Join us as we gather around the hedge,
where we dig into technology,
business, and culture with the finest minds in
computer networking.
Hello, Tom.
Hello, Russ.
Again.
We
recorded
an episode earlier today. So so now here
we are in the afternoon,
talking again, and we won't talk about your
plant. I mean, it looks the same, and
we won't talk about your weird ladder thing,
but I will have you read any more
of the books in your background?
In the last five hours now. Yeah.
Slowpoke.
I know. What is up with you? You're
not like come on. You should be able
to read two or three books in five
hours.
I know. You're you're disappointing me, Tom. Alright.
And today, we are joined by Jonathan Mast.
Jonathan, where are you physically? What city, state,
whatever?
I am down in Foley, Alabama, about 10
miles off the Gulf Coast.
Awesome. So I have yeah. I've thought about
moving
to the Gulf Coast in Florida a couple
of times, never did. Ended up moving to
Knoxville.
I like mountains. What can I say? I
like being near the mountains and Beautiful area.
Yeah. Up in the mountains. I keep talking
about we're trying to build a house actually
on in the foothills of the Smokies
on the West Side Of The Smokies. We
we're trying to get a house built out
there,
which is always
today in today's world,
Oh, you wanted two by fours?
Really? Well, good luck with that.
That's pretty much where we are today.
So, Jonathan, let's talk about AI because because
as you said when we were talking before
we started recording,
a lot of people are really well, let's
even not go their start. Let's start with
what's the status. Like, what are we seeing
today?
What's interesting?
What
is going on in the world
of and I should say large language models
specifically because there are other kinds of AI,
and people get them all mixed up, AI
machine learning, large language models.
They're all kinda like subset, supersets of one
another.
So let's let's start there. Maybe that that's
a better place to start.
Well, I I think, you know, we're gonna
continue to see the an incredibly rapid pace
of development in 2025.
There's so much going on.
The probably the biggest thing in the large
language models in the last few weeks has
been the introduction of deep research models.
The ability for instead of just doing a
search and coming back like Google does with
some sites for you to look at or
like perplexity used to where they would look
at those sites for you and give you
an answer,
Now, there's some deep research tools and most
of the major players have them.
And it's really equivalent to like having an
assistant and saying, I need you to spend
the day
looking at these twenty, thirty, 40 websites
and compiling data and putting it all together.
The difference is, is that, you know, ChatGPT
and tools like that are doing that in
five to ten, maybe on the outside fifteen
minutes.
And you're getting back, anywhere from a five
to 20 page report
about all that information.
It's just truly something I don't think we've
ever had the ability to do before, and
I'm sure it's one of many things like
that we're gonna see this year.
Interesting. So if that's a new development,
so what has been happening
as far as LLMs go? Are we seeing
a lot of people using LLMs in their
daily work?
Is that, like, a thing?
The overall, I think adoption rate is still
pretty low. I think it's definitely improving.
But at the end of the year, when
the last stats that I saw and again,
we we know that most stats are made
up, mine are not, but I don't know
if the ones I'm referring to are made
up or not because I I don't have
all the documentation.
But the estimate was that about 15 to
17%
of people in the workforce are actively using
on a regular basis
these LLM tools in their work.
And they defined that at the time by
using it more than like three times a
day. So the real answer is, you know,
I think people are probably playing
with LLMs at this point. They're trying some
things out. But I think it's still a
very small percentage of the population
that's actually leveraging them as tools on a
regular basis.
Interesting. Yeah. So three times a day and
you think people are exploring.
I know for me personally, I use LLMs
more the way you're talking about
to
find things quickly
and not even necessarily to summarize 15 or
20 pages, but I usually have very specific
questions like
find me every or find me
everything
about
a particular protocol
Yeah. In these in these five websites
or whatever it is.
Not not that I want the LLM to
actually
do the work
of summarizing for me. I wanted to find
the places where I should look. And I'll
tell you why I use it that way.
You might have something to say about this,
is that I fear a lot with LLMs
that they are in their summarization process,
they are leaving out
things I would consider it to be important.
