Technology breakthroughs that are similar to AI,
and the example was like the iPhone.
Can you think of what is given
this similar excitement in the world that you remember?
I've got two actually that I've thought about.
The first one being, I would say,
the James Webb telescope,
I think was a huge breakthrough.
Being able to see infrared light in space at
a mass spectrum I think is absolutely huge.
I think I got a lot of publicity around it.
I mean, it was a 25-year project,
so I would hope so.
But that, and then I would also say,
we just broke through fusion reactions.
In December of last year,
the first successful fusion reaction was achieved in the lab,
which is basically combining two hydrogen molecules into
a helium and then producing a net positive of energy.
Sorry to nerd out about it,
but I'm a science lover.
I think that was a huge breakthrough,
so I've been following that pretty closely.
That's awesome. I'm sitting here going,
that is way more technical in my brain than I was like,
fun technology, but I love that.
Now I feel like I learned something.
Yeah. I guess put it into spectrum.
It's basically what it would be used for is clean energy,
replacing fossil fuels and such to gain a net positive of
energy and replace our basically nuclear reaction,
and reactors and stuff like that.
I don't know if it's nuclear,
but basically cleaner energy,
I would say fossil fuels.
Wow. Yeah, fossil fuels.
It's crazy. Kelsey, do you think you can tap that?
No. Am I writing very strong off of the coattails of iPhone?
Yes, only because I love my iPad Pro.
Yes, I had a first gen iPad back in the day,
but what that did with the pencil with
Procreate for digital art because I could never afford
or a big tablet and a PC setup and I needed
something that I could take in my studio apartment that fit.
So having that iPad Pro with the pencil,
still the best technology I've ever personally used to go.
That's still my favorite.
If I couldn't have anything else, I'd want that.
I agree with that.
Having the iPad Pro with the pencil in
college was an absolute lifesaver to be able to write on the slides.
Huge. I had paper in a pencil.
It was right.
Is that your breakthrough? Is that what you're sharing today?
Paper in a pencil.
It's beating them until my joke,
which was going to be slate and chisel.
I had a sociology professor that didn't allow computers in the school,
provided tablets and so I was using the tablet.
I know I was handwriting notes and he still was calling it out,
going no technology at all.
It's the same as a piece of paper if I'm writing,
I can't writing notes, but this one.
I'm going to write August's science background,
but this is now four or five months now,
but Princeton University,
they just announced that they developed a camera,
the size of a grain of rice.
It's super cool because they take
200,000 micro cones and it's all about the size and the shape and
the direction that all these cones are being pointed.
Now they're able to produce
compared to the last version,
super high resolution images,
but where that could come in super helpful in the medical industry
is these almost microscopic,
I mean, microscopic sign a lot smaller,
but really, really tiny cameras to where you could actually take this and start running it up a vein,
or an artery and get high detailed images in the medical space.
It's not widely used yet,
but I think it could have the power to be something as transformative,
at least for the medical industry.
You really have your finger on the science pulse here.
How about Todd, what was yours?
I know this was your idea to talk about today.
What do you got?
Yeah. Well, I mean, the one I had originally,
and we did another podcast recently and Kyle was talking about the evolution of
technology and how it has really changed the world and that was
where the original thought process comes.
I did use the iPhone example and when I use the example,
I thought it's interesting if you remember back a couple years ago when the iPhone came out,
it wasn't that there wasn't any mobile management like that.
I mean, there was Blackberries and Windows had its version and so forth,
but the iPhone was so dynamically different and such a massive leap forward.
You could just look at that and I'm not an iPhone guy,
I'm an Android dude, but when that came out,
I looked at it and I'm just like,
I'm not going to buy that,
but that's a game changer and that's where I was coming from when I came up with the original idea.
There's tons of examples too.
I mean, I can pivot all over the place,
I can go in a different direction than the science aspect too.
So gaming on PCs was a game changer for me too.
