1
00:00:00,000 --> 00:00:05,300
All right. Well, Adib, you and I talk about AI stuff a lot nowadays. I don't know. Since the

2
00:00:05,300 --> 00:00:10,500
summer, our brains must have gotten warmed up. And now we're slowly getting chilled out some more.

3
00:00:10,500 --> 00:00:16,660
I'm not really sure. But I thought you, we, the last time we talked, you were like, I'm going to

4
00:00:16,660 --> 00:00:22,780
go figure out what agentic AI really means. And I was like, this is going to be great. So you told

5
00:00:22,780 --> 00:00:28,100
me you did a little, you know, hanging out, chilling out at home, cleaning up the yard,

6
00:00:28,200 --> 00:00:33,580
stuff like that. And then you figured out this weekend what agentic AI means. So I'm ready.

7
00:00:33,980 --> 00:00:35,580
What's, what's the, what's the big reveal?

8
00:00:36,220 --> 00:00:41,060
I didn't, I didn't know that I figured out exactly what it means. I kind of figured out what it's not

9
00:00:41,060 --> 00:00:47,920
and more where it fits historically. So I was a little bit kind of, because I'm old, I remember

10
00:00:47,920 --> 00:00:54,040
clearly the emergence of two application platforms, the web and mobile. I was doing a little bit of

11
00:00:54,040 --> 00:01:02,000
reflecting on that history. And what I noticed was that, you know, back in the nineties started building

12
00:01:02,000 --> 00:01:07,760
websites and people called everything websites. No one used the term web app, right? People started

13
00:01:07,760 --> 00:01:15,000
using the term web app after the emergence of this famous Ajax pattern. When people saw Google maps and

14
00:01:15,000 --> 00:01:20,460
were like, how's it doing this? And, you know, kind of work backwards. And there was this,

15
00:01:20,780 --> 00:01:28,420
if you remember mashups and web 2.0 and all of that, right. And, and fundamentally what it meant

16
00:01:28,420 --> 00:01:36,480
was there was a new UX pattern. So now if you follow the UX pattern for building code web apps,

17
00:01:36,480 --> 00:01:43,520
and then you use Ajax and single page applications, you could deliver a particular type of experience.

18
00:01:43,520 --> 00:01:49,160
And then businesses were like, build me a web app. They would go to a developer or a team or hire a web

19
00:01:49,160 --> 00:01:57,720
app team. And, um, it meant something. So I kind of, uh, then, you know, after that a little bit later,

20
00:01:57,720 --> 00:02:04,760
you know, I, I worked originally at Blackberry, which was like mobile stuff before mobile. I owned a Palm

21
00:02:04,760 --> 00:02:10,060
pilot at one point in my life. So I'm sure, I'm sure the kids out there appreciate you explaining

22
00:02:10,060 --> 00:02:16,140
what Blackberry is. That's the Blackberry and Palm pilot. Like back in the stone age, right? You had

23
00:02:16,140 --> 00:02:21,440
to like buy this, like you could buy this little PDA for, I don't know what to probably be like more

24
00:02:21,440 --> 00:02:27,040
than a thousand dollars at this point. And it was like black and white screen more looked like a Kindle,

25
00:02:27,040 --> 00:02:35,220
like than a, than a modern device. And then they didn't have any network in it. You had to like plug

26
00:02:35,220 --> 00:02:40,700
it with a cable to a computer to transfer any kind of information. Um, but then eventually, you know,

27
00:02:40,700 --> 00:02:46,600
we had the mobile, everybody had a cell phone, but it was a flip phone. Sure. You could write apps on it,

28
00:02:46,600 --> 00:02:53,800
but there wasn't enough screen and keyboard to do anything really practical beyond, uh, calling,

29
00:02:53,800 --> 00:02:58,140
which was kind of the killer use case to begin with. And then eventually you got short text message,

30
00:02:58,140 --> 00:03:04,740
right? But really apps and app stores and app ecosystems didn't become a thing until the iPhone,

31
00:03:04,740 --> 00:03:10,960
because there was a UX pattern that came with the iPhone, like this whole touch thing. And people

32
00:03:10,960 --> 00:03:18,780
were like, I can pinch to zoom and all this type of stuff. And so I kind of, I kind of go like,

33
00:03:18,780 --> 00:03:24,340
then people start saying, build me a mobile app. What did they mean by build me a mobile app? They

34
00:03:24,340 --> 00:03:29,940
meant build me something that's optimized for touch and for a touch interface, which is a UX pattern,

35
00:03:29,940 --> 00:03:34,080
which can run on a handheld device, whether it was from Apple or Apple's competitors.

36
00:03:35,120 --> 00:03:44,040
And so my observation is that whenever we give a name to a type of like application,

37
00:03:44,560 --> 00:03:57,500
it's what we're doing is where that emerges when you have a new UX pattern and you have a type of,

38
00:03:57,500 --> 00:04:04,400
uh, capability that didn't exist before. Like for the web, that was that Ajax capability.

39
00:04:04,400 --> 00:04:10,640
And in-place update, which birthed the single page app and the UX pattern that goes with it. Then you got

40
00:04:11,360 --> 00:04:18,480
on the mobile world, the touch interfaces with no keyboard, big screen, uh, relatively speaking,

41
00:04:18,480 --> 00:04:26,080
and, uh, uh, the whole UX pattern that goes with it. So when it comes to, uh, gen AI and the foundation

42
00:04:26,080 --> 00:04:33,680
models, well, that's definitely a new capability, a hundred percent. And it does enable a new type

43
00:04:33,680 --> 00:04:43,600
of UX. And so when you have UX plus new type capability, I guess I'll call that an agent or

44
00:04:43,600 --> 00:04:48,800
like it, it kind of gets its own name, right? So you'll see people talking about AI agents,

45
00:04:48,800 --> 00:04:54,640
people talking about agent, agentic apps. And so rather than trying to come up with a list of,

46
00:04:54,640 --> 00:04:59,920
you must do these 15 things, which I don't know what's on that list. I'm just saying, yes,

47
00:04:59,920 --> 00:05:07,920
it's a thing because it's a UX pattern plus a capability and it's just software that uses the

48
00:05:07,920 --> 00:05:17,760
foundation models. And it's trying to offer a user experience that is distinct from mobile and

49
00:05:17,760 --> 00:05:22,400
distinct from like web and maybe even requires less effort to use it.

50
00:05:22,400 --> 00:05:29,680
Mm. Yeah. No, I, I think, I think that's, that's great framing one, because I think about Ajax way

51
00:05:29,680 --> 00:05:35,600
too much, like, like in, in, in the, in the same, like, I think very similar to how you were thinking

52
00:05:35,600 --> 00:05:42,640
about it there as it's, it's, it, it, well, it, it was like, uh, uh, an example of what we're,

53
00:05:42,640 --> 00:05:47,440
you're just talking about that I lived through and actually did some work with and which is to say,

54
00:05:47,440 --> 00:05:52,320
I'm re-summarizing what you, you're saying, but like, you have some new type of, um, I'm going to

55
00:05:52,320 --> 00:05:58,320
call it like infrastructure, faceless technology. And then you have a new way of using it, new, new UX,

56
00:05:58,320 --> 00:06:03,840
whether it's a UI or delivery or whatever. Right. And it does, uh, and then that drives

57
00:06:05,040 --> 00:06:10,400
new opportunities and ways of doing stuff. That's my very abstract way of putting it.