And this is this is not LLMs, by
the way. This is
any time I get into summaries, anytime I'm
reading a research paper or something and somebody
says
or or the research paper says, as quoted
in or this person makes that argument,
I'm the kind of person who goes back
and says, no. No. I don't believe you.
I'm gonna go find Sure. The original
document
and see what that author said
for themselves.
I I I I'm I'm thankful that you
summarized for me, but I I'm not gonna
trust that your spin
or your take is the same as what
I read into it or I read out
of it.
So I I don't know. There's there's something
around that that we could probably talk about
that might be interesting.
Oh, absolutely. I think, you know, your your
concern there is very well founded because AI
and large language models, in this case, have
been trained on very specific data.
And they're they're taught generically to reply quite
generically to things. And so,
one of the things I would encourage you
to do as you do this, Russ, and
you're probably already doing some of it, but
is to
train train the model that you're using on
the type of responses that you want
and the type of information that you're looking
for so that it can do that. Because
it's a great data analysis model. It's fantastic
at at taking a look at, you know,
reams of data and and analyzing that. But
typically, what we don't do as as users
is we rarely
take the time to go in and say,
alright, here's who I am as an individual.
Here's what I do. Here's where my passions
are. Here's where my interests lie. By the
way, here are my preferences for communication style.
And when we don't do that, it's kinda
like anything else. We get something quite generic
back. And oftentimes, when we do that, we
look at it and we're like, well, that
was unimpressive
because it was.
But if we can take that step to
train the large language models on who we
are, then I really find that they get
a whole new sense of life and value
because they're able to save us a lot
of time by oftentimes finding the relevant stuff
to us so that then I can spend
my time digging into those sections that are
truly relevant to what I wanna learn.
Yeah. And and I think they're also
what I do
think that I find useful sometimes in using
an LLM
is that it also helps me understand
if I'm in a position where I'm arguing
something. Right? Mhmm. Where I'm when I where
I have an opponent per se. Not necessarily.
You know, this sounds very
military or battle, but it's not it's more
like
we should use MPLS. We should use SRV
six. Whatever the question is,
I want to understand the other perspective
because I just want to be able not
only to counter it effectively, but I wanna
make sure I'm right.
I I don't I don't want just my
perspective.
So one thing I also fear about LLMs
is I'm gonna use one enough
to train it so well that it tells
me what I want to hear.
And I'm like, I actually don't want that.
Well and and that's a very legitimate fear.
A AI models, large language models are generally
taught to be pleasant towards you. And,
you know, we've all read these studies that
I got the LLM to admit that it's
x, y, or
z. Well, the problem is with any of
those, if you push hard enough because it
is designed to be agreeable, at some point,
it will decide it's not worth arguing with
you, not that it's sentient, but it's essentially
not worth it. It's not going to win.
And it'll go, okay, you're right. I'm a
Nazi
Because you kept asking it if it was.
And it just finally said, Yes, I'm a
Nazi.
And that's a danger because
not that it happens, but that people aren't
aware that it can happen.
And therefore, as a result of that, I
think that we, as users, just need to
be more informed
and understand that, you know, if I wanted
to admit that it's a pink elephant,
I can essentially berate it to the point
that it gives up and says, you're right,
Jonathan. I'm a pink elephant.
Yeah. Yeah. This is by the way, this
is not just true of LLMs. We've put
a lot of this on LLMs
and AI stuff. This is true of almost
any text. There's a saying in the philosophical
world that I can if I torture a
text long enough, I can I can get
it to confess to anything I want it
to? And that's just an absolute I can
read Plato.
I can read I don't know. I can
read Calvin. I can read
Aristotle or or Augustine,
and I can make them so I can
find things and put things together
to make them say something
I want them to say to support my
point of view. And that's just a fact
of exegesis
and the way we approach a text.
So when I approach text, I always approach
text from what I call the virtue hermeneutic,
which I don't know if that's gonna, like,
freak everybody out, but that's that's what it
is. I'm I'm a big Aristotelian
virtue ethic person. That's what I'm really strong
into, virtue ethics.
And so when I approach a text, I
approach
it from what I consider to be a
virtue hermeneutic.
