Ever met a martial artist who builds AI powerful enough to predict cancer—and still jokes about replacing
himself with it? Meet Ron Green — the CTO of Kung Fu AI, who’s spent two decades turning science
fiction into strategy. In this forward-looking conversation, Ron demystifies AI, unpacks the real risks (and
hype), and shares why the future belongs to leaders who experiment, adapt, and use their data wisely.

Chapters:
00:00 – AI’s Job Takeover? Long-Term Impact vs Now
01:38 – Ron’s AI Origin Story & Why He Chose This Path
05:00 – AI Isn’t Magic—It’s Learnable Software
07:00 – Breast Cancer Prediction Model Approved by FDA
08:40 – What Building Scalable AI Looks Like Today
12:30 – From Early Adopters to Enterprise Transformation
14:30 – Agentic AI: What’s Next and When to Expect It
18:30 – Will AI Replace Leadership and Coaching?
20:00 – The Power of Proprietary Data in AI Strategy
23:30 – AI vs Skeptics: Why You Can’t Afford to Ignore It
26:00 – Ethics & Bias in AI: How to Do It Right
30:00 – Final Advice: Experiment Small, Think Big
35:00 – Where to Find Ron & Learn More About Kung Fu AI

Melissa Aarskaug (00:01.536)

What do martial arts and AI have in common? Discipline, precision, and today’s guest, Ron Green, is the co-founder and CTO of Kung Fu AI. And he’s been kicking down doors in the AI world for over two decades, from launching tech startups to building cutting edge AI solutions for Fortune 500 companies.

Ron’s not here to sell hype. He’s here to talk real results. If you’re wondering how to actually use AI to solve the needle in your, wow, that was, I reread that separately. We’re gonna have to do that again. you’re.

Ron Green (00:43.79)

That was good. That was great right until the end. I’ve done that a million times myself.

Melissa Aarskaug (00:48.516)

how do I actually use it? Ugh, I read it wrong. Dyslexia’s a bitch. Let’s take two.

Ron Green (00:53.966)

actually makes it even harder. Damn, okay.

Melissa Aarskaug (00:58.358)

No, I’m very dyslexic. It’s so funny. like, seven people know this. And so it drives Brian nuts because he’s like, what? And he gets it now though. It still gets a year to go.

Ron Green (01:03.825)

my gosh.

Ron Green (01:09.55)

I’ll watch the read like seven times in a row. So take your time.

Melissa Aarskaug (01:14.124)

Like lifting the weights for reading this stuff is so like, and we’re both engineer brains too, so it’s like, we’re also recovering perfectionist, so it makes it 10 times harder. But let’s do that again, take two. Okay. What do martial arts and AI have in common? Discipline, precision.

Ron Green (01:24.035)

You

Melissa Aarskaug (01:38.912)

And today’s guest, Ron Green, is the co-founder and CTO of Kung Fu AI. And he’s been kicking down doors in AI for two decades. From launching tech startups to building cutting edge AI solutions for Fortune 500, Ron’s not here to sell hype. He’s here to talk real results. If you’re wondering how to actually use AI to move the needle in your business,

You’re in the right dojo. Welcome, Ron.

Ron Green (02:11.896)

Thank you so much for having me.

Melissa Aarskaug (02:14.156)

Now you’ve had the front row seat and the steering wheel and the evolution of artificial intelligence for over two decades. What first drew you to AI and how is your journey from the startup founder and CTO of Kung Fu AI shaped your approach to innovation?

Ron Green (02:34.584)

that’s a great question. So I have a little bit of a different journey than I think, you know, maybe the average person. I was doing my undergraduate degree in computer science at the University of Texas at Austin. And by the end of my program, I was so burned out. I remember I was limping, limping into my last semester and I had one elective left.

And I remember thinking to myself, you know, I don’t know what I’m going to do when I graduate, but it is not going to involve computers because I was just, I was right. And the course I took was AI 101 and this is in the nineties. And I’m not kidding. I think maybe two, three, four weeks in the course, I knew this is what I wanted to do with the rest of my life. But the funny thing is I thought, I thought two things. thought, you know, am I smart enough to actually do this? Because this seems like a really, really hard domain.

