What if technology isn’t replacing humans but revealing what makes us more human? In this episode of Executive Connect, Yuying Chen-Wynn, founder and managing director of Wittingly Ventures, former Microsoft executive, and MIT alum, shares how empathy, adaptability, and experimentation are redefining what great leadership looks like in the age of AI.

With over 20 years of experience leading digital transformation across four continents, Yuying reveals how leaders can balance innovation with ethics, create cultures that embrace change, and implement AI responsibly without losing trust or creativity. She breaks down the mindset shifts every executive needs to thrive in a world where change is constant and curiosity is currency.

Chapters

00:00 Why You Can’t Start with Regulation
00:40 From Microsoft to Global AI Leadership
02:00 Lessons from 20 Years Across Four Continents
04:00 What Global Transformation Teaches About Culture
05:20 AI as a Strategic Business Enabler
07:00 How Natural Language Changed Everything
09:00 The Role of Empathy in Leading Transformation
11:00 Why Leaders Must Spend 80% Listening
13:00 The Hidden Fears Behind AI Resistance
14:00 Building Guardrails for Ethical AI
16:00 What Every Company Needs Before AI Adoption
18:00 Skills That Define Future-Ready Leaders
19:00 Adaptability, Learning, and the Power of Tinkering
21:00 Why Curiosity Beats Credentials
23:00 Human Connection in a Digital World
25:00 How Employers Are Redefining Talent and Hiring
27:00 The Next Five Years of AI and Work
29:00 What Automation Will Really Look Like
31:00 Redefining Productivity and the Workweek
33:00 Embracing AI Without Losing Humanity
35:00 Technology as an Amplifier for Creativity
36:00 Where to Learn More About Wittingly Ventures

Yuying Chen-Wynn

(0:00) Here’s the thing. (0:01) Right now, you cannot start with what regulations do I need to comply with. (0:06) That’s not your starting point right now because AI regulation laws and compliance are all in the middle of changing.

(0:13) And none of them have thought it all the way through. (0:15) Does it actually work? (0:16) Is the liability on the right person?

(0:18) Should OpenAI be more responsible? (0:20) Should me, I use it inside my company, be more responsible if something happened when I used it? (0:25) All of those things are changing as we go.

(0:27) You can’t just look at the law and be like, okay, are we good on this one? (0:30) Good. (0:30) Are we good on this one?

(0:31) Good. (0:31) That’s not where you start. (0:32) The backstop is literally at your company.

(0:35) I go, this is the point where I know nothing happened. (0:38) This is my safe. (0:39) Then.

Melissa

(0:40) Some leaders talk about digital transformation like it’s a future goal. (0:45) Yunying Shen Wen has been living it for 20 years across four continents. (0:51) She’s a former Microsoft, MIT alum and ex-chief product officer and head of AI who’s led global transformation.

(1:01) Today, she’s the founder, managing director of Whitley Ventures, where she has helped executives harness AI to reimagine how their businesses run. (1:13) Yunying’s work blends deep technical expertise with strategic foresight, helping leaders not just keep up with the change, but shape it. (1:22) In this episode of the Executive Connect podcast, we’re diving into how AI is transforming industry’s leadership and the way we think about innovation.

(1:34) Welcome, Yunying. (1:36) Thank you for having me. (1:38) It’s so happy to be here.

(1:41) Now you’ve worked across four continents, advised PE firms, private and public companies and held executive leadership roles. (1:52) I’m just gonna pause on that, cause that’s a lot, a lot. (1:57) So talk to me a little bit, how did global and cross-industry expertise lead you to where you are today in AI transformation?

Yuying Chen-Wynn

(2:11) I will have to say there’s a lot of luck involved. (2:14) So coming out of MIT when the entire .NEB boom was happening afforded me opportunities to kind of take on what you normally don’t get to take on right out of college. (2:27) So there’s a really bit of luck on the timing.

(2:30) So when you start at Microsoft in Windows, working for Bill Gates, you kind of get a little bit of an open door. (2:38) Now the global piece, I love the travel. (2:41) I’m sure you do.

(2:42) I was in some ways when you’re young, you kind of look for opportunities for your own interests. (2:48) I look for opportunities to travel and I had the opportunity to join a Canadian company as acquired by a French company at the same time. (2:56) And my question during the interview was, wait, do I get to go to Paris as a work trip?

(3:00) They’re like, yeah, probably. (3:01) I was like, sign me up. (3:04) So I traveled to Paris for work, got paid for it and got to understand the European market.