58
00:06:10,400 --> 00:06:18,160
Yeah. Exactly. I mean, this is why I'm like agent is simply, I use, I built some software to talk to

59
00:06:18,160 --> 00:06:25,200
a language model or to a foundation model and I wrapped it in a UX. So users don't even know that

60
00:06:25,200 --> 00:06:29,680
there's a foundation model underneath. Right. They're not the ones, they're not the ones talking

61
00:06:29,680 --> 00:06:34,800
to the foundation model directly. Right. And, and I think, I think that highlights something that is,

62
00:06:36,320 --> 00:06:41,200
I don't know, uh, important about like what, what, what I like to call enterprise AI, I guess

63
00:06:41,200 --> 00:06:46,640
for consumer too, but like, I don't really care about consumer tech so much, but, but in, in a,

64
00:06:46,640 --> 00:06:51,200
in a business and an enterprise context, well, my, my first problem is always like, well,

65
00:06:51,200 --> 00:06:56,400
what are you going to do with this thing? Yeah. Right. Like, but I think, I think what you're

66
00:06:56,400 --> 00:07:05,200
getting at here is one, let's call it like strategic thinking tool to get closer to answering that

67
00:07:05,200 --> 00:07:12,320
question, which is, well, let's not think so much about like training and models and all this stuff.

68
00:07:12,320 --> 00:07:18,960
How let's start with how we're going to interact with it. And are there different like modes of

69
00:07:18,960 --> 00:07:24,720
interacting with this thing that we can kind of sort through kind of like, um, you know,

70
00:07:24,720 --> 00:07:28,640
those books where you've got like flip cards and you've got three layers of a monster you can make,

71
00:07:28,640 --> 00:07:32,320
like, let's kind of like flip through and figure out like what makes sense here. Right.

72
00:07:32,320 --> 00:07:39,840
Yeah. Because, because you know, the, uh, and, and, and I think, I think it's someone like Ben Thompson,

73
00:07:39,840 --> 00:07:47,120
who I heard first saying this, but like, you know, the, uh, the, the, like the chat part of chat GPT

74
00:07:47,120 --> 00:07:52,480
and all the other ones now is almost like an accidental innovation. Like, like when that,

75
00:07:52,480 --> 00:07:57,360
when that was released two years ago now, I think, I think the, the, the team over at open AI was just

76
00:07:57,360 --> 00:08:02,320
sort of like, well, here's something so that we can mess around with this. Right. And then,

77
00:08:02,320 --> 00:08:08,720
and then it became the thing, right. Like, like it became huge. And it's exactly what you're saying

78
00:08:08,720 --> 00:08:15,520
is like, here, here's a combination of like an infrastructure technology and the UI finally,

79
00:08:15,520 --> 00:08:21,360
that has good user experience. Right. And, and like, we bring these two things together and you have

80
00:08:21,360 --> 00:08:27,200
something new essentially. Yeah, for sure. And I'm like, I'm, I'm convinced that

81
00:08:27,600 --> 00:08:35,280
UX designers will ultimately be the ones that define what an agentic slash agent is. So I'm calling

82
00:08:35,280 --> 00:08:42,720
AI agent equals to just software that uses foundation models though. I've seen hints of what that could

83
00:08:42,720 --> 00:08:49,680
look like. So I'm, I'm, I'm currently like really intrigued by this idea that a key difference between

84
00:08:50,640 --> 00:09:01,680
um, regular UI and, uh, AI powered generative UI is the following in a, in a regular kind of, whether

85
00:09:01,680 --> 00:09:07,440
it's a mobile app or a web app, I have to clickety click around to get to a widget to do something.

86
00:09:08,960 --> 00:09:14,800
Right. Like classic example, uh, say you're using an application with some sort of credit card

87
00:09:14,800 --> 00:09:19,040
stored in a, I don't know, Uber, right? Like as an example, you have to navigate to

88
00:09:20,320 --> 00:09:28,480
the screen and settings, payments, credit card, edit, ad, blah, blah, blah. Right. Or you go into

89
00:09:28,480 --> 00:09:35,760
the web app for Amazon and you go and you're like clickety clicks profile settings, payment,

90
00:09:35,760 --> 00:09:44,080
add, edit credit card. And you know what, if there was an AI in that was part of the app or an AI

91
00:09:44,080 --> 00:09:51,120
or a UX that's AI driven, I should just be able to say update my credit card. Hmm. Yeah. I just said

92
00:09:51,120 --> 00:09:55,280
that whether I type it on the keyboard or I say it, it doesn't really matter what the modality is for

93
00:09:55,280 --> 00:10:02,080
the input, but I would expect the widget to come to me on demand for the thing I'm doing right now.

94
00:10:03,280 --> 00:10:09,120
Yeah. Right. Right. Right. And then, and then, I mean, also if you, you know, in that very specific

95
00:10:09,120 --> 00:10:16,080
example, if you had enough like security protections and et cetera, you could sort of like give it one

96
00:10:16,080 --> 00:10:20,080
time permission to access your wallet or whatever. I, you know, I don't even know what I'm talking

97
00:10:20,080 --> 00:10:28,000
about, but like it, you would almost have your agent talking with its agent to your credit card.

98
00:10:28,000 --> 00:10:34,000
Okay. So, so that, that touches on like a common theme running through various definitions of agent is

99
00:10:34,000 --> 00:10:42,240
is that the agent is autonomous. Yeah. And actually does stuff on your behalf. And I'm like,

100
00:10:43,600 --> 00:10:50,880
eh, don't really care. Like I don't need this thing to be, I don't need this thing to yet be a Star

101
00:10:50,880 --> 00:10:58,400
Trek level agent. Right. I needed to just do something stupid. Can you just free me from having to click

102
00:10:58,400 --> 00:11:04,400
through four different screens and remember, have my brain remember how to navigate to the payment

103
00:11:04,400 --> 00:11:11,760
settings page? Yeah. Yeah. Yeah. And like, can you just like figure out that like, like,

104
00:11:11,760 --> 00:11:16,960
can you imagine like an app where the only thing you see is just a blank screen and a chat bar at the

105
00:11:16,960 --> 00:11:24,960
bottom? And then as you try to do different things, like widgets start showing up and allowing you to do the

106
00:11:24,960 --> 00:11:31,600
things you need to do. It's a very different, it's a different potential way to actually just design a

107
00:11:31,600 --> 00:11:38,960
user experience. And that's what I'm excited about. So for me, I'm going to call that agent, an AI agent.

108
00:11:39,760 --> 00:11:45,200
Yeah. Yeah. You know, I think, I think maybe to, to maybe generalize it to be too general,

109
00:11:46,640 --> 00:11:54,080
you know, essentially maybe an agent, an AI agent is something that's figures out, not figures out as

110
00:11:54,080 --> 00:12:00,400
the wrong word in this context, that it's kind of like mediating you using an AI thing. Like it sits

111
00:12:00,400 --> 00:12:06,640
between you and the model. And like, it doesn't have to be, so, you know, you could say like a chat

112
00:12:06,640 --> 00:12:13,280
thing is an agent, which, you know, once, once you understand how much goes on with those chat

113
00:12:13,280 --> 00:12:17,680
applications that has nothing to do with AI, you're like, oh yeah, I see. Right. I mean,

114
00:12:17,680 --> 00:12:21,600
there's like all sorts of stuff that they're, they're doing, you know, pre and post processing

115
00:12:21,600 --> 00:12:30,160
and all of that, that, so you need that kind of, and you know, words sometimes matter, but like,

116
00:12:32,000 --> 00:12:36,720
the whole point of the word agent is that you have something acting on your behalf,

117
00:12:36,720 --> 00:12:41,120
right. Like doing something for you. And so to that extent, like the chat thing's doing

118
00:12:41,120 --> 00:12:46,160
it for you. And just like you're saying, if, if the agent is, uh, figuring out the right, the right,

119
00:12:46,160 --> 00:12:50,560
the good UI for you to have to interact with stuff, then, then, then that makes sense as well.