And I was more concerned about the second, which was I was like, I think I’m too late. I think they’ve got this all figured out. And this is in the mid 90s. And so I graduated, went to grad school, did a master’s in artificial intelligence at the University of Sussex. And it was the perfect time for the field to die. Like literally, I graduated and nobody cared. Everybody was about the internet.

and all this stuff. so there was a period of just, you know, kind of feeling like I was in the wilderness and the darkness, you know, one of the few people really, really passionate about AI in that period of time. But I’m delighted to say that it has had resurgence, I think you could say. And now I get to do amazing work and work with amazing clients building just crazy, difficult, state the art AI solutions. And I couldn’t be happier.

Melissa Aarskaug (04:28.098)

Now, I would agree with you. I also did not enjoy computer science and C++ and anything CS was painful as well for me. So I agree with you. Now, how do we demystify AI to business leaders? And let’s talk about the obvious elephant in the room. AI can feel overwhelming for business leaders. How do you help organizations cut through the noise?

and help them make sense of their goals as it relates to AI.

Ron Green (05:00.172)

Yeah, yeah. One of the first things I always tell businesses is AI is not magic. is literally just software. It is deterministic as much as any traditional software solution would be, but it’s really different from that point on. And what I explain is most AI today is based upon machine learning techniques where you can take models and you can…

show them examples, you show them inputs, and you show it what the model’s output should have been, and you can give it enough examples and it can learn to generalize. you know, probably the easiest image to hold in your head is imagine you’re trying to build a computer vision model to recognize animals. Maybe just something simple like to detect and classify whether it’s a picture of a cat or a picture of a dog.

We literally did not know how to do that 20 years ago because even though the human brain, you know, we can look at a picture and instantly tell that it’s a cat or a dog, we don’t actually have the ability to introspect on our brain and understand the steps that we’re taking. And so that meant as powerful as computer systems were, we couldn’t build systems capable of sort of perceptual capabilities like that. With this machine learning technique, we can build systems that

learn to generalize and you can train it on, sometimes it can be as little as a few thousand examples and it will learn to generalize catness and doggness and then you can show it new pictures. The beautiful thing about this is these techniques extend far beyond just computer vision. We now can use these techniques in all the perceptive realms for the most part, language, speech, we can do.

translation, but we can also do things that are superhuman. In fact, I’m really proud of this. Just this week, literally just this week, a project that we worked on for almost four years was approved by the FDA, and it is a state of the art breast cancer risk prediction model. And here’s what’s crazy about it. Most medical models in the breast cancer space look at a mammogram and they detect breast cancer. They’ll look for signs of it.

Ron Green (07:22.048)

This is much, much more sophisticated. It can actually predict the risk of breast cancer up to five years in advance. And it’s using just normal screening mammograms. This is like going from having smoke detectors that go off when your house is on fire to being able to beforehand that your housemate catch on fire. This is gonna save millions of lives. And this computer vision model

works in a way that we don’t totally understand, meaning it’s picking up on biological signs of cancer that we as humans are blind to. And so that’s just one example of how these really advanced AI techniques are changing the field. And it’s happening in every domain, not just healthcare, of course.

Melissa Aarskaug (08:11.204)

First of all, wow, I’ve been reading about this and hearing about this. What is exciting that it was just approved. how special is it to say that you’ve been able to save lives? And I think that’s the beautiful thing of what AI can do. And because we always hear the other side, right? We always hear that we’re all going to lose our jobs and have robots to do everything. So I love hearing these stories. And you develop.

Ron Green (08:32.269)

You

Melissa Aarskaug (08:40.544)

solutions in retail and healthcare and across many different verticals. What does a building a scalable AI service look like today? like, I know you touched on it how it has changed, but how is it different today than it was before?