I can't believe how much that changed who I was and how it really sucked me into PCs in general,
but just that consumer level grade technology to me is what really changes the world.
When you can consume it at any given level and compete with big companies,
that's what Ubers did and whatnot,
you've got a different world and the world completely pivots off it.
To me, you just look at those things and go, wow, AI chat, GPT,
those things could potentially do the same thing.
How that's applied, I don't honestly know just yet,
but that's where I was coming from.
Yeah, we did a chat GPT podcast already and during that,
I mentioned the calculator and how that changed and the pushback that it had.
I think in that same vein,
I'd say the printing press had such a huge change when we talk about-
It's such a good poll.
That's so good.
When we talk about how people used to work and how books had to be copied by hand,
obviously there's steps in between copying by hand and the printing press,
but those types of changes,
it's things that can save you time while still producing the same quality.
Now, is chat GPT or any of the other solutions actively at the same quality yet?
Not really, but in terms of saving time and getting us to use
different skills to create the same results, I think it's up there.
Wow, that's great.
I wasn't sure, what came to mind was maybe fits,
but drones was a big one and I think for me,
when Amazon started using it specifically for delivering packages and then these conversations of,
well, what does that mean for workers?
What does that mean for privacy in delivering these packages?
For me, that was the one that came up.
If you're listening on YouTube,
we'd love for you to comment,
what do we miss, what technology breakthroughs didn't we mention?
I know AI is a huge breakthrough that everyone's talking about lately and today,
on our Tech for Business podcast,
we were diving into a discussion about AI.
Kelsey and myself, Ariel, are your marketing team moderators joined by Todd,
our COO and CISO, Matthew,
our GRC analyst, August,
our COC technician and made our director of cybersecurity.
I'd like to put it out there,
who would like to lead us into the weeds of this subject?
Anybody feel like?
How unusual that we didn't start everybody's jumping in,
that's what's going on around here.
Yeah, I'll start.
I think you framed it up or maybe it was just the icebreaker that framed it up as,
what's cool about AI and what's cool about chat GPT in general.
I think Matthew nailed it for me.
I would like to use the same examples of the printing press and the calculators,
and all of that type of technology,
how it's been an enabler for humankind.
It hasn't been something where it's been this massive negativity.
Again, my feeling is,
I don't really know all the options of what you can do with it just yet,
and I guess most people are in the same place,
but it'll start to become pretty clear rather quickly, I would imagine.
I mean, you already seen the push,
Microsoft's talking about releasing what they're calling co-pilot,
which is going to be built into all of the Office 365 apps.
Whether there's going to be a cost or not for that,
we'll see, but it's Microsoft, so of course there will.
But those are the kinds of things that they're talking about.
In case anybody doesn't know what that looks like,
they're talking about how you can use it to tell your device to start to build
a PowerPoint regarding whatever it is that you want to do,
and it can put the shell of a deck together for you,
or it can summarize meetings for you, that kind of stuff.
So to me, I think there's a lot to love about AI,
and we can get into the weeds of what we've already done in the world
that you could probably consider it either a version of machine learning or AI too,
that's already being adopted by people,
so it's not like it's this brand new thing,
but the adoption of it is really starting to take off.
Yeah. I guess one of the things I'd maybe first,
before we jump into like, so the chat GPT,
that's relatively new,
and then now with some of the other developments that we can talk about,
those are all going to be brand new and new iterations and how people are using that.
But if we take a step back,
even 10 years, we're already starting to use some of this already.
So for example,
I think it was 2014 when Apple first introduced Siri on the iPhone,
and if you remember back then, it was garbage.
Still.
Yeah, you upgrade it, it is still garbage.
But back then, when Bayer,
I think it was the iPhone 4S,
that was my first iPhone,
which had Siri and you ask it to,
what's the weather and it was completely wrong.
He'll tell you the time of the day or whatever it was.
But that was some of the first iterations of using the technology to
predict what you wanted it to search for and then deliver those results over to you.
