August 13, 2025 • 33 mins
Worried that AI is coming for your job ?
This episode is a leader's guide to the truth about automation, job loss, and where humans still win.
We sit down with Ron Green, CTO of Kung Fu AI and a martial artist who builds cancer-predicting AI to pull back the curtain on the future of work.
Ron makes AI feel less like a mystery and more like a toolkit every leader should learn to use.
Learn how to turn data into a competitive advantage and prepare your team for the future without panicking.
This conversation delivers grounded insight and practical strategy.
Timestamps:
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
Guest:
Ron Green
Host:
Melissa Aarskaug Episode
Connect With Us:
Visit our website: www.executiveconnectpodcast.com
Watch full episodes on YouTube: Executive Connect YouTube Channel
Social:
LinkedIn: Melissa Aarskaug
Instagram: @executiveconnectpodcast
TikTok: @executiveconnectpodcast
Facebook: Executive Connect
This episode is a leader's guide to the truth about automation, job loss, and where humans still win.
We sit down with Ron Green, CTO of Kung Fu AI and a martial artist who builds cancer-predicting AI to pull back the curtain on the future of work.
Ron makes AI feel less like a mystery and more like a toolkit every leader should learn to use.
Learn how to turn data into a competitive advantage and prepare your team for the future without panicking.
This conversation delivers grounded insight and practical strategy.
Timestamps:
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
Guest:
Ron Green
Host:
Melissa Aarskaug Episode
Connect With Us:
Visit our website: www.executiveconnectpodcast.com
Watch full episodes on YouTube: Executive Connect YouTube Channel
Social:
LinkedIn: Melissa Aarskaug
Instagram: @executiveconnectpodcast
TikTok: @executiveconnectpodcast
Facebook: Executive Connect
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
AI is coming for all of our jobs.
(00:01):
There are many, many positive elements to that,
and it's gonna be a very, very long cycle.
This isn't something that's gonna happen more up.
This coming wave of AI automation is going to
take away jobs in the long term.
AI is not magic.
It is literally just software.
Now, our clients want to help with everything.
For example, supply chain optimization, fraud detection,
(00:24):
a recommendation engines for products, things like that.
Your models are only as good as the data you have,
the duration of the task that these agents are capable of
performing is doubling every seven months.
- What do Marshall Arts and AI have in common?
(00:44):
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 AI for two decades.
From launching tech startups to building cutting edge AI solutions
for Fortune 500, Ron's not here to sell hype.
(01:08):
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.
- Thank you so much for having me.
Now you've had the front row seat
and the steering wheel and the evolution
of artificial intelligence for over two decades.
(01:31):
What first drew you to AI?
And how is your journey from the startup founder
and CTO of Kung Fu AI shapes your approach to innovation?
- 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
(01:53):
at the University of Texas at Austin.
And by the end of my program, I was so burned out.
I remember I was limping into my last semester
and I had one elective left
and I remember thinking myself, you know,
I don't know what I'm gonna do when I graduate,
but it is not gonna involve computers
'cause I was just, I was right.
(02:15):
And the course that took was AI 101.
And this is in the 90s.
And I'm not kidding, I think maybe two, three,
or 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 two things.
I thought, "Ah, you know, am I smart enough to actually do this
because this seems like a really, really hard domain."
(02:36):
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.
(02:58):
Like, literally, I graduated and nobody cared.
Everybody was about the internet and mobile and all this stuff.
And 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.
(03:18):
But I'm delighted to say that his had resurgence, I think you could say.
And now I get to do amazing work and work
with amazing clients building just crazy, difficult,
stay-the-art AI solutions, and I couldn't be happier.
Now I would agree with you.
I also did not enjoy computer science and C++ and anything CS
(03:44):
was painful as well for me.
So I agree with you.
Now, how do you 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
(04:04):
and help them make sense of their goals as it relates to AI?
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Don't just watch, act.
Yeah, yeah.
Well, one of the first things I always tell businesses is,
(04:27):
AI is not magic.
It 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
(04:47):
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 models output should have been.
And you can give it enough examples
and it can learn to generalize.
And probably the easiest image to hold your head is imagine
you're trying to build a computer vision model
(05:09):
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,
we can look at a picture and instantly tell
that it's a cat or a dog, we don't actually
(05:30):
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
(05:51):
be as little as a few thousand examples.
And it will learn to generalize catness and dogmas.
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.
(06:14):
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.
