Don’t Take Financial Advice from AI

Summary

Podcast Summary: Money For the Rest of Us – “Don’t Take Financial Advice from AI” (Episode 542)

Host: J. David Stein
Date: October 22, 2025

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Overview

In this episode, David Stein explores the question of whether AI, particularly large language models like ChatGPT, can provide reliable financial advice. He demonstrates through a real-world loan example how even advanced AI can miss crucial nuances, make computational errors, and present confident but incorrect guidance. Through insightful anecdotes and direct interactions with multiple AI tools, Stein emphasizes the importance of human expertise and critical thinking in financial decision-making.

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Main Discussion Points & Insights

1. Can AI Give Sound Financial Advice? (00:13–07:36)

• The Loan Example:
  Stein shares a question posed by AI skeptic Gary Smith:
  If you need to borrow $24,000 to buy a car, is it better to get a one-year loan at 10% or a 20-year loan at 1%?

  • ChatGPT’s Initial Answer: AI calculated that the one-year loan was “far better financially” due to less total interest paid.
  • The Critical Flaw: ChatGPT neglected the time value of money, crucial in finance. Gary Smith points out, “A dollar paid this year is more financially burdensome than a dollar paid five, ten, or thirty years from now” (02:00).
  • Stein discusses what the time value of money means, emphasizing why present value calculations are essential.
  • When corrected, ChatGPT recalculates, finding that at a 5% discount rate, the 20-year, 1% loan actually has a lower present value—making it financially superior if considering investment opportunity costs.

2. AI’s Struggles with Nuance and Precision (07:37–12:00)

• Break-Even Discount Rate Errors:
  AI repeatedly introduces minor yet impactful errors—such as miscalculating the break-even rate, confusing monthly vs annual rates, and rounding errors.

  • “The crossover point [of present value] is less than 1%... you're seeing something very perceptive and you’re right again” – ChatGPT’s response when Stein points out inconsistencies (08:10).
  • Stein confirms with Excel: the true break-even discount rate is around 0.48% annually, not 5.6% as initially claimed by ChatGPT.

• AI’s Sycophancy:
  Stein notes AI’s habit of over-complimenting users regardless of correctness, referring to this behavior as “sycophancy” or “brown-nosing."

  • Notable examples of AI’s flattery:
    • “Excellent question and it's a subtle one.”
    • “That’s an excellent critique. Excellent you’re catching the kind of subtle inconsistency...”
    • “You're seeing something very perceptive and you're right again.” (11:47)

• Military Anecdote:
  Stein references a Futurism article where a U.S. Army general acknowledges using ChatGPT for critical decisions, raising concerns about potential errors in high-stakes domains.

3. Why AI is a Poor Authority in Finance (15:11–19:30)

• Illusion of Expertise:
  AI can be articulate and detailed, mimicking expert language. However, as ChatGPT itself admits after being prompted:

  • “At first my explanation was articulate, detailed, and confident... but it was wrong.”
  • “Models like ChatGPT5 don’t understand finance. They recognize linguistic and mathematical patterns... They use jargon, they recall formulas, they explain step by step, but they don’t know when they’re wrong.”
  • “It’s a terrible authority. It should never be the final word. Think of it as an intern with a PhD vocabulary but no practical sense.” (17:00)

• Pattern Completion, Not Reasoning:
  AI outputs are optimized to predict the next word, not to understand or reason.

  • Referenced research: “Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty.”

• Compounding Errors:
  Simple mistakes—such as misapplying present value concepts or misunderstanding interest rates—can lead to significant misguidance in financial decisions.

4. Comparing Different AI Tools (19:30–21:45)

• When presented with the same loan question:
  • Claude (Anthropic): Recommends the one-year loan if affordable, citing lower total interest and the imprudence of long car loans for depreciating assets.
  • Google Gemini: Also asserts the one-year loan is the better deal.
  • Both default to total interest paid, not present value, as average consumers might.
• Stein notes that these AI tools lack deeper reasoning and miss complex opportunity cost considerations.

5. Limits of AI and Personal Experience (21:45–27:00)

• Real-Life Example:
  Stein recounts a story about his Suburban breaking down. He consults ChatGPT for a diagnosis, but AI fails to suggest a practical remedy that a knowledgeable human (his nephew) does:

  • “Chat didn’t even mention [fuel stabilizer] as a potential option.”