I'm trying to understand what the author is
saying. I'm not trying to understand what I
want to say. I'm not trying to
get support for my argument.
I'm trying to find what did Aristotle
actually mean to say
at this point on this topic.
I'm seeking Aristotle's
perspective, not mine.
So I think
we need to find discipline. And by the
way, a lot of readers don't have that
discipline.
A ton Oh, I agree. Tons of readers
don't have that discipline.
And we need to come to the point
where we have that discipline with LLMs.
Perhaps that's part of
we need to seek data or seek information
rather than seeking support
as it were.
Well, I I think you've again, you've stumbled
on to some probably not stumbled. That's not
fair to say, Russ. But you you're pointing
out something that's so accurate. Most of us
are are looking for validation.
We're not really looking for truth, and I
think that's a much larger
discussion that we have. But, yeah, I I
would agree though that the the average person,
if that average person exists,
is definitely looking for that validation.
We're not looking to be challenged. In fact,
it's one of the things that I've been
teaching in a lot of my masterminds recently
that is, in my mind, is incredibly enjoyable,
which is telling the LLM,
do not be agreeable with me for the
sake of agreeing.
Challenge me, push me,
make me better,
help me
improve my perspectives,
don't agree with me for the sake of
doing it. And it really does change the
way When you instruct it to do that,
it really does change how it responds.
It doesn't become rude,
but it it becomes almost a sparring partner,
a verbal sparring partner. And in personally, I
find that a lot more enjoyable. Yeah.
So is there any value in doing something
like having multiple accounts
that you
have tuned
to believe in two different I I don't
know. This is like a thing I've thought
about. I've never done it. Is there any
value in having multiple accounts that say,
you know, I believe x
strongly, and I want you to support that
over time. Or the other account, you say,
I believe y strongly, and I want so
I actually have the two perspectives coming at
me
from the two different account models. I I
don't know if that works because I'm afraid
there's still
you still have
you still have the forgotten middle.
You're still leaving the world. It's an interesting
concept. I think, you know, ChatCPT
is really the only large language model currently
that purports to have memory.
And I admit I have mine turned off
because I do lots of client work, and
I don't want client work filtering across clients.
So I I've turned that off.
My choice, not something NSA recommend, but something
that I've chosen to do.
I find that because I don't have memory
on, I can really get a fairly neutral
position with AI starting as a starting point
from any place I want. And that is
as simple as starting a new conversation because
it doesn't remember things from one conversation to
the other.
It's kinda like texting. If if you and
I are texting together and I'm texting my
wife, you're not gonna see the text messages
to my wife and she's not gonna see
the text messages to you because they're in
different conversations.
And when you don't have memory enabled, I
find that that works really well and it
does not remember,
really anything that's going on as a result
of that. So you're kinda starting off in
that natural or neutral
middle.
So we've we've talked to some about some
good points about philosophy and stuff. What Jonathan,
what in the people that you've worked with,
have you noticed any trends? Like, what are
the types of things that people are most
commonly using the tools for right now?
Well, again, I think it's a relatively small
percentage of people that are. Those that are
actively using them, I find one of the
most common things they're using it for is
literally for, like, decision trees and brain storming
and, you know, challenging ideas that,
that type of thing. I'd say the second
most common thing I see is something as
simple as writing emails. It is really good
at helping out with communication,
whether that's creating an SOP, whether that's writing
an email.
Regardless of our role in an organization, I
think we've all had those situations when we
need to write an email that we maybe
need to establish some boundaries in or we
need to be able to respond
in a way that isn't the way we
emotionally want to respond to things.
Or maybe, you know, I can think of
my son, maybe we need to be a
bit more emotional than we are in in
that way. And AI does a really good
job of helping do that very, very quickly.
While it's not emotionally intelligent, it can mimic
people and things very, very quickly. And so
I think that's those are probably some of
the the biggest things. The other things that
at least, you know, I see from the
people that I work with that are super
popular,
people love creating music with AI, and they
love creating images, and they love creating videos.
Those are all very fun things, so to
speak, to do with AI. And as we
look at the general public, I think those
are probably some of the biggest areas.
