1
00:00:02,840 --> 00:00:06,000
Speaker 1: Today on the Business of Tech powered by Two Degrees Business,

2
00:00:06,040 --> 00:00:09,160
Speaker 1: we're talking to two kiwis who've gone from the bleeding

3
00:00:09,240 --> 00:00:13,080
Speaker 1: edge of Silicon Valley to the frontline of the robotics revolution,

4
00:00:13,640 --> 00:00:16,400
Speaker 1: and are both doing it through what's fast becoming known

5
00:00:16,760 --> 00:00:21,800
Speaker 1: as physical AI. Harry Malsop was an autopilot engineer at Tesla.

6
00:00:22,280 --> 00:00:26,160
Speaker 1: Adrian McNeil helped build out the self driving platform and

7
00:00:26,320 --> 00:00:30,440
Speaker 1: data infrastructure at Cruise. Now they founded their own companies,

8
00:00:30,520 --> 00:00:33,839
Speaker 1: Antioch and Foxglove to solve some of the hardest problems

9
00:00:33,880 --> 00:00:38,800
Speaker 1: in taking robots safely from the lab into the real world. Foxglove,

10
00:00:38,800 --> 00:00:43,159
Speaker 1: which Adrian co founded, has just raised forty million NEWS

11
00:00:43,280 --> 00:00:47,600
Speaker 1: dollars in Series B funding led by Bessemer Venture Partners

12
00:00:47,600 --> 00:00:50,839
Speaker 1: with New Zealand's own ice House Ventures. Also on the

13
00:00:50,920 --> 00:00:55,160
Speaker 1: cap table. It's building the data and an observability platform

14
00:00:55,200 --> 00:00:59,960
Speaker 1: that lets robotics teams capture, search, and visualize the ocean

15
00:01:00,240 --> 00:01:04,000
Speaker 1: of censored data robots generate so they can understand how

16
00:01:04,000 --> 00:01:07,880
Speaker 1: these systems actually behave, why they failed, and how to

17
00:01:07,920 --> 00:01:13,080
Speaker 1: improve them before they're deployed at scale. Antioch Harry startup

18
00:01:13,120 --> 00:01:16,280
Speaker 1: has pulled in four point two million US about seven

19
00:01:16,280 --> 00:01:19,440
Speaker 1: point three million New Zealand dollars in pre seed capital,

20
00:01:19,880 --> 00:01:23,960
Speaker 1: again with ice House Ventures involved, to build a cloud

21
00:01:24,200 --> 00:01:28,640
Speaker 1: simulation platform that lets companies build, test, and validate robots

22
00:01:29,480 --> 00:01:34,040
Speaker 1: entirely in software instead of on costly physical test rigs

23
00:01:34,319 --> 00:01:38,120
Speaker 1: and fake warehouses. We hear so much about humanoid robots

24
00:01:38,160 --> 00:01:43,520
Speaker 1: and optimists coming online from Tesla, the revolution in robots

25
00:01:43,520 --> 00:01:46,200
Speaker 1: being able to work in unstructured environments. What is going

26
00:01:46,240 --> 00:01:49,160
Speaker 1: to make it happen is this type of innovation that

27
00:01:49,200 --> 00:01:53,480
Speaker 1: these two guys are involved in. Between them, Foxglove and

28
00:01:53,560 --> 00:01:58,560
Speaker 1: Antioch sit on the critical plumbing of physical AI, the data, flywheels,

29
00:01:59,040 --> 00:02:03,040
Speaker 1: the simulation, the tooling that let you move fast without

30
00:02:03,120 --> 00:02:06,200
Speaker 1: breaking things in the real world. So we'll talk about

31
00:02:06,200 --> 00:02:09,720
Speaker 1: why AI powered robots have taken so long to arrive,

32
00:02:10,440 --> 00:02:14,840
Speaker 1: how simulation and data pipelines are slashing those development cycles,

33
00:02:15,160 --> 00:02:20,040
Speaker 1: and why safely validating robots in software first is becoming

34
00:02:20,200 --> 00:02:24,359
Speaker 1: non negotiable if you want to deploy fleets off autonomous

35
00:02:24,360 --> 00:02:30,079
Speaker 1: machines into factories, farms, warehouses, and city streets. We'll dig

36
00:02:30,120 --> 00:02:33,000
Speaker 1: into the very key we journeys that took them from

37
00:02:33,320 --> 00:02:37,840
Speaker 1: Treasury Stanford coin based Teslat and Cruise to leading this

38
00:02:37,880 --> 00:02:42,400
Speaker 1: new generation of physical AI infrastructure companies. Here's my interview,

39
00:02:42,480 --> 00:02:45,960
Speaker 1: recorded just before the summer break with Harry Melsop and

40
00:02:46,040 --> 00:02:55,480
Speaker 1: Adrian McNeil. Harry and Adrian, Welcome to the Business of Tech.

41
00:02:55,520 --> 00:02:56,760
Speaker 1: How are you both doing great?

42
00:02:56,880 --> 00:02:59,320
Speaker 2: Thanks for having me, Thanks having us doing well.

43
00:02:59,560 --> 00:03:00,360
Speaker 3: Thank you well.

44
00:03:01,080 --> 00:03:04,120
Speaker 1: It's real honor to have you on the podcast. I've

45
00:03:04,160 --> 00:03:07,280
Speaker 1: been hearing so much about both of your companies and

46
00:03:07,280 --> 00:03:09,320
Speaker 1: we're going to get into exactly what you're doing. But

47
00:03:09,680 --> 00:03:12,720
Speaker 1: what I've found really interesting is the area that you've

48
00:03:12,760 --> 00:03:17,639
Speaker 1: both ended up specializing in after a lot of different

49
00:03:17,639 --> 00:03:21,000
Speaker 1: companies that you have worked at in software engineering, some

50
00:03:21,040 --> 00:03:23,720
Speaker 1: of the big names of Silicon Valley. So I'm keen

51
00:03:23,760 --> 00:03:26,360
Speaker 1: to drill into what you learn from those amazing roles,

52
00:03:27,040 --> 00:03:31,520
Speaker 1: but sort of at the frontier of physical AI and robotics, which,

53
00:03:32,400 --> 00:03:35,840
Speaker 1: as someone who's covered tech for twenty five years, probably

54
00:03:35,840 --> 00:03:38,760
Speaker 1: the most difficult area to cover because there's so much

55
00:03:38,800 --> 00:03:41,560
Speaker 1: hype there and we've been promised so much in robotics,

56
00:03:42,160 --> 00:03:44,800
Speaker 1: but there's always been these hard barriers that have been

57
00:03:45,120 --> 00:03:47,760
Speaker 1: really difficult to get over. So it's great that you've

58
00:03:47,760 --> 00:03:51,080
Speaker 1: got too New Zealand founders with really incredible startups that

59
00:03:51,120 --> 00:03:54,560
Speaker 1: are tackling some of those core issues that are preventing

60
00:03:54,760 --> 00:03:58,720
Speaker 1: robotics from rolling out and giving us the productivity gains

61
00:03:58,800 --> 00:04:01,080
Speaker 1: and making life easier sort of thing. So keen to

62
00:04:01,120 --> 00:04:04,480
Speaker 1: get into exactly how you're tackling some of those key problems.

63
00:04:04,800 --> 00:04:07,520
Speaker 1: But maybe backing up just a bit, keen to get

64
00:04:07,520 --> 00:04:09,960
Speaker 1: your origin story. Maybe if we start with you first,

65
00:04:10,000 --> 00:04:13,840
Speaker 1: Harry about how you got into tech, your pathway in

66
00:04:13,880 --> 00:04:17,560
Speaker 1: New Zealand and then making that jump to the US.

67
00:04:17,920 --> 00:04:19,560
Speaker 3: Yeah. Absolutely so.