(3:12) I moved back to China, worked across Asia-Pac and was able to travel. (3:17) Some of that motivation was probably not for global transformation, but it gave me this view. (3:23) So across the Americas, across Europe, across Asia-Pac and then I had, because I made so many international connections with the expats, I consulted for companies in Africa on emerging ed tech.

(3:36) I was just like, wow. (3:37) But a part of the travel led me to the global experience which actually helps you see transformation very differently because each culture in terms of the internal workings, what works and what doesn’t work, your assumptions just kind of break up and which allows you to really rethink what can and can’t be done, what can and doesn’t work. (4:02) And on top of that, the US workforce is so diverse.

(4:05) So all of those experiences kind of just came to play in terms of first just around being able to deal with a global workforce and culture. (4:17) And then when AI happened, of course, that kind of just naturally added to that piece of the expertise.

Melissa

(4:25) Ready to lead smarter and invest wiser? (4:29) On the Executive Connect podcast, we unpack executive strategies for wealth and influence. (4:35) Hit the subscribe button now.

(4:38) Don’t just watch, act. (4:40) I love it. (4:41) And I love that you mentioned Paris for a work trip.

(4:45) It’s so great to travel to these amazing countries on somebody else’s dime, especially when we’re young, still figuring ourselves out. (4:54) It really changes the way you look at work and life young. (5:00) And I feel like you become such more of a well-rounded employee executive because you’re dealing with different culture, different time zones, different people, different experiences.

(5:13) So I love that you mentioned that because I thought the same thing young in my career. (5:18) I wanna talk a little bit about AI as a strategic advantage. (5:24) I know it’s everywhere.

(5:25) Everybody’s talking AI all the time. (5:29) Everybody’s publishing books, writing articles at a crazy fast speed these days. (5:34) But I know you talk about AI as an actual business fundamental enabler.

(5:41) What does that shift look like for companies when they actually put that into practice?

Yuying Chen-Wynn

(5:47) Yeah, so the thing I say what’s different about this technology. (5:53) So we use AI across way too many technologies to lump all together. (5:58) The fundamental shift is this new type of AI that’s generative AI where you’re able to talk to super intelligence and tech using natural language.

(6:12) So this natural language piece where I go talk to the machine like a human, that’s the fundamental not only enabler but breaking down the barrier to entry of what technology and super intelligent models can do for you. (6:27) Because that just opened it up not to the developers, the data scientists, the researchers. (6:33) That is a very small population.

(6:35) When it’s a fundamental business enabled because literally every single person from the front desk to janitorial to your AI research can now have their work be better, be faster, have more information, have a better starting point. (6:54) And there’s never been a technology that came in that goes literally like I see what my front desk people, I’m just like, oh my gosh, that’s so cool. (7:02) And that totally helps your job and across the board.

(7:06) And you don’t even have to do any customization. (7:09) You can literally go here. (7:10) I’m gonna give you a secure version of ChachiBT and suddenly everybody can make their job better.

(7:17) That switch, that has never happened before. (7:21) So what I say is one of the reasons I realized this much earlier than most, I came from EdTech for language learning. (7:27) We already have the idea that language is power.

(7:32) But I think a lot of the more technical don’t make that switch, that the ability purely to be able to talk like a person, which now you don’t even have to type texts. (7:42) You don’t even have to be literate. (7:44) You can speak that lower, that bigger, even more, democratize it even more.

(7:48) It speaks multiple languages, even more, even more. (7:51) So your entire population, even if they’re not literate, can have access to something that gives them better information, better judgment and faster creation. (8:01) So that is as fundamental as it gets.

Melissa

(8:05) I love that you mentioned the talk. (8:07) I’m just now getting used to that. (8:10) I don’t know, it’s because my fingers are tired of texting on my phone or typing and now I’m going to the talk.

(8:17) I love it, it’s unbelievable how convenient everything is anymore. (8:24) And friends of mine that started businesses that would have never been able to hire and staff and get up and going as fast. (8:33) It’s just amazing what’s going on.

(8:36) So I want to talk a little bit about leading transformation now. (8:43) Again, back to the global scale, you’ve done it many times across the world. (8:49) What are some of the biggest lessons that leaders need to learn before they attempt one in the air of AI?

Yuying Chen-Wynn

(9:00) Wow, this is a big one. (9:02) I was like, where do I start? (9:07) This one, so I’m going to start actually on empathy.

