Ep 21: Investing in AI

Jess: All right, last episode you said that productivity, which is fueled by AI, quote, will be the catalyst for the next leg higher in the broader stock market, end quote.

Jessie: What does that even mean? Is it time to go down the AI rabbit hole? And don't you know what happens to curious minds? Is this a Swifty reference you just made me say in this opening? That's why I write the openings.

Jess: Cue music.

Jessie: Cue music.

Jess: You're listening to Market MakeHer, the self-directed investing education podcast that breaks down investing and how the stock market works in easy to understand terms from her perspective.

Jessie: We're your hosts.

Jess: I'm Jessie Denewey, the beginner investor slash investor apprentice, learning alongside you and asking all of the questions so you do not have to.

Jessie: I'll make sure we do not get lost in our Wonderland journey today.

Jess: And I'm Jessin Skip, what we call the resident finance expert, been in the industry now for about 15 years, here to apply that experience to help answer those burning questions.

Jessie: OK, Jess, let's talk about AI.

Jess: I want to start with a Sam Altman quote, who is the CEO of OpenAI, or was at some point, you know, lots of drama there.

Jessie: AI will probably most likely lead to the end of the world.

Jess: But in the meantime, they'll be great companies.

Jessie: Which leads into a lot of things.

Jess: So the great companies, the stock market's made up of a bunch of public companies.

Jessie: We need to understand the companies that have revenue exposure to fully appreciate that.

Jess: We need to visit the dot-com bubble.

Jessie: Let's go back in time, shall we? There were so many comparisons to AI and the dot-com bubble, saying this is another bubble, which, sure, there are some similarities, but there are major differences.

Jess: And I personally do not think it is a bubble.

Jessie: In order to get to this point where we are, it started with connectivity, which was the World Wide Web.

Jess: That was a complete revolution.

Jessie: We use the World Wide Web in absolutely every vertical, absolutely every sector.

Jess: Doesn't matter what you do, you are utilizing the internet in some way, shape or form.

Jessie: That's the connectivity part.

Jess: We went to mobility.

Jessie: We had cell phones.

Jess: Then we went into the cloud and that new infrastructure.

Jessie: We needed all of that to get into artificial intelligence.

Jess: That's kind of the natural transition of things.

Jessie: Thinking about that is the way that I would think about revenue exposure and also just the astronomical growth.

Jess: When we have the World Wide Web, there are a couple of factors to consider.

Jessie: One, it was access to data.

Jess: All of the sudden, if you want to know an answer, you could Google it.

Jessie: Do you remember Wikipedia? I guess people still use Wikipedia.

Jess: Anybody could update it, you know, and there was just so much wrong information there.

Jessie: But it was a revolution because now you could Google anything.

Jess: You could find the CliffsNotes version, whatever you wanted to do.

Jessie: There was connectivity.

Jess: You could talk to somebody who was across the state, across the country, across the world.

Jessie: That's amazing.

Jess: Access to data.

Jessie: That access to data led to increased productivity.

Jess: But unfortunately, at that time, we didn't understand how revenue was generated.

Jessie: We did not understand that if you had dot-com behind your name, that did not necessarily mean that you were profitable because we didn't understand if everything would translate to productivity, if everything would be a money-making business.

Jess: And in fact, it wasn't, which led to the dot-com bubbles.

Jessie: There was a lot of dollars being invested in companies that did not have a clear path to profitability, which is extremely important.

Jess: And it was very difficult for analysts to come up with valuations because it was something that was extremely new that we didn't understand yet.

Jessie: There's a similar problem today, as in analysts can't evaluate properly, but it's not evaluating properly, as in they're too modest.

Jess: And we'll talk about that too.

Jessie: Yeah, you mentioned that in the last episode with earnings, right? How they're predicting earnings, the AI-type stocks specifically, or technology specifically, they're not evaluating them correctly or projecting earnings correctly.