Ron Green (08:58.158)

Yeah, great question. So, we started Kung Fu AI almost eight years ago. And in the early days of the business, most of our clients were, by definition, early adopters. They were coming to us with really challenging problems that they couldn’t solve any other way. And they were all but desperate. Like, could we somehow use artificial intelligence to address this? And this was things like,

understanding documents, extracting information or detecting fraud or optimizing supply chain, just tons of business related initiatives. Now our clients want help with everything. AI touches everything now and they are not coming to us with point problems. They are coming to us asking for broad help. need help putting together their strategy, their product roadmap, integrating.

I’m so sorry.

Melissa Aarskaug (09:58.98)

Do you wanna stop at take a drink? We can cut it.

Ron Green (10:00.718)

I have no idea, for like the last minute, yeah, let me just, you can hear my voice. my God, that’s never happened before. I just got a quick symptom like.

Melissa Aarskaug (10:04.844)

Yeah. Take a drink. We can cut this. It’s a beautiful thing. Go, go take a drink.

Well, I’m literally taking the breath out of you. Literally. I mean, just my questions are so riveting that you can’t even breathe anymore.

Ron Green (10:16.236)

my god.

my gosh, that has never happened to me before. All of a sudden, I just, well, was trying to, could you tell that I was?

Melissa Aarskaug (10:28.974)

Well, that’s why, yes, that’s why I said stop. We can cut this. Don’t struggle it. We got time. I got all the time in the world. no.

Ron Green (10:31.854)

Woo! I’m so sorry. Wow. I tried to fight through that for like a good 30 seconds, but then my eyes started watering. I don’t know what that was.

Melissa Aarskaug (10:39.108)

Well, it’s funny, last week I did the same thing and I am fair skinned person, so I was coughing and then she’s like, I just took a drink and then I was red at the second half. Like my face didn’t like de-red the second half.

Ron Green (10:54.776)

Wow, that was crazy. That has literally never happened to me before. was all of sudden like I had a catch in my throat. I don’t even know what the physicality of that is. Yeah, if you don’t mind, if you don’t mind, I’ll redo that. Give me one second, I’m gonna grab another water. I’ll be.

Melissa Aarskaug (11:06.062)

Do you wanna redo, do you wanna redo it all? Yeah, yeah, yeah. Yeah, do it, do it, do it, do it.

Ron Green (11:29.23)

This is why it’s good to have a little padding in the schedule. My gosh.

Melissa Aarskaug (11:34.244)

And I never, it’s funny, I never wanna do these live, cause this kind of stuff happens, we’re human, like, you know?

Ron Green (11:41.016)

That’s exactly right. I mean, that is crazy. I mean, that is a…

I’m trying to imagine what physically that could have been. All of a sudden I just had just a frog in my throat. Okay, I am so sorry about that. Tea it up again and I will do my little dance.

Melissa Aarskaug (11:53.572)

It’s just, don’t worry, let’s do it again. You’re gonna do it and if we have to stop again, we can cut this. That’s the beautiful thing about people that help us post-produce this, they’re geniuses. Now Ron, you lead AI solutions across many different verticals from healthcare to retail to finance. What does scaling…

Ron Green (12:06.146)

Good, good, good, good.

Melissa Aarskaug (12:20.596)

AI look like today and how has that changed five years ago?

Ron Green (12:26.316)

Yeah, it’s changed dramatically. we started Kung Fu AI almost eight years ago, all of our clients were really early adopters. You pretty much had to be at that point in time. They would come to us with very specific problems that they couldn’t solve with traditional techniques. For example, supply chain optimization, fraud detection.

a recommendation engines for products, things like that. And these are, these are areas where machine learning and AI really, really can move the needle. Those days are over. Now AI is widely adopted and the clients that come to us now for the most part want really, really broad AI help. They want help with strategy. They want to help. They want help with product road mapping. They want to help with product selection. There are many tools that you can buy right now.

that you can buy off the shelf. And they essentially need a partner to help them make this transition because the reality is most businesses are very, very early on in their AI journey. And it’s complicated because unlike traditional software, there’s quite a bit of math. It’s very data dependent. it’s almost a dirty secret with an AI that

you’re data dependent, right? Your models are only as good as the data you have. so understanding how to make the most of those situations can be the difference between a POC that demos really well and something that actually goes into production.