68
00:04:19,960 --> 00:04:22,960
Speaker 4: I grew up here in New Zealand, did high school

69
00:04:22,960 --> 00:04:27,400
Speaker 4: here in Auckland. Always kind of enjoyed building things in

70
00:04:27,440 --> 00:04:31,880
Speaker 4: the technology space. Had a couple of little commercial ventures

71
00:04:31,920 --> 00:04:35,080
Speaker 4: based out of here in Auckland. One of them in

72
00:04:35,120 --> 00:04:37,919
Speaker 4: particular got a wee bit of commercial traction and it

73
00:04:37,960 --> 00:04:41,919
Speaker 4: got picked up by New Zealand Trade and Enterprise. Pam

74
00:04:42,000 --> 00:04:45,880
Speaker 4: Forward from NZTA very kindly organized a trip for me

75
00:04:46,720 --> 00:04:49,120
Speaker 4: through Silicon Valley. I went to a bunch of different

76
00:04:49,320 --> 00:04:53,240
Speaker 4: companies there, also saw Stanford University and for me that

77
00:04:53,360 --> 00:04:55,280
Speaker 4: was kind of a bit of an eye opening experience.

78
00:04:55,320 --> 00:05:00,119
Speaker 4: I'd never really considered going further afield than maybe Australia

79
00:05:00,240 --> 00:05:01,920
Speaker 4: was born in Australia. I've been over there a bit,

80
00:05:02,000 --> 00:05:04,880
Speaker 4: but you know, I never really thought about living further

81
00:05:04,920 --> 00:05:07,880
Speaker 4: abroad than that. But that really kind of changed my perspective.

82
00:05:07,960 --> 00:05:10,880
Speaker 4: I thought the scale was extraordinary. I thought that the

83
00:05:11,960 --> 00:05:15,640
Speaker 4: you know, work ethic and the innovation was just you know,

84
00:05:15,720 --> 00:05:19,520
Speaker 4: fantastic and really inspiring. And so, you know, towards the

85
00:05:19,560 --> 00:05:21,960
Speaker 4: last couple of years of high school, I think I

86
00:05:22,279 --> 00:05:24,200
Speaker 4: probably did that trip when I was in year eleven,

87
00:05:25,080 --> 00:05:26,960
Speaker 4: and so in year twelve I kind of pivoted and

88
00:05:26,960 --> 00:05:28,080
Speaker 4: I was like, Okay, what do I need to do

89
00:05:28,120 --> 00:05:30,320
Speaker 4: to get into one of these universities ever in the States,

90
00:05:31,360 --> 00:05:33,240
Speaker 4: did a ton of research online about what I had

91
00:05:33,240 --> 00:05:35,600
Speaker 4: to do to make that happen, and did my SATs

92
00:05:35,600 --> 00:05:39,280
Speaker 4: and all that kind of stuff, and then was revery

93
00:05:39,360 --> 00:05:43,640
Speaker 4: fortunate got accepted into Stanford, went across there, did my

94
00:05:43,720 --> 00:05:47,560
Speaker 4: undergrad degree there, did my master's degree there in economics

95
00:05:47,600 --> 00:05:51,080
Speaker 4: and artificial intelligence, and then that kind of spawned my

96
00:05:51,200 --> 00:05:51,960
Speaker 4: career from there.

97
00:05:52,200 --> 00:05:56,240
Speaker 1: Well, yeah, and what years did you study at Stanford?

98
00:05:56,520 --> 00:05:59,200
Speaker 4: So for my undergrad I was there from twenty seventeen

99
00:05:59,240 --> 00:06:01,080
Speaker 4: to twenty twenty one, and then I stuck around for

100
00:06:01,080 --> 00:06:03,880
Speaker 4: another year after that and did did my master's degree.

101
00:06:04,040 --> 00:06:09,200
Speaker 1: Yeah, obviously a legendary place to study, the legacy it

102
00:06:09,240 --> 00:06:13,760
Speaker 1: has seeding innovation and talent into Silicon Valley. But I

103
00:06:13,760 --> 00:06:16,440
Speaker 1: guess at that time, around twenty seventeen and that you

104
00:06:16,480 --> 00:06:19,800
Speaker 1: were hearing a lot studying large language models, seeing the

105
00:06:19,839 --> 00:06:23,239
Speaker 1: potential of all of that and generative AI, which spawned

106
00:06:23,240 --> 00:06:27,800
Speaker 1: the open AI and those sorts of large language model services.

107
00:06:28,080 --> 00:06:29,280
Speaker 3: Yeah, no, exactly.

108
00:06:29,400 --> 00:06:32,240
Speaker 4: So I think, you know, I was really a student

109
00:06:32,600 --> 00:06:35,000
Speaker 4: at the advent of some of these transformer models which

110
00:06:35,040 --> 00:06:37,560
Speaker 4: have kind of formed the basis of open AI and

111
00:06:37,600 --> 00:06:40,400
Speaker 4: some of these other big forefront labs. Now, so, I

112
00:06:40,440 --> 00:06:43,480
Speaker 4: think when I first started doing kind of like language modeling,

113
00:06:43,520 --> 00:06:46,839
Speaker 4: it was a technology called LSTMs and like other very

114
00:06:47,320 --> 00:06:52,080
Speaker 4: comparatively primitive approaches at kind of solving these language problems.

115
00:06:52,120 --> 00:06:54,880
Speaker 4: And then you know, we kind of saw GPT and

116
00:06:54,920 --> 00:06:58,640
Speaker 4: then GPT two come out with these transformer architectures, and

117
00:06:59,040 --> 00:07:00,960
Speaker 4: it was kind of more of a curiosity I think

118
00:07:01,000 --> 00:07:04,120
Speaker 4: initially then anything it was like, okay, this, you know,

119
00:07:04,160 --> 00:07:06,960
Speaker 4: this technology seems to be able to really accurately mimic

120
00:07:07,480 --> 00:07:10,360
Speaker 4: a sort of human speech in a way that you

121
00:07:10,600 --> 00:07:13,320
Speaker 4: sort of look at it, and it sort of looks

122
00:07:13,560 --> 00:07:16,120
Speaker 4: at first glance like somebody could have actually written that,

123
00:07:16,200 --> 00:07:19,000
Speaker 4: or somebody could have actually said that. But then I

124
00:07:19,040 --> 00:07:21,440
Speaker 4: think it wasn't until a couple of years later with

125
00:07:21,600 --> 00:07:25,600
Speaker 4: GPT three and then ultimately chant GPT that I think

126
00:07:25,640 --> 00:07:29,480
Speaker 4: we started to see this is not only an intellectual curiosity,

127
00:07:29,520 --> 00:07:32,600
Speaker 4: but actually this is a really useful tool and a

128
00:07:32,640 --> 00:07:34,640
Speaker 4: multitude of kind of different areas.

129
00:07:34,600 --> 00:07:37,720
Speaker 1: And you quite quickly have to Stanford got into some

130
00:07:37,800 --> 00:07:42,640
Speaker 1: really interesting companies and software engineering, the likes of Transpose

131
00:07:42,800 --> 00:07:47,680
Speaker 1: which became Chainalysis, Stint to Amazon, and in Tesla, which

132
00:07:47,720 --> 00:07:50,480
Speaker 1: is really interesting. But back at school, were you really

133
00:07:50,480 --> 00:07:52,520
Speaker 1: interested in robotics? Who did that come later?

134
00:07:52,800 --> 00:07:56,600
Speaker 4: Yeah, So we did a bit of work in embodied AI,

135
00:07:56,840 --> 00:07:59,040
Speaker 4: so connecting some of these At the time it was

136
00:07:59,080 --> 00:08:04,080
Speaker 4: particularly vision based neural networks, so convolutional neural networks and

137
00:08:04,160 --> 00:08:05,800
Speaker 4: kind of imbuing.

138
00:08:05,320 --> 00:08:07,880
Speaker 3: These systems with a visual understanding of the world.