(06:35):
Most, most medical models in the breast cancer space
look at a mammogram and they detect breast cancer.
They'll look for signs of it.
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.
(06:58):
This is like going from having smoke detectors that go off
when your house is on fire to being able to detect
beforehand that your house may catch on fire.
This is going to save millions of lives.
And this computer vision model works in a way
that we don't totally understand, meaning it's
(07:18):
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.
First of all, wow, I've been reading about this and hearing
about this.
What is exciting that it was-- it's just approved.
(07:40):
And 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.
Because we always hear the other side, right?
We always hear they're all going to lose our jobs and have
robots to do everything.
So I love hearing these stories.
(08:01):
And you develop solutions in retail and health care
and across many different verticals.
What does a building, a scalable AI service, look like today?
And I know you touched on it how it has changed,
but how is it different today than it was before?
Yeah, great question.
(08:22):
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 the desperate.
(08:42):
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.
(09:02):
Now, our clients want help with everything, right?
AI touches everything now.
And they are not coming to us with point problems.
They are coming to us asking for broad help.
They need help putting together their strategy,
their product roadmap, integrate.
Now, Ron, you lead AI solutions across many different
(09:23):
verticals from health care to retail to finance.
What is scaling AI look like today?
And how has that changed five years ago?
Yeah, it's changed dramatically.
When we started Kung Fu AI almost eight years ago,
all of our clients were really early adopters.
(09:45):
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 engine for products, things like that.
And these are areas where machine learning and AI
(10:08):
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 help with product road mapping.
They want help with product selection.
(10:28):
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,
(10:49):
there's quite a bit of math.
It's very data dependent.
And it's almost a dirty secret with an AI
that your data dependent, right?
Your models are only as good as the data you have.
And so understanding how to make the most
(11:10):
in those situations can be the difference between a POC
that demos really well and something that actually
goes into production.
Yeah, I love it.
I 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 an AI is no different.
Now, looking forward and looking in the future,
(11:34):
do you see any emerging trends happening
or anything that people should start paying attention to now?
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Yeah, yes, I do.
I think generative AI is extremely sort of pervasive within
(12:24):
the world at this point.
I think everybody uses chat GBT or cloud or something like that.
And most people are using that for personal 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.
(12:44):
And probably everybody out there has heard of this before.
This is this idea of having AI assistance
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,
(13:09):
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 going
to be incredibly important.
But it's not really ready.
It's not firing for yet yet.
(13:30):
But this is the amazing thing about them.
A paper came out just a couple 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
(13:53):
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 going to have agentic systems
where we can give them enormous responsibilities
like planning an entire vacation, hotels, restaurants,
(14:15):
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
(14:36):
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 going to have, as I said, just complete automation
(14:58):
at human level tasks in the near term.
And what these models can do is handle ambiguity just
like humans.
It's just going to be amazing.
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 mention that.
I look forward to the day that I'm fully able to outflow
(15:22):
to all the bookings to somebody else.
All the annoying legwork that you have to do.
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.
(15:43):
And 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
(16:04):
that AI-powered tools can help organizations thrive?
I'm really a big believer that this coming wave of AI
automation is going to take away jobs in the long term.
And I'm not going to pretend that's not the case.
(16:26):
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.
(16:46):
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 an open source access to all of this decades of code.
(17:10):
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
(17:31):
where there's sort of more proprietary or more sort of narrow.
And I'd love to come back to 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
that GPPT for anything.
(17:52):
They think maybe AI is overhyped.
It is incredibly powerful at not just acting
as in 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?
To go and analyze and put together reports.
(18:13):
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 internal tooling is to leverage proprietary data
that you have within your enterprise that acts as an enabler
(18:36):
for either cost reduction or new capabilities.
I juxtapose this against doing something like going
and spending a bunch of time and money to build a call center
solution where that is, you're thinking to yourself,
oh, while we reduce my call center costs,
(18:57):
that's not a competitive advantage.
That's not a good use of your time of money.
Go buy and 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 modes
for your business and that is the best way to leverage AI.
(19:18):
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,
the horses, they just had different jobs,
people that were driving, they buggy,
and they were just they were displaced in the different places.
(19:39):
And so I don't, I'm not one of those people
that subscribe that we're all gonna be doomed
and 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.
I totally agree.
I totally agree.
And I always think about when the camera was invented,
(20:01):
a lot of people thought that was the end of painting, right?