(9:12) I think we talk about it across the board for any kind of transformation, but the empathy still comes to the biggest piece. (9:21) Now, this one why I just said, it’s a fundamental enabler. (9:23) Everybody should be so excited, but there’s also a lot of fear, resistance.

(9:31) There’s a lot of things we don’t understand about this. (9:34) And a lot of this fear about what does this mean to me? (9:37) And is it going to be good or bad in terms of that impact?

(9:41) That is like bubbling underneath everybody, whether or not they say it out loud. (9:48) So this is a very fast shift, very sudden. (9:52) People haven’t had time to get used to it.

(9:54) Truly understand what it means to me. (9:56) That empathy piece right now, you have to start with empathy. (10:01) You don’t get to just kind of pilot, roll it out and see what happens later.

(10:05) Because I have seen it, even when I come in, there are people who you can tell even in kind of the meetings, they have this wall. (10:15) And if you can get to talk to them one-to-one, they literally said, but if I try to do anything with AI, that means you know, you can replace me with AI. (10:27) And I’m just like, oh my God, that doesn’t mean that at all whatsoever.

(10:32) And I’m so happy for the person that was willing to say that out loud, because it helped me understand that the mindset for some of this that you’re approaching this with, a lot of them won’t tell you, but this is what’s behind it. (10:44) And you will find that you will find the resistance. (10:46) And you’re like, this is so much better for you.

(10:48) Why aren’t you trying it? (10:49) Why are you resisting? (10:52) But you turn up that empathy, get somebody to actually tell you what the concern is.

(10:58) You have a far better starting point than others. (11:01) So it’s like every single transformation, like no matter what, you cannot over-index on understanding what the fears might be for whatever change that you think is better for them and for everybody else, if you don’t speak to their fears.

Melissa

(11:19) Yeah, and I love that you mentioned that, because I think there’s so much in that. (11:24) People fear things because they don’t know, they’re used to things being a certain way consistent, but we’re in a world that is changing so, so fast. (11:34) And so it’s kind of a muscle that we need to get used to, but being able to understand, like you said, is so true.

(11:42) It’s so, so true in leadership and developing products.

Yuying Chen-Wynn

(11:49) Yeah, and I found that I actually spent, the successful ones, if you look at what the leaders spend their time on, they spend 80% of their time, the leader themselves, talking and listening. (12:02) So whether it’s town halls, roadshows, one-on-ones with executives, with individuals, if you’re leading this and your 80% isn’t literally talking and helping me understand what the others are thinking versus you just kind of talking out, I can predict that your success likelihood is not going to be high. (12:25) That’s what you’re indexing on, the 80% that you spend speaking and listening.

Melissa

(12:32) Yeah, I think it’s so true. (12:36) I want to switch gears and talk. (12:38) I’m really interested in the ethics and risk side of AI these days.

(12:45) I know a lot of companies are, like you were saying, issuing people their own chat GPT tool to use, but they’re taking a lot of company data and plugging it into their tools. (12:59) This episode is brought to you by Summit Ventures. (13:04) If you’re an accredited investor, Summit gives you access to one of the greatest tax advantage opportunities, direct ownership in oil and gas.

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(14:02) How can we use it? (14:03) Should we put company or client information in it? (14:07) So I’d love to get your perspective on how leaders should look at AI governance, biases, ethical use, risk, all those kinds of pieces.

Yuying Chen-Wynn

(14:20) Yeah. (14:20) So this is one technology that I say the guardrails have to be up first before anything. (14:27) It doesn’t have to be complete guardrails, but you have to have what I call the backstop where you just know nothing goes beyond this.

(14:35) You have to have one before you even look at, you know, oh, people can use Chattopadhyay. (14:40) It’s free. (14:41) Why not?

(14:42) You have to have one backstop before you look at anything. (14:45) So I literally start all of mine. (14:47) I start my meetings with the chief compliance, chief privacy, chief legal officer, and then I bring in the CIO.

(14:53) Okay, I need one backstop. (14:55) That means no matter what, things don’t go beyond this. (14:59) Here’s the thing.

(15:00) Right now, you cannot start with what regulations do I need to comply with. (15:05) That’s not your starting point right now because AI regulation laws and compliance are all in the middle of changing. (15:14) And none of them have thought it all the way through.

(15:16) Does it actually work? (15:18) Does it, you know, is the liability on the right person? (15:22) Should OpenAI be more responsible?

