00:00:00.171 all right we are live, Gar
00:00:02.293 welcome to the show
00:00:03.074 everybody the show that
00:00:04.595 does not have a name yet
00:00:06.217 because the show that the
00:00:07.238 name that we wanted to pick
00:00:08.519 was taken my name is
00:00:11.001 Yassen Horanszky i'm the
00:00:12.463 founder and ceo of Luniko
00:00:13.844 and your host here for
00:00:14.725 today and i've helped
00:00:16.226 digitally transform
00:00:17.247 businesses of all shapes
00:00:18.288 and sizes from publicly
00:00:19.790 traded multi-billion dollar
00:00:21.411 organizations to local
00:00:23.073 non-profits and everything in between
00:00:25.655 And just like you,
00:00:26.837 I'm wondering where
00:00:27.657 artificial intelligence and
00:00:29.119 the whole disruptive
00:00:30.100 technology that comes with
00:00:31.201 it that's been dominating
00:00:33.103 headlines over the last 12 months,
00:00:35.366 where it fits in and how.
00:00:37.832 So that's why we've created this show,
00:00:40.473 right?
00:00:40.733 To bring on guests of all
00:00:41.793 levels and all types of
00:00:42.754 businesses to talk about AI
00:00:45.135 and its business adoption,
00:00:46.335 but strictly from a non-technical lens.
00:00:50.336 So that means we're gonna be
00:00:51.677 touching on topics of strategy, process,
00:00:54.638 people, we'll get into the emotions,
00:00:56.839 all of that side of thing.
00:00:58.379 And if you're curious about
00:00:59.499 AI and its business impacts
00:01:01.360 and challenges,
00:01:02.080 then this show is for you.
00:01:04.101 So we're going to drop the jargon,
00:01:06.364 the corporate jargon as much as possible.
00:01:08.646 We're going to keep the
00:01:09.387 conversation as simple as we can.
00:01:11.009 And welcome to episode zero, everybody.
00:01:15.754 Now, as you guys know,
00:01:17.255 we don't have a name yet.
00:01:18.837 Thank you, Garlon,
00:01:19.538 for putting that up there.
00:01:21.159 Naming has never been our forte.
00:01:23.440 So we're calling on anybody
00:01:25.580 that thinks of a good name.
00:01:27.541 If you can kind of help us name this show,
00:01:30.362 right?
00:01:30.742 Please drop us any
00:01:31.482 suggestions in the email and LinkedIn,
00:01:33.403 however you choose to do that.
00:01:35.203 But not having a name isn't
00:01:37.004 gonna slow us down.
00:01:40.144 And we're just gonna get on
00:01:41.765 with this until we can kind
00:01:42.885 of figure out what that's
00:01:43.525 gonna look like.
00:01:44.086 So episode zero is gonna be
00:01:46.466 a little bit different than
00:01:47.407 the rest of the episodes.
00:01:49.288 And this is episode zero.
00:01:51.132 This is episode zero.
00:01:52.135 That's right.
00:01:52.576 Yeah.
00:01:53.686 Thank you, Gar.
00:01:54.526 So it's going to be a little
00:01:55.627 bit different than everybody.
00:01:57.188 In future episodes,
00:01:58.028 we'll have some guests that
00:01:59.429 is not Luniko related, of course.
00:02:01.810 But for today's episodes,
00:02:03.631 we're going to talk about
00:02:04.711 what the podcast is about,
00:02:05.912 kind of why we started the podcast.
00:02:08.033 I've got Garlon in here,
00:02:09.073 who's my co-founder.
00:02:11.214 Garlon's typically going to
00:02:12.415 be in the background here.
00:02:14.156 So you guys are not really
00:02:14.976 going to see him as much.
00:02:17.237 But today I am joined by him.
00:02:19.098 He is my business partner
00:02:20.259 for anything and everything,
00:02:21.499 all things Luniko.
00:02:23.140 He is an entrepreneur at
00:02:24.622 heart being in different
00:02:26.524 types of industries and different roles.
00:02:28.987 He's leveraged technology
00:02:30.208 basically in all his
00:02:31.029 startups from a VR startup
00:02:33.192 before VR was cool, I will say,
00:02:35.815 to business intelligence,
00:02:37.417 to using crowdfunding
00:02:38.638 platforms like Kickstarter
00:02:39.980 to successfully launch five products.
00:02:43.303 and to the latest venture in
00:02:44.524 AI with myself here at Luniko.
00:02:47.366 Now,
00:02:47.587 innovation has always been a constant
00:02:49.328 in Garlon's life.
00:02:50.429 And so him and I share those similarities.
00:02:53.171 And because of that,
00:02:53.932 we've worked together on
00:02:55.353 many different projects,
00:02:56.434 on different capacities,
00:02:57.494 various startups and
00:02:59.116 corporate jobs as well.
00:03:00.597 Since 2011,
00:03:02.739 where we met working at an
00:03:04.260 energy firm as he was a co-op student,
00:03:06.321 I was a summer student.
00:03:07.222 So
00:03:10.908 I'm excited because we've
00:03:12.954 been talking about doing
00:03:13.776 this for about three months.
00:03:15.080 So let's see how it goes.
00:03:18.171 That is true, right?
00:03:20.093 So look, if you know Garlon and I,
00:03:21.914 and I think this is the
00:03:22.575 best place to start, Garlon.
00:03:23.556 If you know Garlon and I,
00:03:24.536 you know that we like to
00:03:25.517 start with the why, right?
00:03:26.518 We're big Simon Sinek fans.
00:03:28.600 So we're going to talk about
00:03:29.921 why we started the show
00:03:31.562 from a personal standpoint
00:03:33.744 and a professional standpoint.
00:03:35.986 And then we'll just talk
00:03:37.387 about our experience kind
00:03:38.988 of going through, experiencing,
00:03:40.649 understanding,
00:03:41.870 and learning the
00:03:42.491 intricacies of the
00:03:44.032 different AI kind of models.
00:03:46.654 just so you can kind of
00:03:47.794 get a little bit of insight
00:03:48.975 into some of our journey
00:03:50.015 there right so let's talk
00:03:51.215 with why we started this
00:03:52.495 this show and i will
00:03:53.676 promise to stop talking
00:03:54.596 here and focus on you here
00:03:56.176 Gar but really the start of
00:03:57.996 it is this was kind of a
00:03:59.657 been a passion project that
00:04:00.877 we've decided on and off
00:04:02.657 between different years
00:04:03.718 some years we're going to
00:04:04.458 do it some years we're not
00:04:05.358 going to do it we talked
00:04:06.178 about doing it with a
00:04:06.898 partner a couple years ago
00:04:08.519 but really we just love to talk to people
00:04:11.392 We love to connect.
00:04:12.233 We love to learn.
00:04:12.934 If you've ever seen me at a tech event,
00:04:14.696 you'll see me trying to
00:04:16.738 talk to as many people as possible.
00:04:18.840 So we figured that this
00:04:19.741 would be a good vehicle for that.
00:04:23.144 We both grew up around
00:04:24.806 technology and it's really
00:04:25.927 been at the center of our
00:04:27.969 world for quite a bit,
00:04:29.851 both personally and professionally.