Again, a lot of it depends on the
individual and what they're interested in. But if
we were if we were going to
take a guess on the entire population, I'd
say those are some of the most common
and most popular.
So
in technology,
I wouldn't think well, maybe it is. Actually,
maybe a really good use that I've never
tried it for
is building
graphics
of ideas.
Like, I have
these
20 data points I've measured or these 5,000
data points. Please
draw me draw me
a,
you know,
a visual of this data.
Because because, you know, you play with line
charts and graph charts and
bar charts and blah dee da dee da,
but you don't ever really know
what
is the right thing
to use.
Is that another place? Because I've never tried
it. Have you ever tried it? Does it
does it work? Is it, like, something that
would be useful? I I mean yeah. I
think, you know, AI
visualizing
data, I think, is is a just a
tremendous opportunity
with inside of AI to be able to
do some just amazing things and come up
with ideas
and likely connections that we haven't thought of
just because
we maybe haven't visualized the data in the
same capacity.
Yeah. I but but yeah. Go ahead. I
think it you do have to treat it
though as a very raw
input. Like I was I was doing some
home automation stuff the other day and I
was,
I was using chat gpt and I asked
some question about a circuit I was working
on and it said, would you like me
to draw a circuit diagram? I'm like, oh,
sure. I wonder what'll happen. And, I was
expecting something and got something completely different. It
looked like it was trying to draw,
like, illustrations of the components, not like a
circuit diagram.
And also, it was hilarious. I should have
saved it. It was hilariously bad. Like, there
was a picture of a switch, and I
was like, what is this mangled thing? It
was like sort of the equivalent, you know,
when you see people's hands in AI and
there's like six fingers, how, like, disturbing that
is. It was like that except for an
electronic component, which, you know, very entertaining, but
did not give me any help at all.
Like, I will draw this on a piece
of paper. You don't know what you're talking
about.
But so so I wonder, like, you know,
if you could try to get it to
plot data,
what happens there? I I also have never
tried it. If it it's an interesting experiment.
If we haven't tried it, maybe we should
all go
try giving it giving it data and see
what it does because, I mean, I don't
know. Maybe it doesn't maybe not enough people
have tried to train it
because it because it does rely on people
to train it. So yeah. Very Yes. It's
not something that I've tried. I've run into
what you've mentioned before where what it came
up with is akin to a a hand
with 12 fingers.
Definitely, I think that's an area that we'll
see improvement on over the next couple years.
Yeah. Very, very interesting. So okay. So,
again, going back to what people are using
it for,
again, for me, it's much more about
quickly finding data,
finding contrary views, finding places to go search
for stuff.
And you're saying that people use it a
lot for doing emails,
for emotional,
to make sure the emotional quality of the
email is good. I mean, I can actually
see that from a tone perspective.
Actually, Grammarly does this. If you have Grammarly
on your system, it does this kind of
I don't know if it's better or worse
than chat g p t edit, but it'll
say, if you're really gonna send this to
your manager, you might wanna look for another
job.
Interesting.
You know? It doesn't literally say, but that
but that's kind of like,
your tone is really not good here.
Please try again.
You know, or suggestions for tone changing
type of things.
So I think that is an interesting place.
Are you seeing a lot of coders use
it? Of course, that's the critical
killer question everybody wants to know,
is AI
deeply being deeply used for
coding?
Is that something you see there? Circles that
I yeah. In the circles I'm in, yes.
I'm seeing it a lot,
because
it can do a lot of the mundane
aspects of programming really quickly.
There are certainly people that, you know, if
you're not a programmer, I don't think that
AI is going to make you a programmer.
But if you are a programmer, I believe
that AI can amplify that skill and experience
and can really do some amazing things.
So I think that's probably the the thing
that I'm seeing. I'm seeing
people that are already proficient at a task
that have skills
are leveraging it really well. People without skills
are are playing around with things and maybe
doing something cute,
but rarely incredibly useful.
So so that brings me to my next
question, which is really where we started this.
Didn't really start this, but where we started
emailing back and forth on this is what
is the impact on jobs?
Is it making people more efficient to the
point
that we need fewer people to do certain
things?
Is it like, what is the interaction here?