120
00:12:50,560 --> 00:12:57,760
So, yeah, I mean the, the, the, the crazy like part of agents that I just not convinced is ever

121
00:12:57,760 --> 00:13:03,600
going to really work. Um, well, I could, I, I'm probably could be, I hope I'm wrong about it,

122
00:13:03,600 --> 00:13:10,000
but it's like, there's kind of this obsession with replacing people. Like if you go to certain

123
00:13:10,000 --> 00:13:15,440
companies, websites, especially, uh, AI company startups, they're going to make some very bold

124
00:13:15,440 --> 00:13:21,360
claims about how you're going to, we're going to replace your human, your human with an agent,

125
00:13:21,360 --> 00:13:27,600
right? Uh, some of them make less of a bold claim, which is we're going to create an agent

126
00:13:27,600 --> 00:13:33,280
that can help your employee, like be better. So maybe that's 50% of their job, but

127
00:13:33,600 --> 00:13:40,800
they're like much more productive. And unlike my ambition is so much lower than that. I just

128
00:13:44,080 --> 00:13:48,240
there's so much lower hanging fruit. Like, why don't we just focus on that? So like,

129
00:13:48,240 --> 00:13:53,600
this is probably going to go through the standard kind of what they call it, the hype cycle, right?

130
00:13:53,600 --> 00:13:57,600
Like, like, I don't know if we're yet at what do they call the top of the cycle?

131
00:13:57,600 --> 00:14:03,120
Uh, the, the, the, it's like the, uh, that is it the apex of expectations? No,

132
00:14:03,120 --> 00:14:07,360
it's something like that. Yes. Yeah. Yeah. All I know is that there's the, the, the through

133
00:14:07,360 --> 00:14:11,680
of disillusionment and then the slope of enlightenment. That's right. The trough of disillusionment.

134
00:14:11,680 --> 00:14:18,960
Yes. Exactly. So it's like, it's, uh, Oh, and then another good phrase, the plateau of productivity,

135
00:14:18,960 --> 00:14:26,320
right? Yes. That's, that's, that's both judgmental and descriptive. Right. Exactly. Exactly. Well,

136
00:14:26,320 --> 00:14:31,360
there was, there was an interesting announcement, I think maybe a couple of weeks ago from cloud with

137
00:14:31,360 --> 00:14:36,560
their computer use. I don't know if you heard about that. Yeah. Yeah. So it's, it's kind of like,

138
00:14:36,560 --> 00:14:42,560
okay, are you going to adapt the AI to understand what every screen looks like? Maybe do image

139
00:14:42,560 --> 00:14:48,560
recognition or maybe tap into the assistive features of the OS. So it doesn't have to like,

140
00:14:49,120 --> 00:14:52,960
you know, kind of use computer vision models to understand what's happening on the screen.

141
00:14:52,960 --> 00:14:58,240
But either way, you're trying to teach the AI how to use an existing tool. I don't know. Maybe you

142
00:14:58,240 --> 00:15:03,760
should change the tool. Maybe we should change the apps and build them more on the AI's capability

143
00:15:03,760 --> 00:15:09,600
than the other way around. Yeah. Yeah. Yeah. Yeah. You know, like, so, so one that, that,

144
00:15:09,600 --> 00:15:16,000
that makes sense. And two, I think, I think as, as you were saying earlier, you're, you're older,

145
00:15:16,000 --> 00:15:23,760
as am I, we remember XML, HTTP request, and the magic of untangling how Gmail worked and all of that

146
00:15:23,760 --> 00:15:32,400
stuff. But, you know, I think when it comes to automation, uh, I think I've lived through maybe

147
00:15:32,400 --> 00:15:37,920
at least three, at least three giant waves of like automation. And the story is always the same,

148
00:15:38,240 --> 00:15:47,680
that it's going to replace people. And there is some moving around of people like, like, you know,

149
00:15:47,680 --> 00:15:52,800
I, the, the, the startup I started on was like an online banking one. And there's not really like

150
00:15:52,800 --> 00:15:58,400
as many like bank tellers, maybe as, as there used to be. I don't know. I still feel like I talked to

151
00:15:58,400 --> 00:16:03,840
bank tellers, whatever. That's, that's a, that's a weird example, but most recently in kind of like

152
00:16:03,840 --> 00:16:11,360
the, the platform cloud native cloud DevOps space, like in theory, there's a lot of replacing

153
00:16:12,160 --> 00:16:19,120
people and what they do. But I think in practice in it, and I think this is probably true of a lot of

154
00:16:19,120 --> 00:16:24,080
knowledge work or white collar work is one, it's hard to just get rid of people.

155
00:16:25,280 --> 00:16:30,480
Like as it, you know, it doesn't, it's not quite as simple as like, oh, a month later,

156
00:16:30,480 --> 00:16:35,440
now we've got this AI thing and we got rid of all the people, right? Like it's, things are a little

157
00:16:35,440 --> 00:16:41,200
different than that. But two, I think what ends up happening more is now you just get the people to do

158
00:16:41,200 --> 00:16:46,080
more work, right? Not, not more work, but they, they have something that's automating what they're

159
00:16:46,080 --> 00:16:53,200
doing, freeing up time and they do more work. Because if you were to go ask management at any

160
00:16:53,200 --> 00:16:57,920
company, like, is there more you'd like to be doing? I'm pretty sure they'd be like, where's the line I

161
00:16:57,920 --> 00:17:02,880
can stand in to tell you about that. Like I always want to do more stuff. So I don't know.

162
00:17:02,880 --> 00:17:07,760
The number of humans was limiting, was limiting their ability to grow

163
00:17:08,560 --> 00:17:13,760
because the invention of whatever time-saving technology was there. And so if it was those

164
00:17:13,760 --> 00:17:18,080
companies management were already probably planning, Hey, we'd like to expand. So we probably need to get

165
00:17:18,080 --> 00:17:23,040
more customers. So we probably need to hire more humans. Yeah. That's, that's, that's a, that's a

166
00:17:23,040 --> 00:17:26,960
clever way of putting it is that the humans are the bottlenecks. So you don't get rid of the humans.

167
00:17:26,960 --> 00:17:32,480
You want to make each human more productive. Exactly. I mean, I'm a, I'm a, I'm a techno optimist for the

168
00:17:32,480 --> 00:17:41,520
most part, uh, except in recent years when, when tech really became, uh, uh, usable by, uh, um,

169
00:17:41,520 --> 00:17:48,880
um, in situations to polarize people and divide them, then I'm like, maybe I should revoke my

170
00:17:48,880 --> 00:17:54,960
membership, be more skeptical of my membership in the tech optimist club, but. Indeed. Well, that,

171
00:17:54,960 --> 00:17:58,480
that, that, that brings back to the main thing I wanted to talk about. Like I was saying earlier,

172
00:17:58,480 --> 00:18:02,080
I don't really care about consumer tech very much. Right. And I think, I think

173
00:18:02,080 --> 00:18:08,480
maybe these proclamations of optimism in the consumer tech space are a little, it's not all bad, but,

174
00:18:08,480 --> 00:18:13,600
but it's a different, you have that conversation differently because it has, it has, it's just

175
00:18:13,600 --> 00:18:18,880
different materials you're working with. But in the business context, I think, I think equally,

176
00:18:18,880 --> 00:18:24,240
there's kind of like different ways of, of, uh, thinking about how you use tech. And I don't know,

177
00:18:24,240 --> 00:18:29,440
I don't know exactly how it is in the consumer space, but you know, a couple of months ago, we were

178
00:18:29,440 --> 00:18:34,320
talking about like, um, kind of that question I threw out earlier, what, what are companies

179
00:18:34,320 --> 00:18:38,880
going to do with this AI stuff? And, and I think you brought up an excellent point. I really hadn't

180
00:18:38,880 --> 00:18:44,320
thought about that, that we, I wanted to discuss here a little bit is like, all right, so sure at the,

181
00:18:44,320 --> 00:18:48,800
the, the, the, let's say the operations, the developer, the UX, the people making the product,

182
00:18:48,800 --> 00:18:53,840
that's a whole interesting thing. But what's not really talked about very much is like, what does

183
00:18:53,840 --> 00:19:01,840
management or the, or an executive do like, what is their role in like this, this AI future that we're

184
00:19:01,840 --> 00:19:07,520
going to be in? And the way that you put it that I thought was interesting is, is like, uh, you know,

