February 10, 2023 • 28 mins
We discuss A.I from a business and workforce point of view. We left the SciFi out for now.
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Episode Transcript
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Brian (00:02):
Microphone.
Hey everyone.
Welcome back to another episode ofFour Transplants in a Microphone.
Don't let the name fool you.
We got three transplants with us today.
We are broadcasting kind of livefrom the fox and hound in Bedale.
Oh, we're not dead.
We're not dead.
We're kind of.
But bedale and the Fox and Honda Bedale.
(00:22):
Mostly dead.
Yeah, mostly dead . So we've got anotherinteresting business topic today as we are
gonna kind of delve into AI and some ofthe things that have jumped up about that.
But what does it mean?
Where's it gonna take us?
And how do we fight the machines?
Brandon (00:37):
Yeah.
So we, we'll try not togo down the Terminator
Brian (00:40):
rabbit hole.
Yeah.
We'll try not to.
We'll but real quickintroductions around the table.
I am Brian Johnson, owner of Maya Johnson.
Jack,
Jack (00:47):
Tompkins
Brandon (00:48):
p consulting firm, Brandon with Superior Data Solutions.
Brian (00:51):
And you can look him up just as Brandon.
Brandon (00:53):
Yes.
Avena, . The,
Jack (00:56):
just Google.
Brandon.
It'll be him.
Yeah.
. Brian: Yeah.
There's no other phrasearound Brandon that would, Dr.
Would come up first.
, let's
Brandon (01:03):
see everybody else underneath.
Brian (01:06):
So, okay.
So like, realistically I know as we getinto this subject there, there's varying
levels of knowledge and expertise in it.
At the very least, we've all beenaware that like AI is out there.
It's to the extent that it becomesuseful, harmful what does it all mean?
So what are
Brandon (01:22):
these thoughts?
Yeah.
Yeah.
And I don't think people know howmuch AI is currently being used.
They only hear about it in Yeah.
You know, headlines and stuff like that.
But AI is being used acrossthe board in every Yeah.
Big business that there is out there.
It's being
Brian (01:39):
utilized.
We do use it too.
I just can't.
So it's the wizard behind them?
No, but like, so, we gotaffiliated with a kind of like
a personality assessing system.
And they have a way where it's likea, I think it's a Google plugin,
but it can work on LinkedIn.
And you use it and it can read a messagethat somebody sends you or if you
(02:03):
decided to a post that somebody madewhich I wouldn't recommend posts just
because they're usually not by the actualperson, but the comments usually are
you can see what somebody comments andthen that can be drugged into the ai.
And it can tell you their personalityprofile by how they wrote their comment.
Yeah, that's pretty wild.
And then when you go to respond tothem, it'll tell you how to respond
(02:25):
to them according to their profile.
Look, it'll correct what you'retyping and give you suggestions on
how you could retype it to make itsound more appealing to that person.
So like on LinkedIn for example, likewhen you're trying to connect with
somebody, it's kind of an interestingtool because you just like go boom.
And.
You're speaking to them sortof in their language, if
Jack (02:43):
you wanna call it that.
Yeah, it's, and I like that exampletoo, because like, so Brandon and I were
talking about chatbots, like if you'reon a website, it probably has a chatbot.
If it has a chatbot, it's probably an ai.
Right.
More unlikely.
Right.
So that's a very, like, Ithink most people are aware of
that, at least to an extent.
But like the thing with Brian islike, it gives you the template.
(03:04):
And then says, all right, here's how thismight make sense based on their profile.
This is a good thing to respond.
And then the actual human does itand says, okay, I'm gonna put an
exclamation point instead of a period.
Then we're good to go, kind of thing.
So it has that final check.
I think one of the, one of thebig things that's been happening,
and I talk about this a lot in mybusiness cuz people think of data
and they think of machine learning.
(03:25):
They can think of AI and all this otherstuff and like super cool, super interest.
And it's definitely in placeat the corporate level.
It's kind of in place at the medium level.
It is a little tiny bit in placeat the small business level.
So I always say we're not that,you know, we don't do that.
We deal with PAC isai, , . I'm not a real person.
But we don't deal with that.
Like most small businessesdon't need that from a data per.
(03:49):
It's becoming much more usablethough in like things like chat GPTs.
Brandon (03:53):
It's bigger trends and Right, and one of, one of the things I actually read
is one of the hurdles small businessesface is they don't have enough data points
to even put in AI to make it worth it.
They actually need to take datapoints from a bigger business and
see what the total market trend is.
They can't really apply it toa smaller business just cuz
(04:13):
they don't have enough data.
To give a good analysis of whattruly is happening in the market.
So they have to actually bump up toa higher level and kind of get, which
kind of is a good and bad becauseit may not relate to you directly.
Right.
But you might get a macro trend ofthe market, but just a small business
just doesn't have, I mean, evenif you had a thousand customers,
(04:37):
it's not enough data points for AIto take into account and give you.
Nothing that a spreadsheetand a dashboard couldn't do.
Right.
At much.
Jack (04:46):
Plug.
Yeah, they're, yeah.
Dashboards are much better than ai.
I think that's the main bullet point.
Yeah.
But no it's summary . So like athousand data points like that sounds
like a lot, but like chat, G P Thas 500,000 and they can do a lot,
but that's scratching the surface.
Correct.
Brandon (05:01):
That is like the least amount that any, yeah.
Public ai, I think has, yeah.
Has its data points.
So it's like I said it's crazy whatit can do because to have, I mean,
like in our industry for securityoperations centers, they have basically,
they ingest every file from, notfile, but log from its antivirus.
(05:26):
. So if an end user has antivirus,what would they have to have?
It takes that and analyzesall of it for threats.
Then to have a sock a, asecurity operation center.
Ah, yeah.
I was thinking like
Brian (05:38):
sock on the doorknob.
No, do not Smart.
Yeah.
Brandon (05:43):
Firsty.
So it takes all that information, compilesit, and then decides is this a threat?
Not a threat.
And then it sends it to a personand then that person can decide,
you know, really analyze it andsay, is this legitimate notit?
But if you took all that data,just raw data coming in, it would
(06:03):
take millions of people to analyzeevery bit of data to do that.
Now, can you imagine?
How sophisticated your threat analysiscan be based on just, and people
suck at doing that because, right.
You know, you go numb.
If you start looking at lines and lines
Jack (06:25):
of absolutely very quickly, blah, blah, blah, and.
So that's the pro, right?
Yeah.
The pro can go through a million record,or AI can go through a million records and
be like, here's the five that you should
Brandon (06:35):
care about.
Correct?
Yeah.
And then that five are getanalyzed by a real person.
Yeah.
And then they push it and if it's a falsepositive, they don't do anything with it.
If it's not, then it gets sent up.
But like I said, it's it has the abilityto cipher through thousands and thousands
of records or millions of records.
Yeah.
And not get fatiguedand not make mistakes.
(06:56):
I don't care anymore.
Like , right?
It's, yeah.
I'm gonna look at the next, youknow, 1,000,001 record, right?
I don't care.
. Yeah.
So,
Jack (07:04):
and you can do it quickly.
You can do it ef like
Brandon (07:06):
Yeah.