(15:23) Should me, I use it inside my company, be more responsible if something happened when I use Chattopadhyay? (15:29) All of those things are changing and figuring out. (15:30) You can’t just look at the law and be like, okay, are we good on this one?

(15:33) Good. (15:34) Are we good on this one? (15:34) Good.

(15:35) Are we good? (15:35) That’s not where you start. (15:37) So the backstop is literally at your company.

(15:40) I go, this is the point where I know nothing happens. (15:44) This is my safe. (15:45) Then you start to go, okay, what do I actually want the AI?

(15:49) Okay, so if I want everybody to be able to have quick answers based on internal knowledge, how do I guard that piece? (15:57) You have those discussions of the same and then you implement case by case. (16:02) But I always put in a broad, global, company-wide stop first, right?

(16:07) Even now, some of the extremes when people come and go, okay, block out all AI to start with. (16:13) I can tell you, number one, from a CIO perspective, they can’t, they literally technically can’t. (16:20) But so what is that backstop?

(16:23) Maybe it’s like, okay, we’re just gonna block upload of files larger than a certain size. (16:28) So those are the things to work out. (16:30) But this is one of those things where you have to put in the global backstop first before you start to look at adoption of use cases.

Melissa

(16:39) Yeah, and I love you said a couple things in that. (16:42) I think it’s so important to bring everybody around the table because each division within a company, HR, IT, marketing are using AI in a different capacity and their partners or the other tools that they’re using, they’re already implementing AI. (17:03) So I love that you said CIOs actually cannot, not because it’s spot on because a lot of the products they’re using, whether it be cybersecurity or other tools, they’re all using AI and machine learning right now.

(17:19) And so they can’t not do it. (17:21) So I love what you said. (17:22) I think it’s so true getting everybody in the room and what are we trying to accomplish here?

(17:28) And what are our risks? (17:29) And what are we concerned with our employees having, taking, using and layering that as an entire executive leadership team is so true. (17:41) I think I see it a lot of times where it’s just IT or it’s just legal or it’s just the CEO.

(17:48) And that’s, in my opinion, an immediate way to fail with this. (17:55) So I love that you brought that up. (17:56) I think it’s so true.

(17:57) I know when I think about the future of AI, you know, AI to me when I was a kid was the Jetsons. (18:05) I like, that was- I love the Jetsons, I love Rosie. (18:10) Same, like I know when I was young, I would watch this.

(18:14) I’m like, oh my gosh, it’d be so great to put my food in the microwave, push a button and I’d have this amazing dinner. (18:20) And so when I think the future of leadership in the AI, like my brain automatically goes to the Jetsons. (18:30) So let’s talk about how it’s changing work and what skills leaders need to thrive in a world of AI and technology today.

Yuying Chen-Wynn

(18:45) Yeah, so there’s a couple of ones and I have to tell you that I’ve come to this by trial and error. (18:53) There was no easy way, right? (18:55) We would tend to think, oh, AI technology, you want people that are really smart, have a technology background.

(19:04) I’m sure they would do well. (19:06) Not the case. (19:07) So the depth of AI technology or technology background actually is not an indicator with whether or not you will thrive.

(19:17) So a couple of things. (19:18) So number one, adaptability, right? (19:22) So one of the things that’s happening with this AI technology, but not just AI alone, if you see the speed of change of major revolutions, the period of time it takes is that getting shorter and shorter, okay?

(19:36) So the adaptability, when we say people haven’t gotten time to get used to it, the ones that are the most adaptable when something this fundamental changes, they can wrap their head around it in three to six months, that level adaptability at a leadership going, oh wait, okay, I think the world has changed. (19:54) Cool, okay, let me figure it out. (19:55) And I can figure it out in three to six months.

(19:57) That’s key. (19:58) When I say adaptability, that timeframe is getting mature. (20:00) You don’t have five years to figure it out.

(20:04) Lifelong learning. (20:06) So I obviously, I come from like a decade in ed tech because I love learning. (20:14) I’m actually one of those people, if I stop learning in a job, I cannot go on.

(20:18) I actually, I moved jobs because I have stopped learning. (20:22) I am wired that way. (20:24) But when you see, when I said some of those, like even with 20 years of tech experience, they look like they’ve done all these things.

(20:31) They don’t thrive. (20:33) They have stopped the ability to continue to learn in their 40s and 50s. (20:39) That is not gonna work.