Why would anybody want to 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.
(20:22):
No, it just fosters and whole new medium within art.
And I think that, I think, you know,
as humans, we just get kind of fixated on what we're used to
and we fixate maybe on the loss and not the opportunities.
And I think it's probably pretty clear,
extremely optimistic about the wave of prosperity
(20:46):
that's gonna be coming behind this wave of AI.
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 gonna close our minds to AI
and I often tell them that it's,
don't be feared from the technology,
(21:07):
put policies and practices in place to enable them
'cause 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.
I feel that they'll be so far behind
that they're not gonna be able to catch up.
I'd love to get any of your thoughts on that.
(21:30):
I completely agree.
And I understand, you know, you're running a business,
that is complicated and everything gets so hyped, right?
You know, and I've had people ask me,
what's the difference between crypto, currency and AI,
right, they're both just scams.
I'm sympathetic to that perspective.
(21:51):
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
(22:12):
and I think about the personal computer,
we can talk about the internet,
we can talk about mobile,
and now I really believe AI.
And I would admit, I'm as much as anybody,
I stand again personally by proselytizing AI,
(22:33):
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.
(22:55):
Those semiconductors, though, couldn't build the next generation.
We're 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
(23:16):
that is unprecedented within technology.
And to me, the proofs 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.
(23:37):
Don't put your head in the sand.
There's that old saying that people say,
like, AI, it may not take your job,
but somebody using AI will take your job.
I like that one better.
I think that's more, it's interesting.
I have this discussion a lot where I feel like now,
where we need to be Swiss army and knives.
(23:59):
As workers, we can't just be a one thing.
Like, people were just farmers and they were just,
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 a year ago is totally different today.
And before we know it, 2026 is going to be here.
(24:22):
And I love 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
(24:44):
based on using AI.
So I believe we could do great things,
but there are those that don't.
So how do you guys come from AI?
How do you and your team, not you guys, you and your team?
And sure that AI is both powerful and responsible.
That is one of those questions that I think most people underestimate
(25:08):
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, AI as we talked about earlier today is predominantly based upon
(25:31):
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 valuation pricing for homes.
(25:54):
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
(26:18):
and propagated that bias in its predictions
and the users of that model would not be able to set there and say,
"Oh, it's the AI made the decision, it's unbiased."
You know, whatever it says is accurate because you literally taught the model to be bias.
And the same thing, earlier I mentioned the the breast cancer risk prediction model.
(26:41):
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
(27:05):
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, focus on predicting risk and don't worry about any of these other
(27:26):
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.
I love that you said punish your AI.
(27:47):
That's great.
And that's the way we thought about it too.
We really did.
I love it. It's not like punishing your husband or your wife.
It's not like your AI.
I love it. I'm going to use it.
I'm going to take that one from you.
Ron said, we did a punish our AI.
Yeah, right.
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
(28:12):
that you want to share with our executive listeners.
Yeah, just a couple things.
I'll always tell them, look, you have to have a cultural
experimentation as you're walking into AI.
These models are deterministic, but they're probabilistic.
They are, there is a level of fuzziness with them
that is very analogous to working with another human being.
(28:33):
Like you're just, 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 do more points.
(28:54):
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 day
on initiative.
Those, those almost never work.
Go get a small win and then you'll get confidence
within the organization.
And then lastly, get biased and focus on
(29:17):
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.
(29:37):
Just focus on the ROI and you will be able to get this into production.
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
(29:58):
and I have to hear it directly from you
on which one is better.
Gemini or chat GPT if you could only have one.
That's funny.
Well, 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?
(30:19):
So on paper, Gemini has better metrics, right?
As these models get more and 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,
(30:40):
OpenAI and Anthropic are all equally incredible.
My personal go too is I end up using chat GPT
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.
Well, our family of girls will love this episode.
(31:04):
And my husband and my son will be very sad to hear this.
But as you say, they could change
and who knows who's going to be next with it.
And I use Gemini all the time for other things,
but my go too right now is chat GPT.
It's interesting.
Now that I mentioned kids, it's amazing how I never used any of this.
(31:25):
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.
They're an AI native generation.
(31:50):
They're just not going to know what it was like to grow up without it.
I just, 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
(32:11):
to prevent students from cheating.
It's crazy.
It's happening a lot at the school level.
Just incredible.
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.
So your kids are using it in my opinion in the perfect way,
(32:34):
right?