Melissa Aarskaug (14:09.112)

Yeah, I love it. think it’s so true. Everybody wants the one stop for everything these days, the one stop for the perfect everything. So I could imagine and AI is no different. Now, looking forward and looking in the future, do you see any emerging trends happening or anything that people should start paying attention to now? Maybe those who haven’t fully adopted AI yet.

Ron Green (14:14.316)

Yeah.

Ron Green (14:35.178)

Yeah, yes, I do. think, you know, generative AI is extremely, sort of pervasive within the world at this point. think everybody uses chat, GBT or Claude or something like that. And, and most people are using that for personal, personal, you know, automation or personal work enhancements. We are going to be moving probably beginning in 26. I really think it’s going to be that soon.

towards agentic AI systems. probably everybody out there has heard of this before. This is this idea of having AI assistants that you can give high level goals to, and they can go and achieve them on their own. So for example, something that they can do right now would be make a restaurant reservation. They can navigate the challenges of availability, website.

navigation, registration, all that type of stuff. Pretty simple. They are not quite ready for enterprise systems though. So we tell our clients like, look, agentic AI is gonna be incredibly important, but it’s not really ready. It’s not fire and forget yet. But this is the amazing thing about them. A paper came out just a couple of months ago that showed that the task length, so the duration of the task,

that these agents are capable of performing is doubling every seven months. So right now, agentic AI systems can pretty much handle perfectly with 100 % success tasks that take four minutes or less. And you can actually stack these tasks together. It is very, very likely that within the next five years at the latest, we’re gonna have agentic systems where we can give them

know, enormous responsibilities like planning an entire vacation, hotels, restaurants, plane flights, everything like that. And they may go away and work on that for two or three hours and come back with a complete solution. And that’s on the personal side. There’s going to be unbelievable opportunities on the business side for optimization as well. The challenge is right now, stringing together

Ron Green (16:58.65)

multiple tasks is where they suffer. You have one four minute task, great. You have nine four minute tasks and the likelihood of getting all the way through that chain successfully diminishes quite a bit. But the progress being made on that front is astronomical and we’re gonna have, as I said, just complete automation at human level tasks in the near.

and what these models can do is handle ambiguity just like humans. It’s just gonna be amazing.

Melissa Aarskaug (17:30.06)

And I love that you said the trips, because I have personally used it on my trips, and it has been unbelievably accurate at what they suggest. So I love that you mentioned that. I look forward to the day that I’m fully able to outflow all the bookings to somebody else.

Ron Green (17:46.562)

Yeah, all the annoying leg work that you have to do.

Melissa Aarskaug (17:51.08)

It’s going to be so easy. So I’ve had a fellow friend of mine on the podcast who is a very, very, very successful executive coach for some of the top CEOs across the world. And he, we often talk that he’s going to be fully replaced by AI coming next year. So I want to get your insights. AI doesn’t just change products. It changes the way that we lead people.

So as a tech leader myself, I’m wondering what are effective ways that AI powered tools can help organizations thrive?

Ron Green (18:32.334)

I’m really a big believer that this coming wave of AI automation is going to take away jobs in long term, right? And I’m not gonna pretend that’s not the case. I literally joke sometimes, look, AI is coming for all of our jobs. But.

