In today’s discussion, we’re diving into the world of artificial intelligence and its implications for consumers. We’re joined by Gabe Brenner, a financial advisor at Abacus Wealth Partners, who shares his perspective on the integration of AI tools in financial planning and everyday life. Gabe will walk us through the potential and practical uses that AI holds, from boosting productivity to navigating complex information effortlessly, and even aiding in our creative ventures. We’ll also discuss the dual nature of AI, covering both its benefits and potential risks, including AI-driven scams and the necessary precautions to protect ourselves against them. Gabe will share some of his recommended AI tools and his experiences with them. By the end of this episode, you’ll have a clearer understanding of how to leverage AI effectively, its importance in our rapidly evolving technological landscape, and ways to safeguard yourself against its potential pitfalls!
What You’ll Learn in this Episode:
- Understanding the basics of AI tools and how they show up in the consumer market
- How to use AI for creative tasks effectively
- Leveraging some of the AI tools out there (and which one is best for different tasks)
- The impact AI is having on several careers and industries
- Gabe’s top 4 AI tools
- Why it’s important to verify the data obtained from AI
- Some of the potential risks and scams and how to implement security measures
- Some of the upcoming advancements in AI
- A few of the companies that are working to keep AI ethical and use it for good
- A discussion on how AI is being integrated into educational processes
- Why consumers should integrate AI into their daily tasks for better outcomes
Resources Mentioned on the Show:
Stay Connected:
- Join the Abacus community by connecting with us on Facebook, Twitter, Instagram and on LinkedIn
- Connect with Mary Beth on Twitter, Instagram, and on LinkedIn
- Connect with Neela on Twitter, Instagram, and on LinkedIn
Transcript of the Episode
Neela [00:00:14]:
Hey there. Welcome to the If Money Were Easy podcast, the show where we teach you how to expand what’s possible with your money. We’re your hosts, Neela Hummel
Mary Beth [00:00:23]:
and Mary Beth Storjohann,
Neela [00:00:25]:
certified financial planners and co CEO’s of Abacus Wealth Partners. Today on the show we’re going to talk about the AI tools consumers should know about.
Mary Beth [00:00:36]:
We are very excited to have our returned guest today, Gabe Brenner. Gabe is a CFP, partner and financial advisor with Abacus Wealth Partners. Gabe is dedicated to helping his clients craft a financial plan that aligns with their long term goals, manages financial complexities, and optimizes the environmental and social impact of their portfolios while ensuring their finances remain solid. He specializes in serving attorneys, executives and high net worth individuals. Gabe serves as a member of the Abacus Investment Committee and currently sits on the Abacus board of directors and is our resident AI expert. Welcome Gabe.
Gabe [00:01:13]:
That’s good to be here. I’ve been chomping at the bit to talk about this.
Mary Beth [00:01:16]:
We are very excited to talk about this. We did a episode way back when and we know that you are going to far surpass that and our expectations today
Gabe [00:01:27]:
I set the bar real high.
Neela [00:01:29]:
We had like a little sandbox episode of AI, like kindergarten level. So we’re looking forward to dialing it up from a skill and an expertise level.
Gabe [00:01:38]:
It’s just, it’s a dynamic space and so there’s been so much to learn in the ensuing, I don’t know, four to six months since you guys did that episode.
Mary Beth [00:01:46]:
Yes, exactly. So just as a refresher, you are deep in the AI space and knowledge. I dabble in it. I think Neela dabbles in it as well. I might do like a little dabble plus. Neela dabbles. But for our listeners, just a brief introduction, what is AI? And then talk about the tools that you are seeing and hearing about today and then we can go in from there.
Gabe [00:02:08]:
Well, so artificial intelligence kind of first burst on the scene in late 2022, and it wasn’t until the summer of 2023 that I really started to play with it. And it was mostly just an incredible limerick generator. I was on vacation and I wanted to do a fun toast at dinner with our hosts and we were in Croatia and damn, the thing put together like a, you know, 20 stanza poem that really understood where we were and all the things to mention and who our hosts were. And so you’re like, this is really amazing. And then you find out, oh my God, it just makes stuff up. And so I was already hooked and wanted to become more interested in it. And I’ll say that the thing that I use it for the most now, and that people at Abacus are starting to use it for, is note taking. I know you two have familiarity with that. It’s just amazing. If you look at the transcript, the transcript is perfect. The summarization, I mean, you want to get to it very quickly while your memory is fresh and see whether it’s gotten that correctly and make some quick edits. But all in all, it’s saving me a ton of time. We use something that’s really geared towards the financial industry and being compliant. But if you’re listening and you’re out there and you’re wondering some suggestions, I think PLAUD and Otter.ai are two probably better note taking tools, but they’re not compliant for all the laws and regulations that we need to be aware of. But for the rest of you out there, they could be really great.