00:04:32.093 And, you know, Gar,
00:04:32.853 why don't you tell us a
00:04:33.714 little bit more about you
00:04:34.855 and what this podcast is in it for you?
00:04:38.236 Yeah, for sure.
00:04:40.158 I mean, as you mentioned,
00:04:41.718 I've always been interested in technology,
00:04:44.540 right?
00:04:45.961 Growing up,
00:04:47.021 I would always get the newest
00:04:47.982 cell phones.
00:04:48.822 Before that, as a little kid,
00:04:50.043 I would always get the
00:04:50.863 newest Game Boys to play games and stuff.
00:04:54.265 So when it came to having an
00:04:57.367 opportunity to talk about AI,
00:04:59.388 especially something that is so
00:05:01.550 disruptive something that is
00:05:04.011 so I mean at least right
00:05:05.592 now super talked about and
00:05:07.634 especially with you know
00:05:09.214 you and I being in the
00:05:10.695 business world there's a
00:05:12.957 lot of corporate impact
00:05:15.918 from that sense so having
00:05:18.380 this podcast being able to
00:05:21.281 have conversations with
00:05:22.702 people that are in that
00:05:24.483 realm crossed with how this
00:05:27.625 disruptive technology might
00:05:28.986 impact them on a day to day I think is
00:05:31.191 a fantastic opportunity to
00:05:32.711 kind of just understand and
00:05:35.512 dig into this unknown realm
00:05:37.513 a little bit more together.
00:05:40.093 Right.
00:05:40.793 And it's kind of like, you know, AI.
00:05:42.934 I mean,
00:05:43.194 a lot of people are saying and I
00:05:44.954 think we feel under that
00:05:46.555 same category that it's
00:05:48.435 going to be very disruptive
00:05:49.715 and it's going to be
00:05:50.276 changing the way that we
00:05:52.076 interact both in the workplace, of course,
00:05:55.917 with operational
00:05:56.657 improvements and things like that,
00:05:58.097 but also on a personal side.
00:06:00.718 with, I don't know, man,
00:06:01.738 just many different applications there,
00:06:03.499 right?
00:06:03.979 So it's cool because, you know,
00:06:06.280 when the last time
00:06:07.200 something this
00:06:08.120 revolutionary from a tech
00:06:09.320 perspective happened,
00:06:10.081 it was the internet.
00:06:11.721 I was pretty young, right?
00:06:14.302 I was pretty young.
00:06:15.022 I didn't really understand the, like,
00:06:16.942 I just grew up with the internet.
00:06:18.283 But I do remember my dad,
00:06:20.003 who was a database
00:06:20.843 administrator in the 90s, right?
00:06:23.744 Working with computers.
00:06:24.804 We always had kind of
00:06:25.625 computers growing up.
00:06:27.125 I love playing computer games, right?
00:06:29.586 Game boys two and whatnot.
00:06:30.926 Strategy games were my thing
00:06:32.187 when I was like, you know, six, seven,
00:06:34.167 eight years old.
00:06:35.547 Um,
00:06:35.788 but I didn't really get as much
00:06:37.188 computer time from my dad as I wanted.
00:06:39.569 Right.
00:06:39.829 Like, uh, which, which was good.
00:06:41.889 Right.
00:06:42.109 Not looking back on it.
00:06:43.470 My dad was, you know,
00:06:44.730 he was kind of fucking
00:06:45.450 around with different
00:06:46.191 things on the internet.
00:06:48.031 building he was building
00:06:49.033 some leather keychains and
00:06:50.894 you know he started to
00:06:51.595 build some websites so he
00:06:52.957 showed me like what he was
00:06:54.318 doing there on the website
00:06:55.579 and i had no understanding
00:06:56.941 of it right but i did end
00:06:58.943 up building two websites
00:07:00.785 that were basically codes
00:07:02.586 it was a starcraft and
00:07:04.829 warcraft games back then
00:07:07.291 video game cheat codes yeah
00:07:09.393 you're sharing that with everybody
00:07:11.015 I was sharing the cheat
00:07:11.957 codes with everybody.
00:07:12.758 That's right.
00:07:13.919 And they were public.
00:07:14.941 But actually,
00:07:16.022 you had to buy magazines back then.
00:07:18.446 So I had a magazine of cheat codes.
00:07:20.773 yeah so i built a website
00:07:22.514 around it and and whatnot i
00:07:24.536 don't know what happened
00:07:25.356 and kind of how it went
00:07:27.098 after that but i mean
00:07:29.259 really technology same as
00:07:30.420 you gar has always been a
00:07:31.501 key part of my life from a
00:07:33.322 personal perspective even
00:07:34.523 with my kids now uh we talk
00:07:36.484 about ai you know they're
00:07:38.365 growing up with ai they
00:07:40.007 they really don't know
00:07:40.947 anything different for them
00:07:41.928 things like siri is just a
00:07:43.589 way to get answers to their questions
00:07:46.271 They're talking about Star Wars right now.
00:07:48.012 They're really into that.
00:07:48.953 So it's like, hey,
00:07:50.054 who is Baby Yoda's father?
00:07:52.856 And does he have a brother?
00:07:55.057 So they'll ask Siri those
00:07:56.158 kinds of questions.
00:07:56.819 But we do a lot of things
00:07:57.759 with Dolly in terms of
00:07:59.761 creating visuals and
00:08:01.122 creating coloring pages,
00:08:02.483 things like that that they can do.
00:08:03.984 And it's cool to see how they interact.
00:08:07.166 Anything else you want to
00:08:08.027 say on the personal side, Gar, for tech?
00:08:10.068 Or should we move on?
00:08:11.589 Yeah, let's keep going.
00:08:13.413 Okay,
00:08:13.673 so let's talk about the professional side,
00:08:15.275 right?
00:08:15.415 Because obviously we're both entrepreneurs,
00:08:17.056 right?
00:08:17.256 We've been involved in the
00:08:18.557 management consultant space
00:08:19.518 for a number of years in
00:08:21.080 digital improvements, transformations,
00:08:22.941 that sort of thing.
00:08:24.542 AI really just dominated headlines in 2023,
00:08:29.447 right?
00:08:30.648 And I think it's going to
00:08:32.309 continue to do so, right?
00:08:34.811 Yeah.
00:08:36.012 I think that it's obviously
00:08:37.313 going to be a big
00:08:38.014 capability that
00:08:38.954 organizations are going to
00:08:39.995 look to try to leverage.
00:08:42.658 And it doesn't matter what
00:08:44.779 the technological revolution is,
00:08:46.701 whether it's the internet
00:08:47.762 or whether it's basic
00:08:48.622 systems or AI that we're
00:08:51.004 talking about now.
00:08:53.006 The truth is that people
00:08:54.367 will always be at the center.
00:08:56.929 of it right and they're
00:08:58.650 going to be either the
00:08:59.511 reason for its effective
00:09:00.972 integration or they're
00:09:02.513 going to be the reason for
00:09:03.713 its demise and you know
00:09:07.236 when we talk about things
00:09:08.236 from a professional
00:09:08.957 perspective it's like we've
00:09:09.777 been having a lot of
00:09:10.498 conversations with various
00:09:12.479 people and different
00:09:13.560 stakeholders and what do
00:09:14.801 you think and what do you think is
00:09:17.042 is gonna be good,
00:09:17.922 what do you think is bullshit?