139
00:08:08,640 --> 00:08:12,200
Speaker 4: I did a lot of work there at school, but

140
00:08:12,280 --> 00:08:15,240
Speaker 4: it was really I think at Tesla that started to

141
00:08:15,320 --> 00:08:18,840
Speaker 4: kind of like professionalize and sort of solidify my particular

142
00:08:18,840 --> 00:08:22,080
Speaker 4: interest in that physical AI world, working on models that

143
00:08:22,400 --> 00:08:25,600
Speaker 4: went into the car. And then also at Tesla. At

144
00:08:25,680 --> 00:08:27,680
Speaker 4: least in the early days, the models that were running

145
00:08:27,720 --> 00:08:31,080
Speaker 4: on Optimists, the humanoid robots, were actually the same models

146
00:08:31,080 --> 00:08:32,000
Speaker 4: as those running.

147
00:08:31,720 --> 00:08:35,720
Speaker 1: On the vehicle Adrian. Similar sort of trajectory for you.

148
00:08:36,280 --> 00:08:40,559
Speaker 1: Entrepreneurial from the start in New Zealand with Expresso used

149
00:08:40,559 --> 00:08:42,199
Speaker 1: start up tell us about that, and then also a

150
00:08:42,280 --> 00:08:43,720
Speaker 1: stint at New Zealand Treasury.

151
00:08:43,960 --> 00:08:44,320
Speaker 3: Yeah.

152
00:08:44,400 --> 00:08:46,920
Speaker 2: Yeah, I was at the Treasury for a couple of

153
00:08:46,960 --> 00:08:51,160
Speaker 2: years when I first graduated, and then I spent a

154
00:08:51,160 --> 00:08:54,199
Speaker 2: few years with a buddy of mine. We're running a

155
00:08:54,240 --> 00:08:56,800
Speaker 2: web design company and building websites for people with this

156
00:08:56,960 --> 00:09:00,520
Speaker 2: is back in the day. But yeah, pretty quickly was

157
00:09:00,960 --> 00:09:02,960
Speaker 2: keen to get out out of New Zealand and make

158
00:09:03,000 --> 00:09:04,840
Speaker 2: it over here to the States. I think similar to

159
00:09:05,040 --> 00:09:07,560
Speaker 2: Harry when I first came out to San Francisco and

160
00:09:07,600 --> 00:09:09,280
Speaker 2: so like a valley just kind of you know, your

161
00:09:09,320 --> 00:09:11,840
Speaker 2: eyes are open to just how much was going on

162
00:09:11,880 --> 00:09:14,680
Speaker 2: here and how many companies, and you know how much

163
00:09:14,720 --> 00:09:18,959
Speaker 2: opportunity there was, and so when I moved out. Funny

164
00:09:18,960 --> 00:09:21,240
Speaker 2: actually that we both started in the crypto space too

165
00:09:21,920 --> 00:09:26,000
Speaker 2: when I moved out. Yeah too, too washed up crypto

166
00:09:26,040 --> 00:09:31,520
Speaker 2: founders doing doing physically. I now, yeah, I came out

167
00:09:31,679 --> 00:09:36,160
Speaker 2: to San Francisco in twenty fourteen and was at Coinbase

168
00:09:36,200 --> 00:09:40,560
Speaker 2: for a couple of years and was leading the engineering

169
00:09:40,600 --> 00:09:44,720
Speaker 2: team there for a little bit and then yeah, from

170
00:09:44,800 --> 00:09:48,400
Speaker 2: there went to Cruz. So a friend of mine who's

171
00:09:48,400 --> 00:09:52,840
Speaker 2: now my co founder, was at Coinbase. He bounced over

172
00:09:52,880 --> 00:09:55,600
Speaker 2: to Cruz and got all excited about self driving cars.

173
00:09:55,640 --> 00:09:58,200
Speaker 2: This is early twenty sixteen, so you know, it was

174
00:09:58,240 --> 00:10:02,360
Speaker 2: pretty early for the self driving he you know, one

175
00:10:02,360 --> 00:10:04,240
Speaker 2: thing led to another convinced me to come over there,

176
00:10:04,240 --> 00:10:08,520
Speaker 2: and I spent about five years at Cruz leading various

177
00:10:08,559 --> 00:10:12,800
Speaker 2: aspects of the infrastructure platform, develop a tooling, data, infrastructure,

178
00:10:12,960 --> 00:10:15,680
Speaker 2: things like that, and so really got a first hand

179
00:10:15,720 --> 00:10:18,560
Speaker 2: seats as that whole space evolved in how we kind

180
00:10:18,559 --> 00:10:21,240
Speaker 2: of figured out from first principles what does it take

181
00:10:21,280 --> 00:10:23,800
Speaker 2: to put a self driving car on the road. And

182
00:10:24,080 --> 00:10:27,439
Speaker 2: you know, sure as Horry can attest from the Tesla

183
00:10:27,520 --> 00:10:29,800
Speaker 2: side of the equation to you just a lot of

184
00:10:29,840 --> 00:10:32,360
Speaker 2: these things. You know, there was no book for it, right,

185
00:10:32,400 --> 00:10:33,920
Speaker 2: There was no there was no manual, There was no

186
00:10:33,960 --> 00:10:36,400
Speaker 2: blog posting you go read about what other companies are doing.

187
00:10:36,600 --> 00:10:38,840
Speaker 2: It was you know, it was a few companies are

188
00:10:38,760 --> 00:10:41,480
Speaker 2: all kind of just figuring it out for ourselves and

189
00:10:41,480 --> 00:10:44,040
Speaker 2: figuring it out as you go along. So that was

190
00:10:44,160 --> 00:10:48,319
Speaker 2: that was really interesting, and then that led to twenty

191
00:10:48,360 --> 00:10:51,480
Speaker 2: twenty one is when I founded my current company, box Log,

192
00:10:51,840 --> 00:10:55,080
Speaker 2: which we're building a very similar thing, a data platform

193
00:10:55,120 --> 00:10:58,440
Speaker 2: for a physical WAYI mirroring a lot of the lessons

194
00:10:58,520 --> 00:11:02,320
Speaker 2: learned from the self driving industry, building a platform that

195
00:11:02,360 --> 00:11:05,000
Speaker 2: lets people capture and learn from all of that data

196
00:11:05,040 --> 00:11:07,160
Speaker 2: that they're gathering from robots in the real world.

197
00:11:07,320 --> 00:11:10,240
Speaker 1: Yeah, and that company, Cruise, I think, was considered the

198
00:11:10,320 --> 00:11:14,400
Speaker 1: real leader for many years at least in autonomous car development.

199
00:11:14,400 --> 00:11:16,760
Speaker 1: I think it's been folded back into GM now and

200
00:11:16,760 --> 00:11:19,560
Speaker 1: that technology has been dispersed across GM, but for a

201
00:11:19,600 --> 00:11:22,440
Speaker 1: while it was you know, Cruise and then obviously Tesla

202
00:11:22,800 --> 00:11:25,520
Speaker 1: where the real pioneers. And that incredible that another New

203
00:11:25,640 --> 00:11:29,240
Speaker 1: Zealander is running Wave in the UK.

204
00:11:29,480 --> 00:11:31,640
Speaker 2: Yeah, I like, yeah, he's doing great.

205
00:11:31,760 --> 00:11:34,880
Speaker 1: Yeah, raised something crazy like a couple of years ago

206
00:11:34,920 --> 00:11:38,040
Speaker 1: from Soft Bank of billion dollars or something. So they've

207
00:11:38,880 --> 00:11:42,760
Speaker 1: got a lot of runway to make their technology successful.

208
00:11:42,800 --> 00:11:45,960
Speaker 1: So that's brilliant. But just interested in you know, I'm

209
00:11:46,000 --> 00:11:50,120
Speaker 1: constantly thinking about when I interview people. What is it

210
00:11:50,160 --> 00:11:56,000
Speaker 1: about about us that gives New Zealand entrepreneurs a little

211
00:11:56,000 --> 00:11:57,840
Speaker 1: bit of an edge. I'm just wondering if there's anything

212
00:11:57,840 --> 00:12:00,560
Speaker 1: in your upbringing or your early education here in New

213
00:12:00,640 --> 00:12:02,720
Speaker 1: Zealand that sort of shaped the way that you think

214
00:12:02,760 --> 00:12:05,840
Speaker 1: about tackling some of these hard technology problems.