There are many, many positive elements to that and it’s going to be a very, very long cycle. This isn’t something that’s going to happen tomorrow. Almost ironically, the job that is being most affected by AI is programming, right? It’s like AI developers are putting themselves out of the job first because we have these coding assistants that are incredibly powerful. The reason they’re so powerful is there is so much training data because software

has open source access to all of this decades of code. And so the training data to automate that particular field quite ironically is happening first. We’re going to see this in all the other fields where there is a lot of public data. So within the math domain or within the legal space, there’s just a wealth of data. The other areas are going to be much, much less susceptible where there’s

sort of more proprietary or more sort of narrow. And I’d love to come back that because I actually think that’s where the gold lies for businesses actually. But as far as utilizing itself, I really, I’m stunned. I give presentations all the time and I’ll talk to people and they don’t use chat GPT for anything. They think maybe AI is overhyped. It is incredibly powerful at.

not just acting as a writing assistant or to summarize, but almost all the tools out there on a personal level you can be using for like deep research purposes, right? Go and analyze and put together reports. And then to circle back to the comment about the proprietary data I mentioned a second ago, the best way for businesses to leverage AI from a competitive advantage, not just in efficiency or.

Ron Green (20:48.622)

internal tooling is to leverage proprietary data that you have within your enterprise that acts as an enabler for either cost reduction or new capabilities. I juxtapose this against doing something like going and us spending a bunch of time and money to build a call center solution where

That is, you’re thinking to yourself, I’ll reduce my call center costs. That’s not a competitive advantage. That’s not a good use of your time and money. Go buy an off-the-shelf solution. Instead, you can take the proprietary data you have and build predictive systems or new generative capabilities that actually build competitive moats for your business, and that is the best way to leverage AI.

Melissa Aarskaug (21:41.292)

I love that. And I love what you said because when you were talking about replacing us, I was thinking back in the day when the cars came into play, you know, the horses, you know, they just had different jobs. People that were driving, were buggies and, you know, they were just, they were displaced into different places. And so I don’t, I don’t, I’m not one those people that subscribe that we’re all going to be doomed.

Ron Green (21:58.626)

Right.

Melissa Aarskaug (22:07.81)

the bots are gonna take over. I just think people’s roles and jobs are gonna be forced to expand themselves into other areas maybe that they’re not used to. And I love it.

Ron Green (22:18.574)

I totally agree. I totally agree. know, and I always think about, you know, when the camera was invented, a lot of people thought that was the end of painting, right? Why would anybody want to, you know, pay a painter for a portrait? Well, no, it just opened up entire new art forms and we’ve got impressionistic painting. The same thing happened once film became a reality. People thought, oh, well, that will be the death of plays. No, it just fosters a whole new

medium within art and I think that I think they know as humans we just get kind of fixated on what we’re used to and we we we fixate maybe on the loss and not the opportunities and I’m I think it’s probably pretty clear extremely optimistic about the wave of prosperity that’s going to be Coming behind this wave of AI

Melissa Aarskaug (23:13.572)

100%. And I have some of those people that I work with that say, AI, we’re not allowing AI, we’re not enabling AI, we’re just going to close our minds to AI. And I often tell them that don’t be feared from the technology, put policies and practices in place to enable them. Because at some point, I feel like everybody is going to be using them at some capacity. And for businesses that don’t step up now,

I fear for them. feel that they’ll be so far behind that they’re not going to be able to catch up. I’d love to get any of your thoughts on that.

Ron Green (23:52.446)

I completely agree. And I understand, you’re running a business. That is complicated. And everything gets so hyped, right? And I’ve had people ask me, what’s the difference between cryptocurrency and AI? They’re both just scams. And I’m sympathetic to that perspective. This is what I would say is every technological

advancement comes with people trying to make money off of it. That doesn’t mean there aren’t really transformative moments, right? And you look back, if I look back in the last 50 years and I think about the personal computer, we can talk about the internet, we can talk about mobile, and now I really believe AI. I will admit, I’m, as much as anybody, I stand to gain.

personally by proselytizing AI. But the reality is, and I believe this in my heart, is that this is the most important transformative moment of our lives. And AI is different than every technology we’ve ever had before. Yes, we could build semiconductors and use those semiconductors to help design and build the next generation of semiconductors. Those semiconductors, though,

couldn’t build the next generation. We are literally at the point now with AI and reinforcement learning where we are using the previous generation of foundational model to train the next generation. And so there is this feedback loop within AI that is unprecedented within technology. to me, the proof’s in the pudding. We already have models that are super human capable at…

many, many things that these models couldn’t even do 10 years ago. So the pace of acceleration is just off the chart. So if you’re skeptical, I understand it. Don’t put your head in the sand. There’s that old saying that people say, AI, it may not take your job, but somebody using AI will take your job.