And I have one very specific question, but
I wanna get your reaction or the your
thoughts on that first.
More jobs, less jobs,
causing lots of problems, like, what is going
on with that that part of the whole
AI equation?
I don't think at the moment we're seeing
much impact. There are definitely some new stories
that are making it. And I think, ultimately,
like any new transformational
technology, we're going to see some people that
are displaced,
but they're gonna be displaced not by the
technology so much as they're gonna be displaced
by other people using it. So let's take
a look at coders as an example.
Will there be some coders that lose their
job? Absolutely.
They're not gonna be the expert coders or
even the proficient coders.
They're gonna be likely entry level coders that
now AI can do a lot of that
entry level work. And so they're they're simply
not going to be needed if they can't
learn
how to use that and leverage that effectively.
So I think ultimately though, you know, as
with any technology that we've seen, we're going
to see more opportunities
exist than what did prior to this. You
know, we can look even in the computing
world.
I'm old enough to remember that when I
was getting started in the workforce, PCs were
being put on desks at that point in
time. And there were plenty of people like,
I'm not giving up my typewriter. I'm not
giving up my adding machine. I mean, my
abacus said, Just, we're not going to do
that. And for a few years, that was
okay.
But after a bit of time, what happened
was that
those people had to
change
or they were going to lose their job
to somebody else because
that typist that was doing a hundred words
a minute on the mechanical typewriter could do
120 to 130 words a minute on a
word processor.
That typist that was doing 50 words might
have been able to do 80 words on
a word processor.
And so it closed the gap for a
period of time. But then when those that
were proficient embraced it, again, things took off
and worked super well for everybody.
Interesting.
So I guess another question I have here
is, is there any fear that it's knocking
the bottom rungs out of the job market?
Like, new coders come in,
and
there's really
I worry sometimes that there's not a place
for new coders to start
because all the basic stuff is being done
by AI.
Is that something that is
problematic? Is that something anybody's thought about, or
is that, like,
totally unfounded?
I think we're gonna see a difference. I
think it's a very valid point, but I
think we're gonna see a difference in what
that entry level position looks like.
In in you know, I mean, look at
it in from a network engineering
perspective. I mean, we we have
various levels of proficiency and skill and all
that, but there are still people doing lots
of different jobs. And and here's, I guess,
where the key is to me.
I think that we're going to find that
you're going to have new jobs that didn't
exist and new opportunities that didn't exist
prior.
So in the programming world, are are there
gonna be some jobs lost
100%?
There will be. Absolutely.
Do I believe at the end of the
day that most programmers will be displaced? No.
I don't. I think it'll probably follow a
pretty typical eighty twenty die you know, paradigm
that 80 of the the coders are gonna
keep their jobs and 20% are probably gonna
lose their jobs.
But I do think that initial, that entry
level position
is absolutely gonna change as far as responsibilities
and skills needed to get into that. Because
the new the new up and comers in
that field that are gonna become newbies there,
they're gonna be learning with AI tools to
start with.
And hopefully, they don't treat the AI as
a crutch.
Well, if they do, they're not gonna be
able to very long because those that those
that are excellent at it will be using
it as a tool
instead of a crutch.
And those that use it as a crutch
are just gonna, in my opinion, at least,
are gonna get washed out.
Okay.
That's interesting. Because this also goes to what
I call the more the the moral hazard.
Right? You give people seat belts, they drive
faster
because now they feel safe. Right? And so
there's always a moral hazard. You give people
airbags, they drive even faster.
So there's always a moral hazard here that
when you give people tools,
they shape their lives to the tools. And
and I I don't wanna be like,
they take risk or they become lazy.
And And and there is no doubt some
people will do that, and those will be
the people in my mind that are gonna
be displaced
because that's
you know, I I I will compare it
to just recently. Our neighbors had a new
roof put on.
And I'm not a roofer. I don't know
a lot about it, but I remember watching
roofs go on decades ago.
And, you know, it took men to pick
up those shingles and would carry them up
on, and they had nails and hammers, and
they would go back and forth. And it
was an arduous process.