Neela [00:03:37]:
The way you know from a note taking standpoint that it’s highly leveraged is that oftentimes when we’re doing something, we’re trying to engage with somebody while we’re also trying to take good notes and remember the conversation. This allows you to be fully present when whatever you’re working on and probably have better notes than you would have taken as a human, right?
Gabe [00:03:55]:
Oh, far better. Makes me much more present. I used to have to bring another advisor into the meeting, which is spectacularly inefficient, but in order to really be present. But, you know, note taking just scratches the surface, guys.
Mary Beth [00:04:08]:
I just had a question about how many poems do you typically write for dinners? I just wanted to go back.
Neela [00:04:14]:
That is such a good question. Like, there was a limerick need.
Mary Beth [00:04:17]:
I’m sorry, I needed to circle back. It was just a curiosity. I can’t let it go too far. I couldn’t let it go too far.
Gabe [00:04:23]:
I try not to make a habit of it.
Mary Beth [00:04:25]:
Inquiring minds. That was obviously going to be a question for our listeners. It’s like, how often does this happen?
Neela [00:04:28]:
Like a real problem to be solved.
Mary Beth [00:04:32]:
Great. Yeah.
Gabe [00:04:33]:
People like, oh, it’s great. We’re going to go over to the Brenner’s house for a dinner party. Another poem? No, no, nothing like that.
Mary Beth [00:04:41]:
Just checking.
Gabe [00:04:43]:
But you guys know that I need a creative outlet, and so I write a lot of blogs around here, and we recently posted one on meme stocks. And just to give you an idea, you know, I just interacted with it. I tend to do it verbally. Like, hey, I’m writing this blog post, what are the major points that we might hit? What could be the structure of this? I find that when you interact with AI, if you don’t think of it as a magic tool and you say, all right, let’s break down. What are the steps? Okay, do step one. Write the second section. Write all of them. Okay. Now stitch those together. Great. Now let me take this to another AI and see if it can write it in a more reader friendly style. And so I also use multiple AI.
Mary Beth [00:05:21]:
You AI hop. Basically, you’re AI hopping.
Gabe [00:05:24]:
It’s currently an $80 a month habit. Yeah, I’ve got four subscriptions.
Mary Beth [00:05:27]:
You’re in an open relationship with AI that’s happening. Okay.
Gabe [00:05:31]:
Prolific. I mean it.
Mary Beth [00:05:32]:
But what are the four, what are the four subscriptions before you go on, and then let’s go back.
Gabe [00:05:35]:
No, we should definitely do that. So number one, if you’re only going to pay for one, pay for Perplexity.ai Pro. What it’ll do is it’ll take your, your web searches and it’ll help you refine them into a better web search. Like you asked a question, let me help you refine that and then make it better. And then it’ll go out and identify, show you can see it all happening. Well, we found 20 sources, and then it’ll synthesize an answer. And within those answers there’s like little footnotes. So you can go out to the source and you can say, I don’t like those sources very much. Why don’t you use different sources that come from, and I may constrain it, say, just deal with the top ten newspapers as your sources. Don’t look at anything else. Think of it as more than a high school, less than a college intern. That’s probably going to need a lot of direction. But this perplexity really gets at the issue of hallucination and being able to pin it down. Where did you get this?
Neela [00:06:28]:
Hallucination.
Mary Beth [00:06:31]:
Let’s go back.
Neela [00:06:31]:
Tell me, what do you mean, hallucination? I mean, I know what like, to hallucinate means, but give us more in the context of AI.
Mary Beth [00:06:38]:
Not from personal experience, aware of.
Neela [00:06:41]:
From an academic standpoint. I mean, I did go to Berkeley, but just saying.
Gabe [00:06:46]:
Right on. So the early example in this was an attorney who filed some briefs and he cited case law and he was using the free version of early chat GPT. And it made up cases that were in the exact style of a case with docket numbers that look legit. It mentioned actual judges. It wrote in the style of those judges. But when the opposing counsel and the judge got this brief and they tried to find those cases, they didn’t exist. And so if you say, go find me five examples of something, it will. It won’t tell you, but only one of them is real.
Neela [00:07:26]:
It is the deep fake of AI, right? It is essentially, yeah.
Gabe [00:07:31]:
And so before we go any further, stop using the free versions of anything. They are worse than using nothing at all. They hallucinate so much. The free version of chat GPT, got a 35th percentile on the bar exam. The current version of it got the 99th percentile on the bar. Spend that money.
