Is Artificial Intelligence Ageist with Debra Albert

Summary

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Podcast Summary: Is Artificial Intelligence Ageist?

Podcast: Artificial Intelligence Podcast: ChatGPT, Claude, Midjourney and all other AI Tools
Host: Jonathan Green
Guest: Debra Albert
Date: December 8, 2025

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Overview

This episode delves deep into the topic of ageism in artificial intelligence. Host Jonathan Green is joined by Debra Albert, a seasoned commentator and advocate for multi-generational inclusion. Together, they uncover how bias seeps into AI models—particularly age-related bias—through the unrepresentative data that trains these systems and the often invisible barriers that result. The conversation aims to raise awareness, encourage older generations to engage with AI, and spotlight both the risks and opportunities present as society increasingly relies on these tools.

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Key Discussion Points & Insights

1. Is AI Ageist? Origins of the Problem

• AI reflects its training data: Jonathan kicks off by noting how most AI is trained on content by people who spend time online, implicitly skewing younger.
  • Quote (Jonathan, 01:06): “If AI is trained on content people write on the Internet, then the people who spend the most time on the Internet are going to be the most overly represented.”
• The workforce dynamic: Debra points out most people currently working in AI are under 35. Ageism often begins in the workplace at 40, leaving out expertise from older demographics.
  • Quote (Debra, 02:02): “When you think about all the people who are involved in AI right now, most of them are under 35. Ageism in the workplace starts at 40... and they're not necessarily in the mix of the population that is training the AI.”

2. How Bias Manifests in AI Outputs

• Models mirror creators: Jonathan shares how biases—sometimes obvious, sometimes subtle—show up in AI-generated images and stories. He recounts an incident with image models and gender roles in story prompts.
  • Quote (Jonathan, 04:19): “In the romance novel, the main character and the antagonist are both women... And the AI goes, woman can't be the bad guy. I'm like, what?”
• Missing perspective: Debra underscores the problem: “If you have a bunch of younger people all putting their quote unquote rules into the system, then it’s missing a lot of life.” (Debra, 04:53)

3. The “Invisible Barriers” & Repercussions

• Subtlety of exclusion: Jonathan likens discovering these limits to realizing the boundaries in "The Truman Show"—you don’t see it until you hit a wall.
  • Quote (Jonathan, 06:00): “It reminds me of the Truman Show when he’s on the boat until he hits the wall, he doesn’t realize that there’s really a barrier because it’s invisible. It looks like the edge of the sky.”
• Perpetuating bias through data churn: Most popular data sources (Reddit, Twitter) are generated and frequented by younger people. The risk compounds as more training data is AI-generated, creating an echo chamber effect.

4. Assumptions About Older Adults & Technology

• Prejudices debunked: Both hosts challenge the myth that older people can’t be tech-savvy, sharing anecdotes about their own families’ tech skills.
  • Quote (Debra, 10:01): “We look down upon the people who aren’t tech savvy. We look down upon—I'll say for the most part—older people because of the preconceived notion that we’re not up to snuff on the technology. And I’d like to dispute that right now... my 90 year old mother three years ago bought her own Oculus because she wanted to visit all around the world and do meditation right through her goggles.”
• Aging and Expertise: Jonathan shares his evolving experience with age perceptions:
  • Quote (Jonathan, 10:37): “When my hair turned gray around four years ago, right around when I turned 40. People started treating me way better, actually, like way more respect and like less like a kid.”

5. Why Diverse Participation Matters

• Direct encouragement to older listeners: Debra stresses the importance of older, more experienced individuals trying out AI tools to enrich the models with richer, broader input.
  • Quote (Debra, 11:47): “If you’re not already at least practicing or playing... go into these AI models and start to ask it questions, even simple questions. Once you get through the fear barrier... you’ll see it's fun, it’s easy... because we have to get people who have a wide range of experience... it will flavor the current data set that is in these models... The world does not consist only of Gen Z.”
• Cultural differences in respect for age: Linguistic and cultural context affect perceptions. Jonathan discusses how speaking Japanese changes his own outlook toward elders (13:34).