(20:40) This continuous learning and the fact that the more you learn, the more you wanna learn, that is a key. (20:47) The ones I’ve seen that’s thriving, you’re like, huh, they don’t really have AI. (20:51) But I don’t know, why are they doing so well?

(20:53) You see what they do in their off time. (20:55) They’re like, oh, I’m listening to this podcast. (20:58) I actually try to vibe code it.

(20:59) I’m just like, oh my God, really? (21:01) You’re the head of data, like for the agricultural commission and you vibe coded something on the weekend. (21:06) But that’s the guy that’s gonna thrive.

(21:08) You’re like, oh my God, you deal with what? (21:10) And you did what? (21:12) So the continuous learning and the other one, I call it the tinker.

(21:15) So it kind of goes to the continuous learning. (21:17) You don’t just listen to podcasts. (21:18) You don’t just read everything that comes through.

(21:21) That’s a big, and you go, huh, I wonder if I could do it. (21:24) You go tinker, you try things out. (21:27) At home, like all the, I love it when people go, I have a knack for breaking things.

(21:32) I’m like, yes, that means you’ve tried it. (21:34) Cause you don’t have a knack for breaking things if you didn’t try it. (21:38) Right, so those are the three that I’ve found.

(21:42) And like some of these more traditional, we’re like, oh, look at their background. (21:45) They’re from this big college. (21:47) They have all this university.

(21:48) They have from this big logo, they’re McKinsey. (21:50) That is not an indication. (21:52) It’s the three so far that I’ve seen of adaptability.

(21:56) And there’s a speed to it, the continuous learning and the tinkering that makes. (22:01) And of course, sorry, I just realized I left out empathy, which was the best four.

Melissa

(22:08) Yeah, and I think, gosh, so true. (22:11) I think the continuous learning is so key. (22:16) I don’t know if it’s just me or my engineer brain or my ADD personality, but I have to read multiple books at the same time.

(22:25) I can’t, I don’t know what it is to me, but I’ll be reading six books at the same time and they’re all different subjects. (22:33) And my friends will come over and they’re like, you’re not really reading all six of those. (22:38) I’m like, that one I’ve been reading for a year.

(22:40) I could only get in for a little bit. (22:43) That one I’ve been reading for, but it’s so true, the learning and the human side of things. (22:51) I talk a lot with my kids about this, that their friends are over here and they’re all on their phones or their tablets.

(22:59) I’m like, all right, everybody’s electronics down. (23:01) Now you guys got to talk to each other and no TVs and nothing. (23:05) And I, so I do think the human side, we’re all, we can do things so easy with technology, but it’s the human side and the empathy you were talking about that’s gonna be key in the world today.

(23:20) And I think, I love that you brought that up because I absolutely agree.

Yuying Chen-Wynn

(23:25) I love that you read six books at the same time. (23:28) I’m like, I think my version, everybody always freaks out when I share my whole screen and they’re like, how many Chrome tabs do you have open? (23:35) I’m like, these are my three new ones.

Melissa

(23:36) I just got three out, three in, I love it. (23:44) So, but it’s interesting that you say, I have some of my very educated, Ivy League educated friends that are not really excited about AI. (23:59) They’re not really excited about technology.

(24:03) So they’re not leaning in, not racing it. (24:07) So I’m curious, those kinds of people that are on the sidelines with AI, they’re not gonna use it for whatever the reason is, what’s your advice for them as they make the decision? (24:23) Should I do it?

(24:24) Should I not?

Yuying Chen-Wynn

(24:25) I can tell you from the employer’s perspective, right now, we have stopped looking at those credentials. (24:35) We start looking at what projects have you been tinkering with on your own? (24:40) Can I see them?

(24:42) Which means you actually finish something that people can look at. (24:46) If you’re a coder, we’re looking at GitHub repos, public ones to see what you build. (24:51) We’re looking at how diverse it is.

(24:53) What have you tried? (24:55) When we talk to you, we wanna know what you have broken, what you have failed at. (25:02) That’s what we’re looking at because we’re looking for those people that’s going to thrive, that can move the organization with them.

(25:10) I have changed my job descriptions. (25:12) I am not looking for an AI PhD. (25:15) There’s so many jobs, go look for the jobs.

(25:17) There’s a lot of head of AI. (25:18) I’m like, what do you need an AI PhD for? (25:20) You’re not building the actual foundation model.