Don't use it to, use it to elevate yourself.
Yeah, they're using it really smart, Ron.
They're using it against our parenting.
And, and to solve for problems that we otherwise have
insolved in our house.
So there you have it.
Our kids are using AI.
Now, I want to get kind of,
(32:55):
in closing 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.
Yeah, connect with me.
You shoot me an email.
Very simple, Ron Arlin at kungfu.ai.
We have a podcast.
It's a little bit more on the technical side.
(33:15):
It's called Hidden Layers.
If you, we do episodes on the latest breaking news
with an AI every month.
And then if you, you can follow, you can find me everywhere.
You can find me on X, you know, threads, blue sky.
And we have a website, kungfu.ai.
Check it out, please.
Ron, thank you so much for being here today.
(33:37):
And sharing your knowledge with our listeners.
That's the Executive Connect podcast.
AI is coming for all of our jobs.
(00:01):
There are many, many positive elements to that,
and it's gonna be a very, very long cycle.
This isn't something that's gonna happen more up.
This coming wave of AI automation is going to
take away jobs in the long term.
AI is not magic.
It is literally just software.
Now, our clients want to help with everything.
For example, supply chain optimization, fraud detection,
(00:24):
a recommendation engines for products, things like that.
Your models are only as good as the data you have,
the duration of the task that these agents are capable of
performing is doubling every seven months.
- What do Marshall Arts and AI have in common?
(00:44):
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 AI for two decades.
From launching tech startups to building cutting edge AI solutions
for Fortune 500, Ron's not here to sell hype.
(01:08):
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.
- Thank you so much for having me.
Now you've had the front row seat
and the steering wheel and the evolution
of artificial intelligence for over two decades.
(01:31):
What first drew you to AI?
And how is your journey from the startup founder
and CTO of Kung Fu AI shapes your approach to innovation?
- 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
(01:53):
at the University of Texas at Austin.
And by the end of my program, I was so burned out.
I remember I was limping into my last semester
and I had one elective left
and I remember thinking myself, you know,
I don't know what I'm gonna do when I graduate,
but it is not gonna involve computers
'cause I was just, I was right.
(02:15):
And the course that took was AI 101.
And this is in the 90s.
And I'm not kidding, I think maybe two, three,
or 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 two things.
I thought, "Ah, you know, am I smart enough to actually do this
because this seems like a really, really hard domain."
(02:36):
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.
(02:58):
Like, literally, I graduated and nobody cared.
Everybody was about the internet and mobile and all this stuff.
And 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.
(03:18):
But I'm delighted to say that his had resurgence, I think you could say.
And now I get to do amazing work and work
with amazing clients building just crazy, difficult,
stay-the-art AI solutions, and I couldn't be happier.
Now I would agree with you.
I also did not enjoy computer science and C++ and anything CS
(03:44):
was painful as well for me.
So I agree with you.
Now, how do you 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
(04:04):
and help them make sense of their goals as it relates to AI?
Ready to lead smarter and invest wiser?
On the Executive Connect podcast, we unpack executive strategies
for wealth and influence.
Hit the subscribe button now.
Don't just watch, act.
Yeah, yeah.
Well, one of the first things I always tell businesses is,
(04:27):
AI is not magic.
It 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
(04:47):
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 models output should have been.
And you can give it enough examples
and it can learn to generalize.
And probably the easiest image to hold your head is imagine
you're trying to build a computer vision model
(05:09):
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,
we can look at a picture and instantly tell
that it's a cat or a dog, we don't actually
(05:30):
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
(05:51):
be as little as a few thousand examples.
And it will learn to generalize catness and dogmas.
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.
(06:14):
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.
(06:35):
Most, most medical models in the breast cancer space
look at a mammogram and they detect breast cancer.
They'll look for signs of it.
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.
(06:58):
This is like going from having smoke detectors that go off
when your house is on fire to being able to detect
beforehand that your house may catch on fire.
This is going to save millions of lives.
And this computer vision model works in a way
that we don't totally understand, meaning it's
(07:18):
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.
First of all, wow, I've been reading about this and hearing
about this.
What is exciting that it was-- it's just approved.
(07:40):
And 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.
Because we always hear the other side, right?
We always hear they're all going to lose our jobs and have
robots to do everything.
So I love hearing these stories.
(08:01):
And you develop solutions in retail and health care
and across many different verticals.
What does a building, a scalable AI service, look like today?
