Latest / The Cyber Savvy Woman / Why Digital Literacy Matters In The Age Of AI With Martin Mehl |The Cyber Savvy Woman |Ep 114

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Why Digital Literacy Matters In The Age Of AI With Martin Mehl |The Cyber Savvy Woman |Ep 114 · The Cyber Savvy Woman

• 0:00
  Hey, welcome to the cyber savvy woman where women business owners reclaim their power in the digital world
• 0:06
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• 0:13
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• 0:26
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• 0:32
  Every woman leading a business deserves to feel unshakable in her digital decisions. No matter her technical background
• 0:39
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• 0:46
  Here's your host Rachel Shanker
• 0:48
  Welcome back to the cyber savvy woman where we talk about practical ways women can protect themselves their businesses and their futures and our increasingly digital world
• 1:00
  I'm your host Rachel Shanker a Silicon Valley technologist and owner of the award-winning managed service provider CMIT solutions of San Mateo
• 1:09
  Today we're talking with Professor Martin Mel college professor
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  entrepreneur AI human interface
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  strategist and co-inventor of the AI work score
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  Martin has spent decades bridging academia and industry helping individuals and organizations build digital literacy
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  Strong mentorship and a more human centered approach to technology in this conversation
• 1:33
  We're diving into AI readiness why digital literacy is more than just knowing the tools and what it really takes for an organization to use AI thoughtfully and strategically
• 1:44
  Martin, welcome to the show.
• 1:46
  It's good to be here. And to be honest, I was going to lead with saying congratulations on International Women's Day one day delayed
• 1:53
  And I was a hike yesterday and I actually set this to a family that was hiking and the person's responding was
• 2:00
  It's International Women's Day. What? So I think it just happens to be the month. So we might as well celebrate the month. If it's not the day.
• 2:08
  Exactly. Yes. And I thought, how interesting. It's on a Sunday.
• 2:12
  I know.
• 2:15
  Same thoughts. So it makes sense that everything is a little delayed with time change and with obviously the celebration. So I think your thoughts were just catching up. It all makes sense.
• 2:26
  Yeah, exactly. I love that. You know, you've built a career that bridges, as I mentioned, academia mentorship communication. What first pulled you into this work, you know,
• 2:37
  First of all, it's really interesting when you hear your career out of somebody else's mouth and you hear. So what sticks out and why it stuck out. So obviously I really enjoyed when you and I had a chance to talk to each other first and
• 2:49
  I like being a bit of a jack of all trades, but I also know that that can mean that you spread yourself too thin. I usually tell my students on day one in a lecture.
• 2:56
  So one of the things that I do is I I love research. I think my family's right. I think other things are okay, but I love research.
• 3:06
  And then I usually pressure test them and ask, so what do you know about me and they gently respond with you. Do you love research like no I love my family. I love
• 3:15
  But because I'm a researcher, I get to be a better partner. I get to be a better boss. I get to be a better mentor.
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  And I think part of what drew me into this is that I think I had the privilege growing up in Europe.
• 3:28
  Going to a university and then telling us, you know what computers are a fad. They're going to be just like typewriters. They're going to go and be obsolete. And I thought that's going to be a problem.
• 3:38
  And so I knew technology was going to be a big deal. I knew communication was going to be a big deal and mentorship was going to be a big deal pretty early on in my career when I get a chance to be in a domain where I get to share knowledge use those things wisely because technology plus communication plus mentorship is probably the secret sauce to
• 3:57
  Maybe get to go and be ahead of the game. So that's how I think my headspace has always been there. And then I got the privilege of how to maybe become an expert at it. If that answers your question.
• 4:08
  Yeah, it does completely. You said something in our prep call that really stood out to me that being an educator without field experience isn't enough
• 4:16
  And field experience without passing it on can be selfish. How did you kind of come to that philosophy or what was the point where you're like, huh, drew that conclusion.
• 4:25
  Well, I always felt like I was a bit of an odd duck where I always thought, you know, to all of my colleagues in education. I felt like, but how is that
• 4:32
  Hands on experience you're drawing from. I'm glad you're referring to a textbook chapter and a theory, but
• 4:38
  Have you field tested it and most of my colleagues were like, well, but did you publish something on that in theory. So I always felt a little bit like I had a conversation.
• 4:45
  Where that was not my tribe. And then I was sitting in business and I was kind of holding them accountable on. Well, you know, we have to go and do a good solid ethics analysis and we have to make sure that this is good practice and we do policy analysis.