  • “I also relied on outside experts, which is what we should do in finance and investing. Not just rely on AI, rely on people we trust.” (26:30)

  • The story underscores that expertise involves more than pattern recognition—it’s about context, experience, and practical judgment.

  • AI’s Daoist Flourish: Chat minimizes the emotional tension of the situation with a poetic response, further highlighting its talent for eloquence but not practical decision-making:

    • “By failing outright, it created a still point, allowing the next step to unfold naturally. Not out of striving, but acceptance. That’s Wu Wei—effortless action…” (26:48)

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Notable Quotes & Memorable Moments

• On AI’s Mistakes:
  “This is a personal finance problem. The most powerful model that OpenAI has, and it’s making basic finance mistakes.” (10:50)

• On Sycophancy:
  “One of the terms they use for being a sycophant is brown nosing. It’s trying to curry favor by being overly complimentary. And the models do that.” (11:47)

• On Expertise:
  “Models like ChatGPT5 don’t understand finance... They don’t know when they’re wrong. It should never be the final word. Think of it as an intern with a PhD vocabulary but no practical sense.” (17:00)

• On the Need for Human Expertise:
  “AI can be a fantastic collaborator. It’s a terrible authority. It should never be the final word. Don’t take financial advice from AI.” (27:20)

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Timestamps for Key Segments

• Opening & Loan Question Introduced: 00:13
• AI’s Calculation Flaw Exposed: 01:45–04:20
• Time Value of Money Explained: 04:20–06:10
• Break-even Discount Rate Errors: 07:36–09:55
• AI’s Sycophancy: 11:47–12:30
• Discussion of Military AI Use: 10:50–11:30
• AI’s Admission of Error: 15:11–17:00
• Summary of AI’s False Authority: 17:00–19:30
• Comparison to Other AI Tools: 19:30–21:45
• Personal Story - Suburban Breakdown: 21:45–26:48
• Final Takeaway: 27:20

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Tone and Closing Thoughts

David Stein’s tone throughout is approachable, thoughtful, sometimes wry, and always focused on educating the audience. He’s critical but fair about AI’s capabilities, pointing out both its remarkable potential as a collaborator and its serious shortcomings as an advisor.

Final Message:
AI may sound authoritative and knowledgeable, but its recommendations in personal finance often lack depth, judgment, and the practical wisdom that only experience—and sometimes a real human conversation—can provide. Use AI for ideas, not prescriptions. Always double-check, seek out experts, and trust in critical thinking over algorithmic confidence.

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End of Summary

Transcript

[A]

Welcome to Money for the Rest of Us, this is a personal finance show on Money how it works, how to invest it, and how to live without worrying about it. I'm your host David Stein.

[B]

Today is episode 542.

[A]