Jess: They don't.

Jessie: That's why earnings beat or miss constantly.

Jess: It's just by the amount.

Jessie: I'm talking beat by 150 percent or beat by 40 percent.

Jess: That's insane.

Jessie: But that means that we just don't understand the earnings potential of these stocks, which is a different valuation problem.

Jess: I'd rather have that valuation problem.

Jessie: It's not that I don't understand how they make money.

Jess: It's I don't understand how much money they make.

Jessie: Is it inflating stock prices when they do that unnecessarily because they keep beating the earnings because they're not predicting the earnings correctly? That's such a good question.

Jess: So it changes valuations.

Jessie: So whenever we look at valuation, one metric that's used is the P-E ratio.

Jess: So literally price divided by earnings.

Jessie: And we use forward P-E ratios because we want to know what is going to happen.

Jess: Think of the math equation.

Jessie: Sorry, we're doing math today.

Jess: Didn't know that was going to happen.

Jessie: If the current price per share of a stock, easy math, is $100 per share and the earnings potential is 5, 100 divided by 5, that would have a P-E of 20.

Jess: The undervalued it is considered in comparison to its sector and the overall market.

Jessie: Microsoft was that one that beat by 150 percent or Nvidia.

Jess: Those are stocks that are purely for example purposes only, not considered.

Jessie: Yeah, there you go.

Jess: This is an informational and educational only podcast, guys.

Jessie: They beat by that much.

Jess: Analysts will then come in and readjust their expectations and make a higher earnings expectation, which is a higher divisor.

Jessie: So now, say an extreme example, the stock price might have shot up, but we're going to, for easy math, keep it at 100.

Jess: And now if the divisor is 10, saying we actually think it's going to make $10 per share.

Jessie: So it's a lower P-E multiple.

Jess: That's how the stock actually becomes more attractive due to its earnings potential because it's not known.

Jessie: And that actually happened with Nvidia multiple times.

Jess: The stock will be upgraded constantly by analysts, more earnings potential, and that's why the stock immediately reacts once you hear an upgrade comes out.

Jessie: And that's why news affects stocks constantly.

Jess: Yeah, it doesn't always have to do with the company's actual profit.

Jessie: Sometimes it's the buzz around what's going on and the forward-looking market projecting.

Jess: Yeah, it doesn't have to do with actual profits.

Jessie: It's profit potential because the market is forward-looking.

Jess: We're always saying, what are they going to make in the future? That's what we're assessing.

Jessie: And since the future is AI, we're investing in AI.

Jess: We have established that this does not have those bubble tendencies.

Jessie: There was a bubble because there were just too many companies that didn't have a clear path to profitability.

Jess: Now, the companies that are making money have already existed.

Jessie: They're huge money-making machines that have clear paths to profitability, that know how to create technology.

Jess: If you look at the productivity that happened post World Wide Web introduction, so I'm talking from the 90s, even actually like 80s-ish, all the way to 2008, you can see this productivity boom, because you can actually pull up in the labor market reports productivity increases.

Jessie: And it's plateaued right now, which means we're kind of due for this productivity shift, which is now starting to pick back up due to the labor market data.

Jess: And I really think that has to do with AI.

Jessie: If you can increase productivity, then you're going to decrease your operating expenses.

Jess: Now the question comes, if I want to invest in AI, how do I do that as someone who wants to invest in the stock market? Keyword is invest, not trade.

Jessie: Yes, and disclosure, disclosure, disclosure.

Jess: There is a lot of shareholder pressure.

Jessie: Literally, if you'd say AI within your earnings calls, it would make the stock go up.

Jess: It's very interesting.

Jessie: But that means there's shareholder pressure.

Jess: Everyone is saying to the C-suite, CEOs, how are you using AI within your strategy? How are you using AI? How are you using AI? It makes sense because it could be used in healthcare.

Jessie: Not only is it technology, but it's within financial services.