• 4:58
  And they would be equal doesn't have an impact on the bottom line. Are we going to make more money of that. Is there going to be more profit. And I thought, Oh God, so I'm not in your tribe, either. So I don't really know where I belong. I feel like I'm the ugly duck
• 5:11
  And then I think I had my swan moment when I approached basically zoom before they really became zoom and I had this really bizarre.
• 5:20
  Essay where basically thought in case we have a pandemic zoom could be a really great tool. And I think that's the moment I actually discovered being both academic and business oriented and fusing them.
• 5:32
  The right time and the right place makes you sort of a unicorn. And so being unique. In that case, makes you able to see a world.
• 5:40
  But one of my friends kind of envisioned as if there's a universe that expense where you have the luxury of slightly expended areas that you can look at and see different horizons. And once you look at all of it at once. Then you can connect dots that maybe
• 5:53
  Others will also see but didn't have on their horizons. So I think that's sort of a conclusion based on a lot of running into brick walls in my career of discovering instead of packaging it as if it's a crutch package it as if it's a gift. And I think that really changed a lot of things.
• 6:11
  Yeah, I love it. I love it. What inspired you to create the mentorship Academy and what gap, are you trying to fill?
• 6:17
  You know, I think like nearly all educational experiences, sometimes they're happy accidents that need to be embraced. So I used to really consider office hours were when I get to be myself, tell my students how to hold their heart, tell my former mentees or people that trusted me how to hold things that are not academic. And I think that's really changed a lot of things that I've been able to do in my career.
• 6:47
  One of my students, probably about almost two decades ago, said across from me and said, you know, the way you act in office is so cool. And you're so approachable and so nice.
• 6:59
  And it's not at all how you run your class. And I thought, what, what do you, what do you mean? She's like, I don't feel comfortable. Really, I feel like I might let you down. I might be
• 7:08
  embarrassed or ashamed that I don't know the answer. And when I talk to you, and you approach it in office hours, you're so thoughtful and so open. And I thought, that sounds like I'm a Jekyll and Hyde human. That's not at all what I wanted. I thought I'd set a very strong professional standard.
• 7:26
  And then I would just be a safety network. And it almost felt like my students thought, I don't understand why those have to be separate. I don't understand why you're conducting
• 7:35
  me being safe. It's almost like as if your classroom is unsafe and your office is safe. So why don't you carry that through? And that's when I discovered, I almost have to franchise the way
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  I happen to be in one-on-one needs to become the way I teach, needs to be the way I learn. And then I think you and I had this in our conversation last time.
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  I also understand that mentoring isn't just something that is something I can take for granted. When I can reach to a student and say, why don't you go and have that person mentor you, they almost always respond with, well, they're not you.
• 8:07
  Or they don't seem to approach it like you. And I thought, what does that exactly mean? I like the compliment.
• 8:12
  But what does it mean? And I think my younger ego would have been, I'm so smart. I'm so intelligent. I think I'm the best. And after a while, when I got a little bit of that chip off my shoulder, I realized
• 8:23
  I'm also learning. I'm sitting across from a person and it's a give and take. And it's a lot of humility, being able to actually say, I actually don't know that. But together we can figure it out.
• 8:34
  Because you don't need a person that walks around going, I'm a mentor. I'm a genius. And I'm there for a person that you should worship. Here is my pedestal. Please worship. And then when we're done worshiping, you can go and scram. And I think I discovered
• 8:49
  probably based on the people I had a chance to mimic my mentorship on, that those are the people that almost always were very humble and really brought out the best in me. So I think
• 9:02
  I discovered that I had good role models and then I mimic those.
• 9:06
  Yeah, I love that. You talk a lot about digital literacy not being common sense, but a competency. What do you mean by that?
• 9:15
  I think it's actually really the pandemic that highlighted a lot of people's
• 9:21
  force of not having a choice anymore. So it used to be we're currently conducting this in a space where I don't have the privilege of seeing you in person, but we're quite comfortable and we feel pretty
• 9:32
  in tune with using these resources. But it also means that, and you've learned to appreciate this, that this is not something that just comes naturally. You're looking at
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  you have to consider lighting, you have to consider positioning. I'm standing today, I have a standing desk, not my normal go-to projection. It's how I lecture.