(25:23) You don’t need the AI PhD. (25:25) Those guys need them. (25:27) I will say maybe it hasn’t quite hit you yet, but from the employer’s perspective, that’s what we’re looking for.

(25:35) Either you make it on your own with your specifics, but if you wanna play in the larger space, this is where it’s all going to.

Melissa

(25:46) Yeah, I agree. (25:47) I think it’s very important to even just try it. (25:51) The younger generation, that’s the norm for them.

(25:56) It is just normal to use it to younger kids that have just graduated college or studying for big tests. (26:06) They’re using it to test themselves. (26:09) And so I think it’s just gonna be the way of the future and not to be scared and realize employers are gonna want that is so key.

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(26:57) I’m curious. (26:58) So let’s talk five years out. (27:02) Five years seems like a lifetime away, how fast technology is moving these days.

(27:09) But talk to me about what do you think, where are we going in five years with AI? (27:16) And maybe what surprises do you think may come up?

Yuying Chen-Wynn

(27:22) I think five years is incredibly hard to project out because of the speed of everything changing. (27:29) So I’m just gonna remind you, ChatGPT launched two and a half years ago. (27:35) If you can kind of remember back in your mind before ChatGPT, that’s only two and a half years and where we are now.

(27:42) Okay, we’re gonna go five years out. (27:43) I’m gonna be like, oh. (27:46) Right?

(27:47) Okay. (27:48) So, because when you’re reminded, you’re like, oh my God, it’s only been two and a half years, holy. (27:52) A couple of things, I think.

(27:54) Number one, when we say future of work, that’s gonna look completely different than what we’re used to today. (28:03) And I’m talking about org charts. (28:05) I’m talking about how they lead.

(28:07) I’m talking about how teams are formed and what skill sets and what roles. (28:12) Maybe the titles would have stayed the same, maybe. (28:15) But for sure, what each role does will have been completely blown out of the waters.

(28:22) That’s one. (28:22) In terms of how people work, I know for sure everybody has an army of agents that’s working for them. (28:30) So you with the six books, each one of them, each topic will be an agent that’s off doing something for you and come back and you’d be like, that’s not good enough.

(28:38) You gotta go try again, right? (28:41) That I think will happen in two to three years. (28:43) So five years, I was like, that’s, I don’t know what that is.

(28:46) There might be full businesses. (28:48) That’s running like this. (28:49) And you just kind of redirect them every now and then.

(28:54) I think the thing that’s easier to predict. (28:58) So the autonomous driving cars and the robots in our home. (29:02) These are the two I’m most looking forward to because I do not like to drive long distance, but I love road trips.

(29:08) Number one. (29:10) And number two, I’m like, I also do not like to fold laundry or do dishes. (29:15) And I think we’re going to get to the point where for certain segment, those robots doing those things for you and making smoothies, you can have them in certain amount of homes.

(29:30) It won’t be widespread, but I was like, it’s going fast enough that it can definitely show up in that time.

Melissa

(29:37) It’s interesting in Austin, there’s a lot of, Waymo. (29:44) There’s like the Waymos, the robot lawnmowers that are working now, which is interesting. (29:55) But I did, gosh, it was a few years ago.

(29:58) I did this one year without a car thing. (30:02) And I found it really interesting. (30:03) Everybody thought I had lost my mind, but the productivity I had from being in a car and skipping some of the commute was unbelievable to me.

(30:16) So one of the things I think in five years, the work day, the work week, how many hours we’re working a day, how many hours we’re working a week, I think productivity will go up. (30:30) Therefore the need for us to ruminate about, as a recovering perfectionist, I would ruminate over things all the time. (30:40) Is this good?

(30:41) Good enough, get it out. (30:42) I don’t have to ruminate anymore on, like when I’m writing emails and I’m using different tools, it’ll clean up my writing. (30:51) So I don’t have to go reread what I just wrote.

(30:54) Somebody’s doing it for me. (30:56) And so I feel like productivity for me is gonna go through the roof. (31:01) And I’m thinking like my work day is gonna be like half if I could get it all organized.

(31:07) But it would be cool, to your point, having a robot make my lunches. (31:12) I think that would be amazing.

Yuying Chen-Wynn

(31:14) Or my smoothies and- Yeah, it’s just being less tempted to put in all the wrong things. (31:21) I’m always there like, oh, the chips are fine. (31:24) That should be a little more.