Melissa Aarskaug (26:12.226)

I like that one better. think that’s more, it’s interesting. have this discussion a lot where I feel like now we’re, we need to be Swiss army knives as, as workers. can’t just be a one thing. Like people were just farmers and they were just, you know, engineers and doctors. But I think in the world we’re moving into, like you were just saying, things are changing so fast. What happened, you know, a year ago is totally different today.

Ron Green (26:23.938)

Mm-hmm.

Melissa Aarskaug (26:41.39)

before we know it, 2026 is gonna be here. And I love all that’s going in, all that’s happening with AI. I think it’s such a breath of fresh air. So I see it as a positive, not a negative, but I do know a lot of people have concerns of ethics in AI and the responsibility with innovative designs based on using AI. So I believe

Ron Green (26:55.214)

Absolutely.

Melissa Aarskaug (27:10.678)

I believe we could do great things, but there are those that don’t. So how do you guys at Kung Fu AI, how do you and your team, not you guys, you and your team, ensure that AI is both powerful and responsible?

Ron Green (27:25.26)

That is one of those questions that I think most people underestimate because they are used to dealing with technologies and software and you try to get some system to work and you get the bugs out and you don’t really think too much about maybe ethics or bias or things like that. Well, AIs we talked about earlier today is.

dominantly based upon a machine learning technique called supervised learning, where you show the model examples and it learns to generalize. Well, what that means is these incredibly powerful models will learn everything you show it, including the biases. So for example, we build a system to do auto-evaluation pricing for homes. Well, we went and collected decades of

home pricing data and it was clear as day that sort of racial discrimination was happening in certain periods of time and it was reflected in the data set. We had to work really, really hard to remove that bias from the data set and had we not done that, the model would have learned it and propagated that bias in its predictions and the users of that model would not be able to sit there and say,

it’s the AI made the decision. It’s unbiased. know, whatever it says is accurate because the you literally taught the model to be bias. And the same thing earlier, I mentioned the the breast cancer risk prediction model. One of the best things about that model was we spent so much time, we literally spent years making sure it wasn’t biased to either ethnicity or race.

or age, and that’s something that we could do because we controlled the data. We knew what the actual values were, and as the model got better at predicting these external factors and not focusing on cancer, we could punish the model. So the model got good at guessing age, so we punished it for doing that and made it essentially get so bad at guessing an age, it was like a random number generator. And that’s what you want. Focus on the cancer.

Ron Green (29:45.122)

Focus on predicting risk and don’t worry about any of these other external factors that might bias your judgment. And so it’s a double-edged sword. These incredibly powerful systems can be unbiased or largely unbiased, but you have to do the work. You have to do the leg work, roll up your sleeves and identify it and remove it when you’re building these systems.

Melissa Aarskaug (30:07.724)

I love that you said punish your AI. That’s great.

Ron Green (30:09.902)

And that’s the way we thought about it too. We really did.

Melissa Aarskaug (30:16.068)

I love it. It’s not like punishing your husband or your wife. It’s punishing your AI. I love it. I’m going to use it. I’m going to take that one from you. Ron said, we need to punish our AI. You can’t let him be biased. So I love that. I want to get any final thoughts or anything that I may have missed on that you want to share with our executive listeners.

Ron Green (30:26.126)

You’re damn right.