• 9:49
  And I think literacy is, in its finest way, a way to become competent at things and understanding why. And I think the why part,
• 10:00
  we're kind of circumnavigating. We had this sort of mindset on here is an AI tool and it gives us the preview of maybe talking about artificial intelligence, but just the tool doesn't mean
• 10:10
  you know what to do with it. It becomes truly a tool, but when you can use it, it becomes an instrument. When it's digital literacy,
• 10:18
  you might know how to Google, but you don't really know why certain things, certain algorithms, certain search queries, certain elements of
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  inclusionary, exclusionary search components, slightly academic me showing up, but
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  are actually useful and successful. And so a good amount of times, I think the big fat why, why you're doing something is the foundation of the difference between
• 10:40
  I use a tool because I have a tool versus I understand. And the best analogy, I actually thought about this after our conversation,
• 10:50
  a lot of people in AI, by the way, are mechanics, but they're not drivers. And so the conversations are a lot from people that are mechanics. So if you talk to a person that is a mechanic and you talk about a car,
• 11:01
  they don't care about how it feels. They don't care about some of the, do I want to have a convertible car? Do I want to have a car that goes fast? They just talk about the function.
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  And so when you're talking about literacy, I have the ability to understand all aspects on one of my cars goes grocery shopping. One of my cars goes in the dunes and
• 11:18
  and drives off road. One of my cars is to pretty much have a midlife crisis. One of my cars is just playing to impress my friends, but it means that those are things as humans.
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  We understand when it gets to the point of what literacy really means. I understand the impact and the why. So to me,
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  that is the shorthand answer on when I understand not just simply
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  what a car does, but why I might use it and how it makes me feel I'm starting to become much more capable of picking the right tool, the right instrument, the right car for the right occasion.
• 11:51
  And this kind of leads into this. For small business owners listening, how can they tell the difference between someone who is digitally fluent versus someone who just knows how to use a few tools? You know, like when they're
• 12:04
  assessing or, you know, figuring out where they land?
• 12:09
  Yeah, I think that's probably in three different spaces where this would be incredibly relevant, especially for a small business owner. There is the who you align with, meaning
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  partner. And I think a lot of people like to sound immediately competent as fast as possible. So there's a lot of
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  flex and name dropping on, I read these six things. So I'm going to go and use the buzz terminology that almost always gets me a little concerned.
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  The second thing is also proof is in the pudding. Are you showing me and are you telling me? So I'm a big fan of show me versus tell me. If I hear something that sounds good,
• 12:43
  apply, demonstrate, showcase, use case. It gives me the classic, you know, Cal Poly's motto, the university, I get the privilege to have been for almost
• 12:54
  I don't know, we're celebrating 125 years, literally yesterday, same day, but 25 of those 125 are me in the faculty position. So
• 13:05
  there's a difference. But in this case, I just flex, look, I'm a 25 year veteran, but am I good at what I do? So your question is
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  when the person on the other side is also willing to say, here are things I don't know yet, even if they're an expert, or here are things that I'm limited by, or here are things I'm concerned by.
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  So first off, simple gauge. When the person starts to differentiate on artificial intelligence that it's not a homogenistic term, but there's generative,
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  you know, analytical, predictable, and they start to actually use a certain adaptation. That's like saying, hey, open AI may have been an early adopter and was the first out of the gate, but they might be a good generalist.
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  But maybe Anthropic is a good specialist for coding. Maybe Gemini is a good specialist for art. And so the person you're talking to across from you, you can't be good at everything. So if you're looking at something specific, benchmark also what you know. So when you're talking to the person,
• 14:00
  if you don't know how good the answer is, you didn't ask a good question. So you may want to go and ask something that they can fill in with something that you're an expert in so that you're pressure testing
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  as if you're truth checking the other person's answer with things that have value to you. Because otherwise you're just wooed by the novelty as opposed to being able to gauge any answer.
• 14:21
  Exactly. Yeah, I love that. Let's talk a little bit about AI strategy and implementation. You know, as we know, there's a lot of hype around AI right now. From your perspective, what's the biggest disconnect between AI excitement and AI reality?
• 14:36
  Yeah, I think I just want to make sure that one of the things that can happen is when I get really passionate, I want to say a lot of really important things.
• 14:44
  To follow the story, I think it's important to consider the journey that artificial intelligence has taken. So I think
• 14:51
  if I were to be a mid level to an upper level firm, so it means I have in between 10 to 100 or between 1000 plus organizational members. So let's assume for the time being
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  you're a big company. The first thing you associate with artificial intelligence is
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  we need to contact our information technology division, our IT department needs to go and deploy this tool or this product.