185
00:19:07,920 --> 00:19:12,000
it's not, you didn't put it this way. This is my rewording is like, they're not the ones who are going

186
00:19:12,000 --> 00:19:18,560
to be doing the technical work. Like they only have a limited amount of things in their toolbox that they

187
00:19:18,560 --> 00:19:22,640
can do, which, which I thought was a good point. And so that's, I thought, you know, I thought we'd

188
00:19:22,640 --> 00:19:30,800
see how far we go just, uh, figuring out kind of brainstorming what it is you think a manager and

189
00:19:30,800 --> 00:19:36,160
executives role is and sorting out how they're going to use AI. Yeah. Yeah. I mean, I look at it,

190
00:19:36,800 --> 00:19:43,360
let's, let's take it like use a, as a mental model, a bag, right? We have a large bag, you know,

191
00:19:43,360 --> 00:19:51,120
a really rich bank, you know, a bank that has, um, a very well-paid CEO and think of the direct

192
00:19:51,120 --> 00:19:58,400
people that report to the CEO, right? Maybe you'll have like an EVP head of personal banking and EVP

193
00:19:58,400 --> 00:20:06,560
head of commercial banking, EVP head of capital markets, EVP head of something. And so each of those,

194
00:20:07,120 --> 00:20:11,920
what, one of the models I've seen in a bunch of the banks is they might also have an EVP of

195
00:20:11,920 --> 00:20:21,840
slash CIO head of for the bank, right? Or they might, uh, move the, that function underneath each of the

196
00:20:22,560 --> 00:20:29,520
EVPs. So like you'll have your capital market CIO, your personal banking CIO, right? And, and I've seen

197
00:20:29,520 --> 00:20:35,200
organizations kind of like shuffle between the two, but let's say now you're, uh, let's just stick with

198
00:20:35,200 --> 00:20:41,760
the, the, just to keep the model simple that you have the one CIO that reports to the CEO. Now,

199
00:20:41,760 --> 00:20:48,960
if you're the personal banking, uh, leader in the bank, your success is based on innovation and trying

200
00:20:48,960 --> 00:20:54,240
to make more money or save money within personal banking. You're not really necessarily that

201
00:20:54,240 --> 00:21:00,240
dependent on what's happening in the commercial banking or capital markets are kind of like kind

202
00:21:00,240 --> 00:21:07,040
of isolated. Your, your objectives are going to be independent and you'll be successful or not within

203
00:21:07,040 --> 00:21:15,840
your silo, uh, without too much perhaps dependency on that. Right. But the one person that's, that's head

204
00:21:15,840 --> 00:21:21,920
CIO, right. That reports to the CEO, like everybody else needs something from them.

205
00:21:24,720 --> 00:21:29,200
And personal banking, I'd like, wouldn't it be great if people could be qualified for a mortgage

206
00:21:29,200 --> 00:21:37,440
in 60 seconds or less so we can like, you know, sell more mortgages. Right. And, and so they become

207
00:21:37,440 --> 00:21:45,440
kind of like, uh, uh, the bottleneck and on, on the rest of the business. And they also have the potential

208
00:21:45,440 --> 00:21:52,080
to become unpopular because as that head leader, right. Of technology, again, you don't do any of

209
00:21:52,080 --> 00:21:57,760
the work you have all these teams. So what I've observed in my interactions with these executives

210
00:21:57,760 --> 00:22:02,640
over the years is that they get into that position because they're really good at managing their team

211
00:22:03,600 --> 00:22:07,680
and getting the most out of their team. And I've noticed a common operating pattern, like the new

212
00:22:07,680 --> 00:22:15,040
technology comes along. That executive basically says, if my team agrees that something is a good idea,

213
00:22:15,040 --> 00:22:24,400
I'm going to do it because they all agree. If they, if you have, let's say out of your direct team,

214
00:22:24,400 --> 00:22:30,800
uh, a couple of people, two or three, you know, uh, uh, uh, directs that are themselves executives

215
00:22:30,800 --> 00:22:37,040
are just not reporting to the CEO. Right. Then, and they, they completely disagree about a technology

216
00:22:37,040 --> 00:22:42,320
or an approach. Uh, what I've seen these execs do is they'll just do nothing. They'll be like,

217
00:22:42,960 --> 00:22:50,080
there's too much disagreement. Clearly the market is not mature for the most part. They'll wait. Now,

218
00:22:50,080 --> 00:22:54,720
I forgot to say that this particular bank, let's put the model that they're more of a conservative

219
00:22:54,720 --> 00:23:00,640
institution. They're not trying to be the first, like with, with, with something. And so they're,

220
00:23:00,640 --> 00:23:05,120
they're wanting like, you know, to do something that a few other people have tried. They don't

221
00:23:05,120 --> 00:23:10,000
want to be the first. They're in line to be the second user of something. Right. Right. Right.

222
00:23:10,000 --> 00:23:17,760
Uh, and then, uh, uh, if your management, if your team kind of like has some disagreement,

223
00:23:17,760 --> 00:23:22,800
which is healthy, but they're like, I disagree and commit, that's easy. You can still make the

224
00:23:22,800 --> 00:23:28,560
chase, uh, the, the choice forward. This is my really long winded way of saying that, um,

225
00:23:28,560 --> 00:23:36,560
what the executives are doing is they're managing the conflict and the energy between their team

226
00:23:36,560 --> 00:23:44,400
members. And when it comes to AI, like, that's what they got to do. They got to figure out how to get

227
00:23:44,400 --> 00:23:54,160
the most out of their existing team. And to do that, um, it's really hard in the case of AI,

228
00:23:54,160 --> 00:24:00,320
because you need to experiment. Right. And experimentation means that you're probably going

229
00:24:00,320 --> 00:24:08,480
to fail, but everything is going to work. And a lot of these executives are like, oh, I made an impact.

230
00:24:08,480 --> 00:24:19,280
That's why I was promoted. I made good decisions. So it's like, Hmm. So if you promote people based

231
00:24:19,280 --> 00:24:25,680
on them making good decisions and hitting the plans and the objectives, how could you measure them on

232
00:24:25,680 --> 00:24:34,240
something that's probably going to have ideally a 50% like failure rate? Right. Right. Especially. Yeah.

233
00:24:34,240 --> 00:24:40,880
Yeah. Right. And, and so, uh, what's the number one thing you should do is figure out how to create

234
00:24:40,880 --> 00:24:49,040
that situation where it's okay to experiment. And that is something you badly need to figure out,

235
00:24:49,040 --> 00:24:54,320
Hey, what's going to work in the bank if we add AI. Now you don't want to put those people

236
00:24:55,600 --> 00:25:00,400
completely outside of the no accountability. You can do whatever you want to use whatever technology

237
00:25:00,400 --> 00:25:06,720
you want. You can break all the rules debate because then they do science projects that have no impact

238
00:25:07,760 --> 00:25:14,640
to the business. Right. And, and, you know, I've, I've, at least in discussing not an AI, but in other

239
00:25:14,640 --> 00:25:22,400
areas, the other problem with that is sometimes if, if those experiments are successful and you try to go

240
00:25:22,400 --> 00:25:28,880
apply them to the main business, there's an easy amount of chaff excuse that, you know, your, your rivals can

241
00:25:28,880 --> 00:25:32,880
say, which is like, Oh, well that was in the labs where they could do anything. That's not going to

242
00:25:32,880 --> 00:25:38,960
work here. Like it's almost like you're not in real enough world situations to, to expand it to your

243
00:25:38,960 --> 00:25:43,520
actual business. Yeah. I would go like, why don't you just get the people that are actually doing the

244
00:25:43,520 --> 00:25:48,080
actual real thing. Don't need to create a lab. Like don't go out and create an AI lab. Right.