00:09:19.203 What do you think is more than hype?
00:09:21.864 What do you think it doesn't
00:09:22.725 get talked about?
00:09:23.805 Just to really build our curiosity.
00:09:26.186 And we tend to do that over
00:09:27.627 a beer or a networking
00:09:29.668 event or anything like that.
00:09:32.069 And we thought that like,
00:09:33.270 why have those conversations
00:09:35.671 where nobody else can kind
00:09:36.832 of see them except for us, right?
00:09:38.873 They tend to be pretty dynamic.
00:09:40.154 They tend to be interesting.
00:09:41.715 Everybody has kind of
00:09:42.596 different perspectives and
00:09:44.097 the things that they think about,
00:09:45.898 the fears or the risks or
00:09:47.479 the challenges or the
00:09:48.360 opportunities that we just thought like,
00:09:50.721 let's just get everybody
00:09:51.782 here together one by one.
00:09:53.904 We can chat with them.
00:09:54.924 We can hear their perspectives.
00:09:57.986 And, you know,
00:09:58.367 maybe different people will
00:09:59.387 kind of learn from that, right?
00:10:02.069 Mm-hmm.
00:10:03.990 For me, I think, you know,
00:10:07.011 obviously I won't get too much into it,
00:10:09.091 but Luniko as a business,
00:10:11.131 we're definitely dabbling
00:10:12.592 in AI and we're pivoting
00:10:13.992 our business to integrate AI,
00:10:16.753 not just for us, but for our clients.
00:10:19.553 But what, for me, I thought that, again,
00:10:26.234 going back to the love for technology,
00:10:28.275 right?
00:10:29.075 If I have an opportunity to
00:10:31.395 marry my love for technology,
00:10:34.431 with also just business what
00:10:37.253 i get to do on a day-to-day
00:10:39.055 i mean we spend 40 hours at
00:10:41.216 work if not more out day
00:10:43.318 right so if you can marry
00:10:44.499 those two worlds together
00:10:45.700 it's a win-win in my books
00:10:47.861 and just i know you briefly
00:10:49.202 talked about it before but
00:10:50.764 coming straight out of
00:10:51.504 university i've had that
00:10:52.965 opportunity to dabble in vr
00:10:54.987 when vr was even cool this
00:10:56.848 was like when the first
00:10:57.709 oculus rift came out i had
00:10:59.690 a little startup that um or
00:11:02.132 i was a co-founder of the
00:11:03.133 little startup that
00:11:04.394 said hey there's this brand
00:11:06.014 new technology out there
00:11:07.715 it's going to be the wild
00:11:08.535 west there's so many
00:11:09.615 different applications that
00:11:10.736 could be done what should
00:11:12.756 we do and we went from an
00:11:14.237 idea to an actual execution
00:11:17.558 of creating that product
00:11:19.618 and actually selling it to
00:11:20.759 a couple customers and so
00:11:22.419 that was a really cool
00:11:23.419 experience like you know
00:11:25.000 obviously there's a lot of
00:11:27.031 learnings that I'm taking from that.
00:11:29.395 But just kind of remembering
00:11:32.059 how interesting and dynamic
00:11:34.663 that work environment was like, hey,
00:11:36.546 we're doing things that
00:11:37.367 nobody's done before.
00:11:39.233 and kind of coming back full circle,
00:11:43.315 Luniko, this new version of it,
00:11:45.316 or this new chapter of it,
00:11:48.298 definitely feels like that for me.
00:11:50.359 So same thing,
00:11:51.539 I think even just in this
00:11:53.060 short period of time where
00:11:54.160 we started dabbling from a
00:11:56.381 professional standpoint in AI,
00:11:58.763 I've had to go through a
00:11:59.963 few paradigm shifts,
00:12:01.244 a few new ways of thinking about AI,
00:12:04.645 because I wasn't like, bam, oh yeah,
00:12:06.466 this is the next big thing.
00:12:08.067 I was also caught in the, oh,
00:12:10.329 this might just be hype.
00:12:12.231 I'll dabble in it and kind
00:12:13.512 of make my own opinions.
00:12:14.853 And throughout that journey,
00:12:16.135 I think I have a few things
00:12:17.316 that might be interesting
00:12:18.777 for me to share on this podcast.
00:12:20.839 And from a podcast standpoint,
00:12:22.580 I think it will be cool for us to
00:12:26.061 take a look in about a year, two years,
00:12:28.723 or at regular frequencies
00:12:31.445 to use this podcast as a
00:12:33.746 bit of a journal.
00:12:34.807 It'll be interesting to see, you know,
00:12:36.829 if we keep going three, four,
00:12:38.610 five years down the line,
00:12:40.691 going back to episode number one, two,
00:12:43.253 three, and be like, oh,
00:12:44.354 this is what people were thinking.
00:12:45.615 Think about how much AI has
00:12:46.996 evolved since those conversations, right?
00:12:50.318 So that's kind of why I'm
00:12:51.799 doing this professionally, I think.
00:12:53.813 Yeah,
00:12:54.573 that'll be cool to see because like
00:12:56.274 the internet, man,
00:12:57.075 I don't think anybody back
00:12:58.976 in 1995 when it was really
00:13:00.877 picking up steam thought
00:13:02.158 that you'd be able to pick
00:13:03.819 up a cell phone and, you know,
00:13:06.481 have McDonald's delivered
00:13:07.761 to you in 15 minutes, right?
00:13:10.963 20 minutes at your door, right?
00:13:12.884 Like it's crazy how much we've come.
00:13:14.746 That is cool.
00:13:15.486 Yeah.
00:13:16.046 I mean,
00:13:16.367 think about all these new technologies.
00:13:19.188 Whenever a new platform
00:13:20.869 comes out or a new disruptive technology,
00:13:24.047 It takes time for all the
00:13:25.989 different permutations and
00:13:27.351 ideas to come out of it.
00:13:29.414 When you think about virtual reality,
00:13:31.596 it's kind of the same thing, right?
00:13:32.918 At first, everyone's like, oh,
00:13:33.819 there's so many business applications.
00:13:35.441 There's so many video game applications.
00:13:37.664 But what's interesting about
00:13:38.745 that is that that one
00:13:40.187 didn't quite live up to the
00:13:41.789 hype that it did.
00:13:43.992 And obviously AI,
00:13:45.233 we're having this hype cycle.
00:13:47.054 I think you can see it from the stocks.
00:13:48.735 But what I believe is that
00:13:50.677 there will be value
00:13:51.818 generated from an AI standpoint,
00:13:53.880 regardless of what the
00:13:54.900 stock price is saying of AI companies.
00:14:01.045 In the long run,
00:14:03.327 there will be business
00:14:05.268 value that's generated.
00:14:06.429 And I really firmly believe
00:14:08.431 that that's undeniable.
00:14:10.500 Yeah, I think so too, man.
00:14:12.161 It's kind of like we see systems nowadays.
00:14:13.962 It's not really a thing you do.
00:14:15.603 It's just a core of your
00:14:17.624 organizational DNA, right?