Mary Beth [00:07:52]:
That’s fascinating.
Gabe [00:07:52]:
Just don’t use it.
Mary Beth [00:07:53]:
Yeah, I mean, if you’re a business owner, obviously it’s a write off, so just go ahead and do that.
Gabe [00:07:58]:
Half, half the cost. I should be expensing that, sorry.
Neela [00:08:01]:
So it sounds like the way you’re using it and where you’re getting the most leverage out of it is to save time, to get better information and also to have like a structural friend in terms of, can I bounce this off you without actually bouncing it off a human being? I always think, like in engineering, they have a phrase where they say ask the rubber ducky, which is ask the rubber ducky before you go to a human being. And so you’re essentially using it as a rubber ducky that can talk back.
Gabe [00:08:30]:
It is so great for ideation. It’s a getting unstuck tool. So let’s say you’re like, I don’t even know where to start. Just tell it. Like, I don’t know where to start. And it’s going to give you some great ideas. It’s very good it on process, you know, like when you go ask some a question, you’re like, actually don’t even answer. Just the process of asking. I have the answer. Thank you. But it’s a lot more than that. You’re getting a lot back. But it’s also, let’s say you get a 20 page white paper on how to do some aspect of financial planning better. Don’t get me wrong, I’m excited to read those things. I don’t always have all the time. And so I can upload it and say, summarize it for me. I can ask it questions about it. My daughter was showing me, she’s a neuroscience major, but she has some humanities classes to take. And she had this 80 page PDF that she had to read and she’s showing me how she uses it and she’s like, look at the intersectionality of gender rights and economic opportunities and something else that you see the themes of in this paper. And they gave me four examples from the article.
Mary Beth [00:09:30]:
Wow.
Gabe [00:09:31]:
Like, you go, kid.
Neela [00:09:33]:
Oh, your neuroscience daughter. You must be so upset. She’s the best tragedy.
Gabe [00:09:42]:
But also, like trip or leisure planning. So I have this trip that I’m going on with another friend of mine, and our two daughters are coming along like, that’s the best. We’re going hiking and biking, and we’re going to be in Canada. I don’t know the restaurant where we are. And I’m like, in these two towns, what are five good recommendations? Say I’m pressing it might make one of those up, but I’m confident there are more than five restaurants. And, you know, I’ve got one picky eater, and I’ve got one person who hens mostly vegetarian, and two people who are very adventurous. And it’s more than going to Eater or to Yelp. I’ll tell you. The other sort of brilliant thing I use it for is even though I’m a planner, I can be bad at making reservations. I really prefer restaurants where I can withstand as much pain as anybody else and wait in line. But there’s no list of restaurants that don’t take reservations. A I can find it for you. No problem. Within a ten minute walk of here. What restaurants don’t require reservations and are highly rated.
Mary Beth [00:10:35]:
Interesting. That’s a good one.
Neela [00:10:38]:
Yeah. That is cool.
Gabe [00:10:38]:
I love it.
Mary Beth [00:10:39]:
I’ve used it for the trip planning. So we’ve been talking about doing Italy with our kids, and I have an eight year old, I have a six year old. Here’s the budget. I want four star hotels, all the different parameters. We want activities. Make sure that you link out to the hotels. Put it in a graph format so it spit out the whole itinerary. For me, it linked out to the hotels, gave me a budget for hotels, for accommodations estimated for food and beverage. Everything across the board basically replaces a travel agent in some ways. And that if you give it enough parameters, so I think it’s fabulous. You know, it made me think, too, the ideation, one of the things that I used it for. So our mergers and acquisitions playbook or handbook, if we’re merging in a firm, is drafted in such a way that is the story of getting married. And I’m like, all these stories about getting married for partnership. I’m like, is there a better way? Do we have to then, like, get engaged and blah, blah, blah? So I went to chat GPT at one point in time, I basically said, give me an example. Mergers and acquisition story from, I think from the acquirer. Tell it through a different storylines. And it gave me three different stories. One was planning a surprise party, one was taking a vacation. It basically crafted this six step story through a different lens versus getting married. So it was fascinating. I was kind of stumped, but it was interesting from those ideas what you can then take and then play and run with. So I loved it from that idea generation of just different things. If you’re kind of stalled out.