Ron Green (30:38.69)

Yeah, just a couple things. always tell them, look, you have to have a culture of experimentation as you’re walking into AI. These models are deterministic, but they’re probabilistic. There is a level of fuzziness with them that is very analogous to working with another human being. You’re going to have to live with that, right? If you’re using a generative system and you want it to never hallucinate or never say something,

you know, along some domain, that is really, really hard to do. And I think you have to come to the table with that mindset in place. And then two more points. One, don’t go for the moonshot right out of the gate. Do something small, do something valuable, focus on proprietary data. Don’t try to go and spend $100 million on your first AI initiative.

those almost never work. Go get a small win and then you’ll get confidence within the organization. And then lastly, get buy-in and focus on business outcomes. Don’t focus on doing AI because it’s cool or doing AI because you’re getting pressure from the board. Focus on meaningful business outcomes. They are, because most businesses have done almost nothing with AI yet, there is so much amazing low-hanging fruit, the opportunities will be widespread.

Just focus on the ROI and you will be able to get this into production.

Melissa Aarskaug (32:07.17)

I love that you said that, because that’s such an important part in building a plan for what we’re trying to solve for, not just do it to do it, because as you mentioned, it’s just a waste of money. Now, we have this debate in our household, and I have to hear it directly from you on which one is better, Gemini or chat GPT, if you could only have one.

Ron Green (32:28.719)

That’s funny. you know, that is such a great question. And of course, you know, if you ask this question a month from now, the answer could be different, right? So on paper, Jim and I has better metrics, right? As these models get more more capable and as there is benchmark cheating and all this sort of stuff, it’s really, really hard to tell.

I am blown away by both those models. I would say the work coming out of Google, OpenAI and Anthropic are all equally incredible. My personal go-to is I end up using ChatGPT, if I’m being honest, a little bit more. And they just have a great, great client app that you can download. And I think the interface has less friction for me.

Melissa Aarskaug (33:14.242)

Well.

Melissa Aarskaug (33:23.544)

Well, our family of girls will love this episode and my husband and my son will be very sad to hear this. But as you say, things could change and who knows who’s gonna be next with this.

Ron Green (33:30.734)

you

Ron Green (33:35.214)

That’s right. Yeah. And I use Gemini all the time for other things, but my go-to right now is chatGVD.

Melissa Aarskaug (33:41.764)

It’s interesting now that I mentioned kids, it’s amazing how I never used any of this. And it’s amazing how the kids are using this now and how they’re figuring out how to use it. It’s like baffling to me. Our smart children decide to consult AI if they feel that they’re being unfairly punished.

Ron Green (34:09.114)

that’s great. Yeah, they’re an AI native generation. They’re just not going to know what it was like to grow up without it. One more thing, I just saw an amazing fact. In China this week, they’re holding essentially admission tests, like standardized admission tests. And all of the leading AI companies have turned off AI access to prevent students from cheating.

Melissa Aarskaug (34:36.884)

It’s crazy. It’s happening a lot at the school level. But I do know my kids use it to the other direction. Okay, I’ve got an A on this. Give me harder equations to solve for.

Ron Green (34:41.346)

Just incredible.

Ron Green (34:52.012)

Wow, see your kids are using it in my opinion in the perfect way, right? Don’t use it elevate yourself.

Melissa Aarskaug (35:00.344)

Yeah, they’re using it really smart, Ron. They’re using it against our parenting and to solve for problems that we otherwise haven’t solved in our house. So there you have it. Our kids are using AI. Now I want to get kind of enclosing any way that our listeners, what’s the best way our listeners can connect with you, learn more about your company and kind of any final thoughts.

Ron Green (35:04.956)

Hahaha

Ron Green (35:11.822)

Oh, I love it. Oh, I love it. That’s awesome.

Ron Green (35:28.43)

Yeah, connect with me. can shoot me an email. Very simple, ronron at kungfu.ai. We have a podcast. It’s a little bit more of the technical side. It’s called Hidden Layers. We do episodes on the latest breaking news within AI every month. And then you can find me everywhere. You can find me on X, Threads, Blue Sky. And we have a website, kungfu.ai. Check it out, please.

Melissa Aarskaug (35:57.154)

Ron, thank you so much for being here today and sharing your knowledge with our listeners. That’s the Executive Connect podcast.

Melissa Aarskaug (36:09.419)

All right.