as individuals purchasing apple devices, enterprises, and institution, using Apple devices services and deploying with an IT infrastructure.

Uh, developers and content providers and third party licensing technologies using Apple's Cloud advertising platform. I'd say that that's pretty accurate. Yep. I would a hundred percent say that. Yep. All right. Next, where does the company operate? So Apple operates globally with significant revenue coming from multiple geographic regions.

As of Q2 of 20 25, 40 2% of sales were from the Americas. 92 billion, 26% of sales were from Europe at 58 billion greater China was 16% of sales, and Japan was 7.4% of sales with the other regions making up the rest of the balance. So it says. US Americas, Europe and China are the company's largest and most important regions.

So how useful is that? Right? Immediately we can know that Americas are the most important segment, but Apple has more sales outside of America than inside it. This is part of my argument about the international when looking at index fund portfolios and part of the international exposure of all these US companies that are operating out there, and you can figure out how much international exposure they actually have by doing something like this.

Yep. And figuring this out, you can use SEC filings to figure this out, but isn't it slightly easier to have chat TBT do it for you 100%. Yep. I mean, you're saving hours of time right here, for sure. Next, business dynamics. How often do customers buy? So what's it say here? So hardware tends to be purchased periodically.

Devices upgrade every few years and seasonally around launches. That makes perfect sense to me. Services tend to be recurring in nature, so iCloud, apple Music, apple TV are more subscription based, and App store fees and licensing are more perpetual. Apple reports deferred revenue for some services, like prepaid services and expects portion to be recognized over one to three years, and it had.

13.6 billion in deferred revenue. Two thirds of wisp will be recognized in one year. How's that for granulated information? Yep. And then finally, high retention is services is implied by the ecosystem lockin and switching costs. So I've been an Apple consumer for many years and I would say that that is spot on analysis.

A hundred percent. I think they nailed it right there. Next, can the company raise prices? So what's it say? Apple has some pricing power, especially on premium devices via its brand value in its risk. Exclosure, apple site's currency fluctuations, competition and components cost, pressure and constraints.

Service margins can expand and Apple can raise prices significantly over time on cloud tiers and subscription bundling, however, aggressive increasing risk customer pushback. And competitive situations. Again, I would pretty much agree a hundred percent with this. Me too. Uh, next question. What happens in a recession during economic downturns demand for discretionary hardware?

Like smartphones, computers may soften. Services tend to be more resilient. Apple scale, strong balance, sheet diversified geography and ecosystem helps it to weather volatility. And in past recessions, the company maintain margins through cost control, supply chain management, and focusing on high. Margin services.

So, and then saying, I can build a sensitivity analysis for Apple compared to its peers if we want to, and then we have all of the sources pulled from there. So, yet again, if you are a brand new investor or Apple investor, I bet that you, even if you've been investing in Apple, I bet that this prompt might even teach you something about the company that you've owned and think, you know so well.

I think so, and I think that shows right there. You know, we looked at one example of a company most people probably don't know. We looked at another example of Apple, which most of you will know and you'll know even if you haven't invested in Apple before, you know all about its products and its services and everything it offers.

And this shows how powerful this prompt can be to get you started. It's a starting point where really you can look at the analysis of these companies and save yourself. Hours and hours of time. I mean, to find out all this information, in the past I would've spent hours just reading through 10 Ks and making sure the information is correct and did I remember that correctly?

Did I write that down properly? And so this is all in one spot, and I think it's really, really important for people to be able to utilize this. It's really, really powerful. So this is absolutely awesome. And have you kind of started and utilize this stuff, you know, in your everyday journey? Is this kind of where you start is with a prompt like this when you're looking at a new company?

So I have a series about nine prompts that I go through. Uh, so that's the first prompt that I do like. To me, question number one is always what is the company and what does it do? Because if I, once I understand what the company is and how it works, I'll know, am I interested in this company and learning more, or am I not?

So I think this is a great first, first filter. So this is one of several prompts that I have. I have prompts that just to analyze the company's moat, I have just promise to analyze the company's financials and key metrics. I have promises to analyze the company's management team, the company's risk, the company's valuation, company's opportunity, and more.

So this is the. A first prompt in a series that I use, but it's absolutely been a game changer for me to analyze companies quickly. And so if someone out there wants to start using AI for fundamental analysis, you know, going forward, what are some of the most important things that they should remember based on, you know, what we talked about here today?

Yep. So key things are one, give it a roll. Assign it a roll. Step number two, limit its uses of sources and insist that whenever it gives you information back, that it puts a link to the source that it pulled that information from. And then three, give it a stepwise function. Now do A, do B, do C, do D. If you have a checklist that you used to make investments, upload that checklist.

If you want it to help you create a checklist, give it instructions to help you make a checklist. But putting time into building out a series of prompts for yourself will save you. Hours upon hours of research down the road. But if you adhere to those three core principles, I think that AI can become your best friend when it comes to stock analysis.

Brian, this has been so incredibly helpful and I think it's just been a powerful lesson for a lot of people out there that they go take action right now on this stuff and utilize this. So where can, you know people find out more about this prompt and where can they access this prompt? Where can they find out more about you and everything else you have going on as well?

Yep. So I, I'm available on pretty much every social platform, but if you want a free copy of that full prompt that we just did the business analysis prompt and those seven questions, just go to long-term mindset.co/. PFP for Personal Finance podcast. Uh, and that will take you to a link where you can download a free copy of that full prompt, and you can even use that prompt as a template to create more prompts for yourself.

But I think there's huge value in just seeing the way that the prompts that I've built are structured so you know how to prompt properly. Awesome. This has been so incredibly valuable. Thank you so much again, Brian, for coming on, and we're gonna obviously have you on again soon. Awesome, Andrew. Thanks for having me.

Always fun to be here.