Quickly, efficiently,and it without mistake.
Well, everything has mistakes,but less than a human mistakes.
Yes.
And margin of error is farless than a person, so, right.
That's where the real advances are onthose things that we just physically can.
There's so much dataingested that there's no way
Brian (07:24):
people can do it.
So, okay, so I, and I know this soundslike it's gonna go down like a path, but
like I've always heard, we utilize likenot even 10% of our brain's capacity.
Fake, right.
That's fake or that is fake?
Jack (07:38):
What's the percent, what's the percent they don't know.
Oh,
Brandon (07:40):
I'll fact check this.
Yeah, go ahead.
They, that's because
Brian (07:42):
like there was that movie Lucy.
Yes.
I don't know if you ever saw that,where like she's starting able to
access No, I'm not saying that's real.
I'm just simply saying like, I don't knowthat all of a sudden I'd become magnetic
because I can use more of my brain.
But like, it would make me wonder thatlike with our limited, so I always
look at like AI can't, it's made by a.
(08:02):
Coded in a way or set up in away where it starts to do what
it's actually told consistently.
Where we're inconsistent in what we do.
Brandon (08:10):
Correct.
But it's more than being told what to do.
Brian (08:14):
Well, right, but it's create, it's it is a creation that manmade.
Brandon (08:18):
Correct.
So it is actually set up like thesimilar to what the human brain is.
So the more data points it has the.
Accurate.
The decision can be made by the way itanalyzes the data and produces results.
But
I
Brian (08:34):
Again, like my starting point is somebody had to set
it up to be able to do that.
So somebody's brain decidedhow to tell it to do it.
Correct.
Jack (08:42):
That's where I come back to any sort of AI is like there has
to be a human intervention at.
Whether it's building
Brandon (08:49):
the code correct, but then at what point does the code
become, I'm the code, build a
Brian (08:54):
new one, and yeah.
Yeah.
And I'm not arguing that like it can'tbe better at a simple task that we, we
do because it's gonna be not infallible.
But when you set up like a code ora structure, and I don't know what
I'm talking about, but I mean, I'mjust assuming the way in my mind, I'm
envisioning it like you're telling it.
This is how you follow.
When you get thisresponse, this is what you.
(09:15):
It's going to do it every time.
No,
Brandon (09:16):
it doesn't.
That's not how it
Brian (09:18):
works though.
But it does follow a pattern oflike, it won't go outside of what?
Like it won't be wrong if it'sfollowing the path you set it up on.
Brandon (09:29):
It doesn't see, that's the difference between
AI and like a search engine.
So search engine's justlooking for keywords, right?
And relevant facts where AI isactually producing an answer.
Based on what it has to look at.
Brian (09:46):
So then connect the dot for me because what I'm struggling
with is somebody created it.
How did it get to the pointwhere it creates its own answers?
Brandon (09:57):
Because it's set up the same way your brain is set up, you have a set of
life experience, but somebody set it up.
Correct.
But there's a certain point where the.
, yeah.
Is learning more
Brian (10:07):
and more.
And I, I'm not arguingagainst the validity of it.
I mean, even if you're just, it'swatching the movies like, you know,
that like that has to, it's likepeople that go back and say like, star
Trek was like a joke and like, howmany things in Star Trek now exist
Brandon (10:21):
except the phase?
Or they only get on the
Jack (10:23):
phone,
Brandon (10:23):
but like
Brian (10:24):
Yeah, they phasers are like, Stu guns.
Like, yeah.
Yeah.
So it's not, but my point is like, I agreethat is, I'm just saying that like, I
just don't physically understand the paththat goes from somebody's idea to now
this thing can create its own answers, so
Brandon (10:41):
it's not alive in air quotes, but it queries information and collates
data the same way in your brain does.
So it's not just saying, okay,here's 10 results, searching an index
and kicks back like Google search.
Where you get 500 websitesthat contain that information.
(11:02):
This is actually taking datapoints that it has, that it's
disposal and collates a answer.
To the question you gave it.
So
Jack (11:12):
This is good.
So for one, the fact check someresearch says 65% of our brain,
a couple research said a hundredpercent of our brain is used.
I'm guessing, I didn't read the articles,but I'm guessing it means that like, yes,
a hundred percent of the brain is useful.
We probably only access a
Brandon (11:28):
portion of it.
There's certain parts of the,certain parts of the brain that.
Brain function right at acertain time than others, right?
One
Jack (11:34):
is mobility, one is thinking, and the rest is like, all right,
you know, we'll figure it out.
I don't know.
Anyways, so that aside fact check done my,
Brandon (11:44):
I'm looking for it.
I don't have
. Jack: I've always thought that my brain is very much like I, I think in data points,
which is not surprising given what I do.
And I said that.
So you do believe we're in the matrix.
Oh, a hundred per no.
. That's a funny, that'sfor a different episode.
Nevermind.
Yeah.
Yeah.
I equate that, so Brian, to yourquestion of like, somebody built it
and then eventually it's gonna create,like, parents raised me and I got up
(12:05):
a lot of experience, knowledge, etcetera, whatever from them, and like
whatever books, teaching, learning, et.
And now I'm off on my own, soto speak, and I'm creating new
memories and new data points.
That's how I was thinking of it too.
And it's I have all these data points andlike I have a high eq, but it's because I
have dedicated the memory and the researchto each data point that has happened.
(12:26):
and I like categorize it like an SEO orsorry, like a search engine rather, and
then collate them together to say, thisis the best answer, to talk to this person
who just lost their job.
And that's similar to what that is doing.
It's the same.
So the more data points it has, the moreinformation it has to collate and produce
Brian (12:43):
an answer.
And I think the difference isthat, like, how I've looked at
it is like when, and I'm making asearch engine reference, which I
also am not highly skilled on, but.
You're saying that it eventually,it gets to the point where the more
data points it gets, it's not thatit can give you a longer answer,
it can give you a better answer.
Correct.
(13:03):
That's, and I think that's whatpeople more, more access like you
need to understand is that like,it's not like, oh, I've got access
to more data, so therefore I'mgonna give you more information.
It's actually, no, you asked a question.
I'm gonna scour all the data togive you the best answer possible.
Correct.
Yes.
So that is, and it's including moredata as that grows, gives it a better
chance to give a better answer.
(13:24):
But it doesn't necessarilychange the best answer.
No.
And it's
Brandon (13:27):
not guaranteed to be the right answer.
Right?
Right.
It's just what it thinksis the right answer.
Best based off of the data,
Brian (13:34):
because I've been known to not give the best answer sometimes too.
You've rarely been
Jack (13:38):
known to.
Brian (13:39):
Best answer at all.
Yeah.
Yeah.
Jack's like, ah, it's about 80% close.
And I'm like, oh, good enough for me
Brandon (13:44):
too.
Well, and see, there's thedouble edged sword of that.
If you only limit it to certain data,you can create a bias in the ai where
if it has total free range of data, youcan't see my mind blowing up right now.
Yeah.
Well, but you can create a bias.
So if you only create certaindata points for it to pull from,
that's all it knows, right?
(14:05):
So you can create a bias.
In AI to have a predictable outcome.