00:14:19.566 Just like the dot-com boom too, right?
00:14:21.667 There was the big hype and
00:14:23.428 then it all crashed.
00:14:24.829 But then in the longer run,
00:14:26.090 you've got your Amazons, your Googles,
00:14:27.911 your Netflixes and stuff like that,
00:14:30.052 right?
00:14:31.313 From the ashes, they arose.
00:14:32.854 Yeah, definitely.
00:14:36.335 The ones that made it, right?
00:14:37.916 So let's talk about,
00:14:39.916 let's hone in a little bit
00:14:41.437 on large language models
00:14:43.177 and specifically ChatGPT.
00:14:45.378 The ChatGPT was the catalyst, I think,
00:14:48.538 for the hype last year, right?
00:14:51.239 I mean, geez, just seeing the capability,
00:14:55.200 what could be possible at
00:14:56.760 the first beginning stages
00:14:59.181 of it right and seeing
00:15:00.362 videos of oh in you know 30
00:15:02.784 seconds i created this
00:15:03.945 website in this business
00:15:05.066 and all this kind of stuff
00:15:06.347 that kind of came up from
00:15:07.327 it that you know obviously
00:15:08.308 a lot of it was not very
00:15:10.370 good now that we kind of
00:15:11.711 see it laid it down and the
00:15:13.812 dust has settled but it was
00:15:16.794 and i think it's probably
00:15:17.675 going to be a lot of people
00:15:18.596 it's either going to be
00:15:19.897 chat gpt or some sort of
00:15:21.498 similar large single model variation
00:15:24.340 that people are either
00:15:25.361 exposed to soon if they
00:15:27.724 haven't already been exposed to it,
00:15:29.246 or something like Copilot,
00:15:31.129 where their organization is
00:15:32.390 going to be purchasing Copilot.
00:15:34.193 Or I think some people are
00:15:35.134 already starting to get it
00:15:35.935 as a preview without having
00:15:37.317 to purchase it.
00:15:38.700 Right.
00:15:39.080 So you've gone through a lot of,
00:15:42.262 let's talk about your experience in AI,
00:15:45.504 right?
00:15:45.785 Because, you know, you,
00:15:47.165 you're obviously a big tech advocate,
00:15:49.147 but anytime there's new tech, you,
00:15:51.028 you go through this like motion of, of,
00:15:53.509 you know, your initial journey.
00:15:55.331 So why don't we talk about
00:15:56.912 what your journey was like
00:15:59.093 Gar and maybe provide the
00:16:00.494 audience with a little bit
00:16:01.294 of context as to how that came to be.
00:16:05.817 Sure.
00:16:06.838 Um,
00:16:09.055 Well,
00:16:09.395 when we decided to do our business pivot,
00:16:11.396 Yas came to me and said, hey,
00:16:13.578 you ever heard of this thing called AI?
00:16:15.219 Like, yeah, I have.
00:16:17.700 He's like, well,
00:16:18.021 I'm thinking of building a
00:16:18.981 business around it, right?
00:16:20.002 And I was like, at that point,
00:16:22.664 I hadn't really dabbled with ChatGPT too,
00:16:26.286 too much.
00:16:27.387 I think I had seen some
00:16:29.248 videos and I went in there
00:16:30.869 and I specifically remember that
00:16:34.424 My fiance is a vet and she
00:16:36.645 had this really challenging case.
00:16:38.185 And I said, hey,
00:16:38.806 why don't we ask ChatGPT
00:16:40.427 and see what ChatGPT could come up with?
00:16:43.588 And so we typed in all the
00:16:46.129 parameters of the case and
00:16:47.710 ChatGPT kind of gave a
00:16:49.011 generic answer saying like, okay,
00:16:50.631 it could be any of these things.
00:16:52.492 From my perspective, I was like, oh,
00:16:54.853 is this pretty good?
00:16:56.954 From my fiance's perspective, it was like,
00:16:58.875 well, it's lacking a few things.
00:17:00.796 It's not bad.
00:17:02.844 But it is, you know,
00:17:05.727 in my professional opinion,
00:17:07.709 there are some other things
00:17:08.650 that it hasn't really brought up.
00:17:10.011 So that was my kind of initial experience.
00:17:12.894 And, you know, working with Yas,
00:17:14.937 Yassen had to convince me a
00:17:16.558 little bit about, hey,
00:17:18.000 there really is something here.
00:17:19.822 We should dig into this a bit more.
00:17:22.745 Right.
00:17:23.899 And I think I definitely had
00:17:27.041 my own personal skepticisms
00:17:29.922 of AI as well.
00:17:31.924 I think one of the mental
00:17:33.765 traps that people can get
00:17:35.426 caught in with AI is when
00:17:39.068 you first hop in a chat GPT
00:17:40.829 and ask it a question, you kind of go, oh,
00:17:43.610 that question wasn't very good.
00:17:45.751 But I think there was a bit
00:17:47.933 of a mental gap or a jump
00:17:50.134 that people had that
00:17:53.017 I don't know if people talk about it much,
00:17:54.358 but ChatGPT, you kind of have to,
00:18:02.782 or just AI in general,
00:18:04.123 you kind of have to feed it
00:18:05.403 certain things for it to
00:18:06.624 give you the proper responses, right?
00:18:09.565 For ChatGPT specifically,
00:18:11.606 because that's the AI that
00:18:13.447 everybody knows,
00:18:15.276 lots of people say, hey,
00:18:16.357 give me a recipe for X, right?
00:18:18.798 And it's a very basic recipe for things.
00:18:22.200 And then you look at the
00:18:23.901 recipe it gives you, and you're like,
00:18:25.642 well, that doesn't look very good.
00:18:27.263 It doesn't have very specific things,
00:18:29.264 right?
00:18:30.405 And yet we have given it a simple,
00:18:34.727 simple prompt and expected
00:18:37.049 a perfect answer, right?
00:18:40.311 When in reality, even if you're,
00:18:42.232 let's say I'm asking Gordon
00:18:43.632 Ramsay for something,
00:18:46.064 I probably would give him a
00:18:47.885 lot more context on, hey,
00:18:50.586 I'm looking for this type
00:18:51.787 of recipe that can fit
00:18:53.548 within this time constraint.
00:18:55.048 Oh, I'm allergic to X, Y, Z,
00:18:56.709 so on and so forth.
00:18:58.090 Can you give me a recipe for this?
00:19:00.771 Right.
00:19:01.111 And he probably would have
00:19:02.452 with his experience and
00:19:03.432 understanding of cooking.
00:19:04.960 give me something instead so
00:19:07.741 my mental flaw I think at
00:19:09.301 first was to give it the
00:19:10.461 simplest prompt and
00:19:11.702 expecting the world from it
00:19:14.062 when in reality basically
00:19:16.143 for it to read my mind but
00:19:17.583 you know even you know we
00:19:19.704 expect so much of this
00:19:20.584 technology I think we are
00:19:21.645 taking some of it for
00:19:22.585 granted but the truth is or
00:19:24.565 the reality of is that you
00:19:25.666 have to give it have to
00:19:27.446 have a little bit of give
00:19:28.346 and take it's a
00:19:29.127 conversation it's about
00:19:30.587 elaborating on what your
00:19:31.967 initial idea is and so
00:19:35.369 That was one of the first
00:19:36.850 mental traps that I had about like, okay,
00:19:39.031 I'm kind of skeptical about
00:19:41.032 AI until I had that first
00:19:44.114 paradigm shift of I
00:19:45.795 actually need to give it
00:19:46.976 more information.