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  And we better do a good job. And since we're talking about security and the title of our show,
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  we better not mess up that this is actually going to go and give some kind of pinhole and access to people that could cause a problem. So in its root,
• 15:25
  let's also probably limit it or even just make it illegal for everybody, because if in doubt, might as well just make it illegal and it's bad and it's not a good idea.
• 15:33
  So by root of origin, that's set in a domain of individuals that are responsible for
• 15:39
  deploying a tool by its nature, like Microsoft Word, like any other derivative, we're going to go and use a technology like a calculator that gets added to the arsenal of the tool chest of an employee.
• 15:51
  Then we had a bit of a shift where we get to the point of, oh, this is actually reflective of
• 15:56
  a usage. And the difference here is that we suddenly look at, oh, it's not really an information technology solution.
• 16:03
  Oh, this may be swing the pendulum, the exact opposite direction entirely about talent and people and human resources. And then we got to the point of
• 16:11
  how do we maybe overhaul, how we run the place, how do we maybe organize ourselves from a human resources perspective. Now, if you're a small business owner, you're also wearing both of those hats. You get to be
• 16:22
  the person that first got to go and see if you would want to install it, then you get to figure out how you go and train your people.
• 16:28
  And the pendulum swinging the other direction, like anything else that's extreme, we went from it is purely functional to it is completely applied.
• 16:36
  And in most environments, you're going to look at it going two big problems because oppositions
• 16:43
  are probably that the healthy part of somewhere in the middle. And I think that's where we're starting to settle, where we're going, okay, human resources starting to feel a little overwhelmed with
• 16:50
  what we don't really have all the technical skills and technology is overwhelmed with we don't really have all the human resources skills.
• 16:56
  So it makes it a merged component. I recently had a conversation
• 17:00
  with one of my colleagues from Switzerland, actually, who's now in Luxembourg, but had talked about, hey, if you ever talk to people that are human and AI or human and computer interaction people or experts, they are cool people because they think of stuff we don't think about because they think of psychology.
• 17:17
  And they think of technology and they think of user interface and they think of implementation and they're really cool. But it also means they're very selective group of people that what we're just about to talk about now.
• 17:30
  The reason I think I was traveling this journey, if anybody gets to listen to us and we're going, yep, totally remember the IT phase, totally remember the human resources phase.
• 17:40
  Then what does it mean where we are in 2026? And what does it mean to you? And I think, to me, it's a clinical application where we need to consider it's a lab. And when it is a lab, it resides in a
• 17:51
  mutually beneficial component on, there is a technological side, there is a human resources side, but then there's a business applied side. And before we unleash
• 18:01
  a use case that is a very rapid, okay, we invested in it, we might as well deploy it, we should go ahead and consider what's the reverse engineering of what are you trying to accomplish? And if it's efficiency
• 18:13
  and cost, it may not be good enough. I think it should be about quality. So if we can scale quality, we're going to start nailing the solution to artificial intelligence. And one of the things that I have in my quiver that I have a very strong bias on,
• 18:28
  you don't have a baseline and you can't measure it, you don't know if you're successful. So obviously, that's why my inventor me is also in our conversation on going, well, and that doesn't exist, and that needs to exist. And if it doesn't exist, it's in part because
• 18:41
  there's a vested interest of the artificial intelligence industry, the open AI and others to disseminate their product that they spent a lot of money on to be able to be taken up and be deployed and have no filter and be as fast as possible and rapid deployed.
• 18:56
  And on the other side, you have those that are willing to regulate and govern and happen to be very cautious. And as per se,
• 19:03
  it needs to be a compromise between both. But most industry doesn't self regulate, this is the time to do something not ask backwards, but do something where technology can do a better job than we've ever done in the past. So I think this is the moment
• 19:16
  where we get to go and score, assess, evaluate the human machine interaction, and then figure out what that means. So it's like creating a scale, and then we can go and have Weight Watchers telling us on how to handle it. So in my mind, that's where we are in 2026.
• 19:31
  Yeah, I agree. When I, you know, I focus on small businesses, and I definitely help them vet, you know, where they're going to place AI, what tool they're going to use. And that's one of the first, you know, the first thing in, in addition to vetting out security, what data, those things, it's what is the KPI that we can wrap around this? Like, how are we going to measure how this saved you time or money? And, you know, we can quickly figure it out, back of napkin doesn't have to be overly complicated. But I think that's something that I think is really important.