Yeah.
Based on what you want it to.
Right, so,
Jack (14:16):
so you can, going back to like the parent child analogy, you can raise
a child in a super rich neighborhoodor a super poor neighborhood, and then
they will be skewed, maybe not skewed,probably too strong of a word, but like
that is their mindset going forward.
In later years, they'll eitherbecome richer or poor or
Brian (14:33):
whatever fall.
Well, their actions haveto change their mindset.
Correct.
Brandon (14:37):
Right.
Yeah.
Right, right.
If you were raised in a veryreligious household and weren't
exposed to good to certain things,yeah, that's all you're gonna see the
world through is those experiences.
Yeah.
And if you were raised in, youknow, a more well-rounded home,
not that's the other sound where a
Brian (14:53):
heathen home . No,
Brandon (14:54):
It sounded bad when I said it.
I was kind of focusingon the one of those,
Brian (14:57):
I'm just trying to like, let's put it on
Brandon (14:59):
me now.
No, it's just the, like, if you havean ultra of narrow view of search
based on whatever, politics, religion,whatever, and if it's ultra narrow,
that's the only life experience youhave through that filtered lens.
Right?
Right.
And if you.
More of a broad, you're gonna havemore of a broad, but even people,
when you get to something new that youhaven't done before, all bets are out.
(15:22):
How long it's gonna take you tocomprehend and process all the
new information you're getting.
Brian (15:27):
Yeah it's a good, the real life thing.
I could say that thecorrelates that and it's.
So when I got into the professionalworkforce, like after pro rodeod, pro
rodeo, , rodeo, , what's the AI do that?
Yeah.
After AI . So I got in there andI started, I just had a job that
(15:48):
required some international traveland I never traveled internationally.
And so you have a very Americanperspective of the world.
Russia's, I mean, growing upfor me, Russia, the Soviet
union's bad, Russia's bad.
Like you know, like you have this viewand then you get out in the world and it.
Huh These other countries, likeI was just like, well, America's
(16:08):
the greatest country on earth.
I mean, pretty much.
Hell yeah.
Yeah.
Pretty much everybody else aroundthe world's probably struggling.
And then you get to these other countriesand it's like, huh, it ain't so bad here.
They're doing
Brandon (16:18):
great.
. You know, and I've asked that questionto people because like I was talking to a
German guy and he was like, oh yeah, I didvery well for myself, blah, blah, blah.
And I was like, well,what brought you here?
He goes, there's just more opportunity.
Brian (16:30):
But and that, True.
But my like, my point with it isthen when I got out, like my mindset
had always been like, well, nothinggood can happen anywhere else in
the world, but in America, yeah.
And then you get out and like you'relike, wow, I really like how like
Europeans eat meals and they have likeseven courses and it's a whole thing.
Like, may not be what I would wantto do every night, but like I can all
(16:50):
of a sudden appreciate it and neverhave once had that in the us right?
Like, and then you start to likesee things differently and then
now my mindset's totally different.
I love being in America.
I love a lot about America, butit's like, I mean, there's some
things we just don't do quite right.
You know, and other peoplehave really figured out.
Brandon (17:06):
Do they have yeah.
Corn fed steak in other countries?
Brian (17:08):
Not as much.
I know not as much.
And to be honest, steak is probably theworst example you could give because
like here you can get on average a betterstake at like the Outback or Texas Road.
Than most of the really expensiverestaurants elsewhere now.
Wow.
I know.
Really why I said it.
Yeah.
But like every time somebodycomes over to visit, all they
(17:30):
wanna do is go have a steak.
Yeah.
. Yeah.
Interesting.
Yeah.
So that's probably the numberone request I've ever had
from my international friends.
Take me to a steakhouse.
Get a steak.
Yeah.
Like interesting.
Yeah, everything's grassing.
Take me to the sizzle.
Jack (17:42):
So it's inter, because like the more we're talking about this, the
more it's like AI is being treated.
Human.
The more diverse the experience,the more all sides they can see.
The more countries they visit, the moresteak they eat, like, you know, those
equivalents in ai, the better it becomes,the smarter becomes the best answer or
the better of the best possible answersit gives, which is very interesting.
(18:05):
Correct.
Brandon (18:06):
So like I said, the more rounded the data is, the more correct
the answer will be or not correct.
Just the more realistic theanswer will be to apply.
All things.
Or if you had a very narrow, youknow, and that's what they're worried
about is if you take and have aspecific grudge against certain
people, you can make it biased andit will make decisions based on that.
(18:29):
Right.
And it's no emotion.
No, nothing.
Just data.
Yeah.
So imagine you pure callous responses.
Well, imagine this, imagine you had aAI court system with a built-in bias.
Yeah.
Right.
So then
Jack (18:42):
do you think we could learn things from ai
Brandon (18:44):
because like Absolutely.
There's things that can be learned.
Yeah,
Brian (18:47):
because it's, well be, yeah, because I mean, inherently what it, I
mean, and I'm if you can convince me ofthis, then you've done really well, but.
I would think the answer is yes, becausewhat, where we lack focus and attention
and an ability to drill down on thingsand we have tiredness and sleep.
You know, like all of thatcauses us not to be able to
(19:08):
necessarily get to that point.
But like an AI would, I mean,they'll just give it to you and
be like, oh yeah, that's right.
Because like when we were messingaround with it yesterday, like there
were things popping up and I waslike, you know that, that's actually
Brandon (19:19):
right.
Yeah.
Yeah.
It's a, it's an.
There's no emotion in it.
There's no, that's the thing, how I
Brian (19:25):
feel, and it doesn't consider it right or wrong, it's just
Brandon (19:28):
the answer.
Jack (19:28):
is what it is.
Right.
And that's, it's a intentionallyemotionless answer.
And you could also say,okay, that makes sense.
Also, what's the datathat's supporting that?
So it's like you get toquestion their background.
So,
Brian (19:40):
so, okay.
Because I know we, we don'thave tons of time left all.
I think we've got great contexton the power of it, but like how
is, what are the pros and cons?
Like how do we use it in our lives andbusiness , like, I mean, but like really
Brandon (19:52):
what does it mean to us?
So the pros, I mean, from whatI've read, my data points, my I
would say that we have, the prosare you're going to get data.
on a high level with very fewmistakes and consistent results.
Damn
Well, no, I mean, this islike no more dashboard.
(20:15):
No, I know
. Brian: Yeah.
But
It's more data than people can deal with,
Jack (20:18):
right?
Yeah, a hundred
Brandon (20:19):
percent.
Yeah.
It's just more data thanwhat people can deal with.
So on that level, you'redealing with data analysis.
You have jobs that just aren'tfeasible to have people do.
Right.
The ability to compute and.
Deal with problems on a computerlevel where it's so much faster.
I mean, you watched howfast it typed a report.
(20:41):
It was typing it fasterthan they could read it.
That a Absolut?
Yeah.
So it kicked out a 300 word essay.
Faster than which you could read it.
It kicked it out and literally
Brian (20:50):
typed it in.
People that are going to get theirdoctorates are like 300 word essay.
Like I'd love to only have to
Brandon (20:55):
do that.
Yeah.