215
00:12:06,040 --> 00:12:08,280
Speaker 2: Yeah, I actually, I actually think quite a bit about this.

216
00:12:08,400 --> 00:12:12,600
Speaker 2: I think New Zealand is quite conducive to an entrepreneurial

217
00:12:12,640 --> 00:12:16,280
Speaker 2: mindset because there is so much small business you actually,

218
00:12:16,360 --> 00:12:19,840
Speaker 2: I mean in the States and Silicon Valley. I don't

219
00:12:19,880 --> 00:12:22,120
Speaker 2: know if it's a majority, but certainly, like a large

220
00:12:22,160 --> 00:12:24,280
Speaker 2: portion of the founders that you talk to were not

221
00:12:24,440 --> 00:12:26,560
Speaker 2: born in the States and certainly didn't grow up in

222
00:12:26,559 --> 00:12:28,959
Speaker 2: the Bay Area. You know, you have a lot of

223
00:12:29,360 --> 00:12:33,200
Speaker 2: founders from all over the world here, and you know,

224
00:12:33,280 --> 00:12:38,199
Speaker 2: I found, for example, growing up, like my parents rand businesses,

225
00:12:38,320 --> 00:12:40,400
Speaker 2: various businesses over the time, growing up most of my

226
00:12:40,440 --> 00:12:44,640
Speaker 2: friends parents run businesses. It's actually quite an unusual experience

227
00:12:45,400 --> 00:12:47,920
Speaker 2: compared to the US, where most people are growing up

228
00:12:47,960 --> 00:12:51,400
Speaker 2: with you know, parents with stable jobs, and then they

229
00:12:51,440 --> 00:12:53,040
Speaker 2: get into a good college and then they're you know,

230
00:12:53,080 --> 00:12:55,079
Speaker 2: coming out of college immediately looking for what it is

231
00:12:55,679 --> 00:12:57,320
Speaker 2: a great job that I can get out of college.

232
00:12:57,320 --> 00:12:59,599
Speaker 2: That's kind of the default experience. But I think, you know,

233
00:12:59,600 --> 00:13:01,360
Speaker 2: at least my experience in New Zealand, and I think

234
00:13:01,400 --> 00:13:03,240
Speaker 2: this is true in a lot of New Zealand's just

235
00:13:03,280 --> 00:13:05,400
Speaker 2: there's a lot of small business and that has an

236
00:13:05,400 --> 00:13:07,040
Speaker 2: impact on you from a young age.

237
00:13:07,160 --> 00:13:08,720
Speaker 3: Yeah, I completely agree with that point.

238
00:13:09,559 --> 00:13:13,120
Speaker 4: I think, you know, there are also some elements of

239
00:13:13,160 --> 00:13:15,679
Speaker 4: being at the far end of the world where you

240
00:13:15,760 --> 00:13:17,720
Speaker 4: just sort of have to be pragmatic and just have

241
00:13:17,800 --> 00:13:19,400
Speaker 4: to figure it out. And I think that there are

242
00:13:19,760 --> 00:13:21,319
Speaker 4: a lot of New Zealanders, if you kind of look

243
00:13:21,360 --> 00:13:24,800
Speaker 4: across history, have sort of embodied that. And maybe it's

244
00:13:24,800 --> 00:13:26,640
Speaker 4: a little bit of a New Zealand cultural trope, but

245
00:13:26,640 --> 00:13:29,520
Speaker 4: I think there's also a lot of truth to that

246
00:13:29,559 --> 00:13:34,080
Speaker 4: as well. And I think when you're tackling really hard problems,

247
00:13:34,280 --> 00:13:37,440
Speaker 4: you have to be extremely resourceful and you have to

248
00:13:37,480 --> 00:13:40,360
Speaker 4: sort of look at that problem from a distance and

249
00:13:40,400 --> 00:13:42,520
Speaker 4: figure out you know, I've got a ton of constraints.

250
00:13:43,480 --> 00:13:45,920
Speaker 4: How many of them are often financial, But if you're

251
00:13:46,559 --> 00:13:49,280
Speaker 4: attacking this really large problem, even with a ton of money,

252
00:13:49,880 --> 00:13:52,880
Speaker 4: you're still going to be running up very quickly against

253
00:13:52,880 --> 00:13:54,679
Speaker 4: that and so I think, you know, there's a lot

254
00:13:54,720 --> 00:13:56,920
Speaker 4: of New Zealand history that's that's kind of been written

255
00:13:56,960 --> 00:13:59,000
Speaker 4: around people kind of overcoming a lot of that, and

256
00:13:59,120 --> 00:14:00,760
Speaker 4: I think that layers into culture there too.

257
00:14:00,920 --> 00:14:03,839
Speaker 1: Yeah, and that's something that Sir Peter Beck I think

258
00:14:03,920 --> 00:14:06,599
Speaker 1: is still part of his DNA and the DNA of

259
00:14:06,720 --> 00:14:10,600
Speaker 1: Rocket Lab is that famous quote that Sir Ernest Rutherford said,

260
00:14:10,640 --> 00:14:12,079
Speaker 1: you know, we don't have the money, so we need

261
00:14:12,120 --> 00:14:15,840
Speaker 1: to think I'm paraphrasing it there, and that's that inspired

262
00:14:15,880 --> 00:14:17,720
Speaker 1: Sir Peter, which is why he named the you know,

263
00:14:17,760 --> 00:14:22,120
Speaker 1: the Rutherford Engine after Ernest Rutherford. And I think it's

264
00:14:22,160 --> 00:14:25,040
Speaker 1: still evident in the approach in that company today. You know,

265
00:14:25,080 --> 00:14:28,080
Speaker 1: with the new rocket, it's actually a relatively modest budget

266
00:14:28,080 --> 00:14:31,480
Speaker 1: they've developed that on. So that frugality, I think is

267
00:14:31,560 --> 00:14:35,320
Speaker 1: a benefit that we have. We can do more with less.

268
00:14:35,320 --> 00:14:38,240
Speaker 1: We're used to doing that. And I think Adrian, you know,

269
00:14:38,280 --> 00:14:40,600
Speaker 1: you were interested in robotics all the way back right

270
00:14:40,680 --> 00:14:43,480
Speaker 1: like growing up in rural New Zealand looking at kiwi

271
00:14:43,520 --> 00:14:45,640
Speaker 1: fruit processing and that sort of stuff.

272
00:14:45,920 --> 00:14:48,920
Speaker 2: Yeah. Yeah, I think that definitely had an impact on me.

273
00:14:49,000 --> 00:14:51,880
Speaker 2: I mean, I wasn't so much thinking about robotics at

274
00:14:51,920 --> 00:14:53,560
Speaker 2: the time, although I didn't spend a fair amount of

275
00:14:53,600 --> 00:14:57,720
Speaker 2: time in packhouses doing various jobs and you know, in

276
00:14:57,840 --> 00:15:02,360
Speaker 2: high school summers and things. But once I sort of

277
00:15:03,080 --> 00:15:05,680
Speaker 2: started working in the self driving industry and spending time,

278
00:15:06,680 --> 00:15:09,680
Speaker 2: you know, around self driving vehicles, I reached a point

279
00:15:09,800 --> 00:15:13,360
Speaker 2: where I was like, so, you know, we made a

280
00:15:13,360 --> 00:15:16,200
Speaker 2: car drive around San Francisco, and yet look at what

281
00:15:16,320 --> 00:15:18,840
Speaker 2: we have not why are there no self driving tractors

282
00:15:19,480 --> 00:15:22,800
Speaker 2: in the fields for example, right? And so I reached

283
00:15:22,800 --> 00:15:25,560
Speaker 2: this point where you know, you're sort of living in

284
00:15:25,600 --> 00:15:27,560
Speaker 2: a bit of a bubble at the bleeding edge of

285
00:15:27,560 --> 00:15:30,240
Speaker 2: some technology, right, Like you know, every every week I'm

286
00:15:30,560 --> 00:15:33,480
Speaker 2: going for drives in a self driving car and thinking about,

287
00:15:33,720 --> 00:15:35,680
Speaker 2: you know, how this technology was put together and what

288
00:15:35,680 --> 00:15:38,640
Speaker 2: it took to get there. And then you know, looking

289
00:15:38,680 --> 00:15:42,200
Speaker 2: around and seeing all of the things that would be

290
00:15:42,200 --> 00:15:44,600
Speaker 2: way easier to automate that have not yet been automated.