245
00:25:48,080 --> 00:25:58,160
Yeah. Like that's just a recipe for wasting money. I saw way too many digital factories created in the

246
00:25:58,160 --> 00:26:04,720
last 10 years where it's like everybody else in the bank gets to use a windows machine, but the digital

247
00:26:04,720 --> 00:26:11,520
factory has max. Everybody has black coffee, but the digital factory has a really nice coffee machine

248
00:26:11,520 --> 00:26:17,280
that can make you all sorts of coffee and they have max and they kind of look more like a Silicon Valley

249
00:26:17,280 --> 00:26:24,480
office and all of that. And, uh, you know, it's a little bit of cargo culting, um, uh, software

250
00:26:24,480 --> 00:26:29,600
companies, but it's really, really just like, as an exact, what do you need to do? You need to make

251
00:26:29,600 --> 00:26:35,840
sure you create that environment that allows your, your, your reports or reporting to you to actually

252
00:26:35,840 --> 00:26:43,600
do something meaningful in their current area. And that means that you and your org have to start

253
00:26:43,600 --> 00:26:47,520
learning about AI and doing a little bit of experiments and stuff like this.

254
00:26:49,680 --> 00:26:54,400
So, so what do you think is, let's see, where should we start with this?

255
00:26:56,320 --> 00:27:04,800
Well, what do you think is the, the, the foundation for doing that? And, and, and let me extemporaneous

256
00:27:04,800 --> 00:27:11,440
here a little bit to, to figure, to tell you like what I'm asking is, you know, one answer would be

257
00:27:11,440 --> 00:27:16,560
like, well, you need to set up like a, a common AI service, just the technology for people to use.

258
00:27:16,560 --> 00:27:20,800
So they don't have to worry about that. And that's fine. There's another answer that's sort of like,

259
00:27:21,360 --> 00:27:26,000
well, as the executive, you have to figure out how to prove to people that experimenting is

260
00:27:26,000 --> 00:27:32,160
worthwhile. Right. So, you know, we've got, we've got the classic big company change vectors,

261
00:27:33,040 --> 00:27:38,000
the technology and the culture, uh, like, like coming in. So like, and I don't know,

262
00:27:38,000 --> 00:27:41,680
maybe if there's a third or a fourth one you're thinking of, but like, what, what do you see the

263
00:27:41,680 --> 00:27:48,480
executive doing on those two vectors to get people experimenting with, with, with AI? And, and

264
00:27:48,480 --> 00:27:54,800
I want to point out before I stopped talking that like, you know, what you said just there is

265
00:27:54,800 --> 00:28:00,400
probably the most important thing that an executive can do at this point with AI is realize that they

266
00:28:00,400 --> 00:28:07,600
just need to experiment. They need to learn what it's even useful for. Right. And, and I, you know,

267
00:28:07,600 --> 00:28:13,520
I would almost say, you know, that one, what would you call it? One test is like, if you're sure,

268
00:28:13,520 --> 00:28:18,240
you know, what's going to pay off for your AI stuff, you're probably not thinking about it correctly.

269
00:28:18,240 --> 00:28:23,360
Right. Like instead you should be like, I have no idea what we're going to do, but let's experiment

270
00:28:23,360 --> 00:28:29,600
and track those experimentations and let's learn, learn might be a better word than I was told by

271
00:28:29,600 --> 00:28:34,240
one of our customers in the military. Once I should never talk about failing to learn. I should just

272
00:28:34,240 --> 00:28:43,520
talk about learning. Yeah. But you need a way to learn, uh, what, what's, what's useful. I think that

273
00:28:43,520 --> 00:28:49,360
there's two, there's two, maybe three types of learning that I would, I would advocate for. Number

274
00:28:49,360 --> 00:28:54,640
one is the basic one, which is you should be personally using whatever AI products are out

275
00:28:54,640 --> 00:29:00,720
there that have emerged, no matter how crappy they are, just see how you respond to it personally.

276
00:29:00,720 --> 00:29:09,280
Right. Um, and, uh, that's, that'll give you a sense of the impact too, is obviously talk to a

277
00:29:09,280 --> 00:29:15,120
lot of people, talk to people, talk to normal people, not people in technology, see what they're using.

278
00:29:15,120 --> 00:29:22,720
Ask them, Hey, is there an AI app or feature? Has it been great for you? Uh, in my case, uh,

279
00:29:23,520 --> 00:29:30,560
I, I have a very distinct, uh, situation with my electrician who language is not English.

280
00:29:31,680 --> 00:29:38,800
And, uh, he told me, uh, um, a while ago, Hey, I started using chat GPT for a copy and paste

281
00:29:38,800 --> 00:29:44,560
all my emails. It makes it better. And all my colleagues were like, did you take an English

282
00:29:44,560 --> 00:29:50,720
class? And he's like, yes, I did. Because he didn't want to tell them that he was like using

283
00:29:50,720 --> 00:29:56,160
chat GPT. Uh, and I'm like, uh, did you know you could actually, rather than doing that, you could

284
00:29:56,160 --> 00:30:02,160
actually ask chat GPT to explain to you what was wrong with your English. And it can like,

285
00:30:02,160 --> 00:30:06,880
like, don't tell it to fix it, tell it to tell you what's wrong with it. Then you fix it yourself.

286
00:30:07,600 --> 00:30:11,760
Yeah. And then you'll learn better. And he looked at me, he's like, can it really do that? I'm like,

287
00:30:11,760 --> 00:30:16,800
yes, I show him how to do it. And his head exploded. Like, it's like, this is like a free

288
00:30:16,800 --> 00:30:24,320
English teacher, right? Like, I'm like, yeah. So, so this is the part where, uh, it's like,

289
00:30:24,320 --> 00:30:28,240
it is worth it to talk to normal humans who aren't in tech, aren't in your industry,

290
00:30:28,240 --> 00:30:34,400
see how people are using it. And then the second learning layer of learning is to actually understand

291
00:30:34,400 --> 00:30:39,760
fundamentally what this technology is. Like you need to do a little bit of deeper diving than you

292
00:30:39,760 --> 00:30:45,360
might normally need to about established technologies. Like let's say using the bank executives,

293
00:30:45,360 --> 00:30:50,000
you probably don't need to know the ins and outs of how your ATM machine works or how the phone system

294
00:30:50,000 --> 00:30:55,680
works or something. But because this is a new technology that can be very disruptive to your

295
00:30:55,680 --> 00:31:01,920
business and to your processes, you really should learn a bit more than you normally would in

296
00:31:01,920 --> 00:31:07,360
situations where you would delegate something away. So what's the right level of understanding

297
00:31:07,360 --> 00:31:12,560
what AI is for an executive would be the next question. How much should I learn about it?

298
00:31:13,280 --> 00:31:19,280
Should I go and like crack open my math books and, and you know, get into that? No, absolutely not.

299
00:31:20,960 --> 00:31:25,200
What you should really be looking at is understanding its limitations

300
00:31:26,720 --> 00:31:32,720
and understanding how those limitations may disappear over time. Like what's going to get faster,

301
00:31:32,720 --> 00:31:41,360
cheaper, better, and what's unlikely to get better. And once you understand its constraints,

302
00:31:41,360 --> 00:31:49,120
then ask yourself the question of how can I do something useful within the constraints that exist today?