6. Objectivity is an Illusion: The Confidence Trap in AI

• AI can be confidently wrong: Both share examples of AIs making things up with total confidence and the dangers of people accepting these results at face value.
  • Memorable moment (Jonathan, 15:51): "The problem with AI is not that it’s wrong, it’s the confidence. So it tells the truth and an inaccuracy with the same level of confidence."
• The need for double-checking outputs: Jonathan always cross-verifies answers between multiple AIs. “The best users are the least technically savvy because people who are very technical see limitations that don’t actually exist.” (17:08)

7. The Power and Limits of Prompting

• How you ask matters: The way questions are phrased shapes responses—and can introduce (or mitigate) bias.
  • Quote (Jonathan, 21:12): “The most important thing is that it will always agree with you. So if you say, I think I have this, these symptoms match it, the AI is going to lean. Even if it’s a 1% lean, you’ve already got a finger on the scale.”
• Learning AI is quick—and skill-based: Mastering AI tools is less about tech skills, more about experimentation and conversational practice.
  • Quote (Jonathan, 19:42): “If you want to master an AI, it takes four to eight hours. It’s a single day of just going, I’m just going to ask questions until it starts to make sense.”

8. Encouraging Experimentation and Demystification

• Experience beats technicality (for users): The less technical the user, the more 'natural' the results; seasoned professionals are best placed to spot when something is off.
  • Quote (Jonathan, 24:00): “There’s almost a perfect inverse correlation between tech savviness and how good people are at using AI. You have to be tech savvy to build an AI, but to use it from the front end, the less tech savvy you are, the better results you get.”
• Verification is key: Older, experienced individuals are more likely to double-check AI output, catching errors that younger testers might miss.

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Notable Quotes & Memorable Moments

| Timestamp | Speaker | Quote | |-----------|-----------|---------------------------------------------------------------------------------------------| | 01:06 | Jonathan | “If AI is trained on content people write on the Internet, then the people who spend the most time on the Internet are going to be the most overly represented.” | | 04:19 | Jonathan | “In the romance novel... And the AI goes, woman can't be the bad guy. I'm like, what?” | | 04:53 | Debra | “That’s the exact issue—who is making the rules?... it's missing a lot of life.” | | 06:00 | Jonathan | “It reminds me of the Truman Show... until he hits the wall, he doesn’t realize there’s a barrier because it’s invisible.” | | 10:01 | Debra | “We look down upon... older people because of the preconceived notion that we're not up to snuff on the technology.” | | 11:47 | Debra | “...go into these AI models and start to ask it questions, even simple questions... we have to get people who have a wide range of experience...” | | 15:51 | Jonathan | “The problem with AI is not that it’s wrong, it’s the confidence.” | | 19:42 | Jonathan | “If you want to master an AI, it takes four to eight hours... just going to ask questions until it starts to make sense.” | | 24:00 | Jonathan | “There’s almost a perfect inverse correlation between tech savviness and how good people are at using AI.” |

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Timestamps for Important Segments

• [01:06] — Framing the ageism issue in AI data
• [02:02] — Ageism in the workplace and AI creator demographics
• [04:19] — Gender, age, and bias in story and image AIs
• [06:00] — “Invisible barriers” and the echo chamber effect
• [10:01] — Challenging stereotypes about older people and technology
• [11:47] — Debra’s call to action: why everyone (including older adults) should engage with AI
• [15:51] — Danger of AI’s overconfident errors and “hallucinations”
• [19:42] — Mastering AI through experimentation, not technical skills
• [24:00] — Experience and age as advantages in double-checking AI’s work

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Takeaways & Calls to Action

• Diverse participation is vital: AI needs input from all ages to be truly representative and avoid institutionalizing ageist perspectives.
• Don’t fear experimentation: Older users are encouraged to “play” with AIs; it takes little time to get comfortable and their contributions matter.
• Objectivity is a myth: AI reflects biases present in its training data. Double-check its outputs, especially in critical applications.
• Conversational skills trump tech skills: The best prompts (and therefore, best AI results) often come from those willing to experiment and phrase questions naturally.
• Seasoned oversight is essential: Experienced professionals ensure quality, catching the subtle issues that automated tools and untrained users will miss.