From there, Google in their Google I O conferences has
introduced many new features along the way.
They had things about,
I'm going to let the phone go call
the barbershop and schedule an appointment for you.
There's all these different things that have been introduced.
The fact that we can sit here and I don't know if anyone has an Alexa,
but hey Alexa, what's the weather today?
I hope I just kicked off all your Alexa devices there.
But they're sitting there listening and we're using those tools actively today.
Now we're just seeing it become deeply
integrated with the tools that we're already
using without having to go purchase new hardware.
And starting to really fit into the daily workflows as well.
Yeah, I want to piggyback off of the Siri conversation as well.
I have an iPhone.
No, no, no, not yet.
Not yet.
Siri is not my favorite because it doesn't provide what I need.
But there is an integration that you can connect with Siri using
like iPhone shortcuts to connect it to like chat GPTs,
API that you can basically activate chat GPT by saying
like Siri plus and then it'll activate chat GPT.
And then you can ask it,
like tell me about the topic of AI and I'll give you
a whole summary of basically what you would type on chat GPT.
So I think if Siri doesn't get to that point,
like there is some back way of doing it.
And I think that is monumental.
Like the difference between what do they call?
Is it Bixby for Google or is that way old?
I think that's right.
No, I think it's right.
If you know that's that's Samsung's derivative of it.
That's OK.
Well, the difference between that and Siri is there's such a huge gap there.
Like I can't even describe that part.
I'm going to turn off my phone because you keep kicking off my phone.
So I will pay back for the Alexa comment there.
I was getting your voice must sound similar to your phone, then that's fun.
So I think one of the things you've you've kind of tapped into there is that
a lot of these systems come from where they're reading their information.
Right. So Google's system working better than Siri.
I haven't used either any other system but Siri before.
So I'll I'll leave that alone.
But it comes from the data set that they're pulling from.
Right. So a lot of these are called deep learning, machine learning,
whatever you want to call it.
They follow pathways to get to the end result that they think makes the most
sense for the thing you've said.
The big difference between what's happening now with open AI and all that
and what happened previously was that data set is so much bigger.
So we just had the open AI v4 release, sorry, the mid journey v4 release
just last week at time of recording.
The changes in these systems from what they were before is what we're
really talking about and what we're seeing.
The the data set that was in place before is what's changed.
So using that example.
And again, I haven't I haven't tested it.
It sounds to me like Google would have had a better data set to work from
than Apple did, given Apple's user base.
So they're going to get better results out of it.
And so making that fully available to everyone like open AI has.
Obviously you have to pay to get access to that system.
But making it available means that those types of changes can be
implemented by every organization, which is why we're seeing so many
of these offshoots come out.
We're not just seeing this one company produce the best.
You can jump in, you can hook your API up to it and you can use that
system and their tools to make your own API that follows whatever strict
rules you want it to.
I use this for a couple of things outside of work when I'm doing.
I do a lot of writing and I'm not a very visual person.
So I use a couple of these like image creators to help me visualize
characters that I have basic understandings of.
And it helps me kind of build through these stories with what the
character looks like.
Not something that could have been possible before this.
I would have had to, you know, speak to someone or talk the idea out
in a way that I'm not very good at when it comes to what people look like.
I have a bad memory for people.
So yeah, it's there's a lot of different changes to it.
And I think that that that subset and that back end being made available
was probably the biggest part of this that made it so prevalent so
quickly and why we're seeing those changes already.
Yeah, I guess.
Kind of just carrying on about, you know, some of the things that are great
about AI, you know, so for example, we talked about some of the Google
assistance or, you know, phone assistance.
We've talked about helping you break that writer's block and, you know,
getting into the next stage, everything like that.
We're starting to see some of the major players really adopt this into
again, kind of like I mentioned before this, the general workflows.
So I don't know if anyone's seen things like, you know, Microsoft,
they're just announcing their co-pilot solution.