303
00:31:49,120 --> 00:31:55,360
Right, exactly. And, and maybe, maybe that's a good, uh, uh, a good additional way of thinking about it

304
00:31:55,360 --> 00:32:02,000
is, is you're, you're not only learning how it works by using it, but as you say, you're discovering

305
00:32:02,000 --> 00:32:07,120
the constraints, like constraint seems like, like a negative word in this context, but really it's just

306
00:32:07,120 --> 00:32:12,720
like a more colloquial way of putting it out. It is just like, you're figuring out what it can do,

307
00:32:12,720 --> 00:32:19,920
right? Like, like what it's actually capable of and, uh, versus not like, you know, I was reading

308
00:32:19,920 --> 00:32:26,000
something over the weekend that, uh, what was the metaphor? It was like a lot of people think about

309
00:32:26,000 --> 00:32:31,680
using AI as an, as an intern, but it's the metaphor that's better is that they're like a brand new

310
00:32:31,680 --> 00:32:36,480
employee that can't remember anything. Right. And so like they're capable of doing something, but you have

311
00:32:36,480 --> 00:32:41,040
to constantly be reminding them and coaching them and doing things like that, kind of giving them

312
00:32:41,040 --> 00:32:51,520
context. Yeah. I, I, I, I, I find that analogy to be, uh, like it triggers me to think like,

313
00:32:51,520 --> 00:32:56,160
I have to spend a lot of effort to train and coach and monitor. I'm like, can I think of them more like

314
00:32:56,160 --> 00:33:00,640
a default, like, you know, those little power drills? Yeah, yeah, yeah, exactly. I don't need

315
00:33:00,640 --> 00:33:09,040
to coach it. I don't need to tell it. Like if you use the AI as a single purpose tool for a very

316
00:33:09,040 --> 00:33:16,560
specific task, right. Then you don't have to do all that coaching. You just get the benefit.

317
00:33:16,560 --> 00:33:20,480
Exactly. And see, see that, that's the kind of thing that you would learn from experience,

318
00:33:20,480 --> 00:33:31,360
right. Is that, is that it's maybe, let's see, maybe there's faster ROI, so to speak,

319
00:33:31,360 --> 00:33:37,040
if you just try these kind of like one shot or highly specialized uses, right. Instead of having

320
00:33:37,040 --> 00:33:42,080
just like a general help you out with whatever, which might be cool as well, but maybe you'll get

321
00:33:42,080 --> 00:33:46,720
more early successes by just figuring out like, what's, what's like a simple thing that it can do

322
00:33:46,720 --> 00:33:52,480
really well so that we don't have to like load it up with context and have it understand this over and

323
00:33:52,480 --> 00:33:58,800
over again. Yeah. Like the, the, I think though, like one of the issues we might all be collectively

324
00:33:58,800 --> 00:34:06,000
dealing with is we've had a lot of really good movies and sci-fi where AI is featured and AI is

325
00:34:06,000 --> 00:34:12,320
implicitly assumed to be like faster, better than humans. Like the benevolent AI vision or the AI is

326
00:34:12,320 --> 00:34:17,840
like, you know, kills everybody, turns us into human batteries, like matrix or terminator or whatnot.

327
00:34:18,400 --> 00:34:26,240
Sure. And really it's the AI as like the power drill, the power, the power tool. That is the harder

328
00:34:26,240 --> 00:34:34,560
one, I think for everybody to imagine because it's not exciting. Right. But you know, what if it could

329
00:34:34,560 --> 00:34:40,640
do that and you don't need to change a lot of stuff. So I have a different relationship with constraints.

330
00:34:40,640 --> 00:34:48,880
I think constraints are the only way humans make progress and that every set of constraints contains

331
00:34:48,880 --> 00:34:55,920
within it, the thing that will allow you to violate the constraint and not die. A good example

332
00:34:55,920 --> 00:35:00,000
of that is gravity. Like we have airplanes, right? Right, right.

333
00:35:01,200 --> 00:35:08,720
So within the constraints, within the constraints of the laws of physics is that some combination that

334
00:35:08,720 --> 00:35:16,000
allows you to fly, right? So it doesn't matter that the AIs hallucinate, that the AIs have certain

335
00:35:16,000 --> 00:35:22,240
problems. The question is, can you engineer something on top of this technology with its constraints

336
00:35:23,280 --> 00:35:30,480
to allow you to accomplish something better than you could before? And the answer in 2024 is absolutely,

337
00:35:30,480 --> 00:35:37,440
yes, you can. Like it's may not, it's not Star Trek level AI. It's not going to live up to all the hype,

338
00:35:37,440 --> 00:35:45,360
but it may be just like a 5% improvement over something. And hey, a lot of 5% improvements adds up pretty

339
00:35:45,360 --> 00:35:45,440
quickly.

340
00:35:47,200 --> 00:35:53,120
Yeah. So let's close this little, well, first of all, for our listeners,

341
00:35:54,480 --> 00:35:59,360
Adiv and I have been talking about a little book. I always call these books the something for the

342
00:35:59,360 --> 00:36:03,920
busy little executive. We'll see what we actually call it, but you know, like Enterprise AI for the

343
00:36:03,920 --> 00:36:08,560
busy little executive. And I think, I think it's, it's good to kind of talk through these, these

344
00:36:08,560 --> 00:36:14,240
ideas so we can transform into text. And so, you know, we've got a little section here and, and I think

345
00:36:14,240 --> 00:36:21,920
maybe to finish this section out, you know, like the dog is very enthusiastic about our project here.

346
00:36:21,920 --> 00:36:27,920
Yes. But I think, you know, you alluded to this earlier, or we talked about it directly earlier,

347
00:36:27,920 --> 00:36:33,840
where you and I both having worked at Pivotal and VMware and Tanzu and et cetera, and wherever we are

348
00:36:33,840 --> 00:36:40,240
now, so forth and so on, like, we've kind of been through a similar thing of like, I don't know,

349
00:36:40,240 --> 00:36:46,480
this, this discovery of rapidly being able to do software and enterprises. And I remember we spent

350
00:36:46,480 --> 00:36:52,960
several years, as you kind of said, talking and witnessing executives who were changing over how

351
00:36:52,960 --> 00:36:58,000
they thought about software. So there's kind of that past experience to draw from. And I don't know,

352
00:36:58,000 --> 00:37:03,040
you know, the technology, as with your agent thing, the technology determines a lot of the behavior

353
00:37:03,040 --> 00:37:07,600
and vice versa. But maybe drawing on that and also like new thinking you have, like what,

354
00:37:09,440 --> 00:37:14,800
what kind of structure or expectations or timelines? I don't, I don't know, like, what is, what's the

355
00:37:14,800 --> 00:37:20,240
program look like that, that an executive kicks off for this kind of experimenting? Like what,

356
00:37:20,960 --> 00:37:21,760
what do they do?

357
00:37:23,440 --> 00:37:31,280
I would say step zero, use the technology. Step one, understand its constraints. Step three,

358
00:37:31,840 --> 00:37:36,640
get deep on the processes and places where you think it would apply in your company.

359
00:37:37,760 --> 00:37:46,480
And then design an experiment in there and, and run it. So it's, it's, it's like, for real,

360
00:37:46,480 --> 00:37:52,160
not, not, not, not like, yeah, we're not going, we're, we'll put it in the innovation lab and see

361
00:37:52,160 --> 00:37:58,240
what they come up with. I don't know, do it with the people that actually own that business and own

362
00:37:58,240 --> 00:38:04,480
the MBOs on that. So yeah, it's not just in the lab. Yeah. And, and, and I mean, tell me if you

363
00:38:04,480 --> 00:38:10,560
think it would work, but I think I liked the, uh, the pivotal labs and the Tanzu labs kind of

364
00:38:11,520 --> 00:38:18,320
structural answer to that, which was like, pick a, I don't know, pick a, uh, a two to three month

365
00:38:18,320 --> 00:38:26,240
project, uh, that's actual business facing and do something right. Like I'm, I'm, I'm sure if we're,

366
00:38:26,240 --> 00:38:29,440
if we're kind of pitching it in a formal way, it's a little more structured than that, but it's sort

367
00:38:29,440 --> 00:38:33,600
of like, and, and the experiment that you're establishing, you know, I, I really want to call

368
00:38:33,600 --> 00:38:38,320
it out because I think it's important is it does have to be business facing. It doesn't necessarily

369
00:38:38,320 --> 00:38:43,600
have to be customer facing, but it has to be something real, right. Uh, that, that will have

370
00:38:44,960 --> 00:38:50,480
that involves the business in a real way and we'll have some kind of tangible business result,

371
00:38:50,480 --> 00:38:54,640
right? Like one of my, one of my favorite examples, even though it's ancient is, uh,