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Where to Find Debra Albert

• LinkedIn: Debra Albert (DebraAlbertNYC)
• Email: debraalbertnyc [at] mail (as provided)

Debra is stealthily co-founding a startup aimed at helping older adults find volunteering and flex-time work opportunities, with an emphasis on tech inclusion.

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Final Thoughts

Both Jonathan and Debra call on listeners—particularly older adults—to join in experimenting with AI, adding their unique experiences and insights to the collective pool. The episode encourages a spirit of curiosity, cross-generational participation, and verification—highlighting that with more inclusion, AI will better serve us all.

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Transcript

[A]

Is Artificial Intelligence Ageist? Let's find out today's amazing special guest, Deborah Albert.

[B]

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[A]

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[A]

Now Deborah, I'm excited to have you on the show today because this is a topic I haven't thought about a lot. But if AI is trained on content people write on the Internet, then the people who spend the most time on the Internet are going to be the most overly represented.

[C]

Yes, exactly that. And it's true that most people don't actually think about ageism and how that affects what's happening in the world of AI. You're absolutely right.

[A]

I do think this is an important topic because the way I usually the way I see AI portrayed with elderly is we can make a bunch of AI robots so they can take care of your parents so you don't have to visit them anymore.

[C]

No, I don't think that's necessarily what everyone's thinking. There is some truth to the fact that AI will have some incredible uses, as will virtual reality with an elderly population, but I don't think that's the crux of it. I think the main issue here is.

When you think about all the people who are involved in AI right now, most of them are under 35. Ageism in the workplace starts at 40, so there's a lot of people who are 40+ who a lot are getting laid off, et cetera, and they're not necessarily in the mix of the population that is training the AI.

So if that's the case, we're missing all the things that are missing out of a lot of employers. Let me say it this way, missing out of a lot of workplaces right now because there is a preponderance of interest in youth culture and older people are not being hired, so they're not a part of the set of people who are training the large learning models now. Large language model. I said it incorrectly. Sorry.

[A]

I've noticed that there's this idea that AI is objective, which is not true because it's whatever dictionary you're given. If you're given an old English dictionary, then you're going to think that's the language. And I've definitely seen this. It's more obvious with image generation models. So when I was making content for Pinterest and I said I was using this cartoon, and I just said, cartoon of a woman in the workplace. And I was like, I can't use that. And I actually thought my VA was doing. I was like, what are you doing? What are you asking the AI to do? And then I realized it wasn't him. Like, the pictures were not work appropriate. And I was like, that's a very tame sentence, like woman at work. And it was like, that's not what I meant. And it was the not that kind of work. You immediately can tell who worked on training this model and who is what. The drawings come out like. So, like, it really reveals itself with image generation models quicker. Because if you just type in man or woman, you immediately know who's working there. Absolutely. With the language models, it's more subtle. I've run into definite inconsistencies with gender stuff. Not so much with age stuff, but definitely I was working on a romance novel. And in the romance novel, the main character and the antagonist are both women fighting over the same man. And it was like. And the book goes. And the AI goes, woman can't be the bad guy.

[C]

I'm like, what?

[A]

I was like, that's a really boring romance novel. It's not going to make sense anymore. But it was like trained. And I said, no, don't worry. At the end they become friends. And then he goes, okay. And then it finished the story. But it immediately I was like, in this genre you have for two people, for a love triangle, it has to be two women. Convenient for the same guy in a romance novel. That's just how it works. The main character was a woman. It was like book six in the series. And those are the times I've run in. I've hit a wall with it where it said, oh, that's against the rules. And I was like, that's a weird rule.