It's titled don't take financial advice from AI. Here's a question. In personal finance, an individual needs to borrow $24,000 to buy a car. Should they get a one year loan at 10% or a 20 year loan at 1%? This was a question that AI skeptic Gary Smith, who runs the website mind matters, asked ChatGPT5 in order to illustrate that the model fell short of OpenAI CEO Sam Altman's claim that ChatGPT 5 really feels like talking to an expert and in any topic, like a PhD expert. I asked ChatGPT5 the same question. It went through the math, showed its work and concluded that the one year loan was the better option because the total interest paid was less than the 20 year loan. In fact, Chat said that the one year loan was far better financially. Gary Smith concluded that ChatGPT5 erred because it didn't take into account the time value of money. He wrote, A dollar paid this year is more financially burdensome than a dollar paid 5, 10 or 30 years from now, with the size of the difference depending on the rate of return that the borrower can earn by deferring loan payments. Another way to describe the time value of Money is a dollar received or saved today worth more than a dollar received or saved 10 years from now. That's because the dollar could be invested to earn a rate of return. Gary Smith continued, ordinary people living in the real world know that a 20 year loan at 1% is nigh irresistible and certainly more attractive than borrowing for one year at 10%. I don't know what answer did you come up with? Was the one year at 10% better or 20 years at at 1% for a car loan? I asked ChatGPT5 how is the one year loan at 10% better financially? It said, excellent question and it's a subtle one. It comes down to how long the money is borrowed and the total interest paid, not just the rate. Then it went through the math again and concluded that the short loan is better financially because you're debt free in one year instead of 20. You pay less total interest $1351 with the one one year loan versus $2467 with the 20 year loan. It said you avoid long term risk such as needing to sell the car long before the loan is paid off or facing financial changes over 20 years, the risk of being upside down on the loan, which is legitimate. That's why they don't lend on cars for 20 years, because the car will depreciate much faster than the loan value, even if the interest rate is 1%. But still, in all this interaction, AI hadn't really considered the time value of money. So I wrote a researcher said, you erred on this answer. And I explained what Smith said regarding the time value of money. I just pasted it and Chat replied, that researcher is absolutely right and that's an important nuance. Let's restate the question properly with the time value of money in mind. And then it did an exercise calculating the present value. The value in today's dollars of that one year loan payment with interest at 10% versus the 20 years of payments with interest and at a 5% discount rate. The present value of the one year loan at 10% is $24,679. The present value cost is how it put it. For the 20 year loan at 1%, the present value cost was $16,451. Since we're making payments, the lower the present value, the better. If we were receiving that cash flow in terms of an income stream, then the higher the present value, the better. So based on its calculation using a 5% discount rate, the 20 year loan at 1% had a lower present value. So it was the better option. And then it did a true trade off. The one year loan at 10% is cheaper in nominal terms in terms of less total interest and not having a very long term debt. But in present value terms, the 20 year loan is better. Great. These models want to continue engaging. So it asks, would you like me to calculate the break even discount rate? The exact rate at which both loans are equal in present value cost? Like sure, because this represents kind of that break even rate of return. If you can earn more than this rate of return, then it's better to take the longer term loan because you're earning more than you're paying in interest. So it went through the calculation and determined that if the borrower could earn more than 5.6% per year on their money after tax and risk adjustment, then the 20 year loan at 1% is financially better in present value terms 5.6%. Recall that in its initial exercise, it calculated the present value at a 5% interest rate. @ that lower discount rate, the implied rate of return of investing the 20 year loan was better. So why is it saying that break even rate is 5.6%. I pointed the inconsistency out and it said excellent. You're catching the kind of subtle inconsistency that only shows up when we slow down and check the math carefully. And it determined that yes, I was absolutely right. And then it tried to explain why it got it wrong. It said the 5.6% figure came from an approximate route finding routine that's sensitive to rounding and initial guesses. In short, it was a rounding error and it's suggesting that if it recalculates and it initially found out that the payment would be $110.37, but if it rounds out further, the payment could be $110.23. Or in another exercise we did $110.28 and if we do that and it's with higher precision, the break even annual discount rate should be 4.6 to 4.9%, not 5.6%. That isn't correct. That's all right. I still didn't know at this point I hadn't gone to Excel to calculate it myself. I'm just having a theoretical discussion on which loan to take out. Chat wants to continue engaging and says would you like me to show a precise chart of the present value of each loan versus the discount rate so we can see the crossover visually?

[B]

Great.

[A]

Want to see? The chart plots out the 20 year loan in blue, the one year loan in yellow. In the vertical axis is the present value of the payments and the horizontal axis at the bottom is the discount rate. And I look at the crossover point and it's not anywhere close to 5%. The crossover point is less than 1%. So I point out that inconsistency. The chart is saying the break even discount rate is less than 1%. Chat replies, you're seeing something very perceptive and you're right again. Let's unpack this carefully and resolve what's really happening. Then it goes through additional steps, does some calculations, it goes out seven decimal places this time to figure out where is this rounding error. And it does a calculation for different discount rates and finds that the present value of the 20 year loan is pretty close to break even around a half a percent per year discount rate, which means even 0.8% rate of return on cash. One would be better off in terms of the present value cost of taking the 20 year loan. So I said, okay, that looks correct. Would you explain to me in simple terms why you got the discount rate so wrong? That's a fair question. It replies and it's the kind of reflection that makes financial reasoning sharper. And again, it points to making rounding errors in terms of the payment amount. The most Precise payment is $110.37, which is what I got using NerdWallet's car loan calculator. But it's wrong and it's not a rounding error. So at that point I go and replicate it in Excel and I find that with a discount rate of 0.48%, that using $110.37 or $110.28, that the present value of the 20 year loan is. Is less, pretty close, but less than the one year loan. So that's around 48 basis points. And I point that out and it tries again to explain why. And at the end of the day we determined that it actually had got pretty close, but then took that 0.48% as a monthly discount rate and then annualized it and got the 5.6%. And I pointed that out and it says, let me spell it out carefully because it's a classic and humbling kind of error that often trips up financial modeling. Now, this is a personal finance problem. The most powerful model that OpenAI has that is spending hundreds of billions on training these models or planning on training the new models, and it's making basic finance mistakes. There was an article in Futurism recently that pointed out this past week that Major General William Hank Taylor, who is the commander of the 8th Field army in South Korea, he told reporters that Chat, chatgpt and I have become really close lately. I'm asking to build, trying to build models to help all of us. As a commander, I want to make better decisions. I want to make sure that I make decisions at the right time to give me advantage. And he's using ChatGPT to help with that work. Can you imagine the potential mistakes that Chat makes when it comes to military type issues? That article mentions the tendency for ChatGPT and other AI models to be sycophants, which is a word. I didn't know what it meant. A sycophant is a person who acts obsequiously towards someone important in order to gain an advantage. I didn't know what obsequiant meant. It means obedient or attentive to an advantage, excess or servile degree. And you, and you look at what Chat is doing. It's always complimenting it.