00:19:48.297 I can't give it a simple
00:19:49.438 prompt and expect the best answer.
00:19:53.000 And it should be treated as
00:19:57.923 a back and forth, right?
00:20:00.744 Now, my second paradigm shift was really
00:20:04.522 assuming that AI wasn't
00:20:08.127 going to produce anything good, right?
00:20:10.450 It would help me get a lot
00:20:11.412 of the legwork there,
00:20:13.495 but it would still fall
00:20:14.997 short for a lot of things.
00:20:16.759 Now, the way that I kind of see it,
00:20:21.126 or the best example of this
00:20:22.647 was I remember you
00:20:24.588 specifically asking me like, okay, well,
00:20:26.529 why,
00:20:27.730 why are you scraping information from
00:20:29.871 this document manually?
00:20:31.973 Right.
00:20:32.513 And I said, well, I can't, well,
00:20:35.715 I can't assume that chat
00:20:36.996 GPT would be able to scrape
00:20:39.017 these different fields that
00:20:40.218 I need perfectly.
00:20:41.819 Right.
00:20:42.019 I'm sure it'll give me some
00:20:43.059 flaws and let alone,
00:20:45.121 I'm trying to scrape 50 documents.
00:20:47.042 Right.
00:20:48.162 So you said, well, why don't we just try?
00:20:51.754 And that was a turning
00:20:53.655 moment for me because it
00:20:56.897 was kind of like that's the rule of, hey,
00:20:59.479 innocent until proven guilty.
00:21:01.040 I think from a change
00:21:03.201 management standpoint,
00:21:04.762 a lot of people are resistant to change.
00:21:07.383 But the thing is, you know,
00:21:09.405 the truth was like when you
00:21:11.106 entered those documents,
00:21:12.366 it actually did a really,
00:21:13.367 really good job of scraping
00:21:14.568 that information.
00:21:15.208 Like I would say for that
00:21:16.849 specific use case,
00:21:18.010 it was like 95% accurate.
00:21:20.873 And so that saved me a lot of time.
00:21:23.154 And if I hadn't gone through and said,
00:21:25.935 okay, fine,
00:21:26.536 I'll try and I'll appease you
00:21:28.557 just by throwing all these
00:21:29.797 documents in there and not
00:21:31.198 doing it manually,
00:21:32.078 I would have still been
00:21:33.739 doing it manually.
00:21:34.679 I wouldn't have given the AI a chance,
00:21:37.581 I guess.
00:21:39.222 So now I have this philosophy of,
00:21:43.484 I'm just going to throw
00:21:44.305 everything I can at this
00:21:46.127 tool until it proves me wrong and it says,
00:21:49.770 yeah, it's kind of, you know,
00:21:52.452 this is where the threshold
00:21:53.954 of where I use ChatGPT or
00:21:56.216 this is where I use AI and
00:21:57.497 this is where I don't, right?
00:21:58.958 I'm still on a personal
00:22:00.680 journey to discover where
00:22:02.261 that threshold is.
00:22:03.643 And I'll, you know,
00:22:04.724 as new versions come out,
00:22:06.165 I'll continue to push that boundary.
00:22:08.714 so to speak.
00:22:09.254 Yeah,
00:22:09.475 and as different models come out and
00:22:11.576 whatnot, they change.
00:22:12.717 Sometimes they get better,
00:22:13.598 sometimes they get worse.
00:22:15.139 Exactly.
00:22:16.500 And I think that that's a
00:22:17.321 paradigm shift that a lot
00:22:18.362 of people need to be able
00:22:19.302 to go through with not just AI,
00:22:21.964 but technology in general, right?
00:22:24.366 And you have this technology that is very,
00:22:27.629 very capable
00:22:29.557 And it can do really amazing things.
00:22:33.060 But you've got to know what
00:22:34.781 it's good at and what it isn't.
00:22:36.122 And how else will you know
00:22:37.303 if you're not trying?
00:22:39.044 Yeah.
00:22:39.325 Right.
00:22:39.505 And that was the
00:22:39.985 conversation that we were having.
00:22:41.867 That's like, you got to try.
00:22:42.927 It's like, I don't know if we can do it.
00:22:44.909 You know, it's like,
00:22:45.529 I don't know if we can read
00:22:46.290 a business process flow and tell me,
00:22:47.771 you know,
00:22:48.031 who the roles responsible of it are.
00:22:50.413 Yeah.
00:22:51.214 Right.
00:22:52.301 I don't know.
00:22:52.761 Let's try it out.
00:22:53.602 I never gave it the benefit of the doubt,
00:22:56.764 essentially.
00:22:59.006 It's like if you're a
00:22:59.787 teacher and you just assume
00:23:01.008 your student can't do it,
00:23:02.369 or if you're a parent and
00:23:03.109 you just assume your kid can't do it,
00:23:05.071 you're never going to let
00:23:05.951 them try and do it.
00:23:07.953 What I want to see is I want
00:23:09.494 to see the AI fail,
00:23:11.095 and I want to understand where it fails,
00:23:13.857 why it fails.
00:23:15.330 If I can.
00:23:16.131 Right.
00:23:16.812 And then as a leader or
00:23:19.395 someone that has tasks to do,
00:23:21.157 I will know whether this
00:23:23.720 person or this student is up for the job.
00:23:27.885 Right.
00:23:29.365 Yeah,
00:23:29.825 and that learning piece and having an
00:23:32.647 understanding,
00:23:35.589 it's like why things work
00:23:37.391 the way that they work and
00:23:39.112 where it was successful and
00:23:40.493 where it wasn't successful.
00:23:42.254 Because we've seen this many times, right?
00:23:44.596 One person will do something,
00:23:46.477 they'll get completely
00:23:47.518 different results than the other person.
00:23:49.119 And then one will just think like, oh,
00:23:51.641 tech's not working well.
00:23:53.882 But sometimes it is the tech.
00:23:56.246 The tech's not quite there.
00:23:57.168 It's obviously not perfect.
00:23:58.491 It's got its challenges.
00:23:59.673 But oftentimes,
00:24:01.456 we just don't know how to
00:24:02.217 interact with it.
00:24:04.059 right um and the ceo of
00:24:06.100 nvidia came out recently
00:24:07.840 right um and he basically
00:24:10.582 said that the next
00:24:13.202 generation of workers they
00:24:15.183 shouldn't be learning how
00:24:16.324 to program they shouldn't
00:24:17.364 be learning how to code
00:24:18.504 like that thinking is kind
00:24:20.385 of gone right before it was
00:24:21.786 like you got to learn how
00:24:22.566 to do that because systems
00:24:23.726 are everything and you know
00:24:25.467 you want to be able to
00:24:26.367 operate within various
00:24:27.608 systems and be efficient
00:24:29.108 and effective there now
00:24:30.529 it's like you got to understand how this
00:24:33.524 you know,
00:24:33.844 technology works and what's a
00:24:35.846 good way for you to use it.