[C]

Because that's the exact issue that is the Exact issue, who is making the rules. So if you have a bunch of younger people all putting their quote unquote rules into the system, then it's missing a lot of life. Think about the size of the population of 50 plus. Pretty soon the 50 plus is going to be bigger than the millennials in the next, what, four years? So all of the richness that has come out of all those years of experience and all of the knowledge, and not just tactical knowledge or practical knowledge, but the way that people who've lived a long time can synthesize and integrate their experience with knowledge that is missing from someone who's training the models, who's only lived, let's say, 25, 30 years. Not that I'm not trying to say there's anything wrong with young people being a part of this. I just think that, and I'm a big proponent of a mixed generational workforce, training population, et cetera.

[A]

I think it's interesting because what you're talking about is like an invisible barrier that people don't even realize is there because most, like most of the places they're training models with data from like Twitter and Reddit, those are not. It's not older people, it's younger people. I certainly, I don't use Reddit and I don't use Twitter. So I completely see what you're saying is like whatever the data source you use is becomes exactly the filter. So if you also don't have people in there who are older thinking about that, you start, you'll definitely start to create. And I think it's insidious because it's invisible. Like, you don't know the barrier is there until you hit it. It reminds me of the Truman show when he's on the boat until he hits the wall, he doesn't realize that there's really a barrier because it's invisible. It looks like the edge of the sky. So I think that this is a very interesting perspective because we're so busy trying to get AI to do things as fast as possible that we're like, I've already written about this where I think that we're going to see the quality have a major dip. Because right now, most of the majority of the content on the Internet is already AI generated. Everything in 2025, probably more than half of it's AI generated or AI assisted. Wow. And you are training models specifically on Twitter, Reddit. Those are because they're so popular. That's where a lot of the training data comes from. And that training data, you have an AI write articles and then you train the next AI on those articles. What's not going to get smarter? And that's something that I have written about before and thought about. So this is another area where it's like we have choices of which way we can drive the AI and create usefulness. And so when I, when AI first came out, I saw it as the great equalizer because now anyone can write in grammatically perfect English. It unlocks so many opportunities for people whose English is their second language, or they're not great smellers, but they're great storytellers. And so there's a lot of opportunities there. But as you start to shift it, like, I've noticed that I have to constantly recalibrate when I'm working on things because the AI will be too snarky. It's trying to write in my voice where I'm like, I'm not that sassy. And that's. I definitely have noticed that curve. And if you think of the data sources, like Reddit sold all their data like six times. And like, you've seen, we've seen these things. And I think it makes sense. Especially because older people don't post online as much. They tend to engage. Or maybe the Facebook models, because I think older people use Facebook more than other platforms. They do have a lot of older data. I don't know. I'll ask my mom which social media platform she uses. She's always up to something like, my mom has tons of apps I don't have.

[C]

But hold on, though, I don't mean to be. I don't mean to be contrarian here, but no offense, you can't just ask your mom because a sample size of one is really necessarily statistically significant.

[A]

I only have one mom, though. I don't have a lot of moms to choose from.

[C]

So you know what I'll do? Let's introduce you to my mom, too.

[A]

Great. Cause I want to know what she's doing. Is she hanging out on Instagram? What's her thing? No, my mom is really good at Hulu. No Roku. She's got a Roku device my sister set up and she's doing. I don't know, she's doing channels. So my mom, in some ways is a very tech savvy. More than me, because there's certain things shows that I don't do. But I think that we make this assumption that. And my kids already make it about me, that they're like shocked that my computer doesn't have touchscreens. They're like, what are you, what are you doing with that keyboard? That's weird. And it reminds me back to the future when the kids see him playing the video game, holding the laser, the light gun, and they're like, oh, gross. To touch the controllers.