291
00:15:44,600 --> 00:15:45,960
Speaker 2: And you reach this point you kind of reached us

292
00:15:46,000 --> 00:15:48,280
Speaker 2: like technology overhang, I guess where you're like, look, we

293
00:15:48,320 --> 00:15:50,920
Speaker 2: have this technology, it's just it's not widely distributed in

294
00:15:50,960 --> 00:15:53,480
Speaker 2: the world. Get and so those kind of things always

295
00:15:53,480 --> 00:15:55,800
Speaker 2: really excite me. Where there's kind of an opportunity to

296
00:15:56,960 --> 00:15:57,760
Speaker 2: push the ball forward.

297
00:15:57,880 --> 00:16:00,400
Speaker 1: You're both at that point in your career. You're both

298
00:16:00,400 --> 00:16:05,040
Speaker 1: in really good companies, well paid jobs. You could probably

299
00:16:05,080 --> 00:16:08,760
Speaker 1: have carried on, you know, in big tech companies, gaining

300
00:16:08,760 --> 00:16:10,840
Speaker 1: more experience than that, but you obviously both made a

301
00:16:10,840 --> 00:16:13,200
Speaker 1: decision to go out on your own. What was it

302
00:16:13,200 --> 00:16:15,840
Speaker 1: for you, Harry, that convinced you to leave that, you

303
00:16:15,840 --> 00:16:19,080
Speaker 1: know that the safety of a Silicon Valley job and

304
00:16:19,120 --> 00:16:21,400
Speaker 1: a great company, to forge out on your own. What

305
00:16:21,480 --> 00:16:24,680
Speaker 1: was the painful problem that you really wanted to tackle

306
00:16:24,720 --> 00:16:26,520
Speaker 1: that you saw you couldn't do as part of the

307
00:16:27,040 --> 00:16:27,720
Speaker 1: current system.

308
00:16:27,880 --> 00:16:29,120
Speaker 3: I think it's a really good question.

309
00:16:29,360 --> 00:16:32,760
Speaker 4: I think for me it was kind of a confluence

310
00:16:32,960 --> 00:16:35,200
Speaker 4: of different factors coming together.

311
00:16:35,280 --> 00:16:36,000
Speaker 3: I think I've.

312
00:16:35,840 --> 00:16:39,600
Speaker 4: Always liked the process of building something from a more

313
00:16:39,600 --> 00:16:43,680
Speaker 4: holistic perspective, and so the engineering is a is a

314
00:16:43,760 --> 00:16:46,200
Speaker 4: key component of that, but it's far from the only piece.

315
00:16:46,680 --> 00:16:49,120
Speaker 4: There's the interpersonal piece of it. There's connecting that to

316
00:16:49,160 --> 00:16:52,680
Speaker 4: some kind of concrete business outcome. There's everything else that

317
00:16:52,760 --> 00:16:56,440
Speaker 4: kind of sits around what it takes to actually commercialize something.

318
00:16:56,760 --> 00:17:00,440
Speaker 4: And I really enjoy all of that personally, and so

319
00:17:00,480 --> 00:17:05,280
Speaker 4: I think for me, going to Tesla initially was me

320
00:17:05,359 --> 00:17:08,840
Speaker 4: thinking to myself, Okay, I know that ultimately I kind

321
00:17:08,840 --> 00:17:11,639
Speaker 4: of want to go and I want to build technology companies,

322
00:17:12,000 --> 00:17:13,760
Speaker 4: But first of all, I want to kind of surround

323
00:17:13,800 --> 00:17:17,920
Speaker 4: myself with, you know, as many kind of brilliant minds

324
00:17:17,960 --> 00:17:20,240
Speaker 4: who challenged me, who are much better than me in

325
00:17:20,560 --> 00:17:23,080
Speaker 4: a multitude of kind of different ways, to kind of

326
00:17:23,359 --> 00:17:26,879
Speaker 4: optimize for that learning gradient as quickly as possible, and

327
00:17:26,920 --> 00:17:28,879
Speaker 4: just sort of optimize for talent density so I can

328
00:17:28,960 --> 00:17:32,240
Speaker 4: kind of, by process of osmosis of absorb as much

329
00:17:32,280 --> 00:17:34,639
Speaker 4: of that as possible. So I think for me it

330
00:17:34,720 --> 00:17:37,600
Speaker 4: was less of a push to get out of Tesla

331
00:17:37,760 --> 00:17:40,240
Speaker 4: and more of you know that I kind of always

332
00:17:40,359 --> 00:17:43,080
Speaker 4: saw that experience at some of those larger companies as

333
00:17:43,080 --> 00:17:45,720
Speaker 4: a bit of a means to an end to sort

334
00:17:45,720 --> 00:17:47,360
Speaker 4: of take that out and build something myself.

335
00:17:47,560 --> 00:17:50,600
Speaker 1: Yeah, and take us through Antioch. What you're trying to

336
00:17:51,080 --> 00:17:57,119
Speaker 1: achieve there you've said, I think integration testing for atoms,

337
00:17:57,800 --> 00:18:01,400
Speaker 1: which is a really interesting phrase that probably you keep

338
00:18:01,440 --> 00:18:05,879
Speaker 1: talking about, and simulation. Tell us what Antiocha is actually

339
00:18:05,880 --> 00:18:08,719
Speaker 1: setting out to achieve when it comes to robotics, AI

340
00:18:09,119 --> 00:18:09,879
Speaker 1: and simulation.

341
00:18:10,240 --> 00:18:14,800
Speaker 4: Yeah, so today if you're building a physical AI company,

342
00:18:14,840 --> 00:18:18,639
Speaker 4: if that's self driving vehicle or drones or robots that

343
00:18:18,680 --> 00:18:22,240
Speaker 4: are in factories. The general kind of development process that

344
00:18:22,280 --> 00:18:26,120
Speaker 4: you're taking is you're buying all of these physical components,

345
00:18:26,240 --> 00:18:28,400
Speaker 4: you're getting them on a workbench in front of you,

346
00:18:28,400 --> 00:18:30,800
Speaker 4: you're writing the software for them, and you're kind of

347
00:18:30,840 --> 00:18:34,760
Speaker 4: testing them manually out there in the real world. And

348
00:18:34,760 --> 00:18:37,119
Speaker 4: there's a multitude of kind of problems with that. But

349
00:18:37,160 --> 00:18:39,480
Speaker 4: the biggest problem comes when you kind of hit scale

350
00:18:39,560 --> 00:18:43,080
Speaker 4: and you start thinking about the tail cases of when

351
00:18:43,080 --> 00:18:45,520
Speaker 4: I'm deploying this out in reality, what are all the

352
00:18:45,600 --> 00:18:48,600
Speaker 4: edge cases that I'm running into that I didn't foresee

353
00:18:48,600 --> 00:18:51,440
Speaker 4: when I was developing, or that are actually really difficult

354
00:18:51,440 --> 00:18:54,520
Speaker 4: and really prohibitive to kind of replicate in my local

355
00:18:54,600 --> 00:18:58,159
Speaker 4: kind of development and kind of testing environment. And so