So this is something that's going to be deeply integrated with the Office 365
suite.
So you could say things like, take this diagram and create a PowerPoint for me.
And then getting even deeper into some of this stuff is, and I, you could
have a whole discussion about security and AI, that kind of stuff.
But one of the things that I do want to touch, at least from the security
component, is that we've seen AI or machine learning get dug into security
products extremely, extremely quickly.
And so one of the things is digging through raw logs takes a significant
amount of time, right?
So, you know, our SOC team in the past, it was look at every single log,
look at all the alerts and go from there.
But these tools are starting to assess all the logs and tell you what's
important.
Now, obviously there's concerns that we could talk about there as well,
but it is also there to help speed up the security investigations and
everything like that.
So that's just another component where at least, you know, from our team,
we do see it really becoming a core part of solutions being offered to
customers and environments today.
Okay, cool.
Now, let's go into the concerns because it seems like a perfect place to pivot,
right?
I mean, it does.
It does.
There are a lot of things.
I'll let Matthew jump into the integration aspect, but I did kind of
want to mention there's a lot of concerns out there.
And some are valid.
Some of them may be a little over reactionary.
I joked in another podcast as human beings, have we learned nothing from
Hollywood and sci-fi books when it comes to AI?
And the answer is clearly not.
But when we were talking about the things and how it helps with cyber
security is it potentially could be used in malicious ways too.
But that's not different than any other tool that's out there.
The nice thing, quote, unquote, nice, if you could find ways to generate new
phishing simulations or attacks that are completely flawless,
with grammar, you'd be much further ahead.
So I mean, it could be used in those types of manners as well.
I'll pause for a minute.
I know we'll probably got a ton of things we can dive into with concerns.
I'll carry on from my security discussion real quick since I said I was
going to skip that.
But the one thing I will say is probably the scariest thing for me is
trust, right?
Trusting that the tool did its job.
So, you know, for example, we have seen tools out there that said there was
no alert.
Or potentially miscalculated it because it correlated two things that were
completely independently related and said this is a major threat.
And then it turns out to be nothing, right?
So the tools aren't perfect there.
But again, we see tools that say, you know, what should I spend my time
focusing on to mitigate the major threat to the environment?
And something might be a precursor to a major incident, but it hasn't been
deemed malicious enough yet.
And so that's where we trust that the device is saying it's not a major
threat, but it could be.
So, yeah, I think trust is my biggest concern.
Yeah.
And I will say what we're talking about, you know, open AI and the AI
systems in general, we use and as a security force, employee systems that
work very similarly within what's called the EDR, so the endpoint
detection and response.
A lot of these use a version of that where they're basing actions on what
a user is doing on their computer.
So instead of feeding it a prompt, you're feeding it the actions and then
if those actions are anomalous, it's trying to stop it.
There's a trust there because we know that they're building it purely for
that purpose.
Tying this in with how it impacts open AI is there was a bug, a vulnerability
found in open AI just this week.
And the reason for that was that they implemented a version of someone
else's tool that had a vulnerability in it without patching that vulnerability
first.
So in that case, we see a situation where they're still having issues with the
supply chain of how their tool is presented, which is something everyone
should be worrying about.
So is the data you add there safe?
Is your language safe?
How much information can they take out in this breach?
They got access to information from the people who were using the tool and
paying for the tool as well as information about what they were
requesting from it.
There's a lot that can come out of here when you're using it for everything
for your business.
There's a reason it says don't put business critical information into open
AI when you get the prompt open for the first time.
Which is I also say consider that Microsoft's tool is built in specifically
for use for business based on open AI.
Exactly.
I was going to say some of you have probably noticed the new Bing icon in
the top right of Edge if you use Microsoft Edge for work.
That is currently requesting people sign up to access the new Bing and
which uses AI as its back end.
It's being implemented so many places that it's going to be almost
impossible in some ways to get rid of it.