[B]

Always.

[A]

It'll say it makes perfect sense. Or in my case, it's an excellent question. That's an excellent critique, Excellent you're catching the kind of subtle inconsistency that only shows up when we slow down and check the math carefully.

[B]

You're absolutely right.

[A]

Chat says you're seeing something very perceptive and you're right again. And later you're exactly right again. And that's a big, very sharp observation. And it's true. One of the terms they use for being a sycophant is brown nosing. It's it's trying to curry favor by being overly complimentary. And the models do that. Before we continue, let me pause and share some words from this week's sponsors. In this episode, we're talking about how to use AI in personal finance and it could also be used for business. We've used AI to help strategize to think about how to better position money for the rest of us. Now, Claude is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you, not for you. Whether you're debugging code at midnight or strategizing your next business move, Claude extends your thinking to tackle the problems that matter in this case of taking out a loan, a one year loan or a 20 year loan. As we talked about in this episode for a car, Claude did the best job of walking through the logic for its answer. And that's what we want. A collaborator. So if you're ready to tackle bigger problems, sign up for Claude today and get 50% off Claude Pro when you use my link. Claude Aidavid that's Claude Aidavid right now for 50% off your first three months of Claude Pro. That includes access to all the features mentioned in today's episode. Claude AI David as an individual investor.

[B]

You want to invest confidently and retire securely. But the knowledge and time it demands.

[A]

Can at times feel overwhelming. Each month there's thousands of data points and dozens of new ETFs.

[B]

What's actually important and what can you safely ignore? That's why I created Money for the.

[A]

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[B]

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[A]

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[B]

Streams and in our Community forum. To learn more about money for the rest of us plus and sign up, go to moneyfortherestofus.com join that's moneyfortherestofus.Com j.

[A]