Gabe [00:11:58]:
Like imagine, even if you’re like, the marriage template just isn’t, that isn’t everyone’s cup of tea. What’s something that’s a little bit more generic? You could give it the document and say, can you rewrite it with one of those themes? It’s absolutely capable of doing that. But here are the things that people don’t think about doing. I will step up to my refrigerator just because I’m an AI geek. I will take a picture for chat GPT4-o, and I’ll say, with what you see in my fridge, what might I make tonight? I also have this in the pantry.
Mary Beth [00:12:23]:
Whoa.
Neela [00:12:24]:
Wow.
Gabe [00:12:26]:
Yeah.
Mary Beth [00:12:26]:
Just the picture of the refrigerator. The picture of the pantry.
Gabe [00:12:29]:
Yeah. It’s like, I see. It’ll tell you the things it sees, and it’ll even say things like, well, since that’s probably the most perishable, let’s include that in it.
Neela [00:12:37]:
Wow. Mind blown. Okay, so you’re obviously spending $700 a month on all of these tools.
Gabe [00:12:46]:
80, please. My wife could be listening to this.
Neela [00:12:49]:
There’s a range, you know, and it sounds like there’s a lot of different uses and different tools that are better for different usage. So you said to start do Perplexity.ai Pro. If you were to go order of priority of what are your top four and how would you use them to get the biggest bang for your buck?
Gabe [00:13:08]:
So Perplexity.ai is Google on steroids. And when you need to get accreditation or citation from your AI, funnily, it leads to my next two choices, because the engine underneath it is either chat GPT or Claude from Anthropic, and so you can actually tell it which one to primarily use. And my daughters have even showed me how it can help you with physics and equations within Perplexity. I digress. The next one that’s the most important to use is chat GPT4-o. Now, it’ll give you like six or seven searches a day for free. That’s pretty cool. Didn’t used to do that. There was no amount of access to it unless you paid, but pay for it. It’s $20 a month. It is unbelievably powerful, and I’ll give you a more technical example. So financial advisors, occasionally we go crazy with spreadsheets. I had disparate data sets that I needed to kind of combine. My fingers are going to fall off. It was going to be a terrible task. So I described to it, I’m like, in column a, I have this, column b, I have this, c, d, I have this. And here’s how I want to combine the data. What formula will do that for me? And then I’ll feedback the result. Hey, that didn’t work. This is why it got spit out. Try again. Because if you’re thinking it’s going to get it right the first time, this is not how AI works. And it’s like, okay, try this. And that didn’t work either. All right, we better take a different approach. Boom, it worked. I wasn’t going to do the project. It would be a ridiculous amount of work. I would have abandoned it. It’s possible to do something in a spreadsheet, and you can imagine what that possibility is. You can implement it. It’ll give you the formula to copy and paste and then tell you how to drag it down and implement it. Chat GPT is great at that. Gemini advanced, which is from Google, if you can figure out how to sign up and pay for it, I applaud you. I did. But I mean, I had to use other AI’s to figure out how to subscribe to the thing. Google, you are a great company. Your marketing is the worst. Gemini advanced is the one that I use the least. But since I like to compare them all, it’s still in the stable of pay for apps.
Gabe [00:15:10]:
And then Claude Opus is their largest language model and it’s the one you need to pay for. And it is just great at writing in a way that doesn’t seem like AI. And so I had basically a four page memo that I did for a client, and I kind of knew that client, like, there’s no way they’re going to read that. That’s just not going to land. And so I gave it to Chat GPT and it was like, I gave it to Claude and it condensed it to a page, and the instructions were, make this as understandable brief as you can without losing any of the meaning or content. And it gave me this one page output that was so much better. I have a colleague you probably know, who sometimes can write long emails. And I told him like, hey, man, run that stuff through Claude. You’re going to get a lot more response.
Neela [00:15:59]:
I need the TLDR.
Mary Beth [00:16:04]:
So our team members are going to start doing, we’re going to start running it through. It comes to us.
Gabe [00:16:10]:
But let me rattle them off real quick. So, perplexity pro, chat GPT4-o, Claude Opus, and then Gemini advanced. If you’re feeling charitably towards Google. But if you wanted to leave one off, you probably could.
Mary Beth [00:16:21]:
And Claude is the writer.
Gabe [00:16:23]:
Claude is the writer.
Mary Beth [00:16:24]:
So go back to, you mentioned the meme stock. Did Claude assist on the most recent meme stock blog?
Gabe [00:16:29]:
Yes, it did very much. But let me tell you something, none of them is perfect. Where Claude falls down is its knowledge ends in, I think, November of last year. And it’s not connected to the Internet because Anthropic, the company that owns it, they’re probably the leaders in trying to figure out how to make this stuff not dangerous.
Mary Beth [00:16:48]:
That’s nice. Appreciate that.