But imagine you put 1500words or 2000 put 3000.
Yeah.
It would have it done in two minutes.
Kicked out and you just corrected.
I mean, like, so the, there's realbenefits to it and in those aspects,
and like I said, the downfalls are gonnabe, anything can be used for nefarious
Brian (21:14):
purposes.
So if I were to, if I were togeneralize and again I fall back on,
I'm probably the person that like,would be the one you'd want to convince.
Not the one that's going toconvince people, but like, I
look at like, what's gone on.
Like McDonald's for example.
One of the complaints theyheard of all was like, it's
the opposite of Chick-fil-A.
(21:36):
I come in, I go to placemy order, the person at the
register doesn't seem to care.
You know, they mess it up and blah blah.
Like it's all these things.
And what is the first thing they did?
Touch screen ordering.
When you walk in, they have somebodyup at the register, but that
person's there mostly to just handyou your food, not to ring you up.
They can, cuz there's people thatcome in and still want to get.
(21:56):
But like that change to me is a changethat is a business positive change.
It may not be a people positive change,but it's a business positive change.
And I think that like AI tome represents more of that
Brandon (22:09):
well, it does in business.
So that varies scenario.
It's not a politicalstatement, it's just fact.
You want to get, oops, you want to getpaid 15, 20 bucks an hour for burgers.
And work at McDonald's.
Yeah.
The threshold has just become superaffordable to have machines and ai Right.
Run the store and literally you havetwo people there to feed the machine
Brian (22:32):
or flip burgers.
Brandon (22:33):
Yeah.
They don't even flip 'em.
The machine does.
They already have these machines.
Yeah.
It literally, you put a stackof patties in it, put all the
condiments, everything in there.
You put the kiosk in, ittakes the order, it makes the.
Literally you take it.
I think it even wraps.
See,
Brian (22:48):
this is where I'm gonna have to draw a hard no on this.
Like, if all of a sudden we'regonna let machines feed us, and
that's the only way we get fed.
. Brandon: But that threshold is there right now.
It's why wouldn't you buy the machine?
And take your workforce down totwo machines, never call out sick.
They don't act like fools,you know what I mean?
Product consistency.
They don't hook up with the other machines in place,
(23:09):
. Brandon: So you don't have all those issues.
And you just made it a very, areyou doing ? You just made it a
very kind of a feature that youwould want to put in your business.
Right.
And now the threshold's there.
Yeah.
I could pay employees and deal with.
Or you could just have this machine
And I think that's the part for business that, like, generally
(23:30):
speaking, it's not going to be themind of like the consultant or the mind
of the CEO or the, it's not gonna bethose things that get no eliminated.
It's going to be the task oriented job.
So like right when you look at like youremployee group, you've got like people
that like, Hired hands are like, they'rebasically mercenaries in your business.
Like they're there for apaycheck and that's it.
(23:52):
They have no loyalty to you.
All they're doing is a task.
That's the first tier thatyou're gonna outsource to the
Brandon (23:57):
machines.
Correct.
I think a lot of it's newfactory stuff that will be, yeah.
I don't see like a mechanic shophaving AI to be able to take a car
apart and do whatever it needs to do.
I just, See, because there'sso much variability Correct.
About car consent.
Exactly.
I could see
Jack (24:12):
eventually, but Right.
It's definitely not in your turn.
Brian (24:15):
I could see all the diagnostic testing and stuff done.
Yep.
By ai.
But I think you're right.
Like when it boils down to it,somebody's gonna unscrew the thing
because it's gonna be twisted on
Brandon (24:25):
incorrectly.
Correct.
Or it's just
Brian (24:26):
not reachable.
Right.
And that's the other thingwith like machine and.
It's set up to do what it's supposedto do best, and let's be efficient.
If it has to be efficient withsomething that's not correct, it's,
it throws the whole system off.
Correct.
It becomes
Jack (24:39):
inefficient,
Brandon (24:40):
right?
Yes.
So that's where you have likeTesla in their gigafactory.
They can turn out a car from every day itruns through and they have zero people.
It every bit of thatTesla is made on site.
Everything shows asrolled steel or aluminum.
And they literally press fenders.
Every part is pressed and made on site.
(25:02):
That's pretty impressive.
And they just kick out andthis's a machine that basically
runs through every bit of it.
All ai, all computer operated,there's people there to run the
machines, you know, make sure theydon't attack each other, but Right.
Other than that, I mean, and that's.
It's gonna be the future of manufacturing.
It's just Well, that's,
Brian (25:23):
and that's the thing.
It's been the future ofmanufacturing for a long time.
Right.
It's just some manufacturingoperations haven't adopted it, but,
Jack (25:28):
And I'd say even broadening past manufacturing, back to like
McDonald's and stuff like that,technology is always supposed to
like raise the bar for the minimum.
Like it, you know, thatcould be the right phrasing.
It might not be, but if thesimple, very task oriented.
, admittedly, boring jobs are taken away.
(25:49):
Then like the next person thatcomes in does the job above that.
And so you just keep kind of levelingup over time is at least a theory.
So AI will potentially takeout all of the silly, boring,
useless jobs that people hate.
Right?
Lower skill jobs.
But then you just get anotherskill and you're, yeah,
Brandon (26:09):
the problem is those people aren't gonna level up with it.
They, that's, I don't want to saythat's their lot in life, but.
That's kind of what they've Well
Jack (26:17):
was so then you either gain the skills, you learn the ai or
you go to a different industrythat hasn't adopted it yet.
Right.
I don't
Brian (26:24):
know.
So it's a different revolution ofwhen, like back in the like eighties
and nineties when the mass outputof manufacturing went to Asian, we
moved things to Taiwan, to Japan, toChina, and all of a sudden we were no
longer manufacturing anything in the.
Us, but it's still, people adapt.
People got left behind for sure, butlike the system adapted to where, okay,
(26:48):
now we're gonna focus on technologyand leading through different areas.
And so those people that were comingup into the workforce, instead of
learning how to be a shift manager at gm.
They were studying, how do I be likean IT professional or something that's
gonna be more relevant in the future?
It is trade-offs.
There will be people left behind, butit also creates just a new generation
(27:11):
of people leading something that'sdifferent than we've seen before.
And it's hard to think aboutthat because we don't see it.
Brandon (27:18):
Yeah.
We went from manufacturing to service.
Yeah.
And from service.
Brian (27:21):
We're gonna have to go some somewhere and we're still, like
the US is still the largest economyin the world despite all that.
So it's like it.
It doesn't have to belike negative and harmful.
No, it doesn't.
It just forces change becausethe inevitability of it is, it's
Jack (27:35):
arrived.
I, and I think you said twokeywords, new and different.
Yeah.
Not bad, not worse.
Not even better necessarily, but, and
Brian (27:44):
change is always met with fear first, right?
A hundred or It'sembraced, so, absolutely.
All right.
We probably gotta wrap this suckerup, but I mean, do you have AI
to take us out or do I have to.
Brandon (27:54):
No we don't
Brian (27:54):
have a yet.
Well, thanks again to theFox and Hound in Bedale for
hosting and all right, let's go.
If I had a perfect day, Iwould it start this way.