• 20:01
  I don't necessarily hear a lot of people talking about when they're implementing AI inside a business is no one is, you know, tracking that ROI or the KPI to see how it's going to affect.
• 20:13
  When I think, if you don't mind me sort of tagging on anything, that's an investment, let's say your chief financial officer. And in this case, again, we've just talked about each of these executive roles on a mid level or small businesses. Hi, I'm the CEO, the CTO, the CFO, and most of the staff. But what you're looking at as wearing the hat that ultimately becomes the financial responsibility, you've already maybe made a commitment on, okay, we're going to deploy AI.
• 20:42
  And there's a very strong knee jerk. And I think this is the dangerous part. You're mentally able to justify it is probably faster, and it is probably going to be cost effective, where your checks and balances and I will sound so risk averse and insurance businessy. But what I want to make sure is, it's part of investment.
• 21:02
  I agree. Yes. It's part of training. And in the past, education has always been like, well, we're kind of doing this unnecessary evil, where we're letting people also guess get smarter, but somehow, that's such a waste of money. And it's the same way with tools and humans combined. It's the smart investment where you go, how good is it really? So do we vet our product? So to me, if we're looking at the laboratory, they're like, okay, we're going to do this.
• 21:31
  And at the laboratory, there's the door at the entrance where you come in, that should be ideally a tool and a technology that measures like a scale, which is what we consider calling AI work score, where you are, so you have a benchmark in the middle in a way to improve that, like a Weight Watchers or a fitness, you know, supporter, we should have some consultation that talks about this sort of from a human in the loop, adding emotional intelligence into the equation, which is all from a data or analytics perspective,
• 22:00
  unstructured data, which is really hard to identify, because it's the intangibles. And then on the tail end, when you leave the door, you should check the truth of the product that you've done. So these different inception points, if your small businesses has already bought into AI, you want to do the checking, because that's the first thing you want to do, how good is what I'm doing? And why is that working? So truth checking it. In the middle, you may be still able to figure out and refine it. At the very beginning, you have to make sure that you don't forget these other two steps.
• 22:30
  Because otherwise, you don't have a strategy, you just have a knee jerk fear of missing out. And if you're acting on a formal in a business sense, I'm not really fond of the word by coding, but you're much more of the new generation than the old generation that happens to be responsible for governing the workplace.
• 22:48
  So if you're responsible for finance, maybe be a little less hip and be a little bit more up to what needs to actually be analyzed, because you can't be cool when it comes to you having a negative inflow in your budget.
• 23:00
  Yeah, no, that makes sense. And you know, it's a perfect segue, because you kind of mentioned it. AI work score, let's talk about that. What problem were you trying to solve with AI work score?
• 23:10
  Well, I think it's interesting to see, if you're looking at companies like Anthropic, that basically have said to OpenAI, hey, there's some things that you're missing, and we're going to focus on coders. But there are also companies that have basically say, we're going to specialize and maybe be a small language learning model.
• 23:27
  We're actually looking at what we often talk about as LLMs, which to me is almost the equivalent of an elementary school education, as opposed to a bachelor's, a master's or PhD. So you can't get specialized unless you have specialized education. So we're at the infancy of barely between kindergarten, and maybe middle school in the infancy of AI.
• 23:52
  And if you're looking at the ability to score, it obviously means there's a complexity to it. How complex is it that you're working on? There's a difference. Is this a fifth grader problem, which sometimes is a problem for all of us? But is it quantum physics? Or is it kindergarten? And we can't batch it in the same bucket.
• 24:13
  So one of the problems about artificial intelligence, unlike any other technology, is it is additional intelligence. So when you're looking at the work product, the problem becomes not being able to discern what is truly your own staff members contribution, and what was augmented.
• 24:31
  And my concern isn't so much the world as we know it is going to end. My concern is that we're also losing the critical thinking capabilities along the way. And if we don't have a way to check that the expertise that we have at this point, we need to have a way to measure it. And to me, that means that the problem at hand is we all feel and have a perception of what AI could be doing. But how do we know?