Gabe [00:16:50]:
Thank you Anthropic. They’re really the ones who are most concerned about alignment, making sure that it’s working for us, not against us, keep humans safe. All right, let’s lock them in a room. You know, you don’t want it doing that. But that’s the other thing is when you worry about AI, it hasn’t been connected to the world where it can do anything like it can tell me, here’s a great place to make a reservation, and then I have to go make the reservation. The next thing, which is coming very soon, is where there’ll be agents that will then take the output, and then it’ll create your calendar or make a post for you. Eventually it’ll start doing some of the work. But that separation between here’s how you do it and actually doing it is very purposeful. The AI companies are proceeding with caution.
Neela [00:17:34]:
So the agency, you still are required to take action on anything, and it’s working around things, but to do anything concrete, you still need to basically press the button.
Gabe [00:17:45]:
That’s right. And as we talk about sort of the consumer element of this, what we’re missing is, you know, the scientific and medical applications, which are really beyond me, and I don’t understand how they work, but they’re taking large language models and they’re teaching them to look at different all the proteins that we know exist and then to hypothecate, like, what other proteins could we create and what might they do? And it’s very good at that. And I think we’re going to see all sorts of medical breakthroughs, and neither of you guys nor I are going to be using those forms of AI, but they’re going to be insinuating themselves into other parts of our life in ways that we’re not going to see and that I think are going to be profound and which should make us feel rather optimistic about the future.
Mary Beth [00:18:28]:
So that actually may influence our work because then people would live longer, so therefore we’d need to stretch out their financial plans, maybe.
Gabe [00:18:35]:
Yes. And countervailing that,
Neela [00:18:37]:
newer models.
Gabe [00:18:38:
Prior to 1972, the United States had productivity growth of what, two and a half percent. And then briefly during the 1990s, I think we got up to 2%, but we kind of been cranking along at 1% here. Wealth creation comes from productivity, and if you’re a software coder, you’re probably two x productive to what you were before artificial intelligence, if you’re in marketing or you’re writing, remarkably more efficient if you’re someone who reviews mammograms, remarkably more efficient. And so that will translate into wealth effects that will also impact our clients and us.
Neela [00:19:14]:
I will say married to a software engineer who is an aggressive AI user, he said it’s like having his own mini team of junior engineers. He’s like, I’ve never felt more productive in terms of the coding that I’m able to do. I think it presents some interesting issues for apprenticeship going forward, and I have some concerns about that, but at least it kind of gets some of the grunt work stuff off the table and maybe we can then be more intentional about how we mentor.
Gabe [00:19:45]:
Yeah, I mean, I talk to my wife, who’s an attorney, and so some of the young lawyers go and do a lot of the grunt work and that’s kind of how they, they learn. And eventually, you know, I don’t think it’s been widely deployed in law because of the high stakes there, but eventually it will be, and that will be. I don’t know how we’re going to solve that. But, you know, we figured out what to do with all the buggy whip makers when cars came in, so…
Neela [00:20:08]:
Phone operators, there wasn’t a total breakdown of society.
Mary Beth [00:20:12]:
I mean, it’s a risk across the board, right, for students too. You know, we see students incorporating AI into the educational system, elementary to high school, those papers to college to scholarship reviews, viewing applications. I can very clearly tell when AI is used, used in some ways, and those ones obviously haven’t mastered AI yet to figure out how to customize it. But it’s very interesting in how the education system and how the next generation will learn and adapt with this and how much knowledge they’ll carry around. Same influence as Google. So I think it’ll be interesting to see that cohort. I mean, Gabe, you’re already seeing it with your daughters and how they’re able to do more research or be more efficient with it.
Gabe [00:20:47]:
My oldest just graduated from college and she moved to New York City. You know, it’s New York, I grew up there. It’s great. But I wanted to know about pepper spray and tasers and their legality. And now let me say it.
Neela [00:20:59]:
So I just want to make sure I understand the order. First limericks, then pepper spray. I’m just like, this is wonderful descent into your mind, Gabe.
Mary Beth [00:21:07]:
I love it.
Gabe [00:21:08]:
Limericks, recipes, and then self-defense recipes.
Mary Beth [00:21:12]:
Recipes. Right.
Neela [00:21:12]:
Obviously.
Gabe [00:21:13]:
It wasn’t so much about my daughter’s self defense, so much it was like, I don’t want her to get inadvertently arrested.
Neela [00:21:19]:
Right.