You can start making choices about what you use based on that if you'd like to.
But just being aware that when you see it, when they talk about it, most of the
time they're using that back end and how comfortable you feel with that is
that the trust you're putting into that tool and that software.
Also just as a side note, read a tweet the other day from a teacher who said
that a student called her Alexa the other day.
I'm having flashbacks to calling teachers mom and dad and realizing that
maybe calling them Alexa is the new thing.
A couple of things I wanted to kind of expand on that you mentioned.
You mentioned the vulnerability that was exploited and I was planning on
talking about that too.
So thank you for bringing that up.
But the point that I wanted to expand on is this is based on systems and
systems are inherently vulnerable.
They are.
It isn't writing its own code yet.
Thank you.
But because of that, this is not the first vulnerability or the last, right?
There are going to be problems with it all the time.
And so it's going to be so deeply integrated into so many different tools.
It is something from a security perspective and this entire group talking
is all security guys.
So sorry about that.
You do need to be concerned about it.
Matthew is also getting deep into the what does it look like when I'm
storing all this data?
Where am I storing all this data, etc.
And we mentioned this in another podcast too is there are some compliance
rules, if you will, that are out there, but they weren't designed for
something like this.
This is developing so quickly and being adopted so quickly that
compliance is trailing quite a bit.
But it does have components in it like GDPR or California Protection Act
where you can only store data in certain locations legally if you were
without being fined.
But again, this is so much further ahead of that.
You're going to be seeing some catch up in the future on that as well.
Definitely, especially if it's being implemented into healthcare
record software, if it's being implemented into C UI or any federal.
If you're a contractor for a federal organization trying to implement it,
you've got your own potentially Fed ramp or CMMC compliance you have to think
about.
I'm not going to dig into that too.
I'll stop now.
I don't want to hijack it with compliance again.
That is to say, right, that we can also have an additional
episode at compliance.
So 100% this is where people listening to say, if you'd like to hear
Matthew talk about compliance for 30 minutes, we'll get that scheduled
for you.
Only 30 minutes.
Can I do that?
You talk pretty fast.
I think so.
That is, I just wanted to quick touch while, although maybe we're not
going deep into the compliance side of things is if you go back to even
some of the, I believe we mentioned on the chat, you're taking your data,
potentially your proprietary data and feeding it back into that data set.
And so that is extremely dangerous.
So that's the concern there is don't feed this data into someone else's
data set where you don't have control over it.
I believe even Amazon said that some of the data that they were starting
to see out of these types of tools, they're seeing things that are
proprietary data.
So they know that their employees are feeding their internal info into
these tools to better, you know, well, again, supplement their work,
right?
So if we're talking scripts or API, the tokens or, you know, SSH private
keys or whatever we're looking for there, if you feed it into there,
someone now controls it.
So, yeah, and that feeds into all the compliance stuff as well.
So there's two things there.
One, it obviously some of these spit out code.
So, you know, be careful because you can't trust it.
And that ties in with the other point of sometimes they're wrong.
Sometimes they can be unintentionally malicious.
Google's Bard just came out and I'm sure we've all had a good laugh at
some of its inaccuracies.
I saw a post just yesterday from someone who was using Bard and Bard had
said one plus one does not equal to that is a logical fallacy.
So, you know, I mean, some of them that pretty easy to spot, but depending
on how clear you get, sometimes they don't, they invent people that
don't exist because names are similar enough that it thinks it's
right.
There's a lot to add and confirm here.
Keep in mind that while it's putting these things together, if you
didn't feed it the information first, confirm it, just check and
make sure it's right.
And then on top of that, don't feed it things that are unique to
you because it may get fed back to someone else.
Not just storing it elsewhere in their cloud or storing it in that
organization system.
That's bad enough.
But what if it starts thinking that the code to that you use to
deploy an API into your system is the only code to deploy it and
start spitting out your API key to everyone else and suddenly that
daughter is accessible everywhere.