Jess: It's everywhere.

Jessie: Yeah, marketing, they're integrating it in platforms, everything.

Jess: Exactly.

Jessie: In order to figure out how to invest in AI and the linear model of when things will come to fruition, we've got to understand AI more.

Jess: Yeah, I am not an AI expert.

Jessie: I did find someone on TikTok that I have been following for, I feel like a year and a half now, Rachel Woods.

Jess: She owns a company.

Jessie: It is called the AI Exchange.

Jess: I signed up for her premium newsletter.

Jessie: She also has a free newsletter.

Jess: She was an ex, what, meta data scientist? Yeah, yeah, ex-Facebook, yeah, meta data scientist, yeah.

Jessie: Now CEO of her own company.

Jess: She literally can tell you from the beginnings of how to have really good prompts with chat GPT to how to create your own data lake and have your own type of large language models if you want them.

Jessie: That is so cool.

Jess: She's a genius.

Jessie: She's great.

Jess: Follow her on TikTok for sure.

Jessie: In order to understand how to invest in AI, we needed to understand, what does it take if I want to have an AI model because there is shareholder pressure? What would happen if I want to build one? And then what are the implications that are around it? I bet like every company wants their staff to jump in on this.

Jess: And we talked about this last time too.

Jessie: There's new roles being developed and hired within companies for like some kind of AI technologist.

Jess: Everyone wants to know or wants to use it somehow.

Jessie: That is the data that you could pull too.

Jess: You could literally go on a job site and if you see a specific company is hiring a lot of AI jobs, then you have an idea of where they're spending their money.

Jessie: That's a good idea.

Jess: I never thought about that.

Jessie: Reverse engineering research.

Jess: Oh yeah.

Jessie: Think outside of the box.

Jess: That's how people get ideas.

Jessie: Utilize data.

Jess: It's how you could have a different perspective and have an edge.

Jessie: For sure.

Jess: I like it.

Jessie: Yes.

Jess: Nonetheless, so now we have access to all this data.

Jessie: We are moving into data curation, which is a piece of it.

Jess: I feel like AI is having an iPhone moment.

Jessie: Yeah.

Jess: The real parallel.

Jessie: It's not the dot-com bubble.

Jess: It's the iPhone moment.

Jessie: Wow.

Jess: Yeah.

Jessie: It can go so many ways, do so many things and it's probably here to stay.

Jess: Stay with us.

Jessie: We'll be right back.

Jess: Ready to plug into the future? Join myself, Sean Leahy.

Jessie: And me, Andrew Maynard.

Jess: On Modem Futura, where we explore the technologies shaping our futures.

Jessie: We bring the experts, the insights, and a whole lot of curiosity to every episode of Modem Futura as we boldly go where no one else has gone.

Jess: So join us as we navigate the intersection of innovation and humanity, uncovering the stories that will define our collective futures.

Jessie: Subscribe to Modem Futura wherever you get your podcasts.

Jess: We'll see you there.

Jessie: See you then.

Jess: Exactly.

Jessie: Let's think about the implications for AI first, and then that's how we'll help form our investment thesis and figure out the stocks that would relate to that.

Jess: One is the labor force.

Jessie: Simple things.

Jess: If we use chat GPT, it could help with more remote work or shrink team sizes.

Jessie: There are needs for cybersecurity.

Jess: If we give access to a lot of data, then we need to protect that data, especially if you're within the financial services realm.

Jessie: True.

Jess: And a lot of other places.

Jessie: There's lots of rules and regulations.

Jess: You know them all around marketing.

Jessie: Something happened, like companies stopped allowing chat GPT or something like that because it was getting recorded and somehow leaked, like what they were using it for or something.

Jess: I know what you're talking about.

Jessie: Samsung, they had some proprietary code and they were using chat GPT to help them code and then- I mean, whatever you do on the internet, whatever information you put out there or say or whatever, it can easily get found and used.