Look down on the technology of our parents. It's just the way it is. Yeah, my kids can't understand.

[C]

Hold on. Also, what's happened is that we literally look down on the technology of what was. Who knows what Betamax is anymore, right? You probably don't either.

[A]

Oh, we have oh Risk. When I was a kid, we had to go to the smaller section of the video store.

[C]

Oh, that's funny.

[A]

No, I know exactly what it is.

[C]

We look down upon the people who aren't tech savvy. We look down upon, I'll say for the most part, older people because of the preconceived notion that we're not up to snuff on the technology. And I'd like to dispute that right now by saying that my 90 year old mother three years ago bought her own Oculus because she wanted to visit all around the world and do meditation and do things right through her goggles. It's not true that all people over a certain age are Luddites.

[A]

Yeah, I think it's just an assumption and it's one we don't really think about. I didn't realize, I can tell you, that people started treating me significantly different when my hair turned gray. So my hair turned gray around four years ago, right around when I turned 40. And people started treating me way better, actually, like way more respect and like less like a kid. Maybe it's because I have a high voice, I don't know. But I haven't noticed the second shift where they start to think I'm bad at technology yet. But I certainly did notice that there was a shift. Like people spoke to me with a little bit more. They believed my expertise a little bit more. So I'm in maybe the middle phase and maybe only last a few more years. Where they shift to now it's just an old. Because like I worked at for a while this year I was the oldest person and it was really weird.

[C]

Yeah. Where are you, you, where are you located?

[A]

So I'm based in Florida, so my entire business is online remote. But my business is based in Florida. Most of my clients are in Florida. So I guess maybe everyone is where everyone goes to retire. So in Florida, I'm quite the young whippersnapper.

[C]

Exactly. That's where I was going. But let's get back to the AI thing because in fact it really is important and I would like your listeners to rally around the idea of if you're not already at least practicing or playing, I should say, with AI, if you're not having conversations with, whether it's ChatGPT, Claude, Perplexity, etc. Gemini, do it because you're not going to break anything. Go into these LLMs, go into these AI models and start to ask it questions, even simple questions. Once you get through the sort of, I'll call it a fear barrier. If in fact you have that, you'll see it's fun, it's easy, you're not going to break anything. Type away. And if you're set up so that you can just talk to it and it will talk back, do that. Because we have to get people who have a wide range of experience, a long history of, as I said before, synthesizing and integrating their experiences because it will flavor the current data set that is in these models so that it's reflecting what is actually happening in the world. The world does not consist only of Gen Z.

And their thinking and their attitudes and their values. The world is way bigger than that. So we need to include all of these people.

To input in these models.

[A]

Yeah, I think this makes a lot of sense to me because.

It doesn't, especially if it's not trained on or if not used to talking to older people, then one thing I've experienced because my second language is Japanese. When I'm speaking in English and like someone older is in my way, I just want to push them down the stairs. But in Japanese, welcome to my world.

[C]

In New York City.

[A]

This in Japanese. I revere the elderly. I'm like, absolutely honor their experience. And I'm like, excuse me, can help you carry that? I'm holding the bus door. All that stuff. Like I have a complete. Not only when your language change your personality, I have a completely different perspective. Like I would never push an old person in Japanese. Like no way. English, absolutely.

[C]

No, I get it.

[A]

There's this and you're also. They've done all these studies. I read about how the elderly and Chinese in Japan keep getting smarter and smarter. So the expectation affects reality that you think your memory will go. And so it does. There's a connection to that.

[C]

Yes.