My neighbor who got a roof put on
didn't have to carry and his contractors didn't
carry any shingles to the roof. They were
literally lifted up there by a high low.
They were essentially deposited on the roof.
They didn't take nails and have to pound
them in one at a time. They had
pneumatic cameras, and it was pop pop pop
pop pop pop. In fact, they put an
entire roof on this house. What would have
probably taken a week when I was growing
up, they did it in an afternoon.
So
have there been roofers that have been displaced
because of the pneumatic hammer?
Probably.
But that roofer that did a rotten job
with a hammer still does a rotten job
with a pneumatic nailer.
Yeah. And and there because there's still a
skill associated with that. And I think we're
gonna see that same thing in in virtually
every occupation as it relates to AI.
Will there be some people that try to
take shortcuts and win? Absolutely. We're human. There's
that's going to happen.
Just like there's people that'll like you said,
because I have seat belts and an airbag,
I can drive I can drive faster and
and and push the limits.
And then there'll be people like me that
drive a minivan and go, okay. You're gonna
crash because you just did that.
So, you know, I think it it goes
both ways. And I think what we're going
to see, as we've seen with so many
technologies, is that
society will continue to evolve. And ultimately, we're
going to create a whole level of jobs
that didn't exist two years ago
that two years or ten years from now
will exist and will become
really important jobs
because of the fact that they're needed. You
know, it's, I can remember being in the
IT world.
Thirty years ago is probably not long enough.
But, you know, there was a time when
Cisco routers were not something that people understood
and did because there weren't that many networks.
It just didn't didn't exist like it does
today,
nowadays. And while I'm out of that world,
so I hope it's still Cisco. But, you
know, it's
now that's commonplace and understanding those technologies is
commonplace.
So I think we're just gonna see an
evolution of jobs as as we move through
this.
Well, I would say what's happened in the
network world is we've gone from understanding
routers
to understanding how to configure routers.
Oh, interesting.
Yeah. I I
And I And really where we should be
going and maybe AI can take us there.
The LMS can take us there where we
should be going is going from understanding routers
to understanding networks
and understanding completely assembled systems.
And I wonder if
maybe we can reverse the the negative trend
that you see for us if there's a
way to use these tools in that way.
Yeah. It would be interesting to figure out
a way to target AI tools at,
a way to build an LLM or something
that's trained specifically
to get when you ask for configuration,
oh, you say, what is it you're trying
to do? What problem are you trying to
solve?
Right?
Rather than handing you the configuration,
you know. When I was working at Cisco,
we had a bad habit of putting,
special images in the CCIE lab. I shouldn't
tell all these secrets.
But when you would when you would type
in a question mark, you know, to figure
out what it was, it would just come
back with what? It wouldn't, like, give you
any prompting.
Cruel. You are cruel.
Because I guess the point was, you know,
if you're on the CCIE exam,
you really shouldn't need I mean, I say
that, but then we always used to have
long list of things we could talk about
in the tech
because people would ask us questions, and we'd
be like,
so, how's the weather there while you're looking
up the command structure because you can't remember
every command in iOS
type of thing. And then and then you
take it away from the CCIE candidates. Nice.
Nice.
Yes. That's that's that's, you know, the world
I lived in.
It's terrible, but that's the world I lived
in. So I do I do wonder if
there's a way to structure AI. And see,
I consider
the movement from understanding networks to understanding configuration
to be a product of moral hazard.
Things work well enough now that we don't
have to understand the network.
People can just configure things, and it works,
And they don't need to know how it
works or why.
And I wonder if there's a way to
structure AI
tools
in a way that reverses that, like Tom
said. I I don't know that there is,
but it seems like
something worth exploring.
I think it'd be very interesting, and I
think, again, it's gonna largely depend on the
individual at that point in time. You know,
there's always individuals, at least in in my
perception, and I certainly could be mistaken.
There's always that individual that's looking for the
shortcut,
and then there's the individual that's looking to
understand and comprehend. And I think AI is
going to
facilitate some shortcuts that certain users want, but
it's also gonna facilitate the opportunity
for that network engineer or anybody else that
wants to dive deeper into a topic and
really get behind the scenes and get to
the why of what's happening.