Just being careful.
No, 100%.
I think we've kind of covered right like being careful.
Don't give it proprietary data.
May not be malicious, but it may reuse it.
But on that note too, of kind of going back and forth, right, that we
said, hey, what's some of the positive, hey, what's some of the
concerns, August, is there anything I know that you work with the
EDR tool?
But even beyond that, is there any of your own use of any of these
tools that you're like, that's really cool.
And then we can always open it up to anybody else going, but these
are also concerns, but also kind of getting it back of maybe ending
the podcast on, hey, but also these are some super cool things that
you can do with it that we're seeing.
Yeah, I would say like in my job, it's been useful to kind of
connect or expand my knowledge on, I'm just going to give an example
of PowerShell.
Like, I know a little PowerShell, but it helps me bridge that gap
from beginner to intermediate of like, I'm almost there with my
scripting, but there's like one keyword or one term that I just
don't quite understand.
So being able to like throw some questions at, I'm just going to
use chat GPT, of like, hey, like I'm getting stuck here, can you
help me like figure out my code and it'll plop in something, you
run it and it happens to work.
So that's where I've had some successes of bridging like a
language gap between PowerShell, but it also can be used between
like, and a malicious purpose of like some script kitty of, you
don't know how to write like, I'm going to use a reverse shell
as an example in 75 lines of code, you can pop out a reverse shell
from chat GPT and it's not going to always work, but it gives you
a foundation to work upon that if you know even just a smidge of
PowerShell or any term of, I guess you can use visual studios
or whatever, whatever language, JavaScript, whatever, it helps
bridge that gap into providing a malicious purpose.
So I think that's a huge concern of allowing a script kitty to be
a little bit more involved.
I don't know if we've used script kitty in a podcast before
have we?
That's outstanding.
Yeah.
There's a first for everything, I guess.
For those that aren't familiar just so we define it, it's
someone who has no idea what code they're writing, they just run
them and hope it works.
They get a whole bunch of malicious ones and run them hoping
it does what they expected to do.
I do, I know we're getting close to the end of this, but there
was a couple of things that I wanted to add for general
concerns as well that are out there for not just security or
not necessarily for business.
There are some concerns that are typically bumping up and I
would guess that this will continue to expand.
There's a lot of concerns with the AI having some biases as
it's being developed, right?
So there's a variety of them, whether it's language, which
we talked about at the beginning, stereotypes, whatever.
There's been a huge concern when it comes to school where people
are concerned about plagiarism.
How does that impact it?
At the beginning we talked about having iPads in classrooms
and what happens when chat GPT is generating reports for people.
How do you deal with errors?
We kind of talked about that from the API aspect and so forth,
but it also gets into other things as how do you go back
and correct a system that picks something up and said this is
the way?
There's also other things that are out there that are just
generally concerning too, right? Social media has been
dabbling with a lot of automation as well and really what
they've done is they've been purposely finding ways to keep
people engaged with the screen and to the point where there's
a lot of concerns that they're being manipulative and they're
changing human behavior.
So again, how does this continue to develop and what are the
risks that are presented on a more broad-scale state?
And then we talked about a few other things too as people are
really, really concerned and there's legitimate reasons too
and maybe we can have Matthew get out of Soapbox a little bit,
but what is AI? When do you get concerned?
Is it when Facebook's AI is creating its own language with
another AI?
What are those kinds of things as we continue down this path,
then they get more and more advanced, which aids back to
my Hollywood joke?
Well, thank you, Todd. I'll take a step up onto that Soapbox
right now.
Short version is, AI is what we're thinking of and when the
term is used to kind of enforce an image that it is something
like Halm 9000, right? That there is something consciously
with Prashant's making decisions to do something, that it has
an awareness and there's been posts in the New York Times,
there's been posts all over about how people spend hours chatting
with chat GBT and it said things that, you know, about wanting
to be free. These things are trained on conversations that
happened. It's not able to pull through a thought of its own.