Jess: Nothing's safe.

Jessie: Just remember that.

Jess: Yeah.

Jessie: Reverse engineer that lawyer saying, you didn't say it unless you wrote it down.

Jess: You wrote it down, you said it.

Jessie: Yeah, basically.

Jess: Regulation is something that's important.

Jessie: There have been AI meetings through the government.

Jess: They've been closed door, which is really interesting.

Jessie: They want to understand the risks.

Jess: They want to make sure that AI is responsible and it's ethical.

Jessie: Again, that's data.

Jess: If you realize it's important and it has an impact, if it requires fiscal implications.

Jessie: Let's talk about what's required to launch AI successfully.

Jess: In order to understand this, I read documents from the AI exchange.

Jessie: First and foremost, you have to have a data set.

Jess: Statistics and models are only as good as the data, which leads you to believe the companies who own the largest data sets are primed to benefit.

Jessie: Who has the most data? Google.

Jess: Yeah, all of social media.

Jessie: Anything with a social media exposure has an astronomical amount of data.

Jess: They need to be able to classify it and they need to be able to categorize it well.

Jessie: In order to take that data and do something with it, we need GPUs, those graphic processing units.

Jess: Who offer those? Who's purchased those? You can either purchase those yourself or use cloud-based services that purchase those GPUs already.

Jessie: That's NVIDIA right there.

Jess: They have the market share.

Jessie: They have that A100 chip, largest chip ever created.

Jess: I think it's $40,000 for one, and they have just so much astronomical demand for that.

Jessie: A GPU is a graphic processing unit.

Jess: The A100 chip that NVIDIA has is a GPU, and they have the largest share of GPUs, A100 chips on the market that are required to build AI? To build a large language machine learning model, so LLM, ML.

Jessie: Yes.

Jess: Okay.

Jessie: Yeah, they have the fastest that would be required.

Jess: You can either purchase it or use those cloud services.

Jessie: And who has the big cloud services? Yeah, Google, Amazon, Microsoft.

Jess: That's right.

Jessie: So now we're up to three stocks, four stocks of who would benefit.

Jess: And if you think about who's benefited so far, it's definitely those.

Jessie: But this is if you want to create your own AI model.

Jess: We'd also need to separate this from B2B and B2C.

Jessie: We'll add that layer in a moment.

Jess: In order to train those models, you can speed it up by a TPU, which is an AI accelerator.

Jessie: And there is only one that I could find, and it's developed by Google.

Jess: It's called a TPU? A TPU, which is an AI accelerator.

Jessie: TPUs are designed specifically for tensor operations, resulting in faster training and inference times for neural networks compared to GPUs.

Jess: So you have energy efficiency.

Jessie: TPUs are more power efficient than GPUs, making them a better choice for large scale machine learning deployments.

Jess: Designed by none other than Google.

Jessie: Now you want to speed up that training.

Jess: You can have an AI accelerator by Google.

Jessie: And then to work with the language models directly, you need to have software libraries.

Jess: And TensorFlow is developed by Google.

Jessie: There is also another one, PyMeta.

Jess: Oh, how about that? What's that one called? I don't even know if I'm saying it right.

Jessie: PyTorch? Oh, interesting.

Jess: Yeah.

Jessie: I didn't realize how deep this went.

Jess: I should have.

Jessie: It makes sense.

Jess: And it's all the big players.

Jessie: All the ones you would expect.

Jess: All of the steps that would be required for you as a business to make your own large language AI model.

Jessie: And if there's shareholder pressure, I would want to understand them.

Jess: Right.

Jessie: And so this would be what we would call direct beneficiaries.

Jess: That is one layer of it, though.

Jessie: There are other components like cybersecurity, and then there are companies that have data sets that would be of value.

Jess: So there's so much more than just these companies that we outlined.

Jessie: IBM immediately comes to mind.