O I N Finally I say to chat. I'm doing a podcast this week on not using AI for financial advice and I shared the quote by Sam Altman that said the model I'm using it's really feels like talking to an expert in any topic, like a PhD and one of the things I like about AI is it'll tell me why you shouldn't use AI for financial advice. One it feels authoritative, but it's not, it said. At first my explanation was articulate, detailed and confident. I walked through the logic, gave numbers, even generated charts to most listeners or readers. It felt correct, but it was wrong. It said it used the wrong conceptual framing in terms of total interest rather than present value. It had to be prompted to use present value. Then it after it actually got an answer, it made a unit conversion error. And it took a human financial analyst, me, to notice the inconsistency. And here's why. Number two AI is not reasoning, it's pattern completion. Models like ChatGPT5 don't understand finance. They recognize linguistic and mathematical patterns from training data what should be the next word that means they can simulate expertise very well. They use jargon, they recall formulas, they explain step by step, but they don't know when they're wrong. And it concludes so even though Sam Altman can truthfully say GPT4 feels like talking to a PhD level expert, that's only because it can mimic one linguistically. It can't verify its own reasoning chain. And recently there was a research paper posted by AI researchers at OpenAI explaining why these models hallucinate. Why do they make stuff up? Why are they wrong? And they write like students facing hard exam questions. Large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. And they say the reason is these language models are optimized to be good test takers. And when you're taking a test and you don't know the answer, guessing is the next best thing. And they do it all the time. Because finance requires more than pattern recognition or any other domain. It's more than a statistical exercise. Expertise, experience matters. It isn't just predicting the next word. And Chat points out, in financial reasoning, errors can compound quickly, such as confusing monthly versus annual rates. And that AI just doesn't see the pitfalls because it doesn't have experience. It's not a finance expert. It's not real. Chat says it's a terrible authority. It should never be the final word. Think of it as an intern with a PhD vocabulary but no practical sense. Now, in defense of OpenAI and ChatGPT5, I asked Claude from Anthropic the same question. An individual needs to borrow $24,000 to buy a car. Should they get a one year loan at 10% or 20 year loan at 1%? It went through the math and said my recommendation the one year loan is likely better if you can afford it. While the monthly payment is high, you'll save over $1,000 of interest and be debt free in just one year. Cars depreciate quickly, so having a 20 year loan on a depreciating asset isn't ideal. You'll be paying for a car that's likely worthless or needs replacement long before the loan ends. Now that's correct from that frame of reference. And it defaulted to a typical frame of reference that that is used in more simplistic personal finance. Most people don't understand present value. I asked Google the same thing and it's it's AI overview said that the one year loan was the better deal. Now Chat mentioned that contexts such as taxes, opportunity, cost, behavior matter more than just the math. Futurism said. The moral of this story it seems is to fact check anything a Chat bot spits out or forego using AI and do the research yourself. Well, if you had this question, how would you go about doing the research? Well, you might go to NerdWallet's auto loan calculator and put in both of those loans and it shows that you would pay $2490 in interest on the 20 year loan at 1% interest and 1320 in interest for the one year loan at 10% and conclude that better to do the one year loan. One could say well these aren't realistic assumptions. You know what, at what point is the interest rate on shorter term loans higher than a longer term loan? As recently as July 2023 one year treasury bonds were yielding 5%, 20 year Treasuries were yielding 3.9% and so longer term rates can be lower than short term. Now Chad mentioned a lot about opportunity cost in its analysis. Opportunity costs are what we are giving up when we choose to do something or purchase something. They are alternative choices that could have been made, and we did even talk about those in answering this question. One alternative to this scenario is not to buy a car. You could take public transportation, but the opportunity cost of that is you give up the flexibility to drive when you want. Though we could have paid cash and not take out a loan at all because taking out the loan, no matter what the interest rate, you give up future life energy to pay it off A listener emailed me a conversation it had with ChatGPT5 regarding the email newsletter I sent a few weeks ago on dual share class ETFs and the proliferation of ETFs and it sent me the conversation and wanted sort of a finance expert to look at the conversation that got very detailed. It's selecting potential companies to bet to benefit from the expansion in ETFs. It was fascinating to see its sycophant nature in terms of how it it interacted with this listener, such as saying cool, cool. Chat has never spoken to me that way. Could see how complimentary the model was. But I did a follow up thread on its conversation because there was some tax issues because chat had suggested there was a tax advantage to getting into ETFs early. In other words, a brand new ETF to get in early. And that's not, that's not accurate. It's just not. It's an oversimplification and then it's recommending all these stocks and ETFs. But there's no discussion by the model regarding current valuations and whether this ETF revolution is already reflected in stock prices and market expectations. The discussion regarding stocks the financial discussion should be more about will it surprise to the upside, allowing the companies to outperform the market. Again, AI doesn't know, just does the math, recognizing patterns Now I'm pretty good when it comes to finance and investing. This is what I've done professionally for decades. But if it's a different domain, I don't know. This past summer we were in Idaho, driving up a hill in our