Gabe [00:21:20]:
And I double checked it, right? Once I got an answer, it was very quick to validate that it was correct. And actually, with Perplexity, it would take me right to the source and I could see that it was credible. And I was like, hey, the last resort. And you better only use it if you’re truly in danger, otherwise you’re going to be the one who’s in trouble. I had a client, I won’t be specific, who had a tragic thing happened. And, you know, sometimes you can freeze up, like when you need to write a condolence.
Neela [00:21:44]:
Yeah.
Gabe [00:21:45]:
And I was like, boy, this just whatever I’m coming up with myself just isn’t ringing good. And so I described the situation to AI and I said, what notes might I hit in my condolences? And nailed it. My colleague was like, that email was so good, man. And I was like, oh, thank you.
Neela [00:22:02]:
Nice, yeah, which the value of just like, unsticking us, it’s not like you’re outsourcing your empathy. You are an empathetic person who deeply cares about what happened to your clients. And sometimes we’re at a loss for words. And so helping you unstick and just come across with the same emotion that you feel, that’s like, hugely valuable, I think.
Gabe [00:22:22]:
I think you’ve found something very important there, Neela, think of it not as an intelligence apart from you, but as a co intelligence. Think of it as like your husband. These are junior software engineers, truly junior, but they can really help you. There’s some places where it’s not so junior, but best to think of it that way.
Mary Beth [00:22:40]:
From an efficiency and productivity, I mean, we do use it quite a bit. We use it on the podcast too. We have a software that we use that from the, the audio file creates the transcript, then from the transcript it creates the summary. Then from the summary, it creates three to four bullet points for the post. It also creates a social media. We pay for $20 a month that we pay, and it’s with the click of a button, it spits it all out. We might need to edit it, but from a productivity standpoint, it is well worth the investment to have that draft and that start versus paying somebody their hourly rate to do it manually. So there are so many different versions of AI popping up around the Internet, I guess, but the way that you could use it in your everyday life, I think of, I have friends that are on the PTG at school, and they’re sending out letters. There’s just so many ways in your everyday life that you can lean on something like this to create drafts that just eases up the time commitment for you.
Gabe [00:23:29]:
Yeah, it’s more than that. Like, there’s that benefit of it’s going to provide you with some efficiencies in your life. This is true. But you might just say like, it’s not for me. You know, I’m just not an AI person. And I would encourage you not to think that way. I would bring AI with you to every task that you do to think about, like, hey, how can I integrate this? And frankly, at the beginning, that’s hard. And then it gets easier. But here’s why. So I used to be in the semiconductor industry a long time ago. And the thing with semiconductors, there was Moore’s law that said every 18 months, the power of a computer chip is going to double and its price is going to halve. Now Moore’s law has eased off a little bit. It might be closer to 24 or 30 months per cycle, but that is still occurring. And the silicon is what underlies this intelligence. When you double the size of a server room, let’s just call it that, or a compute array, you don’t double the capability of the AI. It’s a tenfold increase. And so in the same way that you have this almost law of nature unfolding with silicon development, because it’s connected to silicon, the same thing is happening with artificial intelligence. And then people talk about a GI alternative, general intelligence. What is that? That is when the AI is as capable as a human being. Step one. People used to call that the singularity. Like, I’ve been aware of this for like 15 years. And I’m like, oh, the singularity. That’s so cool. It’s such nerdy, geeky stuff. But anyway, some people are saying by 2027 we could be there. And so it’s unavoidable. I think it’s important to learn how to use it. And if you were just like, eh, electricity, that’s not, for me, that’d be a pretty limiting stance.
Neela [00:25:16]:
But we do have a few years before we are living ex machina. Is that what you’re telling me.
Mary Beth [00:25:24]:
On that point? Let’s talk about the risks. Let’s talk about some of the risks.
Neela [00:25:27]:
We’ve got a few more good years. That’s what I’m hearing.
Gabe [00:25:31]:
You better make your money now because yeah.
Mary Beth [00:25:33]:
We just talked about being increasingly optimistic, but yet…Wait.
Gabe [00:25:40]:
Well look, so nuclear energy power, it is both potentially destructive and potentially creative. And you’re going to have good people and bad people harnessing artificial intelligence. And in that sense, I think there’s always that yin and yang, that’s always going to be going on. But in the same way that technologies have, what was it, 95% of Americans used to be engaged in agriculture. Now I think it’s somewhere between two and 5%. I don’t have my hands on the statistics, but that’s directionally correct. And yet there’s plenty of food to eat. And here’s the hard part. Maybe those farmers didn’t find other occupations, but their progeny did.
Mary Beth [00:26:17]:
Right. Those that would have become farmers, now, they found other, other paths.