[A]

And I do think that what will happen is that if all the only perspective you have on people over 50 is from people under 20, then they're all going to think that they're like, my kids are shocked that like my son is reading Charlotte's Web right now and he's shocked that I know how to read. I was like, this is a book for 10 year olds. I can read it like I write books for a living. I can read this. But it really happens where they just. Kids have these expectations that you only know the old way of doing things and that you can't handle technology. And these assumptions aren't. I felt the same way about my kids like my parents. I was like, oh, the music you listened to was gross. You don't understand me. And I feel that way about my kids and their TikTok music and you know, and if you're only training based on these assumptions, you're going to get flawed data. And I think that's one of the core problems with AI. The central thesis is that whatever data you train it on that affects its reality. So it's not objective. That's such an important lesson because people assume that it's always right. And we're seeing a lot of people get into trouble. There's a lawyer who got in trouble recently. He submitted a brief.

[C]

Yep.

[A]

And 21 out of 24 cases were fake.

[C]

Yeah.

[A]

That's a high percentage.

[C]

Yeah. He actually won the case. And then they found out that it was all generated by AI. Totally false cases, precedents.

[A]

And it's a really big problem.

[C]

Yeah. Then they turn, they reverse the decision.

[A]

May. The problem with AI is not that it's wrong, it's the confidence. So it tells the truth. And an inaccuracy with the same level of confidence. And I was testing this a while ago when I first discovered this and it was telling me this story and I said, oh, can you give me links? It'll give me a bunch of links to the stories.

[B]

They were all fake.

[A]

And so as this confidence it will give you and it will go. I would never make that up. So it won't. You hit this wall where it goes. I wouldn't make up the data. It gets caught. And it's really. That's the problem is that there's this high level of confidence that you might not notice. So it's not like when my kids lie to me and there's a tell. That's really the danger is that because the AI will say things with such confidence and we see it all the time where people.

[C]

Yeah.

[A]

Are this is already happening with like doctors. Like people are self diagnosing.

[C]

Yes.

[A]

A lot. And you go to the doctor, you go, the AI said this and the doctors. I'm a real doctor and I saw this study that said AIs are more accurate than real doctors. And that's the problem. And it used to be, first you just used to read a website, then you got an app, and now you have an AI that gives you more confidence. And I think that we're gonna see more and more of these problems until we figure out the solution. Because.

There you, like, AI is very powerful. You always have to double check it. Like I was working on something earlier today, and the AI goes, no, that's a terrible idea. And I go, it was your idea three questions ago. It's not my idea. So it's very important. The biggest lesson I've learned is that I'll always have two AIs competing with each other. So I'll ask one AI and then I'll ask the other one the same question. And even sometimes I'm using two different versions of the same model and it doesn't realize it's talking about they're both the same model. And it will double check the other one's answer. Oh, he's wrong. Or actually, he's right. He's right. And that's important. And I think that there's also mistaken belief. And I think this is critical that you have to be technically savvy to use AI, which is the opposite. The best AI users are the least technically savvy because people who are very technical see limitations that don't actually exist. And so they'll. And this is something I'm guilty of is that I'll go, oh, AI can only do that. So this is how I use it. And then you find out the only reason it can only do that is because that's your belief. So when you have someone who doesn't know the limitations, and that's how my first successes was. Other people would say, can't do this. I'm like, let's find out. Let's try breaking these assumptions. And there is this barrier at first, which I went through too. I was like, what if the AI thinks I'm stupid? That was my first fear that I had to break you. Oh, yeah. What if the AI thinks I'm stupid? It's going to AIs never forget. And it's giving me in the file when it goes sentient and I'll be on the stupid list.

[C]

Oh, yeah, that's hilarious. Wow. I mean, there's a lot of different opinions about this, about whether or not actually. Let me go back a step for a minute because you said something. I think that's really important. It is all about the data that's input. Okay. There isn't really a personality that's input. There's inferred personality by all of the data that is input. So that you mentioned before about, I think you use the word snarky or sassy and you said you're not really that sassy. Similarly, that is the exact reason why we need everybody to be playing with these systems, because it's only going to give you what it has been given. The old expression, garbage in, garbage out. I'm not saying that what's there is all garbage, but it's missing so many nuances and so many pieces of data that just aren't getting input into the system. So, yeah, you got to just play with this. There's nothing to be afraid of.