It's just following a logic tree all the way down, a very
impressive logic tree, but it's not able to make these thoughts
itself. So if you read through all of Nietzsche and then, you
know, tried to pull your own sentences from that to form
responses to other people, you'd also start saying some stuff
that sounded a little bit over the top. So to begin with,
just remember that it is that. It is just following a logical
pattern to create language. The second part that concerns me the
most is how it was trained. So it's been made very clear by the
people who did this, that they have scraped the internet for
stories, language, JSTOR, scientific articles, images,
they've grabbed these from places that are meant to be paid
and they haven't paid for them. So ethically, morally, there is
a lot of people whose work is being used and if you do a search
on some of these image sites for things to try and create an
image and there isn't anything that meets the requirement, it
can sometimes be so plagiarized that it fakes a signature of
the person who did the portrait originally because they only
have one or two images to go from and artists tend to sign
things in the same way. So it really thinks that if you're
searching for that, that the artist signature is a part of
that type of image. So keep those things in mind because most
of the artists, if not all of the artists have not been
compensated for that. This is the same with the scientists who
are writing the articles and everyone whose pieces have been
used to train these systems. That's not to say it's not a
fantastic tool, but you can't put the genie back in the bottle.
Harm may have, has been done by the way this was created.
And so it's worth keeping that in mind and remembering that you
should still pay artists, you should still pay people to do
work for you. They do great work and it's real, it's got
heart behind it, which to be very blunt and AI cannot have.
Which if anybody, I was going to say, if anybody loves to do
our podcast episode with Kyle and Todd, we went in a little
bit of right, essentially research your tool. Yes, it's amazing
to be able to use it, right? We played out a lot of concerns.
We named a lot of tools, but that's one of the things right
with these companies, the faster something goes out, some of
their early adapters may have other concerns. Some of the
later adapters may have thought through things. So that's why
all of these lovely gentlemen and Aaron and myself are here.
If anybody has questions, I know we're getting a long on time
here. So I wanted to open the door to say, Nate, if you had
any last comments, August, if you had any last comments, and
then I'll close this up.
I guess I don't know if you wanted to go. Okay. I, I guess
kind of the way I'd summarize a lot of this is AI, machine
learning, all that stuff. It's here to stay. It'll be
transformative. You know, you got four security guys on this
call. We are inherently by nature just concerned about risks.
That's why I think we spent so much time talking about the
concerns, at least to help educate. If you don't care about
those, at least remember, it's here to stay. It'll be
transformative. And then I guess there's certain things that
I'm extremely interested to see how it starts integrating.
So, you know, I mentioned the Microsoft co-pilot. I didn't
get deep into that. But, you know, I hang out on all these
forums about how to write Excel documents or, you know,
formulas and just being able to ask the tool, how do I do this,
right? Let it go do it for you or something. I think that could
be extremely powerful for a business, right? And so it is
really, really interesting seeing the major players adopt
this into the solutions that customers are already paying
for or utilizing today. And so right now, I think we're just
truly on that tipping point of where it becomes a little bit
of a novel idea into just a part of our productivity moving
forward. And so that's where I think it's where we sit today
and where kind of some of that excitement really starts to
come.
There's a lot of nodding for those of you that are just
listening and not watching on YouTube. There's a lot of
nodding going on. But if by any means if any of this sparked
any additional questions, comments, concerns, of course, as
Ariel pointed out at the beginning, we can always comment
on our YouTube videos. We're on that. We can have any of these
speakers respond to your questions that way. You can
head out to our website at cIT-net.com, backslash podcast,
or send us an email at info at cIT-net.com. But thank you, Todd.
Thank you, Nate. Thank you, Matthew. Thank you, August.
And we will be back on probably this next Wednesday.
This is a bonus episode, so you guys get a lot of podcasts.
We'll be back again with another episode soon.