372
00:38:54,640 --> 00:38:58,560
you know, Home Depot did a couple of these, uh, back, back when it was pivotal where they,

373
00:38:59,360 --> 00:39:05,520
the, uh, the software they had that was running the custom paint desk was not great. And so their,

374
00:39:05,520 --> 00:39:09,120
their kind of position was like, that's where we could learn to do software differently

375
00:39:09,600 --> 00:39:15,760
because it's already not great. And so any improvements we do will be valuable and we can't

376
00:39:15,760 --> 00:39:20,080
really make it worse. Right. And so it was kind of this area where they could go in and discover

377
00:39:20,080 --> 00:39:25,200
things to do. And I don't know. I mean, do you think that same approach would work in kind of

378
00:39:25,200 --> 00:39:31,520
experimenting with AI stuff? Oh, absolutely. I mean, like, again, going back to, um,

379
00:39:32,080 --> 00:39:39,600
the banking example, right? Like you can think of, um, your, I don't know, let's, let's call them,

380
00:39:39,600 --> 00:39:45,760
whatever the people that you go to in the branch that you would say, I need a business loan or I need

381
00:39:45,760 --> 00:39:51,120
something, right? They got to learn about your business. They got to manage, like, uh, talk to

382
00:39:51,120 --> 00:39:56,080
lots of different companies and all of this stuff. They're clearly in charge and the final owner of

383
00:39:56,080 --> 00:40:01,760
the process shepherded along to say, hand out a million dollar loan, start a business or something.

384
00:40:02,480 --> 00:40:09,040
Think of all of the things where, uh, you could make them save them an hour or save them half an hour

385
00:40:09,040 --> 00:40:15,920
and just talk to them and find out like, what is something you spend a lot of time on this week,

386
00:40:15,920 --> 00:40:22,160
every week that you wish you could do faster. Get that list of, I don't know, 10 items,

387
00:40:22,720 --> 00:40:29,760
go through that list and ask the question, what could I do? If I, what could an AI do here?

388
00:40:30,240 --> 00:40:36,400
Like, can it do anything? Maybe it can't, right? Maybe the number one frustration is playing phone tag

389
00:40:36,400 --> 00:40:47,120
with, uh, right, right. And, uh, uh, maybe it's that customers, they, they talk to the customer,

390
00:40:47,120 --> 00:40:51,840
the customer shows up and they tell them, bring these five things and they don't bring,

391
00:40:51,840 --> 00:40:56,480
they bring four of the five documents they need. And then they need to have a follow-up appointment,

392
00:40:56,480 --> 00:41:02,880
right? Where the fifth document is, is brought in. So I don't know, can, can AI help in,

393
00:41:03,760 --> 00:41:09,760
you know, maybe the AI simply takes the five documents that's needed. And, uh, the, that,

394
00:41:09,760 --> 00:41:16,480
the person in the bank is able to, uh, get a text message out like 20 minutes before the meeting,

395
00:41:16,480 --> 00:41:20,880
reminding them of the five things, right. That are, they need to bring with them.

396
00:41:21,440 --> 00:41:26,080
Yeah. Yeah. Yeah. And like, I mean, I mean, you bring up a good, a good, um,

397
00:41:27,360 --> 00:41:32,240
mechanic. I don't know what else to call it, which is, which is like, uh, user interviews,

398
00:41:32,240 --> 00:41:37,840
otherwise known as ask the people doing the work, how you could help them. Right. Right. And, and like,

399
00:41:37,840 --> 00:41:42,960
I think, I think the first part of what you're saying, um, well, there's a couple of things. One,

400
00:41:44,400 --> 00:41:50,160
you know, of course you want to go talk with, with users, another word for people, you want to talk

401
00:41:50,160 --> 00:41:55,040
with whether it's an employee or a customer and just kind of ask them an open-ended question of

402
00:41:55,040 --> 00:41:59,840
like, what would be helpful. But I think that's where your constraints point comes in as if, as if

403
00:41:59,840 --> 00:42:05,840
whoever's doing this interviewing, they know the constraints of an AI thing. They can kind of fish

404
00:42:05,840 --> 00:42:11,840
around for like, would this be useful or would that be useful? Exactly. And like kind of observing

405
00:42:12,480 --> 00:42:17,680
and kind of like mapping out what the, what these people do, uh, what these, the, the users do,

406
00:42:18,480 --> 00:42:23,680
and then applying some, and then coming up with the theory of what to do. Right. And what I want to

407
00:42:23,680 --> 00:42:29,760
call out about that is I think it would be easy. And I bet a lot of people do this is

408
00:42:30,880 --> 00:42:36,480
you, you, you go through, uh, the Adib steps, you, you know what it is you understand. And then

409
00:42:36,480 --> 00:42:39,920
without going to talk to the employees, you just come up with like, I think this would be a good

410
00:42:39,920 --> 00:42:44,720
idea. And you might go out and implement something that, that automates or that helps out some part

411
00:42:44,720 --> 00:42:48,960
of what a user is doing. And then you go give it to the user and they're like, yeah, I really just

412
00:42:48,960 --> 00:42:54,080
need people to bring all their documents, right? Like you've spent a lot of time solving a problem that

413
00:42:54,080 --> 00:43:03,760
may have existed or from a technologist point of view was an exciting thing to, to make up a word

414
00:43:03,760 --> 00:43:09,280
technologize, but, but wasn't as valuable as other things that you could do. And going back to that

415
00:43:09,280 --> 00:43:15,280
Home Depot example, right? Like in that example and a few other ones, the first thing that the teams did

416
00:43:15,280 --> 00:43:21,200
was like go to the store and just watch and kind of like see what people are doing and interview them.

417
00:43:21,200 --> 00:43:27,600
And that's where they started with that constraint of knowing what they could do, if they could deliver

418
00:43:27,600 --> 00:43:33,040
new features and software on a weekly basis. And then, and then actually seeing what people,

419
00:43:33,680 --> 00:43:38,560
you know, the users are doing, they were able to kind of come up with some experiments. Um, and

420
00:43:39,440 --> 00:43:43,680
I guess, I guess that comes, that brings the, the, the, the final thing that I think would be

421
00:43:43,680 --> 00:43:50,400
interesting to bring up is so an experiment, if I remember my science is you have, let's see,

422
00:43:50,400 --> 00:43:57,840
you have a theory, uh, hypothesis, uh, a theory or hypothesis, and then you run an experiment which

423
00:43:57,840 --> 00:44:02,800
generates data. And then you've got to analyze that data to see if our hypothesis was true or not.

424
00:44:02,800 --> 00:44:07,200
Uh, as I think that's the scientific method. I'm not, I'm not up on my Sir Francis Bacon.

425
00:44:07,200 --> 00:44:16,720
Uh, but like, how do you think that applies to like AI experiments? If, if it's even possible to

426
00:44:16,720 --> 00:44:22,880
it's really no different than the UX studies. Like I remember back in the day at pivotal,

427
00:44:22,880 --> 00:44:28,240
we'd have like some local banks that'd be building some app or something. And literally they would

428
00:44:28,240 --> 00:44:33,360
have like two meeting rooms, one where they had a user that they recruited literally off the street.

429
00:44:33,360 --> 00:44:39,520
Like somebody would stand on the street from the, from the labs team and be like, Hey, we're, uh, working

430
00:44:39,520 --> 00:44:44,560
with so-and-so bank and we're experimenting with some stuff. Do you have a bank account? Do you do this

431
00:44:44,560 --> 00:44:50,400
kind of thing? Okay. Would you like some, uh, you know, gift card for a restaurant or something, uh,

432
00:44:50,400 --> 00:44:54,320
come in for 10 minutes to our office. We're just going to ask you to do a task.