00:24:37.247 And I think about the
00:24:38.047 Ironman as an example, right?
00:24:40.349 I don't know what it's called.
00:24:41.289 Like what, you know, Marble, right?
00:24:45.112 Gar, what's his AI called?
00:24:47.293 Jarvis.
00:24:48.014 Jarvis, right?
00:24:48.774 There you go, Jarvis, right?
00:24:49.675 Like he basically, you know,
00:24:51.616 he's just talking to it.
00:24:53.257 He's doing things and it's kind of,
00:24:55.239 he understands its
00:24:56.019 strengths and its
00:24:56.660 limitations and its weaknesses.
00:24:58.381 And because he has such an
00:24:59.341 amazing understanding,
00:25:01.082 he's basically become one with tech.
00:25:04.626 Quite literally, I guess,
00:25:05.487 as part of the movie, right?
00:25:07.069 And I would challenge people
00:25:10.253 to start to do that, right?
00:25:12.235 And I would challenge people
00:25:13.377 to start to play around with it and see,
00:25:15.860 can this be done or not?
00:25:17.642 And if it can't be done,
00:25:19.104 then you've learned that, okay,
00:25:20.165 you know what?
00:25:20.506 This is not...
00:25:21.667 an application that is good
00:25:23.068 for ai to to to well it's
00:25:25.230 not good for us to leverage
00:25:26.751 ai to use to effectively
00:25:29.052 execute it but then you're
00:25:30.433 going to be surprised and i
00:25:32.114 have been surprised many
00:25:34.256 times with the things that
00:25:35.577 it can do and that i can
00:25:37.778 get better at it and i
00:25:39.099 think the generative ai has
00:25:40.500 such a huge potential in
00:25:42.501 the business world to do
00:25:43.602 good and to also do some bad um
00:25:46.917 because it isn't great at everything.
00:25:49.258 And just like you said, Hey,
00:25:50.419 innocent to proving guilty.
00:25:52.060 You've also got to look at
00:25:53.001 it from the other way around, right?
00:25:54.381 Like you can't just assume
00:25:56.042 that it's all good and that
00:25:57.783 the quality is there
00:25:58.904 because it provides you
00:26:00.165 some very logical answers
00:26:02.686 and it sometimes makes shit up.
00:26:04.607 Yeah.
00:26:05.548 I think that the role of the human,
00:26:08.790 at least right now or in
00:26:10.791 the near future is that it,
00:26:15.665 The human almost evolves
00:26:16.746 into a quality assurance type of role.
00:26:21.830 Speaking from personal experience,
00:26:23.392 I've tried to get ChatGPT
00:26:24.773 to build me code for certain things.
00:26:28.136 And it gets it to about 80%
00:26:30.778 of the way there.
00:26:32.439 And then you put the code into, let's say,
00:26:34.761 Power BI.
00:26:35.362 I'm using DAX or whatever, Python.
00:26:38.544 And Python comes back and
00:26:40.526 spits back a bunch of errors.
00:26:43.368 And where the human has to come in,
00:26:46.091 or sorry, let me backtrack for a second.
00:26:49.113 I've used the tool or tell ChatGPT to say,
00:26:52.356 hey, your code has returned this error.
00:26:56.500 It will say, oh, sorry.
00:26:59.142 You're right.
00:26:59.763 Let me fix that code for you.
00:27:01.204 It gives me a new code.
00:27:03.766 But I put it back in.
00:27:05.368 Either new errors occur or
00:27:07.289 the same error continues.
00:27:09.932 And
00:27:11.558 where the human's role
00:27:12.659 shifts is that I don't have
00:27:14.401 to do the heavy lifting for
00:27:15.643 coding anymore.
00:27:16.504 I have to become a very good debugger.
00:27:19.267 I have to be very good at
00:27:20.869 ensuring the quality of that code.
00:27:23.652 So rather you learn to
00:27:28.293 program still to understand
00:27:31.855 how to debug those things
00:27:33.455 or alternatively you know
00:27:35.716 maybe another idea is to be
00:27:37.817 very good at figuring out
00:27:39.578 how to debug specific or
00:27:41.439 common problems that the ai
00:27:42.899 tool comes up with so we're
00:27:44.780 in a little bit of that
00:27:45.660 transitionary period where
00:27:48.401 AI can do a lot of the heavy lifting.
00:27:49.921 You don't have to do things from scratch.
00:27:51.422 It's going to make your
00:27:53.843 ability to be more efficient,
00:27:55.283 save you time at work.
00:27:57.504 Right.
00:27:58.404 Yeah.
00:27:59.104 And I think that like
00:27:59.904 obviously quality assurance
00:28:00.985 is a big component of it.
00:28:02.485 But I would say that before
00:28:04.066 quality assurance,
00:28:04.946 because obviously quality
00:28:05.786 assurance comes much later
00:28:06.867 in the process.
00:28:08.607 It's you've got to be able
00:28:09.467 to guide it correctly.
00:28:11.108 And I think that that's
00:28:11.829 where the majority of the
00:28:12.729 people struggle right now.
00:28:14.730 Like it's a new thing.
00:28:15.670 And I think that most people
00:28:17.991 haven't really grasped
00:28:20.394 like a good enough
00:28:21.155 understanding of what it is,
00:28:23.696 let alone like how it
00:28:24.716 functions and why it
00:28:25.677 functions the way that it functions.
00:28:27.998 That if you're not guiding
00:28:29.559 it correctly and you're not
00:28:31.500 structuring things well,
00:28:33.380 and we won't get into too many details,
00:28:34.801 right?
00:28:35.001 But obviously like you've
00:28:36.082 got to think about, you know,
00:28:39.083 how you lay out the instructions,
00:28:40.804 the things you say,
00:28:41.624 the things you don't say,
00:28:43.185 when you say certain things,
00:28:44.486 how often you say certain things, right?
00:28:46.447 Like,
00:28:47.327 it's a very powerful request
00:28:50.389 yeah yeah all right there's
00:28:52.130 so many different factors
00:28:53.171 that you have to be able to
00:28:54.052 consider um and and it
00:28:57.054 starts with guiding it and
00:28:58.315 if you guide it well from
00:28:59.595 the first time and let's
00:29:00.596 say that you're coming up
00:29:01.357 with this project that
00:29:02.798 requires a whole bunch of
00:29:03.798 different prompts and you
00:29:05.339 know it's a very complex
00:29:06.580 thing to kind of build out um
00:29:09.482 sure.
00:29:09.762 The quality assurance at the
00:29:10.742 end of it is going to be important,
00:29:11.943 but like you need to guide
00:29:13.243 it well from the beginning
00:29:14.883 and guided well throughout the process.
00:29:17.504 And you've got to know where
00:29:18.765 to put the human in and
00:29:20.725 what role the human needs
00:29:22.005 to play to make sure that
00:29:23.726 you get something good at the end of it.
00:29:25.086 Otherwise it's just going to
00:29:25.946 be garbage in garbage out.
00:29:28.267 Yeah.
00:29:28.507 And we've seen lots of
00:29:29.367 stories and news about that.
00:29:32.128 Right.