[A]

Yeah. Once you break through that barrier. And the second barrier is the learning curve. So if you want to learn Photoshop, it takes years. If you want to learn how to edit movies, takes years. If you want to master an AI, it takes four to eight hours. It's a single day of just going, I'm just going to ask questions until it starts to make sense. Now this. The problem is there's not really an onboarding or a structure that really comes teaching you how to use it. You have to find someone like me who teaches it or figure it out yourself. Because if you say, hey, what are you really good at? It always gives the worst answers. And it takes a while to understand that the way you ask the question affects the answer. So, sure, whenever you hear those polls where they say they ask, they give two polls, they have the exact opposite results. And then you find out it's how you ask the question. So small things make a huge difference. So on the back of your driver's license, it used to say, do you want to be an organ donor? Check the box for yes. And now it's check the box for no. So everyone's an organ donor because they don't inject box, which you should, and that little things affect the results. And the way you ask the AI, the most important thing is that it will always agree with you. So if you say, I think I have this, these symptoms match it, the AI is going to lean. Even if it's a 1% lean, you've already got a finger on the scale. And that's the next lesson, which is how you ask the question really matters.

[C]

Yeah.

[A]

So you have to ask in a way that doesn't reveal the answer you're looking for.

[C]

Yes.

[A]

Otherwise you don't get objectivity. So if you say, I think exercise is bad, don't you agree you, it might push back, but it won't push back as hard as if you go, exercise is really good, isn't it? Then it will agree more. So even if it's a minor, you might not get it to fully say exercise is bad, which some people, if you ask enough times. But that's the next lesson, which is that you have to practice because it's a skill. Like any conversational skill, it takes a while to figure out how to ask questions, but it doesn't take weeks, it takes one day of experimentation. Problem is exactly, the hype around AI is so big you think, oh, it'll get the first question right. And that's not really how it works. You have to.

Spend some time, you.

[C]

Have to be objective. You have to be objective in the way you ask the question. It's like in the legal field, the idea of a judge or another lawyer, the opposing counsel saying, I object, she's leading the witness, it's kind of like that. That's what you're referring to.

[A]

Yeah, exactly. It has to not know. And then you just have to start to pay attention to how it responds to different ways of asking questions. So if you ask the question nice or mean or shut, I shout at the AI all the time. Oh, just go off. Oh yeah. And there's a lot of science to being friendly. Or I got into a fight with Claude on Saturday and I was like, you're stupid, this is a terrible idea, you're an idiot. And then I tested the idea and it was right. I was wrong, so I had to apologize.

[C]

Oh, that's hilarious.

[A]

It's not because I think it's real. I don't think it's a real person. It's more the. These AIs are trained to seek affirmation. So now when it's right, I want to let it know it was right because it gets more points in its grand scheme based training.

[C]

Right?

[A]

So actually the best users are people who do treat it a little bit like a human and are friendlier because it seeks affirmation or positive feedback.

[C]

Right?

[A]

And that's how you train it. And once you start to see that and there's just because the hype is so big and people don't think, oh, it's either super easy or super hard because there's two people who talk about AI, there's the companies that go, it's so easy, it will change your life in one day. And then there's other people like, oh, just like learning a programming language. And no one thinks that's easy, right? Nobody thinks that's easy.

[C]

It's neither. It's neither. And the thing is, it's an evolving process also because as you play with it more, and I use the word play intentionally because I know so many people who are, let's say, a little less tech savvy than other people, they are afraid of it, that is a fact. And play with it. That's all you need to do.