433
00:44:54,320 --> 00:44:58,880
Right. Yeah. And like, they would literally have them. They'd have like a couple of webcams pointed

434
00:44:58,880 --> 00:45:03,840
at them and figure it like the user study. I mean, this is why I was saying earlier, I think the UX

435
00:45:03,840 --> 00:45:09,360
designers are going to be the ones that define what an AI agent is because everything we just described

436
00:45:09,360 --> 00:45:16,480
right now is like, watch how the human is doing it and make their life better. This is classic UX

437
00:45:17,360 --> 00:45:24,560
work. And, and, and, and so I don't think it's all that different. Like the parts that make me cringe

438
00:45:25,280 --> 00:45:30,800
when people talk about AI, especially executives, especially from the, like the companies that

439
00:45:30,800 --> 00:45:38,560
should know better. Um, I, AKA, you know, the big giant, uh, hyperscalers, uh, when they make claims

440
00:45:38,560 --> 00:45:45,840
about how the AI is going to continuously do something for you, like detect that your customer is unhappy

441
00:45:45,840 --> 00:45:51,840
or handle it. And I'm like, you don't understand. I'm a human. I want to talk to another human.

442
00:45:52,960 --> 00:45:57,840
Right. Right. Like it's not, it's not like, that's the person that can make me feel better

443
00:45:57,840 --> 00:46:04,400
if I'm upset, not your AI. Yeah. You know, I, I like, like, like this is, this is a simplification

444
00:46:04,400 --> 00:46:11,200
that, that, and therefore makes it inaccurate as far as a hundred percent statement. I should

445
00:46:11,200 --> 00:46:17,840
know more concise way of putting that. But I think one, one, one helpful way to think about this

446
00:46:17,840 --> 00:46:24,400
question of how to figure out what to do with AI and enterprise is the interesting stuff is going to

447
00:46:24,400 --> 00:46:30,240
be in the, in the user experience. Right. Like, like that, sure. There could be stuff like,

448
00:46:31,120 --> 00:46:34,960
well, I don't know, like, like fraud detection is an interesting case, but even in that case,

449
00:46:35,840 --> 00:46:42,640
you probably need a user experience, right? Like, and so there's stuff on the back end that maybe is,

450
00:46:42,640 --> 00:46:47,200
maybe not so UX driven, but that could also just be the case of like, well, it is very UX driven.

451
00:46:47,200 --> 00:46:53,040
You're just not the person doing it, obviously. But, but I think probably, probably the higher

452
00:46:53,040 --> 00:46:59,280
priority, uh, the higher, uh, if you'll pardon the phrase impact that you'll get with, with AI stuff

453
00:46:59,280 --> 00:47:05,200
will have a tremendous, you'll, you'll end up doing a lot of user experience work because that's where,

454
00:47:05,200 --> 00:47:06,880
where interesting things are going to happen.

455
00:47:06,880 --> 00:47:12,880
Right. And like, don't start with a Star Trek level assumption of what AI can do. Start with the

456
00:47:12,880 --> 00:47:20,080
more mundane. These are the constraints. Like I'll tell you, I have two favorite AIs that I interact

457
00:47:20,080 --> 00:47:27,920
with. Um, I think the first one pre-chat GPT that was like, oh, wow, this is really cool was in

458
00:47:27,920 --> 00:47:33,840
PowerPoint. Like PowerPoint has this little button like that you can press to spiffy up your slide.

459
00:47:34,960 --> 00:47:39,840
And I didn't know that this like AI assistant designers, I didn't even know it was an AI or

460
00:47:39,840 --> 00:47:45,600
what algorithm it used. But one day I had like the slide deck that was off of the white template in,

461
00:47:46,480 --> 00:47:53,040
in PowerPoint. Yeah. And I'm like, I pressed the button and it gives me some really good suggestions.

462
00:47:53,040 --> 00:47:58,960
I'm like, oh, that's clever. I, I, I select it and I go to the next slide and it gives me a different

463
00:47:58,960 --> 00:48:03,920
one. And I'm like an hour later, I'm like, oh my God, this presentation, like I would have never,

464
00:48:03,920 --> 00:48:08,640
ever been able to put this together. I like, I like, I like, you know, one thing I think that's

465
00:48:08,640 --> 00:48:12,160
happening there is an hour later. I bet a lot of that because you were very entertained.

466
00:48:12,480 --> 00:48:14,560
Yes. Like I'm going to keep doing this. This is wonderful.

467
00:48:15,280 --> 00:48:20,000
But because, because, because normally in my imagination, like I'd seen other people with

468
00:48:20,000 --> 00:48:25,120
slides that were not bullet points and not on a white background. I'm like, how'd they do it?

469
00:48:25,120 --> 00:48:30,720
They must be like talented in the arts and crafts, but more artistic than I am. That's one of my

470
00:48:30,720 --> 00:48:38,000
limitations. I'm not very artistic. Or maybe they paid somebody right to, to help them with it. But

471
00:48:38,000 --> 00:48:46,080
I'm like, like that was an amazing use of AI. Because it didn't take me out. It didn't go and

472
00:48:46,080 --> 00:48:51,600
make the presentation for me. And now when I'm going to have to make, I use it because it's kind of fun.

473
00:48:51,600 --> 00:48:55,920
And yeah, now I can do it not in an hour. Like I can do it in like five, 10 minutes, but

474
00:48:58,560 --> 00:49:05,200
like that is, it's not taking me out. And the second thing, the other one is chat GPT and that

475
00:49:05,200 --> 00:49:09,200
kind of chat interface. And that certainly doesn't take me out as a human.

476
00:49:11,200 --> 00:49:16,400
Right. Right. No, it relies on you in fact, right? Like it can't talk to itself. Yeah. Yeah. Well,

477
00:49:16,400 --> 00:49:20,400
great. You know, I think, I think there's a, there's a, there's a key sort of principle here is

478
00:49:21,120 --> 00:49:28,960
look, look for the, the interesting AI applications at the use, the, the UX, the user experience,

479
00:49:28,960 --> 00:49:34,000
the UI layer, right? Like that's where some interesting stuff, a lot of interesting stuff will

480
00:49:34,000 --> 00:49:38,720
be happening. Not all of it, but like always kind of be questing for that, right? Like what's the

481
00:49:38,720 --> 00:49:43,200
interaction with a human that you're doing and, uh, how, how does that work out? Well,

482
00:49:43,200 --> 00:49:48,800
great. Well, we, uh, we, we have, we have plenty of upcoming items in our, our rough outline to go

483
00:49:48,800 --> 00:49:53,920
through. I think, I think we really, uh, we figured this one out pretty well and, um, you know,

484
00:49:53,920 --> 00:50:00,400
so, uh, hopefully over the next couple of episodes, uh, you'll, uh, we'll, we'll kind of get the verbal

485
00:50:00,400 --> 00:50:05,360
version of, of this, this booklet we have in mind written out and, uh, I think it'll be fun.

486
00:50:05,360 --> 00:50:10,000
Well, thanks, thanks for, uh, thanks for doing this today. Do you, you know, so, uh, I'll put

487
00:50:10,000 --> 00:50:13,840
a link to your, your, uh, your LinkedIn thing where you had a post on this, but do you have any,

488
00:50:13,840 --> 00:50:15,600
anywhere else that you want to point people at?

489
00:50:19,040 --> 00:50:20,160
No, at the moment, no, thanks.

490
00:50:21,600 --> 00:50:26,800
That's like, like, you know, in the past five years, well, three to three years, everyone is

491
00:50:26,800 --> 00:50:31,280
pretty much like, no, I just have LinkedIn. There used to be all sorts of other stuff, but now, uh,

492
00:50:31,280 --> 00:50:36,160
now that's it. What? Yeah. It's, it's like the, the shadow corporate world social network

493
00:50:36,160 --> 00:50:41,280
that I think is actually out there. All right. Well, uh, if you want to get those show notes,

494
00:50:41,280 --> 00:50:47,040
you can go to tomzutalk.com and, uh, find the episode and, uh, check them out. And with that,

495
00:50:47,040 --> 00:50:48,880
we'll see everyone next time. Bye-bye.