356
00:18:58,800 --> 00:19:01,560
Speaker 4: you know, at anti K what we're doing is we're

357
00:19:01,600 --> 00:19:04,840
Speaker 4: shifting as much of that development and that evaluation process

358
00:19:04,880 --> 00:19:08,280
Speaker 4: as possible into software simulation, where you don't have the

359
00:19:08,359 --> 00:19:13,000
Speaker 4: constraints of the real world. You know, getting actors, getting scenes,

360
00:19:13,080 --> 00:19:16,040
Speaker 4: getting objects, and a multitude of kind of differentch cases

361
00:19:16,119 --> 00:19:19,679
Speaker 4: is effectively free. And so you know, we talk to

362
00:19:19,800 --> 00:19:22,639
Speaker 4: a lot of customers now who spend you know, tens

363
00:19:22,680 --> 00:19:26,280
Speaker 4: to hundreds of millions of dollars every year on their

364
00:19:26,359 --> 00:19:29,520
Speaker 4: kind of physical testing infrastructure. I kind of saw this

365
00:19:29,960 --> 00:19:32,000
Speaker 4: firsthand when I was at Tesla, Right, we would have

366
00:19:32,400 --> 00:19:35,480
Speaker 4: hundreds of cars on the road, hundreds of drivers collecting

367
00:19:35,640 --> 00:19:39,480
Speaker 4: these real driving miles, and it's a huge problem for

368
00:19:39,520 --> 00:19:41,679
Speaker 4: other companies as well. So by kind of shifting that

369
00:19:41,760 --> 00:19:45,439
Speaker 4: into simulation as much as possible, you're slashing a lot

370
00:19:45,480 --> 00:19:48,800
Speaker 4: of cost. But you're also kind of democratizing the access

371
00:19:48,840 --> 00:19:51,639
Speaker 4: to building something in the physical AI world, not just

372
00:19:51,680 --> 00:19:53,720
Speaker 4: to a company like Tesla who can afford to spend

373
00:19:53,760 --> 00:19:57,000
Speaker 4: one hundred million plus dollars a year on that testing infrastructure,

374
00:19:57,359 --> 00:20:00,000
Speaker 4: but because you can do it in software, it's really approachable.

375
00:20:00,200 --> 00:20:03,600
Speaker 4: Anyone could sign up for an account and get started there.

376
00:20:03,800 --> 00:20:06,879
Speaker 1: Yeah. And is there any particular type of robotics or

377
00:20:07,000 --> 00:20:10,280
Speaker 1: physical systems that you're focusing on at the moment or

378
00:20:10,320 --> 00:20:11,560
Speaker 1: is this the whole point of it. It's sort of

379
00:20:11,640 --> 00:20:15,439
Speaker 1: democratizing simulation. So it could be a robot in a

380
00:20:15,480 --> 00:20:18,600
Speaker 1: factory doing manufacturing, it could be an agritech robot, or

381
00:20:18,600 --> 00:20:20,000
Speaker 1: it could be an autonomous vehicle.

382
00:20:20,240 --> 00:20:24,359
Speaker 4: Yeah, that's a great question. I think there's sort of

383
00:20:24,359 --> 00:20:26,359
Speaker 4: two answers to that. So there's an answer from the

384
00:20:26,359 --> 00:20:29,439
Speaker 4: platform capability, like what can we do? And the answer

385
00:20:29,480 --> 00:20:32,679
Speaker 4: to that is it's actually very generally capable. You know,

386
00:20:32,720 --> 00:20:37,000
Speaker 4: we've got high quality sensor simulation, so cameras, radars, lighters,

387
00:20:37,440 --> 00:20:39,239
Speaker 4: all of these different kinds of senses that you have

388
00:20:39,280 --> 00:20:41,959
Speaker 4: on your robots, and those are kind of actually common

389
00:20:42,000 --> 00:20:45,320
Speaker 4: across you know, all sorts of different morphologies of robots

390
00:20:45,320 --> 00:20:50,199
Speaker 4: generally speaking. And the sort of second answer to that

391
00:20:50,320 --> 00:20:53,080
Speaker 4: is from a pragmatic perspective, what are we going after first?

392
00:20:53,119 --> 00:20:55,800
Speaker 4: Because you know, you chase two rabbits, you catch either.

393
00:20:55,840 --> 00:20:57,399
Speaker 4: We sort of have to focus on a couple of

394
00:20:57,400 --> 00:21:01,320
Speaker 4: particular industries to just think our teeth too, and they've

395
00:21:01,359 --> 00:21:03,600
Speaker 4: really been two so far.

396
00:21:04,000 --> 00:21:04,680
Speaker 2: So there's a lot of.

397
00:21:04,640 --> 00:21:08,240
Speaker 4: Manufacturing applications that that we've kind of seen, and that

398
00:21:08,320 --> 00:21:11,280
Speaker 4: takes a lot of different forms, everything from like bringing

399
00:21:11,320 --> 00:21:16,560
Speaker 4: together packaged meals using robots to assembling hardware components using robots.

400
00:21:17,040 --> 00:21:20,320
Speaker 4: But also I think what's kind of surprised US has

401
00:21:20,359 --> 00:21:23,280
Speaker 4: been actually a second market, which is kind of like

402
00:21:23,320 --> 00:21:27,160
Speaker 4: a smart security system market, and so you don't tend

403
00:21:27,240 --> 00:21:29,560
Speaker 4: to think of that when you think of robotics, because

404
00:21:29,560 --> 00:21:32,320
Speaker 4: these things aren't often moving around, but the problems are

405
00:21:32,320 --> 00:21:34,560
Speaker 4: all kind of the same. You've got cameras mounted to

406
00:21:35,000 --> 00:21:38,040
Speaker 4: you know, warehouses or construction sites or things like that,

407
00:21:38,320 --> 00:21:41,600
Speaker 4: and you need to sort of understand track progress of construction.

408
00:21:42,040 --> 00:21:44,399
Speaker 4: You need to track, you know, where agents are in

409
00:21:44,400 --> 00:21:46,359
Speaker 4: the scene, if they're in places that they shouldn't be,

410
00:21:46,520 --> 00:21:48,840
Speaker 4: So people have kind of come into the property and

411
00:21:48,840 --> 00:21:51,720
Speaker 4: they shouldn't be there, for example. So the kind of

412
00:21:51,760 --> 00:21:54,400
Speaker 4: testing problem is actually very very analogous. So we're sort

413
00:21:54,400 --> 00:21:57,800
Speaker 4: of starting with those two markets, but broadly the product

414
00:21:57,880 --> 00:21:59,560
Speaker 4: is capable of going a lot wider.

415
00:22:00,040 --> 00:22:02,600
Speaker 1: Yeah, that's a hugely promising market I think. I mean

416
00:22:02,800 --> 00:22:05,280
Speaker 1: several years ago in Silicon Valley, I think it was

417
00:22:05,440 --> 00:22:08,800
Speaker 1: US Robotics. I saw their little dog they were demoing,

418
00:22:08,800 --> 00:22:12,720
Speaker 1: and they were saying, instead of having a big security

419
00:22:12,720 --> 00:22:15,240
Speaker 1: guard force, you can send out these dogs. They've got

420
00:22:15,359 --> 00:22:19,120
Speaker 1: covered in sensors are constantly giving you real time intelligence,

421
00:22:19,880 --> 00:22:21,720
Speaker 1: night vision and all of that back. So if you

422
00:22:21,760 --> 00:22:25,960
Speaker 1: have a big sort of manufacturing site or retail premises

423
00:22:26,040 --> 00:22:29,159
Speaker 1: or something, but it's always just taken so long to

424
00:22:29,680 --> 00:22:32,600
Speaker 1: get to fruition, and you know they're expensive robots, but

425
00:22:32,680 --> 00:22:37,000
Speaker 1: I'm surprised it's one area that has taken so long.