They're gonna allow them to do that faster
than they can today and likely provide them
some some assistance and guidance as they as
they walk down that path.
Yeah. It's interesting. I I I certainly hope
so. I hope that's correct.
So I actually don't have any other questions
than that. I've kind of run out of
my questions.
Maybe if you want to, Jonathan, talk to
us about
what it is you're doing with AI
and just, like, where you think the future
is going so we kinda, like, wrap up
and think about, you know, what what's gonna
happen here?
My passion with AI is that I truly
believe that at my core
that AI amplifies
skill and experience.
And as such,
I wanna really help those with skill and
experience
learn how to leverage AI in a way
that creates
efficiencies,
that provides more flexibility through time savings,
and hopefully at the end of the day,
because ultimately what we do with in one
capacity or another is serving others, that it
allows us to bring more value to to
those as well.
Do I think we're gonna go to George
Jetson style where people could get finger cramps
because they've been pressing a button all day
long? Not in my lifetime. I don't think
we're gonna see that.
Might we see that at some point in
the future? Who knows?
But I really believe that AI is going
to be a just a fundamental
shift
in how we do things, and it's going
to allow those that wanna skate by to
skate by by a little bit easier.
But as in anything else in life, I
think it's going to allow those of us
who want to become experts in a topic
to become experts
at an even greater level and a greater
depth
than maybe we've ever had the possibilities in
the past. So getting back to me, I
I wanna teach
the teams, the individuals that not not just
the companies, but their teams, their employees,
how they can leverage AI so that at
the end of the day, they have a
better work experience
and a better deliverable for their their ultimate,
whatever that's a product or service. So so
you're actually doing courses in
how to use AI, but it's on-site with
Teams right now. Right? It's not like a
generic class that you're teaching.
Or is it? I'm I'm asking, like, what
format is this? No. That's a good question.
So pretty much everything I do now is
I do a lot of public speaking, and
a lot of that's in person.
But other than that, the courses and stuff
that I do, they're almost all done via
Zoom and are typically topic based where we're
teaching a specific skill set
to a specific group or a group of
individuals regarding something that fits. So, you know,
that ranges anything from,
one of the large national profit nonprofits that
I work for where we're working with each
department to help them kind of address three
their top three goals in that department and
how to leverage AI to help them achieve
those goals.
Two, working with the local small business, you
know, chiropractor in your town,
helping him figure out or her figure out
how they can leverage AI
inside of their business to,
again, provide more value to the community that
they're trying to serve. Okay. That's really cool.
That's interesting.
Alright. I don't have any other questions. Anything
from you, Tom?
No. No. I'm good. Thanks for coming on,
Jonathan. It's been great. Yeah. And anything else
you want to talk about, Jonathan? Like, you
wanna say I mean, like,
you know, famous last words before the
if I have any famous last words at
all, I think AI is gonna present more
opportunities
than it does anything else. And I think
it's there for those that are ready to
embrace it like any new technology. So I'm
excited about the future. Alright. Cool. Awesome.
Okay.
So, Jonathan, where can people follow you, catch
up with you? I assume you blog. I
assume you're on LinkedIn, etcetera, etcetera.
Yeah. The best way is just if you
remember my name, it's Jonathan Mast, m a
s t. If you go to jonathanmast.com/linktree,
I've got all my contact bits there, tons
of free resources.
Just, you know, that's the best way to
connect with me and reach out, and you'll
see everything from Facebook, LinkedIn, Twitter, email, phone,
you name it. Whatever is the favorite technology,
There's a way to reach out, and I'd
love to add value if I can. Alright.
Awesome. Tom,
just say it. LinkedIn. Okay.
You have to say it again.
LinkedIn.
Tom only does LinkedIn, so we have to
give him a hard time about it.
Okay. So I'm Russ White. You can always
find me here on the hedge at rule
eleven dot tech. On LinkedIn,
I do occasionally log in to x, me,
Twitter, whatever you wanna call it today. I'm
pretty easy to find. Other than that, we
know that we live in an attention driven
economy. We thank you for listening all the
way to the very bitter end of this
episode of The Hedge,
and we will catch you next time.