Open up the tall boy.
Yeah,
head up to again, we don'teven really care who.
Microphone.
Hey everyone.
Welcome back to another episode ofFour Transplants in a Microphone.
Don't let the name fool you.
We got three transplants with us today.
We are broadcasting kind of livefrom the fox and hound in Bedale.
Oh, we're not dead.
We're not dead.
We're kind of.
But bedale and the Fox and Honda Bedale.
(00:22):
Mostly dead.
Yeah, mostly dead . So we've got anotherinteresting business topic today as we are
gonna kind of delve into AI and some ofthe things that have jumped up about that.
But what does it mean?
Where's it gonna take us?
And how do we fight the machines?
Brandon (00:37):
Yeah.
So we, we'll try not togo down the Terminator
Brian (00:40):
rabbit hole.
Yeah.
We'll try not to.
We'll but real quickintroductions around the table.
I am Brian Johnson, owner of Maya Johnson.
Jack,
Jack (00:47):
Tompkins
Brandon (00:48):
p consulting firm, Brandon with Superior Data Solutions.
Brian (00:51):
And you can look him up just as Brandon.
Brandon (00:53):
Yes.
Avena, . The,
Jack (00:56):
just Google.
Brandon.
It'll be him.
Yeah.
. Brian: Yeah.
There's no other phrasearound Brandon that would, Dr.
Would come up first.
, let's
Brandon (01:03):
see everybody else underneath.
Brian (01:06):
So, okay.
So like, realistically I know as we getinto this subject there, there's varying
levels of knowledge and expertise in it.
At the very least, we've all beenaware that like AI is out there.
It's to the extent that it becomesuseful, harmful what does it all mean?
So what are
Brandon (01:22):
these thoughts?
Yeah.
Yeah.
And I don't think people know howmuch AI is currently being used.
They only hear about it in Yeah.
You know, headlines and stuff like that.
But AI is being used acrossthe board in every Yeah.
Big business that there is out there.
It's being
Brian (01:39):
utilized.
We do use it too.
I just can't.
So it's the wizard behind them?
No, but like, so, we gotaffiliated with a kind of like
a personality assessing system.
And they have a way where it's likea, I think it's a Google plugin,
but it can work on LinkedIn.
And you use it and it can read a messagethat somebody sends you or if you
(02:03):
decided to a post that somebody madewhich I wouldn't recommend posts just
because they're usually not by the actualperson, but the comments usually are
you can see what somebody comments andthen that can be drugged into the ai.
And it can tell you their personalityprofile by how they wrote their comment.
Yeah, that's pretty wild.
And then when you go to respond tothem, it'll tell you how to respond
(02:25):
to them according to their profile.
Look, it'll correct what you'retyping and give you suggestions on
how you could retype it to make itsound more appealing to that person.
So like on LinkedIn for example, likewhen you're trying to connect with
somebody, it's kind of an interestingtool because you just like go boom.
And.
You're speaking to them sortof in their language, if
Jack (02:43):
you wanna call it that.
Yeah, it's, and I like that exampletoo, because like, so Brandon and I were
talking about chatbots, like if you'reon a website, it probably has a chatbot.
If it has a chatbot, it's probably an ai.
Right.
More unlikely.
Right.
So that's a very, like, Ithink most people are aware of
that, at least to an extent.
But like the thing with Brian islike, it gives you the template.
(03:04):
And then says, all right, here's how thismight make sense based on their profile.
This is a good thing to respond.
And then the actual human does itand says, okay, I'm gonna put an
exclamation point instead of a period.
Then we're good to go, kind of thing.
So it has that final check.
I think one of the, one of thebig things that's been happening,
and I talk about this a lot in mybusiness cuz people think of data
and they think of machine learning.
(03:25):
They can think of AI and all this otherstuff and like super cool, super interest.
And it's definitely in placeat the corporate level.
It's kind of in place at the medium level.
It is a little tiny bit in placeat the small business level.
So I always say we're not that,you know, we don't do that.
We deal with PAC isai, , . I'm not a real person.
But we don't deal with that.
Like most small businessesdon't need that from a data per.
(03:49):
It's becoming much more usablethough in like things like chat GPTs.
Brandon (03:53):
It's bigger trends and Right, and one of, one of the things I actually read
is one of the hurdles small businessesface is they don't have enough data points
to even put in AI to make it worth it.
They actually need to take datapoints from a bigger business and
see what the total market trend is.
They can't really apply it toa smaller business just cuz
(04:13):
they don't have enough data.
To give a good analysis of whattruly is happening in the market.
So they have to actually bump up toa higher level and kind of get, which
kind of is a good and bad becauseit may not relate to you directly.
Right.
But you might get a macro trend ofthe market, but just a small business
just doesn't have, I mean, evenif you had a thousand customers,
(04:37):
it's not enough data points for AIto take into account and give you.
Nothing that a spreadsheetand a dashboard couldn't do.
Right.
At much.
Jack (04:46):
Plug.
Yeah, they're, yeah.
Dashboards are much better than ai.
I think that's the main bullet point.
Yeah.
But no it's summary . So like athousand data points like that sounds
like a lot, but like chat, G P Thas 500,000 and they can do a lot,
but that's scratching the surface.
Correct.
Brandon (05:01):
That is like the least amount that any, yeah.
Public ai, I think has, yeah.
Has its data points.
So it's like I said it's crazy whatit can do because to have, I mean,
like in our industry for securityoperations centers, they have basically,
they ingest every file from, notfile, but log from its antivirus.
(05:26):
. So if an end user has antivirus,what would they have to have?
It takes that and analyzesall of it for threats.
Then to have a sock a, asecurity operation center.
Ah, yeah.
I was thinking like
Brian (05:38):
sock on the doorknob.
No, do not Smart.
Yeah.
Brandon (05:43):
Firsty.
So it takes all that information, compilesit, and then decides is this a threat?
Not a threat.
And then it sends it to a personand then that person can decide,
you know, really analyze it andsay, is this legitimate notit?
But if you took all that data,just raw data coming in, it would
(06:03):
take millions of people to analyzeevery bit of data to do that.
Now, can you imagine?
How sophisticated your threat analysiscan be based on just, and people
suck at doing that because, right.
You know, you go numb.
If you start looking at lines and lines
Jack (06:25):
of absolutely very quickly, blah, blah, blah, and.
So that's the pro, right?
Yeah.
The pro can go through a million record,or AI can go through a million records and
be like, here's the five that you should
Brandon (06:35):
care about.
Correct?
Yeah.
And then that five are getanalyzed by a real person.
Yeah.
And then they push it and if it's a falsepositive, they don't do anything with it.
If it's not, then it gets sent up.
But like I said, it's it has the abilityto cipher through thousands and thousands
of records or millions of records.
Yeah.
And not get fatiguedand not make mistakes.
(06:56):
I don't care anymore.
Like , right?
It's, yeah.
I'm gonna look at the next, youknow, 1,000,001 record, right?
I don't care.
. Yeah.
So,
Jack (07:04):
and you can do it quickly.
You can do it ef like
Brandon (07:06):
Yeah.
Quickly, efficiently,and it without mistake.