And I know you touched on it how it has changed,
but how is it different today than it was before?
Yeah, great question.
(08:22):
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 the desperate.
(08:42):
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.
(09:02):
Now, our clients want help with everything, right?
AI touches everything now.
And they are not coming to us with point problems.
They are coming to us asking for broad help.
They need help putting together their strategy,
their product roadmap, integrate.
Now, Ron, you lead AI solutions across many different
(09:23):
verticals from health care to retail to finance.
What is scaling AI look like today?
And how has that changed five years ago?
Yeah, it's changed dramatically.
When we started Kung Fu AI almost eight years ago,
all of our clients were really early adopters.
(09:45):
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 engine for products, things like that.
And these are areas where machine learning and AI
(10:08):
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 help with product road mapping.
They want help with product selection.
(10:28):
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,
(10:49):
there's quite a bit of math.
It's very data dependent.
And it's almost a dirty secret with an AI
that your data dependent, right?
Your models are only as good as the data you have.
And so understanding how to make the most
(11:10):
in those situations can be the difference between a POC
that demos really well and something that actually
goes into production.
Yeah, I love it.
I 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 an AI is no different.
Now, looking forward and looking in the future,
(11:34):
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?
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Yeah, yes, I do.
I think generative AI is extremely sort of pervasive within
(12:24):
the world at this point.
I think everybody uses chat GBT or cloud or something like that.
And most people are using that for personal 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.
(12:44):
And probably everybody out there has heard of this before.
This is this idea of having AI assistance
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,
(13:09):
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 going
to be incredibly important.
But it's not really ready.
It's not firing for yet yet.
(13:30):
But this is the amazing thing about them.
A paper came out just a couple 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
(13:53):
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 going to have agentic systems
where we can give them enormous responsibilities
like planning an entire vacation, hotels, restaurants,
(14:15):
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
(14:36):
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 going to have, as I said, just complete automation
(14:58):
at human level tasks in the near term.
And what these models can do is handle ambiguity just
like humans.
It's just going to be amazing.
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 mention that.
I look forward to the day that I'm fully able to outflow
(15:22):
to all the bookings to somebody else.
All the annoying legwork that you have to do.
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.
(15:43):
And 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
(16:04):
that AI-powered tools can help organizations thrive?
I'm really a big believer that this coming wave of AI
automation is going to take away jobs in the long term.
And I'm not going to pretend that's not the case.
(16:26):
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.
(16:46):
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 an open source access to all of this decades of code.
(17:10):
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
(17:31):
where there's sort of more proprietary or more sort of narrow.
And I'd love to come back to 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
that GPPT for anything.
(17:52):
They think maybe AI is overhyped.
It is incredibly powerful at not just acting
as in 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?
To go and analyze and put together reports.
(18:13):
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 internal tooling is to leverage proprietary data
that you have within your enterprise that acts as an enabler
(18:36):
for either cost reduction or new capabilities.
I juxtapose this against doing something like going
and spending a bunch of time and money to build a call center
solution where that is, you're thinking to yourself,
oh, while we reduce my call center costs,
(18:57):
that's not a competitive advantage.
That's not a good use of your time of money.
Go buy and 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 modes
for your business and that is the best way to leverage AI.
(19:18):
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,
the horses, they just had different jobs,
people that were driving, they buggy,
and they were just they were displaced in the different places.
(19:39):
And so I don't, I'm not one of those people
that subscribe that we're all gonna be doomed
and 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.
I totally agree.
I totally agree.
And I always think about when the camera was invented,
(20:01):
a lot of people thought that was the end of painting, right?
Why would anybody want to 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.
(20:22):
No, it just fosters and whole new medium within art.
And I think that, I think, you know,
as humans, we just get kind of fixated on what we're used to
and we fixate maybe on the loss and not the opportunities.
And I think it's probably pretty clear,
extremely optimistic about the wave of prosperity
(20:46):
that's gonna be coming behind this wave of AI.
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 gonna close our minds to AI
and I often tell them that it's,
don't be feared from the technology,
(21:07):
put policies and practices in place to enable them
'cause 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.
I feel that they'll be so far behind
that they're not gonna be able to catch up.
I'd love to get any of your thoughts on that.
(21:30):
I completely agree.
And I understand, you know, you're running a business,
that is complicated and everything gets so hyped, right?
You know, and I've had people ask me,
what's the difference between crypto, currency and AI,
right, they're both just scams.