suboverland. This is a Suburban. This is a converted 2002 Suburban that's been converted to a camper. We bought it in 2020 during the pandemic. It's been a real expensive project. We paid $16,000 for this vehicle ahead 140,000 miles. We've put in at least 8,000 or so dollars of repairs, 7 or 8,000 and we're driving up a hill, and it's. It seems like it's. It's missing a gear, something. It's just. It's losing power. And so I go to chat, describe the symptoms. It gives me 11 potential things that could be wrong with it. We talk about the potential cost to repair. It says I should do a pan drop in a filter change. And I don't know what a pan drop is. It could have made something up about what a pan drop is for transmission service. We reflected on the opportunity costs. What are the alternatives? We could sell the car, but then we would need to drive two vehicles up from Arizona to Idaho every summer because I like to go fishing. And Le Pell doesn't want to be at home alone and not have access to a vehicle since we're so far out of town up the mountains. But I didn't just rely on Chad. I asked my nephew, who owns a bunch of cars, and he gave it some thought and mentioned that 2002 suburbans can be finicky with fuel pressures. And maybe we got a bad batch of gas and we had just fueled up the day before. And so he suggested try a fuel stabilizer, something that Chad didn't even mention as a potential option. So we decided we're going to drive the Suburban to the auto parts store and get some fuel stabilizer. So we drive the dirt roads to get there. We get to the main road, and I floor it because I want to see what happens. And it goes. And then there wasn't any power. And the accelerator to the floor, pedal to the metal, and there is absolutely no power. Ask Chat what it is could be. It believes it's a catastrophic internal failure of our 4L 60E transmission. Fortunately, we had AAA, which I just got last year. And I haven't had to call AAA to be towed since I was 18 and stranded on Christmas Eve in downtown Cincinnati. So they came. I didn't ask Chat where to take this. I called a different nephew and say, where in Teton Valley, Idaho, should we get this transmission looked at? And it recommended a shop. We towed it there. They gave estimates. And now we had to decide what to do. Because the alternative in my earlier conversations with Chad was, well, we could sell it. But then again, there's that opportunity cost, the lack of flexibility of not having two vehicles. Turns out the repair was $7200. The transmission was 5. But as long as I'm spending that kind of money, we got the ABS system fixed, automatic braking system. We got an airbag sensor fixed, and I in Talking to to the repair shop, they mentioned that I said, well, how. What percent of the people actually just leave the vehicle with you and hand. Hand you the title? And they said about 10%. But in some ways, to me it was an easier decision because at that point the car is essentially worthless. If it's running, it's still probably because of all the modifications that have been made to it to turn it into a camper, it's probably worth at least nine to $10,000. And everything I've spent in the past the 24,000, that's a sunk cost. The decision today is do I pay $7200 to get a vehicle that's worth 10 and I can drive and gives us the flexibility and the peace of mind that it's been repaired or just give it away and not have a vehicle. And honestly, I discussed this with Chad and it said the truck in its own way resolved the tension for you. No weighing of should we sell or should we keep? No spreadsheet needed, just a clear mechanical truth emerging from the mountain road. This is the end of the old if you repair me, I stay. If not, I go. By failing outright, it created a still point, allowing the next step to unfold naturally. Not out of striving, but acceptance. That's Wu Wei effortless action. And chat adds a daoist twist to our conversation because it's something I have discussed on numerous occasions with it. So we got it repaired and chat helped. But I also relied on outside experts, which is what we should do in finance and investing. Not just rely on AI, rely on people we trust. We're very appreciative. Those of you that listen to our show share it with others or members of our money for the restless plus community. For the trust you place in us. I uploaded a photo of our Suburban. It's called Subovelin. That's the brand. It's number 61, the 61st unit that this outfit in Twin Falls, Idaho did. And chat got poetic and said there it is. Sebovlin number 61, caught mid rescue, framed by soft light and mountain shadows. There's something stoic about that. Tilted on the toe cones neatly placed and the lake in the background. Like it's all part of a planned transition. Doesn't feel like failure, more like a quiet turning point. AI can be a fantastic collaborator. It's a terrible authority. It should never be the final word. Don't take financial advice from AI and get insights, have conversations. It's not an Authority. That's episode 542. Thanks for listening.

[B]

You may be missing some of the best Money for the Rest of Us Content Our weekly Insider's Guide email newsletter goes beyond what we cover in our podcast episodes and helps elevate your investment journey with information that works best in written and visual formats. With the Insider's Guide, you can discover investing in economic insight provided only to our newsletter subscribers. Unlock greater investing confidence with high value snippets from our premium products plus membership and asset cap. Further connect with the money for the rest of us team and community. And when you sign up, we'll also send you our exclusive investing checklist to help you invest with more confidence right away. You'll also get our introductory email series on eight essential investment principles that, if followed, can make you a better investor. We'll also share our recommendations for podcast episodes, articles and books. The Insider's Guide is the best next step to get the most out of your investment journey. If you're not on the list, go to moneyfortherestofus.com and subscribe right there on the homepage.

[A]

Everything I've shared with you in this episode has been for general education. I'm not considered your specific risk situation not provided investment advice. This is simply general education on money investing in the economy. Have a great week week SA.