Well, everything has mistakes,but less than a human mistakes.
Yes.
And margin of error is farless than a person, so, right.
That's where the real advances are onthose things that we just physically can.
There's so much dataingested that there's no way
Brian (07:24):
people can do it.
So, okay, so I, and I know this soundslike it's gonna go down like a path, but
like I've always heard, we utilize likenot even 10% of our brain's capacity.
Fake, right.
That's fake or that is fake?
Jack (07:38):
What's the percent, what's the percent they don't know.
Oh,
Brandon (07:40):
I'll fact check this.
Yeah, go ahead.
They, that's because
Brian (07:42):
like there was that movie Lucy.
Yes.
I don't know if you ever saw that,where like she's starting able to
access No, I'm not saying that's real.
I'm just simply saying like, I don't knowthat all of a sudden I'd become magnetic
because I can use more of my brain.
But like, it would make me wonder thatlike with our limited, so I always
look at like AI can't, it's made by a.
(08:02):
Coded in a way or set up in away where it starts to do what
it's actually told consistently.
Where we're inconsistent in what we do.
Brandon (08:10):
Correct.
But it's more than being told what to do.
Brian (08:14):
Well, right, but it's create, it's it is a creation that manmade.
Brandon (08:18):
Correct.
So it is actually set up like thesimilar to what the human brain is.
So the more data points it has the.
Accurate.
The decision can be made by the way itanalyzes the data and produces results.
But
I
Brian (08:34):
Again, like my starting point is somebody had to set
it up to be able to do that.
So somebody's brain decidedhow to tell it to do it.
Correct.
Jack (08:42):
That's where I come back to any sort of AI is like there has
to be a human intervention at.
Whether it's building
Brandon (08:49):
the code correct, but then at what point does the code
become, I'm the code, build a
Brian (08:54):
new one, and yeah.
Yeah.
And I'm not arguing that like it can'tbe better at a simple task that we, we
do because it's gonna be not infallible.
But when you set up like a code ora structure, and I don't know what
I'm talking about, but I mean, I'mjust assuming the way in my mind, I'm
envisioning it like you're telling it.
This is how you follow.
When you get thisresponse, this is what you.
(09:15):
It's going to do it every time.
No,
Brandon (09:16):
it doesn't.
That's not how it
Brian (09:18):
works though.
But it does follow a pattern oflike, it won't go outside of what?
Like it won't be wrong if it'sfollowing the path you set it up on.
Brandon (09:29):
It doesn't see, that's the difference between
AI and like a search engine.
So search engine's justlooking for keywords, right?
And relevant facts where AI isactually producing an answer.
Based on what it has to look at.
Brian (09:46):
So then connect the dot for me because what I'm struggling
with is somebody created it.
How did it get to the pointwhere it creates its own answers?
Brandon (09:57):
Because it's set up the same way your brain is set up, you have a set of
life experience, but somebody set it up.
Correct.
But there's a certain point where the.
, yeah.
Is learning more
Brian (10:07):
and more.
And I, I'm not arguingagainst the validity of it.
I mean, even if you're just, it'swatching the movies like, you know,
that like that has to, it's likepeople that go back and say like, star
Trek was like a joke and like, howmany things in Star Trek now exist
Brandon (10:21):
except the phase?
Or they only get on the
Jack (10:23):
phone,
Brandon (10:23):
but like
Brian (10:24):
Yeah, they phasers are like, Stu guns.
Like, yeah.
Yeah.
So it's not, but my point is like, I agreethat is, I'm just saying that like, I
just don't physically understand the paththat goes from somebody's idea to now
this thing can create its own answers, so
Brandon (10:41):
it's not alive in air quotes, but it queries information and collates
data the same way in your brain does.
So it's not just saying, okay,here's 10 results, searching an index
and kicks back like Google search.
Where you get 500 websitesthat contain that information.
(11:02):
This is actually taking datapoints that it has, that it's
disposal and collates a answer.
To the question you gave it.
So
Jack (11:12):
This is good.
So for one, the fact check someresearch says 65% of our brain,
a couple research said a hundredpercent of our brain is used.
I'm guessing, I didn't read the articles,but I'm guessing it means that like, yes,
a hundred percent of the brain is useful.
We probably only access a
Brandon (11:28):
portion of it.
There's certain parts of the,certain parts of the brain that.
Brain function right at acertain time than others, right?
One
Jack (11:34):
is mobility, one is thinking, and the rest is like, all right,
you know, we'll figure it out.
I don't know.
Anyways, so that aside fact check done my,
Brandon (11:44):
I'm looking for it.
I don't have
. Jack: I've always thought that my brain is very much like I, I think in data points,
which is not surprising given what I do.
And I said that.
So you do believe we're in the matrix.
Oh, a hundred per no.
. That's a funny, that'sfor a different episode.
Nevermind.
Yeah.
Yeah.
I equate that, so Brian, to yourquestion of like, somebody built it
and then eventually it's gonna create,like, parents raised me and I got up
(12:05):
a lot of experience, knowledge, etcetera, whatever from them, and like
whatever books, teaching, learning, et.
And now I'm off on my own, soto speak, and I'm creating new
memories and new data points.
That's how I was thinking of it too.
And it's I have all these data points andlike I have a high eq, but it's because I
have dedicated the memory and the researchto each data point that has happened.
(12:26):
and I like categorize it like an SEO orsorry, like a search engine rather, and
then collate them together to say, thisis the best answer, to talk to this person
who just lost their job.
And that's similar to what that is doing.
It's the same.
So the more data points it has, the moreinformation it has to collate and produce
Brian (12:43):
an answer.
And I think the difference isthat, like, how I've looked at
it is like when, and I'm making asearch engine reference, which I
also am not highly skilled on, but.
You're saying that it eventually,it gets to the point where the more
data points it gets, it's not thatit can give you a longer answer,
it can give you a better answer.
Correct.
(13:03):
That's, and I think that's whatpeople more, more access like you
need to understand is that like,it's not like, oh, I've got access
to more data, so therefore I'mgonna give you more information.
It's actually, no, you asked a question.
I'm gonna scour all the data togive you the best answer possible.
Correct.
Yes.
So that is, and it's including moredata as that grows, gives it a better
chance to give a better answer.
(13:24):
But it doesn't necessarilychange the best answer.
No.
And it's
Brandon (13:27):
not guaranteed to be the right answer.
Right?
Right.
It's just what it thinksis the right answer.
Best based off of the data,
Brian (13:34):
because I've been known to not give the best answer sometimes too.
You've rarely been
Jack (13:38):
known to.
Brian (13:39):
Best answer at all.
Yeah.
Yeah.
Jack's like, ah, it's about 80% close.
And I'm like, oh, good enough for me
Brandon (13:44):
too.
Well, and see, there's thedouble edged sword of that.
If you only limit it to certain data,you can create a bias in the ai where
if it has total free range of data, youcan't see my mind blowing up right now.
Yeah.
Well, but you can create a bias.
So if you only create certaindata points for it to pull from,
that's all it knows, right?
(14:05):
So you can create a bias.
In AI to have a predictable outcome.
Yeah.
Based on what you want it to.