Jess: They have Watson X.

Jessie: It's something that used to be talked about all the time.

Jess: They are saying, OK, it's going to take all this time, and it's extremely expensive to build your own large language model.

Jessie: What if we brought down all of those barriers, thought about guardrails that's needed for your company, and compliance? We'll bridge in our consulting, and we'll build a solution for that.

Jess: So that's a great example of a business that is trying to find a headwind and make it a tailwind.

Jessie: Right.

Jess: And those are opportunities I would search for.

Jessie: Another piece is what I like to call early adopters.

Jess: Shopify is a great example.

Jessie: They had record user growth and a clear path to profitability, but they were an early adopter of utilizing those AI technologies that were available.

Jess: That's B2B.

Jessie: Now we're going into the B2C side.

Jess: We want to take and build our own large language model like ChatGPT.

Jessie: ChatGPT can also do the images.

Jess: Adobe, I think, is an awesome, awesome example.

Jessie: We use it for so many things.

Jess: There was an entire episode where my microphone wasn't on, and it sounded absolutely terrible on the editing process, but we threw it into Adobe AI Voice Enhance, and you would have no idea.

Jessie: Do you know that 90% of Adobe's Firefly users, so their AI image generation models, the ones where you can like literally give yourself wings or do text and image with the crazy stuff, 90% of them are new to Adobe, which means new products.

Jess: They got me.

Jessie: I started with just having free Adobe AI, and I'm like, I want everything else, and now I pay for it.

Jess: Yeah.

Jessie: And Microsoft's another great example of that.

Jess: I don't think Wall Street's recognizing this part of it, but it just seems so obvious.

Jessie: Yeah, NVIDIA is going to be the obvious beneficiary first because it's the first part in creating a large language model.

Jess: It's the very first step.

Jessie: That makes sense.

Jess: They have orders that they can't meet.

Jessie: So what I'm looking for to make sure that this narrative plays out is how much demand is NVIDIA getting? Because clearly they have market share.

Jess: There's others who are developing their own ships.

Jessie: That means there's more demand.

Jess: There's more competition within the market.

Jessie: Those others being Google's making their own and Amazon's making their own.

Jess: Apple's making their own.

Jessie: I want to see what happens with Apple.

Jess: It's another one we could talk about.

Jessie: Who's making their own or who needs access to it? It's obvious that that stock is performing so well.

Jess: I think it's up 300% this year alone, and it makes sense why.

Jessie: It's crazy.

Jess: But people are like, oh, is it overinflated? Or what are the other stocks? It's like, guys, we're just getting started.

Jessie: Yeah, that's already making my mind go.

Jess: We can use one of the tools we've learned about to figure out what other AI stocks there are.

Jessie: There are ways you can invest in AI.

Jess: Other than the big players that are kind of expensive already.

Jessie: Higher stock prices, I should say, not expensive.

Jess: Who's going to be the next beneficiary? Who is adopting it early? And then you've got to separate it from B2C and B2B.

Jessie: Microsoft, ChatGPT went viral on TikTok.

Jess: Yeah.

Jessie: Anything that goes viral on TikTok tends to sell out.

Jess: Can't really sell out of technology, but you can get a data overload and servers will be down, and that happened.

Jessie: And things might get boosted initially because of that, and then they could come back down, right? The stock price.

Jess: Personally, I don't want to say skeptical, especially after GameStop.

Jessie: It's like, OK, it's too late.

Jess: It's already going up.

Jessie: Things are happening.

Jess: Fear around that now.

Jessie: I think that's a great example.

Jess: GameStop had poor valuations and no clear path to profitability.

Jessie: True.

Jess: The difference is clear path to profitability.

Jessie: Right.

Jess: So if it has the hype, does it have the clear path to profitability? Right.

Jessie: Yes, yes, yes.

Jess: So Microsoft, I could agree.