00:29:33.268 Have you heard of the, um,
00:29:37.150 question that helps people
00:29:39.571 determine your work style,
00:29:41.933 where it's like, hey,
00:29:42.713 would you rather fight a
00:29:47.616 horse-sized duck or fight a
00:29:52.739 hundred little duck-sized horses?
00:30:04.848 Yeah, basically it's like,
00:30:06.990 would you rather take one
00:30:08.652 big task and just tackle
00:30:11.094 the whole task or chop up
00:30:13.095 the little task into
00:30:14.196 individual bits and then
00:30:15.818 tackle every single one and
00:30:17.359 kind of divide and conquer, right?
00:30:19.181 I'm not sure if I got that analogy right,
00:30:21.423 but you get the point, right?
00:30:24.565 And what we've discovered,
00:30:25.806 at least from my standpoint,
00:30:28.609 is that this current state of
00:30:32.062 these large language models
00:30:33.544 is that it works well with
00:30:34.905 these smaller series of
00:30:37.768 prompts and having you as a
00:30:40.551 human being figure out what
00:30:41.752 those little questions are
00:30:43.154 going to be to help direct
00:30:45.556 and create what your end
00:30:46.677 product with the language model will be.
00:30:49.580 Right.
00:30:50.060 So there is a specific approach.
00:30:53.281 Let's move on to the psychology of it.
00:30:55.722 Right.
00:30:55.842 Because one of the things
00:30:56.642 that I know that you kind of went through,
00:30:58.463 Gar,
00:30:59.923 and you use your experience with it
00:31:01.823 was this feeling of guilt.
00:31:04.984 Right.
00:31:06.245 So you want to talk us through that?
00:31:08.424 Yeah, sure.
00:31:10.565 I think it won't just be me.
00:31:12.346 And this really circles back
00:31:13.927 to some potential
00:31:15.728 resistance to using AI tools.
00:31:18.989 I think some people in
00:31:21.731 essence think it's like
00:31:23.092 cheating because the AI
00:31:26.033 tool is doing so much of
00:31:27.394 the heavy lifting for you
00:31:28.835 and doing all the thinking.
00:31:29.855 Well, you know,
00:31:31.176 what am I good for if it's doing all this,
00:31:33.537 right?
00:31:34.097 Something that used to take
00:31:35.078 me an hour now takes me, you know,
00:31:38.603 two minutes, am I still as valuable,
00:31:42.345 right?
00:31:43.346 Now,
00:31:44.006 I want to bring up a story that one of
00:31:46.908 our colleagues, Catherine,
00:31:48.909 got to give her credit for
00:31:49.770 the story brought up,
00:31:50.691 but I think it's extremely
00:31:52.492 relevant to this scenario
00:31:54.373 or this particular topic
00:31:55.774 we're talking about.
00:31:57.700 is that in 1947, Betty Crocker,
00:32:00.441 you're familiar with Betty Crocker,
00:32:01.561 right?
00:32:01.802 The cakes.
00:32:03.082 Yeah.
00:32:03.842 The cakes and the different
00:32:06.523 mixes that they make that
00:32:08.544 you can buy in grocery stores.
00:32:10.044 They essentially created
00:32:13.385 this new product that was a cake mix.
00:32:17.506 And all you had to do was just add water.
00:32:20.810 They figured out how to dehydrate eggs.
00:32:24.053 They figured out how to
00:32:25.053 dehydrate the milk so that
00:32:26.935 everything could be on a shelf.
00:32:29.137 And really you just needed
00:32:30.337 the waters to kind of combine everything,
00:32:33.220 the baking powder or soda or whatnot.
00:32:35.682 It's all in there.
00:32:36.542 So, you know,
00:32:39.084 they thought that they hit it
00:32:40.345 out of the park for your
00:32:41.827 typical busy homemaker.
00:32:44.610 All they have to do is add water,
00:32:46.691 put it in a pan, throw it in the oven,
00:32:48.593 and bam, homemade cake, right?
00:32:53.376 Don't have to buy eggs.
00:32:54.437 You don't have to buy all
00:32:55.277 these other ingredients and
00:32:56.778 figure out the ratios.
00:32:57.819 Like, man, it saves you so much time.
00:33:00.321 But then what was
00:33:02.662 interesting was that they
00:33:03.683 had a really hard time
00:33:05.004 convincing people to use
00:33:06.765 the product because people
00:33:09.447 started to think, well,
00:33:11.028 if I'm just adding water, you know,
00:33:14.870 I feel like I'm not really doing the work.
00:33:18.192 They brought in a bunch of
00:33:19.093 psychologists to analyze
00:33:22.195 and ask the homemakers or
00:33:23.876 the people that would
00:33:25.037 potentially buy this product,
00:33:26.458 why aren't you buying the product?
00:33:27.618 And that's what they found,
00:33:28.679 that there's this element of guilt.
00:33:31.781 They felt like there should
00:33:32.942 have been more work that
00:33:34.223 they were supposed to do.
00:33:37.412 It was really clever what they did.
00:33:39.456 I think what they changed
00:33:42.543 the box to say and changed
00:33:44.106 their formula was to say, add eggs,
00:33:47.032 add milk.
00:33:49.368 so that the homemaker or
00:33:51.289 whoever's buying the
00:33:51.969 product felt like they had
00:33:53.090 to do a little bit more,
00:33:54.071 and then the sales just
00:33:55.592 took off after that.
00:33:57.853 So it's interesting to think
00:33:59.494 that there's a bit of a
00:34:00.914 human psychology standpoint
00:34:03.696 to this as well,
00:34:04.897 that there's this perception of, oh,
00:34:07.218 I'm using this AI tool and
00:34:11.781 it's just not gonna be as good or,
00:34:15.247 Am I really, like,
00:34:17.729 should I be spending more
00:34:18.870 work and more of my time
00:34:21.612 doing this work instead of
00:34:23.894 getting AI to do all the heavy lifting,
00:34:26.016 right?
00:34:26.977 And I think that we need to
00:34:29.659 take a step back and be like, okay,
00:34:31.320 what is the task that I
00:34:32.841 have at hand and what is my
00:34:34.262 end deliverable?
00:34:36.304 How can I use AI or whatever
00:34:39.527 tool to help me either do the job faster,
00:34:45.822 And at the same time,
00:34:48.148 me applying whatever I need
00:34:50.975 to do to ensure that the
00:34:52.138 quality is still there.
00:34:54.638 So that's just kind of the
00:34:56.019 interesting story about the guilt part,
00:34:57.500 right?
00:34:57.700 So I definitely went through
00:34:59.200 that experience of like, you know,
00:35:00.981 I'm supposed to do all this research.
00:35:03.283 I'm supposed to comb through
00:35:04.383 all these reports to figure out X, Y,
00:35:06.344 and Z. If I just ask ChatGVT,
00:35:08.746 I'm sure it's going to miss
00:35:10.546 the X in the X, Y, and Z, right?
00:35:13.428 So, but you just don't know until you ask.
00:35:16.730 And then once it does spit
00:35:18.171 something out for you,
00:35:19.031 my brain shifts to thinking, okay, how
00:35:21.357 Is there anything missing here?
00:35:22.618 Do I need to go and ask it
00:35:25.340 more questions or just supplement it?