Right, so,
Jack (14:16):
so you can, going back to like the parent child analogy, you can raise
a child in a super rich neighborhoodor a super poor neighborhood, and then
they will be skewed, maybe not skewed,probably too strong of a word, but like
that is their mindset going forward.
In later years, they'll eitherbecome richer or poor or
Brian (14:33):
whatever fall.
Well, their actions haveto change their mindset.
Correct.
Brandon (14:37):
Right.
Yeah.
Right, right.
If you were raised in a veryreligious household and weren't
exposed to good to certain things,yeah, that's all you're gonna see the
world through is those experiences.
Yeah.
And if you were raised in, youknow, a more well-rounded home,
not that's the other sound where a
Brian (14:53):
heathen home . No,
Brandon (14:54):
It sounded bad when I said it.
I was kind of focusingon the one of those,
Brian (14:57):
I'm just trying to like, let's put it on
Brandon (14:59):
me now.
No, it's just the, like, if you havean ultra of narrow view of search
based on whatever, politics, religion,whatever, and if it's ultra narrow,
that's the only life experience youhave through that filtered lens.
Right?
Right.
And if you.
More of a broad, you're gonna havemore of a broad, but even people,
when you get to something new that youhaven't done before, all bets are out.
(15:22):
How long it's gonna take you tocomprehend and process all the
new information you're getting.
Brian (15:27):
Yeah it's a good, the real life thing.
I could say that thecorrelates that and it's.
So when I got into the professionalworkforce, like after pro rodeod, pro
rodeo, , rodeo, , what's the AI do that?
Yeah.
After AI . So I got in there andI started, I just had a job that
(15:48):
required some international traveland I never traveled internationally.
And so you have a very Americanperspective of the world.
Russia's, I mean, growing upfor me, Russia, the Soviet
union's bad, Russia's bad.
Like you know, like you have this viewand then you get out in the world and it.
Huh These other countries, likeI was just like, well, America's
(16:08):
the greatest country on earth.
I mean, pretty much.
Hell yeah.
Yeah.
Pretty much everybody else aroundthe world's probably struggling.
And then you get to these other countriesand it's like, huh, it ain't so bad here.
They're doing
Brandon (16:18):
great.
. You know, and I've asked that questionto people because like I was talking to a
German guy and he was like, oh yeah, I didvery well for myself, blah, blah, blah.
And I was like, well,what brought you here?
He goes, there's just more opportunity.
Brian (16:30):
But and that, True.
But my like, my point with it isthen when I got out, like my mindset
had always been like, well, nothinggood can happen anywhere else in
the world, but in America, yeah.
And then you get out and like you'relike, wow, I really like how like
Europeans eat meals and they have likeseven courses and it's a whole thing.
Like, may not be what I would wantto do every night, but like I can all
(16:50):
of a sudden appreciate it and neverhave once had that in the us right?
Like, and then you start to likesee things differently and then
now my mindset's totally different.
I love being in America.
I love a lot about America, butit's like, I mean, there's some
things we just don't do quite right.
You know, and other peoplehave really figured out.
Brandon (17:06):
Do they have yeah.
Corn fed steak in other countries?
Brian (17:08):
Not as much.
I know not as much.
And to be honest, steak is probably theworst example you could give because
like here you can get on average a betterstake at like the Outback or Texas Road.
Than most of the really expensiverestaurants elsewhere now.
Wow.
I know.
Really why I said it.
Yeah.
But like every time somebodycomes over to visit, all they
(17:30):
wanna do is go have a steak.
Yeah.
. Yeah.
Interesting.
Yeah.
So that's probably the numberone request I've ever had
from my international friends.
Take me to a steakhouse.
Get a steak.
Yeah.
Like interesting.
Yeah, everything's grassing.
Take me to the sizzle.
Jack (17:42):
So it's inter, because like the more we're talking about this, the
more it's like AI is being treated.
Human.
The more diverse the experience,the more all sides they can see.
The more countries they visit, the moresteak they eat, like, you know, those
equivalents in ai, the better it becomes,the smarter becomes the best answer or
the better of the best possible answersit gives, which is very interesting.
(18:05):
Correct.
Brandon (18:06):
So like I said, the more rounded the data is, the more correct
the answer will be or not correct.
Just the more realistic theanswer will be to apply.
All things.
Or if you had a very narrow, youknow, and that's what they're worried
about is if you take and have aspecific grudge against certain
people, you can make it biased andit will make decisions based on that.
(18:29):
Right.
And it's no emotion.
No, nothing.
Just data.
Yeah.
So imagine you pure callous responses.
Well, imagine this, imagine you had aAI court system with a built-in bias.
Yeah.
Right.
So then
Jack (18:42):
do you think we could learn things from ai
Brandon (18:44):
because like Absolutely.
There's things that can be learned.
Yeah,
Brian (18:47):
because it's, well be, yeah, because I mean, inherently what it, I
mean, and I'm if you can convince me ofthis, then you've done really well, but.
I would think the answer is yes, becausewhat, where we lack focus and attention
and an ability to drill down on thingsand we have tiredness and sleep.
You know, like all of thatcauses us not to be able to
(19:08):
necessarily get to that point.
But like an AI would, I mean,they'll just give it to you and
be like, oh yeah, that's right.
Because like when we were messingaround with it yesterday, like there
were things popping up and I waslike, you know that, that's actually
Brandon (19:19):
right.
Yeah.
Yeah.
It's a, it's an.
There's no emotion in it.
There's no, that's the thing, how I
Brian (19:25):
feel, and it doesn't consider it right or wrong, it's just
Brandon (19:28):
the answer.
Jack (19:28):
is what it is.
Right.
And that's, it's a intentionallyemotionless answer.
And you could also say,okay, that makes sense.
Also, what's the datathat's supporting that?
So it's like you get toquestion their background.
So,
Brian (19:40):
so, okay.
Because I know we, we don'thave tons of time left all.
I think we've got great contexton the power of it, but like how
is, what are the pros and cons?
Like how do we use it in our lives andbusiness , like, I mean, but like really
Brandon (19:52):
what does it mean to us?
So the pros, I mean, from whatI've read, my data points, my I
would say that we have, the prosare you're going to get data.
on a high level with very fewmistakes and consistent results.
Damn
Well, no, I mean, this islike no more dashboard.
(20:15):
No, I know
. Brian: Yeah.
But
It's more data than people can deal with,
Jack (20:18):
right?
Yeah, a hundred
Brandon (20:19):
percent.
Yeah.
It's just more data thanwhat people can deal with.
So on that level, you'redealing with data analysis.
You have jobs that just aren'tfeasible to have people do.
Right.
The ability to compute and.
Deal with problems on a computerlevel where it's so much faster.
I mean, you watched howfast it typed a report.
(20:41):
It was typing it fasterthan they could read it.
That a Absolut?
Yeah.
So it kicked out a 300 word essay.
Faster than which you could read it.
It kicked it out and literally
Brian (20:50):
typed it in.
People that are going to get theirdoctorates are like 300 word essay.
Like I'd love to only have to
Brandon (20:55):
do that.
Yeah.
But imagine you put 1500words or 2000 put 3000.
Yeah.