426
00:22:37,119 --> 00:22:40,119
Speaker 1: So it sees as some of the barriers that for

427
00:22:40,160 --> 00:22:43,720
Speaker 1: a safety point of view, being able to simulate if

428
00:22:43,760 --> 00:22:47,200
Speaker 1: we put these into an unstructured environment, what is actually

429
00:22:47,240 --> 00:22:48,639
Speaker 1: going to happen? Is it going to be safe to

430
00:22:48,720 --> 00:22:51,760
Speaker 1: unleash these in an industrial or even in an environment

431
00:22:51,760 --> 00:22:53,200
Speaker 1: where civilians are Yeah, there.

432
00:22:53,119 --> 00:22:54,360
Speaker 3: Have been so many different barriers.

433
00:22:54,400 --> 00:22:57,560
Speaker 4: I mean, I think the cost of hardware has historically

434
00:22:57,600 --> 00:23:01,280
Speaker 4: been a huge one. You know, even some of the

435
00:23:01,320 --> 00:23:05,439
Speaker 4: sort of like table stakes, perception and planning algorithms that

436
00:23:05,480 --> 00:23:08,000
Speaker 4: go into this as well have started to be more

437
00:23:08,040 --> 00:23:11,639
Speaker 4: commoditized in recent years too. And then I think, you know,

438
00:23:11,680 --> 00:23:14,280
Speaker 4: you bring up the safety point, which is an absolutely

439
00:23:14,320 --> 00:23:16,920
Speaker 4: critical one, and that's one that I think that we're

440
00:23:16,960 --> 00:23:19,040
Speaker 4: thinking a lot about because if you can kind of

441
00:23:19,440 --> 00:23:21,320
Speaker 4: you know, go through a lot of that safety and

442
00:23:21,359 --> 00:23:25,360
Speaker 4: that validation process in simulation for really really cheap you're

443
00:23:25,400 --> 00:23:27,840
Speaker 4: able to slash a lot of development cost out of

444
00:23:27,840 --> 00:23:30,720
Speaker 4: what you would otherwise be validating in the real world

445
00:23:31,000 --> 00:23:33,720
Speaker 4: shutting down a real construction site in this case, right

446
00:23:33,880 --> 00:23:36,080
Speaker 4: to test that your robot dog is not going to

447
00:23:36,080 --> 00:23:38,200
Speaker 4: flip itself over or not not going to run into

448
00:23:38,200 --> 00:23:40,040
Speaker 4: a person or get itself in front of a car

449
00:23:40,160 --> 00:23:43,000
Speaker 4: or something like that. And so I think lots of

450
00:23:43,000 --> 00:23:45,800
Speaker 4: different barriers, but there are lots of different companies kind

451
00:23:45,800 --> 00:23:48,040
Speaker 4: of across the ecosystem targeting each of those, and so

452
00:23:48,080 --> 00:23:50,960
Speaker 4: I'm pretty excited about what the next few years look

453
00:23:51,080 --> 00:23:52,680
Speaker 4: like as those start to get eroded.

454
00:23:52,920 --> 00:23:57,400
Speaker 1: So you're looking at simulation Adrian fox gloves. It's sort

455
00:23:57,400 --> 00:24:01,879
Speaker 1: of at the data and observe ability layer of robotics.

456
00:24:01,880 --> 00:24:04,960
Speaker 1: For listeners who aren't really familiar with what that actually means.

457
00:24:05,040 --> 00:24:07,160
Speaker 1: Take us through what that means day to day for

458
00:24:07,640 --> 00:24:09,919
Speaker 1: a robotics engineering team trying to put one of these

459
00:24:09,960 --> 00:24:10,719
Speaker 1: systems together.

460
00:24:11,040 --> 00:24:14,200
Speaker 2: Yeah, So fox gott is a platform for managing data

461
00:24:14,200 --> 00:24:17,960
Speaker 2: that you're getting off of these robots or physical AI systems.

462
00:24:18,680 --> 00:24:21,720
Speaker 2: You can think about most robotics company have some kind

463
00:24:21,720 --> 00:24:25,080
Speaker 2: of data flywheel, right. They're logging data in the real world,

464
00:24:25,119 --> 00:24:27,560
Speaker 2: they're bringing their data back, they're analyzing that data to

465
00:24:27,640 --> 00:24:29,840
Speaker 2: understand what was the performance of the robot that using

466
00:24:29,840 --> 00:24:32,159
Speaker 2: that analysis to drive you know, what do we need

467
00:24:32,160 --> 00:24:34,520
Speaker 2: to improve in the robot simulations? Do we need to

468
00:24:34,560 --> 00:24:37,520
Speaker 2: create you know what what do we need to work on?

469
00:24:37,840 --> 00:24:37,879
Speaker 3: That?

470
00:24:38,240 --> 00:24:40,640
Speaker 2: Quite often training on and learning from their data. Now

471
00:24:40,680 --> 00:24:42,520
Speaker 2: as the AI is getting better and better, they're actually

472
00:24:42,560 --> 00:24:45,399
Speaker 2: like learning directly from recordings that we've got from the

473
00:24:45,400 --> 00:24:49,200
Speaker 2: real world. They're needing to build up these data sets.

474
00:24:49,240 --> 00:24:51,480
Speaker 2: They're needing to search across data and find like, hey,

475
00:24:51,480 --> 00:24:54,359
Speaker 2: we recorded all of this, but we're at specific individual

476
00:24:54,400 --> 00:24:57,960
Speaker 2: points in time where we're you know, the analogy the

477
00:24:58,000 --> 00:25:01,439
Speaker 2: example with self driving writer is you can have millions

478
00:25:01,480 --> 00:25:03,080
Speaker 2: and millions of miles and a lot of it is

479
00:25:03,119 --> 00:25:04,720
Speaker 2: very useless. A lot of it is okay, we have

480
00:25:04,760 --> 00:25:07,160
Speaker 2: plenty of data of people driving along the straight road

481
00:25:07,200 --> 00:25:10,080
Speaker 2: with a nice clear, sunning sky or whatever, but we

482
00:25:10,119 --> 00:25:13,200
Speaker 2: need more examples of us trying to make you know,

483
00:25:13,240 --> 00:25:16,600
Speaker 2: a left turn across a divided highway with like pedestrians

484
00:25:16,600 --> 00:25:18,439
Speaker 2: in the way while it's raining or whatever right, And so,

485
00:25:18,880 --> 00:25:21,760
Speaker 2: you know, how do you navigate through all of this data,

486
00:25:22,240 --> 00:25:24,520
Speaker 2: find the interesting examples to build out their data set

487
00:25:24,560 --> 00:25:28,000
Speaker 2: and really really understand what they robot is doing. So

488
00:25:28,080 --> 00:25:33,360
Speaker 2: we're fundamentally a platform for collecting, searching, storing, and visualizing

489
00:25:33,600 --> 00:25:36,400
Speaker 2: and debugging all the data that you're getting off these systems.

490
00:25:36,640 --> 00:25:40,800
Speaker 1: And similarly to Harry's approach, you're sort of horizontally integrated.

491
00:25:40,800 --> 00:25:44,920
Speaker 1: It's not as say you've chosen one particularly promising vertical

492
00:25:45,000 --> 00:25:49,200
Speaker 1: or like manufacturing that is ahead of others. You're actually saying,

493
00:25:49,640 --> 00:25:51,560
Speaker 1: we can apply this and we can probably improve the

494
00:25:51,600 --> 00:25:55,200
Speaker 1: product and learn from deploying it as widely as possible.

495
00:25:55,440 --> 00:26:00,560
Speaker 2: Yeah, exactly, we have hundreds of paying customers across autonomous vehicles, drones,

496
00:26:00,600 --> 00:26:08,439
Speaker 2: across agriculture, across ocean, robots, manufacturing, logistics, constructions. It's a

497
00:26:08,480 --> 00:26:11,399
Speaker 2: very generally applicable problem, as is I think you know,

498
00:26:11,480 --> 00:26:15,160
Speaker 2: Harry's Harry's company. We're trying to solve these horizontal plays.