Melissa

(31:27) It’s true, it’s true. (31:29) And in the fast-paced world of tech that we live, it’s like, what’s the fastest? (31:34) What’s the quickest?

(31:35) And I think when you said that, I’m like, gosh, smoothie, think about how healthy I’d be if I had a robot cooking me healthy food.

Yuying Chen-Wynn

(31:43) I was like, I would totally have a smoothie, but I just don’t wanna make it.

Melissa

(31:49) It’s true. (31:50) And you know what? (31:50) It’s funny that you say that.

(31:52) And that’s why I went to a lot of subscription food delivery because I knew I’d have nine meals a week that were healthy and easy to make. (32:04) And then, so I think it’s convenience, it’s time efficiency, it’s shorter work weeks, it’s clearer minds. (32:16) I know that a lot of the executives and founders I talk to, a lot of them are stressed out, burnt out.

(32:25) And I often question, what area could they use technology to support some of these areas of their life? (32:35) I think of Waymo, like all my friends in LA, they use it to sit, they take a Waymo and it takes away their commute of an hour or more each way to and from work. (32:50) So yeah, five years out is a far away, but I like to think of like, back to the Jetsons, back to the Jetsons.

Yuying Chen-Wynn

(33:00) I’m so glad that your inspiration is the Jetsons. (33:04) I don’t know how many times I’ve heard the Terminator reference. (33:09) I’m just like, oh my God, guys, there’s so many AI.

(33:12) Can you guys stop it with Terminator?

Melissa

(33:15) Mine’s the Jetson. (33:16) I think like the spaceship, right? (33:19) The self-driving spaceship, this was way back in the day and now we’ve got the Waymos.

(33:24) So I think it’s just how we choose to live our lives and not to be fearful of technology and change because there’s so many, we’re solving cancer with some of these tools. (33:42) Although I’m not too excited about talking to my insurance company and their bots and AI agents, it’s been very frustrating given my last name and it’s harder, right? (33:58) But I do think there’s so many positives as we move through transformation.

(34:07) I wanna get in closing kind of, we’ve covered a lot of ground in a short period of time. (34:12) Anything that we didn’t touch on or anything that you wanna leave with our listeners?

Yuying Chen-Wynn

(34:21) I always just remind everybody that technology is a tool and an amplifier. (34:25) It can do everything you want it to do if you focus on using it for how you want to be. (34:35) So when I hear people say, oh, it takes away your ability to do critical thinking.

(34:40) I’m like, no, it depends on how you use it. (34:42) You can actually do more critical thinking, strategic thinking if you use the tool in the right way. (34:47) Well, it takes away your creativity.

(34:50) This morning I generated four different AI songs about vibe coding, cause I was teaching a class and I thought that would be a fun breakaway, right? (34:58) I can’t write a song to save my life or draw to save my life, but I’ve been creating art, I’ve been creating music. (35:06) So there are tools to unleash every aspect, focus on what you want to do and find the tools that help you do that.

(35:15) It’s across the board that it’s there. (35:17) So don’t just kind of fall back though. (35:20) Oh, that’s making it worse.

(35:22) Well, did you make an effort to use it in the way that you want to use it and do things that you never thought before? (35:29) So that’s gonna be my first and foremost and final. (35:34) It’s just an amplifier.

(35:36) Use it for how you want it to work for you.

Melissa

(35:39) Oh, so good. (35:41) I love that. (35:42) I think it’s funny that you said art as an engineer-brained person.

(35:47) I’m also not an artiste, but I’ve been using some amazing tools to create artwork. (35:56) I wasn’t too excited about me as a doll in a box. (36:01) That one didn’t work so great for me as an AI prompt, but I think it’s such an amazing…

(36:09) I gotta try the music side of it. (36:11) Maybe I’ll create… (36:12) You’ve inspired me to write a song.

(36:16) Oh, this has been so fun. (36:18) I love your insights and all the things you shared with us, different perspectives. (36:25) Where can listeners go to learn more about Wittingly Ventures and you and connecting with you?

(36:34) What’s the best way?

Yuying Chen-Wynn

(36:36) Yeah, so easiest, of course, on LinkedIn. (36:39) You can follow and connect. (36:41) And Wittingly Ventures is literally wittinglyventures.com.

(36:44) So very easy to connect with me. (36:47) I also have newsletters, but all of those things get pointed if you connect in one of those ways.

Melissa

(36:53) I love it. (36:55) Thank you so much for being here today. (36:58) That’s the Executive Connect podcast.