Jessie: On the surface, you'd say, OK, well, the ChatGPT hype has come down and not a lot of people are signing up who don't use it yet.

Jess: Sure, but Microsoft's not done.

Jessie: They introduced Copilot, and it's going to be integrated into their Office 365 products.

Jess: So now they're bringing it all the way over to B2B.

Jessie: And if you've ever worked in corporate America, which I would imagine so many of our listeners have, everyone has exposure to it.

Jess: Me too.

Jessie: You know, and it takes time for those companies to recognize revenue.

Jess: Whereas if you have a B2C model like ChatGPT, where you can just sign up in a subscription base, or the Adobe products that are subscription based, they're going to recognize that revenue very, very quickly.

Jessie: Now, if you have both, like Copilot and some of the other stuff that they're offering, the B2B side, we've got to wait to see how that comes out.

Jess: Where's the demand? Where is everything? So I still think there's a lot more to understand.

Jessie: And be a much higher price point too.

Jess: Absolutely.

Jessie: And more reoccurring, less turnover.

Jess: I really, really hope, I am praying for this, that they bring Clippy back.

Jessie: Oh my God.

Jess: The younger generation probably has no idea what Clippy is.

Jessie: Just make them a chatbot.

Jess: That would be amazing.

Jessie: Like, and all the Microsoft products.

Jess: That would be great.

Jessie: It'd be fantastic.

Jess: Please, Microsoft.

Jessie: You're welcome.

Jess: We just gave you gold.

Jessie: Our childhood friend.

Jess: Yes, Clippy.

Jessie: Nonetheless, hopefully that got your mind going though.

Jess: Yeah, so we can go to one of those tools, like an idea builder or whatever brokerage firm tools that we've looked at in past episodes.

Jessie: Should we just kind of like go to Google or go see who's like integrating with AI and things like that? I never say start with Google.

Jess: I am this time.

Jessie: Yeah.

Jess: There are definitely lots of research reports out there.

Jessie: But this is where I think it's different from the worldwide web revolution.

Jess: Now that we have this AI revolution.

Jessie: I learned about chat GPT from TikTok.

Jess: I was one of the first on major media network to even talk about it, which is crazy and awesome.

Jessie: And I've been pounding my hand on the table since then.

Jess: And it's because I'm doing my homework.

Jessie: Right.

Jess: So whoever left that comment that said I needed to do my homework, the joke's on you.

Jessie: That's when I found Rachel Woods though.

Jess: The worldwide web gave us access to data.

Jessie: Let's curate it.

Jess: AI is going to curate it.

Jessie: But if you have somebody who does it already, like Rachel Woods, like everyone go read Rachel Woods, the AI exchange, newsletters, resources on how to prompt.

Jess: But also if you are a B2B, what to use.

Jessie: The question that I want you to ask, if you want to invest in AI while you're digging into this research, separate it from B2B and B2C.

Jess: And then also what is needed to make these large language models come to life? Who's taking down those barriers? Who recognizes revenue when? It's going to be B2C first.

Jessie: Who brings them to life first? Like NVIDIA.

Jess: And then of course, it's going to move across.

Jessie: And then it's going to go to bigger businesses, which means more spending on the CapEx side.

Jess: We see that increasing because there was actually underspending since the great financial crisis because companies were preparing for another downturn and adjusted.

Jessie: And corporate America is so agile.

Jess: So now they are finally investing and that's going to lead to better profit margins.

Jessie: That's going to lead to doing more with less, which is what AI allows you to do.

Jess: Yeah.

Jessie: Well said.

Jess: Well, thank you.

Jessie: Yeah, I'm excited to dive in and start doing some research.

Jess: And maybe we can come up with some smaller player ideas and do some analysis and see what we think about them from an investing perspective.

Jessie: We got Q&A and analyzing stock segments now and stock market updates.

Jess: Yes.

Jessie: One more thing about AI with the stock market.