Gabe [00:26:21]:
That’s right. Right. So it’s good for society, potentially painful for individuals. And, you know, there’s my own personal belief society should probably do something since it’s going to have such a huge benefit. Maybe we should find a way to share that with the people who are affected. But that’s beyond my pay grade.
Mary Beth [00:26:35]:
So let’s talk about this, digging a little bit deeper, because we’re talking about what consumers should know. So if we’re talking about an individual consumer, what are the risks to me as a consumer, aside from this? Obviously, there’s the career, there’s the career, but go ahead.
Gabe [00:26:47]:
Trusting it too much. Verify, verify, verify. There’s an old blog of mine that is finally getting pulled up for use. And so compliance was checking it. And there was one statistic where I was talking about the S& P 500. What percentage of the world equity market capitalization is it? I asked all of my AI’s it that simple question, and they all got it wrong. And then I just said, like, I know for a fact that’s wrong. Go back, try harder. Check your work.
Mary Beth [00:27:16]:
Yeah, try harder.
Neela [00:27:18]:
Try harder. Be better.
Mary Beth [00:27:20]:
Just be better.
Gabe [00:27:21]:
But you know what else, you know what else you can say to it? Your job depends on this, or I’ll tip you $20. Those things tend to make AI perform better. Also, AI doesn’t give you as good an answer in December because it seems to have internalized the idea that December is a time where we don’t work very hard.
Neela [00:27:37]:
My head just exploded.
Gabe [00:27:39]:
Vertigo. So you’re going to have moments of AI vertigo where you’re just like, what’s happening? Yeah.
Neela [00:27:45]:
So I love it. It’s this idea of trust, but verify and using it as a tool. And it’s, like, easy to ignore because it’s such a rapid way of changing how we’ve been working for so long that it’s easy to kind of ignore the potential. And there’s some roadblocks in the beginning. How do I use it? What tools do I use? Et cetera. But if you approach every action that you take in your day to day life, and you’d be like, hmm, could AI help here? You probably are pleasantly surprised at how powerful it actually is and so dabble, because you probably have no idea the ways that it can change your life.
Gabe [00:28:21]:
My wife had a first impression with Google Maps. She had been a MapQuest user and it may have gave her bad directions. And like, four years later, she’s like, nah, it sucks. I don’t use it. Like, when’s the last time you used it? She’s like, that’s amazing. It’s gotten better. And so that’s the thing. You keep checking in with these things and don’t let me forget to talk about the HR thing. That’s where people can really get tripped up when you’re applying for a job. Maybe just launch into that. So my eldest daughter applying for jobs after college, but she was getting rejected within a minute, though clearly it wasn’t a human being that was rejecting her. So then we found an AI tool that would help her rewrite the each and every submission based on the job description, being sure to embed keywords that would get it past the AI filter. And so if you’re looking for a job ever again, you may have no choice but to get AI savvy.
Mary Beth [00:29:12]:
And what was that AI tool that you found to rewrite based on the job descriptions? I mean, that’s probably. People will be curious.
Gabe [00:29:19]:
We can put it in the podcast notes. I’ll have to put it out to my daughter because. Yeah, yeah. Because she’ll be able to tell me what it was.
Mary Beth [00:29:27]:
One of the things that you and I talked about, maybe it was a few weeks ago, offline, if we have clients, and one of the things we’d want them to be aware in terms of scams and the AI usage, what the risk is there from a client perspective, if we’re talking to our clients right now, what do we want them to be aware of in how AI can work against them in terms of getting money out of their hands?
Gabe [00:29:48]:
I so appreciate your asking this question, and let me give you the solution. Before I describe the problem within your family, you should have a pass phrase. Everyone in my family and my immediate family knows a passphrase that we can challenge each other with. And let me tell you this scary story. There was a comptroller in a company in Hong Kong where on a video meeting, his CEO was directing him to wire money to an account out of the country. And then when he asked some questions, they brought one of the board of directors onto the Zoom call. These were all real time deepfake videos. And some millions of dollars later, the guy’s like, this just doesn’t make any sense. And he stopped. It used to be you would need a large audio sample to replicate someone’s voice. Now you can do it with a 30 second sample. And so we’ve told our parents, our kids, grandparents, like, if you get a call from one of the grandkids and they’re in trouble, do not respond because you could get a call, like, when someone’s very upset, it’s hard to tell whether their voice is true, but in this case, it’s going to sound just like their voice. And grandma, I just got arrested. If you don’t wire money to this account, I’m in big trouble, and it’ll sound very convincing.