I'm sympathetic to that perspective.
(21:51):
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
(22:12):
and I think about the personal computer,
we can talk about the internet,
we can talk about mobile,
and now I really believe AI.
And I would admit, I'm as much as anybody,
I stand again personally by proselytizing AI,
(22:33):
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.
(22:55):
Those semiconductors, though, couldn't build the next generation.
We're 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
(23:16):
that is unprecedented within technology.
And to me, the proofs 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.
(23:37):
Don't put your head in the sand.
There's that old saying that people say,
like, AI, it may not take your job,
but somebody using AI will take your job.
I like that one better.
I think that's more, it's interesting.
I have this discussion a lot where I feel like now,
where we need to be Swiss army and knives.
(23:59):
As workers, we can't just be a one thing.
Like, people were just farmers and they were just,
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 a year ago is totally different today.
And before we know it, 2026 is going to be here.
(24:22):
And I love 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
(24:44):
based on using AI.
So I believe we could do great things,
but there are those that don't.
So how do you guys come from AI?
How do you and your team, not you guys, you and your team?
And sure that AI is both powerful and responsible.
That is one of those questions that I think most people underestimate
(25:08):
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, AI as we talked about earlier today is predominantly based upon
(25:31):
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 valuation pricing for homes.
(25:54):
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
(26:18):
and propagated that bias in its predictions
and the users of that model would not be able to set there and say,
"Oh, it's the AI made the decision, it's unbiased."
You know, whatever it says is accurate because you literally taught the model to be bias.
And the same thing, earlier I mentioned the the breast cancer risk prediction model.
(26:41):
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
(27:05):
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, focus on predicting risk and don't worry about any of these other
(27:26):
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.
I love that you said punish your AI.
(27:47):
That's great.
And that's the way we thought about it too.
We really did.
I love it. It's not like punishing your husband or your wife.
It's not like your AI.
I love it. I'm going to use it.
I'm going to take that one from you.
Ron said, we did a punish our AI.
Yeah, right.
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
(28:12):
that you want to share with our executive listeners.
Yeah, just a couple things.
I'll always tell them, look, you have to have a cultural
experimentation as you're walking into AI.
These models are deterministic, but they're probabilistic.
They are, there is a level of fuzziness with them
that is very analogous to working with another human being.
(28:33):
Like you're just, 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 do more points.
(28:54):
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 day
on initiative.
Those, those almost never work.
Go get a small win and then you'll get confidence
within the organization.
And then lastly, get biased and focus on
(29:17):
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.
(29:37):
Just focus on the ROI and you will be able to get this into production.
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
(29:58):
and I have to hear it directly from you
on which one is better.
Gemini or chat GPT if you could only have one.
That's funny.
Well, 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?
(30:19):
So on paper, Gemini has better metrics, right?
As these models get more and 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,
(30:40):
OpenAI and Anthropic are all equally incredible.
My personal go too is I end up using chat GPT
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.
Well, our family of girls will love this episode.
(31:04):
And my husband and my son will be very sad to hear this.
But as you say, they could change
and who knows who's going to be next with it.
And I use Gemini all the time for other things,
but my go too right now is chat GPT.
It's interesting.
Now that I mentioned kids, it's amazing how I never used any of this.
(31:25):
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.
They're an AI native generation.
(31:50):
They're just not going to know what it was like to grow up without it.
I just, 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
(32:11):
to prevent students from cheating.
It's crazy.
It's happening a lot at the school level.
Just incredible.
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.
So your kids are using it in my opinion in the perfect way,
(32:34):
right?
Don't use it to, use it to elevate yourself.
Yeah, they're using it really smart, Ron.
They're using it against our parenting.
And, and to solve for problems that we otherwise have
insolved in our house.
So there you have it.
Our kids are using AI.
Now, I want to get kind of,
(32:55):
in closing 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.
Yeah, connect with me.
You shoot me an email.
Very simple, Ron Arlin at kungfu.ai.
We have a podcast.
It's a little bit more on the technical side.
(33:15):
It's called Hidden Layers.
If you, we do episodes on the latest breaking news
with an AI every month.
And then if you, you can follow, you can find me everywhere.
You can find me on X, you know, threads, blue sky.
And we have a website, kungfu.ai.
Check it out, please.
Ron, thank you so much for being here today.
(33:37):
And sharing your knowledge with our listeners.
That's the Executive Connect podcast.
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