• 24:55
  And if we don't know, we're making speculative guesses for our customers tell us what we should do, because it should be faster, it should be cheaper. So therefore, my price should be better. And if I could prove to my customer that their perception is perhaps based on misconception, and that I actually happen to be educated on my product without being a condescending business owner, I might have a customer that I can appreciate that I'm on the cutting edge and pioneering that I am not just providing a service, but I'm actually doing something.
• 25:25
  But I am aware that I'm not just following a trend.
• 25:28
  Yeah. And how do you define AI readiness? When you talk about that, are you talking about the team? Are you talking inside the business? Are you talking outside? Or, you know, when you talk about AI readiness, who are you evaluating, I guess? Or is it self evaluation?
• 25:44
  So to be able to have an objective, subjective assessment, which means that obviously, all of us have a perception. And I actually just had this beautiful conversation with my business advisor. And he had this really blatant analogy. He said, when you feel like you have this market that only you can see and nobody else can see, there may be a reason that's going on. And it's not your genius, it might be that's not the market.
• 26:10
  So if you're looking at AI ready, don't have the product drive your decision making. Because what that means is that you are letting a tool and its skills tell you what your decisioning is. I'm a big fan of, first of all, when it comes to the decision making, it not being your primary focus.
• 26:29
  So there are good tools to assist you in wealth of research. I talked about how much I like research. They're a good tool in allowing you to pressure test deeper maybe than what you would do with normal or traditional tools. But please do not use it for decision making. It'll undermine everything that you use between gut instinct and experience that you're throwing out of the window.
• 26:53
  It's very easy to be seduced by what we all think looks like cohesive words on a page because it doesn't read in Chinese or Russian, it reads in a language that we understand. So the seduction of the word on the page might look like, this is my wording, this is my sentencing, and it's not. So we can't supplement that.
• 27:16
  And I think the one other thing, if you don't mind, by context, AI readiness, you will never really be AI ready, but you will be ready to know what you don't know. And I think the most blatant ignorance of most individuals that are not digitally literate is overconfident. I don't think it's a good idea to walk into a room going, I googled, and it's basically Google on steroids, and you're looking at going, do you know what agentic AI is?
• 27:46
  Do you understand what really building a bot is? And by the way, I do find it problematic that open AI assumes that a college student would use anthropic or any other derivative for building an agent. Students are inherently looking for the path of least resistance.
• 28:04
  And the path of least resistance is a good reflection of if the youth is not wanting to go and put the extra step in, then it's a good sign for that this is a very convenient tool to get a lot of things done with a little amount of effort. That is the sexiest thing to do for somebody that is young and not wanting to put extra effort in.
• 28:25
  If you have the ability to go and say, hey, to be AI ready means you understand the context in which we're using it, and then become an apprentice of an individual that says, hey, I'm an expert, this is not something the tool can teach you. I think that's where you kind of balance being ready with being able to sound it off against your own expertise, if that makes sense.
• 28:46
  Oh, completely. And the point that I'm going to go back to that I enjoyed was how you said, don't come with the AI solution ahead of time. As I was a business systems analyst, and you know, you are trained, you do not come with a solution, you have to gather all the requirements, you have to, you know, you have to get your use cases, you have to understand, and then that will lead you to the right solution.
• 29:12
  I see that all the time today where people just want this or that, and we don't even know if it's the right solution for them. So I have to like dial it back, roll it back, and like take them through that process to make sure, you know, that they do find the right solution. It's, you know, and I think I hadn't really applied that to AI yet, but now as you mentioned it, I was like, yeah, that's the same thing. It's the same things we're doing.
• 29:37
  Yeah. To go one step further, one of my friends jokingly sometimes calls me Martin GPT, and she might say, I'd rather use Martin GPT than Chet GPT, which I've actually started to use in my classroom when I have an exercise with a student going, can you be a student GPT and pretend to have the answer, it'll make them actually feel a little bit more at ease about maybe getting it wrong.
• 29:58
  But what you just said is sort of like the personification on would you hire this machine for the job? And what are your criteria for the job?
• 30:09
  So I think a little, and I'll stay out of the political realm, but imagine if you were to write down blind, not here the candidates and here's what I'm picking from, but what are the actual criteria, what I need to do to hire the job.
• 30:23
  And if you're a small business owner, you've put a business plan together, you know what it takes to file your taxes, to maintain being afloat, to do partnerships. So use that same capability of critical thinking, and then go, if I were to hire a human or non-human, these are the criteria of what it would entail.