00:35:27.902 Right.
00:35:28.983 Right.
00:35:30.023 And it's funny because decades later,
00:35:31.524 you see all these bakeries
00:35:32.885 that are basically just, you know,
00:35:37.198 Creating the products of a Betty Crocker,
00:35:39.459 right?
00:35:40.539 Putting some decoration on
00:35:41.739 top and selling it on their own, right?
00:35:44.840 It's interesting to see how
00:35:45.860 perception has changed so much.
00:35:47.000 I didn't realize that people
00:35:48.061 were so hesitant about it before.
00:35:51.482 That's interesting.
00:35:52.002 Imagine, yeah,
00:35:52.682 you could have Betty
00:35:53.542 Crockers that are just add water.
00:35:56.143 It could have been simpler.
00:35:57.912 But now we have to add egg
00:35:59.253 and milk just because of a
00:36:02.515 perception thing.
00:36:03.675 That's cool, man.
00:36:05.016 So look,
00:36:05.977 we are about almost 40 minutes into this,
00:36:08.358 right?
00:36:09.119 What do you think about just AI in general,
00:36:11.400 Gar?
00:36:11.740 Is this a good thing?
00:36:12.541 Is this a bad thing?
00:36:13.481 And what would you say to
00:36:15.583 the listeners here who are
00:36:18.865 thinking about AI and what
00:36:21.887 would be a good way for
00:36:22.847 them to start to understand
00:36:25.249 it or continue to
00:36:26.329 understand it a little bit better?
00:36:30.254 I think that if you're relying on it,
00:36:34.840 not just for entertainment purposes,
00:36:37.543 you should be skeptical
00:36:38.785 because it's kind of your
00:36:40.107 name on the line at the end
00:36:41.388 of the day for whatever
00:36:42.750 work product you're delivering.
00:36:45.874 But, you know,
00:36:49.300 challenge your original
00:36:51.302 thought of like oh
00:36:52.163 everything has to be done
00:36:53.165 by myself use these tools
00:36:56.609 that are available for you
00:36:58.090 to see how it can change
00:37:00.253 your workflow right it
00:37:02.716 could be i know for me
00:37:04.418 specifically i work better
00:37:05.840 with a draft one that
00:37:07.922 someone on our team may have
00:37:09.721 brought up and i'm really
00:37:11.102 good at building on that
00:37:12.664 right that's why you and i
00:37:14.145 work so well together
00:37:15.186 because you come up with
00:37:17.047 first draft and i kind of
00:37:18.869 give it the polish right if
00:37:20.450 you will so if you're
00:37:23.393 relying on it be skeptical
00:37:25.635 but at the same time push
00:37:27.617 those boundaries again
00:37:31.000 innocent until proven
00:37:31.921 guilty assume that ai can
00:37:33.602 do it until it tells you that it can't
00:37:36.906 But don't assume so much
00:37:38.327 that you don't check the quality.
00:37:40.007 That you don't check your work.
00:37:41.448 Get yourself in trouble, right?
00:37:43.969 Still, yeah.
00:37:45.569 Right.
00:37:46.150 That's good, man.
00:37:46.750 Good advice, Gar.
00:37:47.430 I think I hope our listeners take on that.
00:37:49.611 So guys, everybody,
00:37:50.971 thank you so much for listening.
00:37:52.332 If you stayed here till the
00:37:53.432 end of the show,
00:37:54.913 we're very excited about it.
00:37:56.354 We've got a lot of exciting
00:37:57.454 guests that we'll be having
00:37:59.035 on from different paths of life,
00:38:02.316 different careers, different industries,
00:38:04.136 different roles.
00:38:05.917 different levels of the
00:38:07.137 hierarchy and everything else in there.
00:38:10.478 And I think it's going to be
00:38:11.319 very interesting to see different inputs.
00:38:14.140 The next episode that we will have,
00:38:15.420 it's going to be an IT
00:38:16.260 director of a biotech
00:38:17.460 company that's based out of California,
00:38:20.021 but he has spent the
00:38:20.921 majority of his career in
00:38:23.322 aerospace and aviation manufacturing.
00:38:26.243 So it's going to be
00:38:26.743 interesting to kind of hear
00:38:27.764 from his perspective, right?
00:38:29.084 Where does he see manufacturing going?
00:38:31.185 Where does he see tech going?
00:38:32.825 He started his career in
00:38:33.926 operations before moving
00:38:35.446 into the more IT and
00:38:37.647 digital transformational kind of world,
00:38:40.128 right?
00:38:40.348 So he's got that perspective
00:38:41.828 of process and technology
00:38:43.129 and kind of working together.
00:38:44.149 So it'll be interesting to
00:38:45.270 see what his input is on
00:38:47.510 some of these topics.
00:38:48.671 We'll be a lot more structured, right,
00:38:50.051 in terms of some of the
00:38:50.731 questions we might want to ask him.
00:38:52.672 about there um we're always
00:38:54.453 looking for feedback right
00:38:55.633 this is episode zero um you
00:38:57.433 know we will we're always
00:38:59.154 looking to kind of improve
00:39:00.234 as well if you've got any
00:39:01.134 suggestions any thoughts
00:39:02.855 any things you want us to
00:39:03.775 talk about anybody that you
00:39:04.935 think should be on the show
00:39:05.936 or we should chat with
00:39:07.556 please let us know right um
00:39:10.837 reach out to me on linkedin
00:39:12.298 or or Garlon or linkedin
00:39:14.018 right or send me an email
00:39:15.218 comment on wherever this
00:39:16.619 video is posted yeah
00:39:18.139 comment whatever you want there right um
00:39:21.380 Let us know.
00:39:22.681 And we still need a name, right?
00:39:24.682 As you guys can see here, you know,
00:39:26.303 placeholder for a cool podcast name,
00:39:28.404 right?
00:39:28.564 So give us a cool podcast name.
00:39:31.105 We're looking for them.
00:39:31.805 We're not very good at naming things.
00:39:33.886 Remember that.
00:39:34.667 So please help us out on that.
00:39:36.688 We're going to be publishing
00:39:37.508 this every couple of weeks.
00:39:39.129 Two to three weeks is kind
00:39:40.130 of what we're aiming for
00:39:40.950 each of the episodes.
00:39:42.271 Follow us on LinkedIn to get
00:39:44.152 more information on when
00:39:46.594 the next episode is going to be.
00:39:48.235 So, Gar,
00:39:48.856 thanks a lot for being on the show, man.
00:39:50.276 I think we'll bring you back
00:39:51.237 every now and then to kind of see.
00:39:52.858 I like that whole thing that
00:39:53.899 you're talking about,
00:39:54.639 kind of like documenting
00:39:56.401 things and how things evolve.
00:39:57.761 I think it'll be cool to see later on,
00:40:00.443 like, hey,
00:40:00.964 some of the things that we were
00:40:01.964 talking about,
00:40:02.625 whether they came to life
00:40:03.665 or they didn't come to life or whatnot,
00:40:06.387 man.
00:40:06.607 So thanks for coming on.
00:40:08.128 Yeah, thanks for having me.
00:40:09.871 All right, guys.
00:40:11.034 See you next time.
00:40:12.337 See ya.