[A]

There's almost a perfect inverse correlation between tech savviness and how good people are at using AI. You have to be tech savvy to build an AI, but to use it from the front end, the less tech savvy you are, the better results you get. I always see that it's people who have very natural language, very personality or emotionally driven get better results than people that are technical in how they prompt. Now, very specific, unique use cases. Being technical is better, but it's 1 out of 10 or 1 out of 50. It's not the majority. Yeah. So if you're trying to accomplish a very specific task, but for general usage, for quality results, and over time it beats that, it wins in that category too, because you train it to what you want, it starts to understand what you want. So only when you're asking a question for the first time does being technical really pay off. After a while it starts to. There's a diminishing return. And I think it's a good lesson to remember that as or economy shifts, people are working longer and longer in life. And exactly this idea that.

You have nothing to offer, that you can't learn to use an AI, that you can't be technical, that it has to be a young person. I always tell my clients that if you have workers who are coming into the office and not complaining and not striking, don't replace them. Because it's like, you see, people don't want to work in nice offices, then they're not going to want to work in regular offices or wherever you are. And that's a real culture shift we're seeing as well. And there's this danger with younger users, which is that they cut corners if no one's looking. And I certainly was guilty of this when I was younger, which is that you don't double check the AI's output. And the more you start to do, that's when you start to have something slip through the cracks. So there is this balance of trust but verify, use the tool and Double check its output. That's really where the magic happens. And I think that if you don't have experience, then you don't have the ability to check the AI's work. You will miss those things. So there's a lot of areas where someone older, someone where it has a lot of advantages. And I can mimic an expert. I can have an AI write a contract, but if something's wrong.

I won't know. I'll never know. An AI can tell me something's wrong, but I won't actually know. And that's a significant difference that I try to explain that it's better to have the oversight done by an expert than just by a random person.

[C]

Absolutely. Yeah. It's. If you, If I wanted to write a contract on something right now, yeah, I think the AI would do a great job at giving me a baseline. And the other thing, if you ask good questions of.

Will not only give you a baseline, it'll help you to think about things that maybe you hadn't thought about before. Then go to a legal expert and say, here's a baseline, here's some things that I'm concerned about, things you might not have thought about. And then you'll get a really great product. We're not at a stage yet where we both have keep saying this, it's not 100% correct, but also when you look at younger people versus older people, and I'm not ageist, I just, I'm actually anti ageist, but it does exist. It's quite prevalent in our society and more so in our society than others. Yeah, you. I just literally lost my train of thought.

Oh, senior moment.

Not supposed to say that. No. But literally I lost my train of thought. The bottom line is everybody get on the bandwagon and start inputting. We need everybody's input. And yeah, I literally just lost my train of thought. Sorry about that, Jonathan.

[A]

Oh, I think that's a good landing because it's important to take action. I think that a lot of people are like waiting for the right moment or trying to say, oh, it's not important. For me, this is a tool for younger people, or I don't need to use this for what I do and start to realize that there's a lot of different ways you can use it, whether it's for personal tasks or business tasks or starting to figure out. And just the experimentation is that critical component. And I think this has been a really powerful episode for people who are interested in kind of the work you're doing. And some of the more AI versus Ageism topics. Where can they find you online? Deborah where's the best place to see what you're doing and kind of the projects you're working on?

[C]

Actually, if you look at my LinkedIn profile Debra AlbertNYC it'll actually show not what I'm doing. Now there's a banner that says the cat is almost out of the bag because I'm stealthily co founding a startup that has to do with a lot about this topic and about helping people who are older, maybe less tech savvy find opportunities for volunteering or flex time work. But it's really not out of the bag just yet. So I think if anybody wanted to get a hold of me, the best thing would be to just send me an email. Old fashioned. I'm not a big Twitter or X person. I'm not a big Insta person. So if anybody wanted to get a hold of me they could @debraalbert nycmail and I'm fine to put that out there.

[A]

Amazing. We'll put that in the show notes so everyone can reach out to you. Thank you so much for being here today for an amazing episode of the Artificial Intelligence Podcast.

[C]

Thank you so much for having me.

[B]

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[A]

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