• 30:43
  And it can't just be money, it can't just be it's cheap, it can't just be it's fast, it needs to be quality. And what are the criteria where you do a job interview, and six candidates walk in, and five of them are AI, one of them is a human being, do you maybe hire half of one and half of the other? Do you only hire the human or do you only hire AI? But have the same criteria, especially on a smaller scale. Do not get seduced by cheaper is better. Cheaper is never better. You get what you pay for.
• 31:13
  That's so true. What gives you optimism right now when you think about AI and humanity?
• 31:19
  Having conversations like this, having the ability to remember that we live in a democracy where when we have a lot of people having the ability to go and speak about, hey, I think this is concerning. I think that this is concerning in education. I think that this is concerning in business. I think that we have ways to express our distrust, our discomfort.
• 31:41
  I think there's an element on communicating, just like anything else, is the core on being able to publish something, write something, having a forum. I'm so appreciative of having the opportunity to sit next to you and have a chance to sound some of the thoughts out that we're covering.
• 31:57
  Because it gives me also a chance to say to somebody, hey, we exchanged some really thoughtful ideas. I might not be able to meet with you, but there's this beautiful podcast. Why don't you spend 10, 15 minutes and listen to some of our conversation? And then maybe that'll have a graduate student go, hey, I actually came up with an idea on how to, maybe it's not measuring, maybe it's an element on checking a gauge.
• 32:21
  I think it's really important to be able to say, hey, I've actually put a speedometer on one of those tools or actually start to write on a policy for these tools. So to me, and that's inherently being an educator, it means that we don't assume that we can't make an impact because we're too small. And these are the big cogs and the big wheels.
• 32:46
  And I think that's an important thing that keeps me not just happy, it makes me aware of the power of what democracy really means.
• 32:53
  Yeah, I love that. So towards the end of our podcast, we do a quick little rapid fire where I'm going to ask you some questions and you're just going to tell me the first thing.
• 33:02
  Okay.
• 33:03
  There's no right answer. And you can say both if you like.
• 33:07
  Anthropic 20X.
• 33:09
  Okay. And what's one technology habit every professional should have or build out?
• 33:14
  I have a to do list that is formally tracked in technology so that you don't happen to have things in your head and you can't relay and it can also convert into scrum meetings and agenda setting that you also check on if you were a successful candidate.
• 33:25
  I have a to do list that is formally tracked in technology so that you don't happen to have things in your head and you can't relay and it can also convert into scrum meetings and agenda setting that you also check on if you were a successful at doing it, not just have it and then be proud that you own it, but use it.
• 33:40
  Yes, I like that second part. What matters more in the AI era, speed, strategy or judgment?
• 33:47
  Judgment.
• 33:48
  And what's one word you wish more people understood when it came to digital literacy?
• 33:53
  Respect.
• 33:55
  What's your favorite way to unplug and recharge?
• 33:58
  Family and or reading.
• 34:02
  And what's one skill students or young professionals should be developing right now?
• 34:07
  Critical thinking, critical thinking, critical thinking, critical thinking, critical thinking.
• 34:11
  Yeah, I love it. What's one thing AI can do well, one thing it should never replace?
• 34:16
  It is fantastic at aggregating information at a speed that is unprecedented to anything else that I've ever seen in our entire human history.
• 34:24
  And it shouldn't pretend that is the equivalent of also having generated it in the first place.
• 34:28
  Yeah, I like that. It's a nice balance. Martin, this was such a thoughtful conversation. I really appreciate the way you challenge people to think about AI, not just as a tool, but something that touches culture, leadership, education and human potential.
• 34:42
  I love your reminder that digital literacy is a competency we have to build and that the goal isn't to become more robotic, but more thoughtful, more strategic and more human.
• 34:51
  Thank you so much for joining me on the Cyber Savvy Woman.
• 34:54
  And to our listeners, thank you for tuning in, where we explore practical ways women can protect themselves, their businesses and their future in our increasingly digital world.
• 35:02
  To connect with Martin, be sure to check out the show notes for links and resources mentioned in today's episode.
• 35:08
  If you've enjoyed this conversation, don't forget to follow, rate and share the show and join us next time.
• 35:13
  Thank you so much, Martin. I really appreciate your insight.
• 35:16
  My absolute pleasure. It's great to be here.
• 35:18
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• 35:23
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• 35:27
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• 35:31
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• 35:35
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• 35:41
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• 35:47
  Hey, thanks for listening and see you on the next episode.