Mary Beth [00:31:09]:
So that’s one of the things we talked about. So not just having the password or safe word with your family, we have one as well for our family, and we’ve created one with the grandparents, but also one of the things that we talk about now, a risk for the podcast. Neela and I are out, so having one for your business as well. So if your voice is out there on the Internet at all, if you have social media, if you do a podcast, if you’ve done recorded speaking engagements, your voice is out there and it’s easily able to be replicated. So making sure that you’re aware of that, figuring out how you want to cross verify, and having those systems in place for your family and for your business, if you have one, is very important.
Gabe [00:31:40]:
At the time you asked my daughters, Mary Beth, you asked my daughters to buy gift cards for a client because I couldn’t get around to it. But I needed a lot right away. I know a lot.
Mary Beth [00:31:50]:
It happens. You know, I was on sabbatical last year and I was getting text messages. Is this you? No.
Neela [00:31:55]:
And, you know, I wonder if it was the fact that they were gift cards to outback Steakhouse that threw off or…
Mary Beth [00:32:01]:
Maybe.
Gabe [00:32:04]:
My daughters were legitimately confused.
Neela [00:32:07]:
Why is your CEO texting me?
Mary Beth [00:32:09]:
That’s crazy. That’s crazy. But I mean, that’s very real. You know, that happened to my grandma. She got a scam phone call pretending to be my sister and she was ready to send the money. How often does that happen to our clients, though, when they get those phone calls and they don’t know? There is the wonderful productivity in terms of life enhancements, optimistic side of AI. And I think there is the downside as well from an individual consumer basis and making sure that you’re protected and aware of some of the ways that it could negatively influence your life as well.
Neela [00:32:40]:
Yeah.
Gabe [00:32:41]:
It positively influenced you in ways that you’re unaware of, too, in the sense that large financial institutions are using it to screen for fraud and detect it in ways that human beings couldn’t, or there was just too much data. So it’s yin and yang power for good. Power for bad.
Mary Beth [00:32:55]:
Yin and Yang, yes.
Neela [00:32:56]:
And we’re gonna have to have one of these. You just come back in six months, Gabe.
Mary Beth [00:32:59]:
Right.
Neela [00:33:00]:
Because I’m sure everything will be completely different in six months, and we’ll just keep up on it. Have it be like a regular slot.
Gabe [00:33:06]:
Whatever tool you’re using today, I didn’t coin this phrase, will be the worst AI tool you’ve ever used.
Mary Beth [00:33:11]:
I just want to keep tabs on the AGI. Where are we in that timeline?
Neela [00:33:14]:
The ex machina?
Mary Beth [00:33:16]:
Yes, yes. Yeah, that’s what I think.
Neela [00:33:17]:
Careening towards that little tracker for that along the way.
Gabe [00:33:22]:
Yeah. So the criticisms I see seem ignorant. I’m like, that’s not going to be a limiting factor. That’s just someone who hasn’t really done the research. You know, I think there are many people saying 2027, so just really not that far off.
Mary Beth [00:33:34]:
Not that far.
Gabe [00:33:35]:
OpenAI and Microsoft are creating a data center. It’s got like starship in the name. I can’t remember what it is, but it’s on the order of a hundred times larger than anything which exists today. What are they going to be running on that? By the way, that thing’s going to take until about 2027 to build, so.
Mary Beth [00:33:52]:
There you go.
Gabe [00:33:53]:
Yes.
Mary Beth [00:33:54]:
All right. Well, with that, is there anything else that we’ve missed, or should we close here with that cliffhanger?
Gabe [00:33:59]:
I don’t think anyone could possibly absorb more than we’ve gotten them today.
Neela [00:34:04]:
My brain hurts.
Mary Beth [00:34:04]:
Neela and I have notes for us to go back to. So obviously so that we can now implement these in our lives.
Neela [00:34:11]:
I’m very busy. Must go sign up for $80 a month plan.
Mary Beth [00:34:15]:
Our CFO is going to be so annoying when we all start. We’ll submit all of of our expense reports for individual $20 for all of our subscriptions. Sorry, George.
Gabe [00:34:24]:
Sorry, George.
Mary Beth [00:34:26]:
All right. Thank you so much, Gabe, for being here.
Gabe [00:34:28]:
This is really fun, guys. Thanks for having me.
Neela [00:34:31]:
All right, see you soon.
Mary Beth [00:34:32]:
See you soon.
Neela [00:34:35]:
Thank you for listening to today’s episode of If Money Were Easy. If you’re looking for more information on how you can expand what’s possible with your money, head to abacuswealth.com. that’s abacuswealth.com for more analysis and resources created by our team.
Neela [00:35:17]:
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