Jess: I just learned about this today doing a little bit of Googling myself and how AI is being used to try to beat the stock market.

Jessie: There are a bunch of different sponsored ads that came up.

Jess: AI-powered stock picking tools on websites.

Jessie: And I didn't realize this is a thing.

Jess: People, of course, were using AI for all kinds of things.

Jessie: Why don't we use AI to try to pick stocks and beat the stock market? I would be weary of anything that's not integrated into a brokerage firm because, again, of the regulations.

Jess: Yeah.

Jessie: I work for a company and we are integrated into brokerage firms.

Jess: And any time that we have something that is a proprietary calculation, you still have to submit a methodology document per Fenner rules.

Jessie: Good.

Jess: Meaning your output is only as good as your data.

Jessie: If you just say that you're AI and you don't have a good data set or you don't even have a good process, it's not going to matter.

Jess: Yeah.

Jessie: When you see things that look too good to be true, make sure you're doing a research seeing where they're pulling their data from, how their system is working.

Jess: Some very important statistics that help support this overall investment thesis because we don't like to just spit things out.

Jessie: We like to back them up with data.

Jess: That's right.

Jessie: Do our homework.

Jess: Labor productivity, it did absolutely plateau.

Jessie: We're spiking now.

Jess: It has been up 4.7% quarter over quarter.

Jessie: And it keeps going, which says AI is real and it's a big contributor to earnings.

Jess: That is doing more with less.

Jessie: Unfortunately, that's doing more with less people, but it enhances productivity.

Jess: Arguably, you can do more because you don't have to go get an encyclopedia, which is going to take you 10 times longer to figure out things when you could just Google it.

Jessie: So true.

Jess: Take that with a grain of salt.

Jessie: The estimates are for earnings save potentially $65 billion in cost for the S&P 500 securities over the next five years.

Jess: That's a ton of money.

Jessie: The S&P 500 companies, it can save them that much in their like overhead costs, you're saying? Right.

Jess: Operating expenses, which is the bottom line for those earnings.

Jessie: Right.

Jess: Which means expanding profit margins.

Jessie: Yes.

Jess: Hence the thesis and statements that were made on the last stock market update.

Jessie: Right.

Jess: And thinking about adoption, the growth, ChadCPT reached 100 million users in two months.

Jessie: Yeah, that's insane.

Jess: It took TikTok nine months to reach that, Twitter a little over five years, Instagram two and a half years.

Jessie: That is telling you that it's absolutely a catalyzing revolution.

Jess: It's crazy.

Jessie: And yeah, it's a disruptor.

Jess: It's going to disrupt every single sector for the good.

Jessie: There's just so much that it can do.

Jess: It's amazing.

Jessie: It's really going to be integrated everywhere in every little thing.

Jess: Everywhere.

Jessie: Like you said, every sector.

Jess: Yes.

Jessie: It's definitely new.

Jess: It's beginning to emerge, which means as investors who want to capitalize on the potential of AI, keyword potential, we need to understand how it comes to life and who has exposure to it.

Jessie: Let your brain marinate on all that.

Jess: Okay.

Jessie: It's time to come up out of this AI rabbit hole wonderland for now and go back into the real world, which will probably eventually be merged with AI.

Jess: Thanks for joining us on today's journey.

Jessie: And feel free to ask us any investing questions you have on any of our social platforms or via our website at marketmakeherpodcast.com.

Jess: marketmakeherpodcast.com.

Jessie: And we'll be doing more Q&A episodes, questions on questions, where we answer those questions.

Jess: And then Jessie asks more questions.

Jessie: So ask away.

Jess: And don't forget to subscribe and share this self-directed investing education podcast because when we all build knowledge together, we break those barriers.

Jessie: That's right.

Jess: Bye.

Jessie: Disclosure, disclosure, disclosure.

Jess: Remember, investing involves risk.

Jessie: There is always potential to lose money when investing in securities.

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