It would have it done in two minutes.
Kicked out and you just corrected.
I mean, like, so the, there's realbenefits to it and in those aspects,
and like I said, the downfalls are gonnabe, anything can be used for nefarious
Brian (21:14):
purposes.
So if I were to, if I were togeneralize and again I fall back on,
I'm probably the person that like,would be the one you'd want to convince.
Not the one that's going toconvince people, but like, I
look at like, what's gone on.
Like McDonald's for example.
One of the complaints theyheard of all was like, it's
the opposite of Chick-fil-A.
(21:36):
I come in, I go to placemy order, the person at the
register doesn't seem to care.
You know, they mess it up and blah blah.
Like it's all these things.
And what is the first thing they did?
Touch screen ordering.
When you walk in, they have somebodyup at the register, but that
person's there mostly to just handyou your food, not to ring you up.
They can, cuz there's people thatcome in and still want to get.
(21:56):
But like that change to me is a changethat is a business positive change.
It may not be a people positive change,but it's a business positive change.
And I think that like AI tome represents more of that
Brandon (22:09):
well, it does in business.
So that varies scenario.
It's not a politicalstatement, it's just fact.
You want to get, oops, you want to getpaid 15, 20 bucks an hour for burgers.
And work at McDonald's.
Yeah.
The threshold has just become superaffordable to have machines and ai Right.
Run the store and literally you havetwo people there to feed the machine
Brian (22:32):
or flip burgers.
Brandon (22:33):
Yeah.
They don't even flip 'em.
The machine does.
They already have these machines.
Yeah.
It literally, you put a stackof patties in it, put all the
condiments, everything in there.
You put the kiosk in, ittakes the order, it makes the.
Literally you take it.
I think it even wraps.
See,
Brian (22:48):
this is where I'm gonna have to draw a hard no on this.
Like, if all of a sudden we'regonna let machines feed us, and
that's the only way we get fed.
. Brandon: But that threshold is there right now.
It's why wouldn't you buy the machine?
And take your workforce down totwo machines, never call out sick.
They don't act like fools,you know what I mean?
Product consistency.
They don't hook up with the other machines in place,
(23:09):
. Brandon: So you don't have all those issues.
And you just made it a very, areyou doing ? You just made it a
very kind of a feature that youwould want to put in your business.
Right.
And now the threshold's there.
Yeah.
I could pay employees and deal with.
Or you could just have this machine
And I think that's the part for business that, like, generally
(23:30):
speaking, it's not going to be themind of like the consultant or the mind
of the CEO or the, it's not gonna bethose things that get no eliminated.
It's going to be the task oriented job.
So like right when you look at like youremployee group, you've got like people
that like, Hired hands are like, they'rebasically mercenaries in your business.
Like they're there for apaycheck and that's it.
(23:52):
They have no loyalty to you.
All they're doing is a task.
That's the first tier thatyou're gonna outsource to the
Brandon (23:57):
machines.
Correct.
I think a lot of it's newfactory stuff that will be, yeah.
I don't see like a mechanic shophaving AI to be able to take a car
apart and do whatever it needs to do.
I just, See, because there'sso much variability Correct.
About car consent.
Exactly.
I could see
Jack (24:12):
eventually, but Right.
It's definitely not in your turn.
Brian (24:15):
I could see all the diagnostic testing and stuff done.
Yep.
By ai.
But I think you're right.
Like when it boils down to it,somebody's gonna unscrew the thing
because it's gonna be twisted on
Brandon (24:25):
incorrectly.
Correct.
Or it's just
Brian (24:26):
not reachable.
Right.
And that's the other thingwith like machine and.
It's set up to do what it's supposedto do best, and let's be efficient.
If it has to be efficient withsomething that's not correct, it's,
it throws the whole system off.
Correct.
It becomes
Jack (24:39):
inefficient,
Brandon (24:40):
right?
Yes.
So that's where you have likeTesla in their gigafactory.
They can turn out a car from every day itruns through and they have zero people.
It every bit of thatTesla is made on site.
Everything shows asrolled steel or aluminum.
And they literally press fenders.
Every part is pressed and made on site.
(25:02):
That's pretty impressive.
And they just kick out andthis's a machine that basically
runs through every bit of it.
All ai, all computer operated,there's people there to run the
machines, you know, make sure theydon't attack each other, but Right.
Other than that, I mean, and that's.
It's gonna be the future of manufacturing.
It's just Well, that's,
Brian (25:23):
and that's the thing.
It's been the future ofmanufacturing for a long time.
Right.
It's just some manufacturingoperations haven't adopted it, but,
Jack (25:28):
And I'd say even broadening past manufacturing, back to like
McDonald's and stuff like that,technology is always supposed to
like raise the bar for the minimum.
Like it, you know, thatcould be the right phrasing.
It might not be, but if thesimple, very task oriented.
, admittedly, boring jobs are taken away.
(25:49):
Then like the next person thatcomes in does the job above that.
And so you just keep kind of levelingup over time is at least a theory.
So AI will potentially takeout all of the silly, boring,
useless jobs that people hate.
Right?
Lower skill jobs.
But then you just get anotherskill and you're, yeah,
Brandon (26:09):
the problem is those people aren't gonna level up with it.
They, that's, I don't want to saythat's their lot in life, but.
That's kind of what they've Well
Jack (26:17):
was so then you either gain the skills, you learn the ai or
you go to a different industrythat hasn't adopted it yet.
Right.
I don't
Brian (26:24):
know.
So it's a different revolution ofwhen, like back in the like eighties
and nineties when the mass outputof manufacturing went to Asian, we
moved things to Taiwan, to Japan, toChina, and all of a sudden we were no
longer manufacturing anything in the.
Us, but it's still, people adapt.
People got left behind for sure, butlike the system adapted to where, okay,
(26:48):
now we're gonna focus on technologyand leading through different areas.
And so those people that were comingup into the workforce, instead of
learning how to be a shift manager at gm.
They were studying, how do I be likean IT professional or something that's
gonna be more relevant in the future?
It is trade-offs.
There will be people left behind, butit also creates just a new generation
(27:11):
of people leading something that'sdifferent than we've seen before.
And it's hard to think aboutthat because we don't see it.
Brandon (27:18):
Yeah.
We went from manufacturing to service.
Yeah.
And from service.
Brian (27:21):
We're gonna have to go some somewhere and we're still, like
the US is still the largest economyin the world despite all that.
So it's like it.
It doesn't have to belike negative and harmful.
No, it doesn't.
It just forces change becausethe inevitability of it is, it's
Jack (27:35):
arrived.
I, and I think you said twokeywords, new and different.
Yeah.
Not bad, not worse.
Not even better necessarily, but, and
Brian (27:44):
change is always met with fear first, right?
A hundred or It'sembraced, so, absolutely.
All right.
We probably gotta wrap this suckerup, but I mean, do you have AI
to take us out or do I have to.
Brandon (27:54):
No we don't
Brian (27:54):
have a yet.
Well, thanks again to theFox and Hound in Bedale for
hosting and all right, let's go.
If I had a perfect day, Iwould it start this way.
Open up the tall boy.
Yeah,
head up to again, we don'teven really care who.
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