499
00:26:15,280 --> 00:26:17,560
Speaker 2: A lot of this infrastructure needs to exist because if

500
00:26:17,560 --> 00:26:20,919
Speaker 2: you look at a large, a large mature self driving

501
00:26:20,960 --> 00:26:24,080
Speaker 2: program like today at a Tesla or Awaimo, there are

502
00:26:24,200 --> 00:26:26,800
Speaker 2: you know, a thousand plus engineers and the bottom of

503
00:26:26,800 --> 00:26:29,480
Speaker 2: the iceberg is building all of this tooling and infrastructure.

504
00:26:29,520 --> 00:26:31,960
Speaker 2: Right like, you have a small number of people who

505
00:26:31,960 --> 00:26:35,040
Speaker 2: are really working on like novel model architecture, and then

506
00:26:35,080 --> 00:26:36,680
Speaker 2: you have all the people that are dealing with.

507
00:26:36,600 --> 00:26:39,680
Speaker 5: The oceans of data that you get with the complexity.

508
00:26:39,200 --> 00:26:42,600
Speaker 2: Around simulation, with the training pipelines, with the monitoring and

509
00:26:42,640 --> 00:26:45,240
Speaker 2: observability of production and understanding like you know, how is

510
00:26:45,280 --> 00:26:48,120
Speaker 2: the fleet managing production and like building this flow. Also

511
00:26:48,119 --> 00:26:50,000
Speaker 2: that you're constantly learning from all of the driving that

512
00:26:50,080 --> 00:26:53,000
Speaker 2: you're doing out in the world. This is how we

513
00:26:53,080 --> 00:26:56,240
Speaker 2: have been able to build self driving cars in twenty

514
00:26:56,280 --> 00:26:58,080
Speaker 2: twenty five. But if we want all of these other

515
00:26:58,080 --> 00:26:59,600
Speaker 2: things to exist, if we want there to be a

516
00:26:59,600 --> 00:27:03,840
Speaker 2: lot more autonomous robots and manufacturing or logistics or any

517
00:27:03,880 --> 00:27:06,400
Speaker 2: of these other areas, that platform needs to be available

518
00:27:06,400 --> 00:27:09,920
Speaker 2: off the shelf. We can't have thousands of robotics. Companies

519
00:27:09,920 --> 00:27:12,199
Speaker 2: and startups cannot be successful unless they can buy the

520
00:27:12,200 --> 00:27:14,720
Speaker 2: bulk of this kind of tooling and infrastructure off the shelf.

521
00:27:14,800 --> 00:27:16,560
Speaker 2: And that's how every other industry works, right, Like in

522
00:27:16,560 --> 00:27:18,080
Speaker 2: the as SASS industry, you want to go build a

523
00:27:18,320 --> 00:27:20,440
Speaker 2: you want to launch an e commerce store, you don't

524
00:27:20,440 --> 00:27:23,760
Speaker 2: start like writing code about how your checkout's going to work. Right,

525
00:27:24,000 --> 00:27:25,760
Speaker 2: you have all of those things available off the shelf

526
00:27:25,840 --> 00:27:28,600
Speaker 2: so that you can focus on what's unique to your business.

527
00:27:28,359 --> 00:27:32,280
Speaker 1: And what was there anything really that crystallized it for

528
00:27:32,320 --> 00:27:36,879
Speaker 1: you at cruise where you thought, wow that if I

529
00:27:36,920 --> 00:27:39,800
Speaker 1: ever do my own startup, which I will do. I

530
00:27:39,840 --> 00:27:42,840
Speaker 1: want to encapsulate that in my approach. Was there anything

531
00:27:42,880 --> 00:27:45,520
Speaker 1: we thought, Wow, even if it was a thousand person team,

532
00:27:45,760 --> 00:27:48,400
Speaker 1: you thought I can on a smaller scale replicate that.

533
00:27:48,600 --> 00:27:51,000
Speaker 2: Yeah. For me, that was the visualization piece. And I

534
00:27:51,000 --> 00:27:53,840
Speaker 2: think maybe even backing up, I agree with the comment

535
00:27:53,880 --> 00:27:57,119
Speaker 2: Harry made earlier, which is that I think if you

536
00:27:57,280 --> 00:28:00,439
Speaker 2: have a sort of startup founded mentality a lot of

537
00:28:00,440 --> 00:28:03,000
Speaker 2: the time, at least for me, I had, you know,

538
00:28:03,000 --> 00:28:05,960
Speaker 2: I had that from a very early age, and most

539
00:28:06,000 --> 00:28:10,600
Speaker 2: of the companies that I joined was intention was with

540
00:28:10,680 --> 00:28:12,879
Speaker 2: the intention of learning and getting a front receipt to

541
00:28:12,880 --> 00:28:15,440
Speaker 2: a high growth startup and seeing what the environment was saying,

542
00:28:15,760 --> 00:28:18,159
Speaker 2: understanding what it took to make a company successful, and

543
00:28:18,200 --> 00:28:22,920
Speaker 2: getting to work really closely with these founders. So in

544
00:28:22,920 --> 00:28:25,439
Speaker 2: that respect, the entire time, you know, you're always on

545
00:28:25,480 --> 00:28:28,720
Speaker 2: the lockout for what a great sort of business opportunities,

546
00:28:28,800 --> 00:28:31,879
Speaker 2: And that is a very common thing when you're working

547
00:28:32,680 --> 00:28:35,359
Speaker 2: full time in a new growth industry. There are a

548
00:28:35,359 --> 00:28:37,879
Speaker 2: lot of opportunities that haven't yet been kind of productized

549
00:28:37,880 --> 00:28:40,720
Speaker 2: and turned into companies yet. So that was always on

550
00:28:40,760 --> 00:28:43,960
Speaker 2: my mind. I think for me, you know, really the

551
00:28:44,000 --> 00:28:47,840
Speaker 2: thinking really crystallized around the data, and initially gio entry

552
00:28:47,840 --> 00:28:51,000
Speaker 2: point more specifically even than the data, was this aspect

553
00:28:51,040 --> 00:28:54,640
Speaker 2: of visualization and debugging. So at Cruz we built very

554
00:28:54,680 --> 00:28:58,800
Speaker 2: sophisticated tools for doing multimode also sort of three D

555
00:28:58,880 --> 00:29:01,760
Speaker 2: and two D visualization of the vehicle and of the data,

556
00:29:01,920 --> 00:29:04,640
Speaker 2: being able to see the cameras alongside the three D

557
00:29:04,800 --> 00:29:06,880
Speaker 2: visualization of where the car thought it was on the

558
00:29:06,960 --> 00:29:10,960
Speaker 2: road and you know, understanding things like hey, why did

559
00:29:10,960 --> 00:29:12,880
Speaker 2: we slam on the brakes here? Well, it was because

560
00:29:12,920 --> 00:29:14,960
Speaker 2: we we thought that this other car was going to

561
00:29:14,960 --> 00:29:16,640
Speaker 2: pull out in front of us, or you know, like

562
00:29:16,840 --> 00:29:18,600
Speaker 2: how do you understanding what the AI on the car

563
00:29:18,880 --> 00:29:21,120
Speaker 2: is sort of thinking you need to do that. It's

564
00:29:21,160 --> 00:29:23,120
Speaker 2: inherently a kind of a three D problem, and so

565
00:29:23,160 --> 00:29:24,640
Speaker 2: that was one of the things that we built our

566
00:29:24,720 --> 00:29:30,000
Speaker 2: crews that from very early on I saw, as you know,

567
00:29:30,040 --> 00:29:32,920
Speaker 2: it was, it was a differentiator at Cruise. We had

568
00:29:33,000 --> 00:29:36,040
Speaker 2: we invested a lot in that. But then you know,

569
00:29:36,160 --> 00:29:39,120
Speaker 2: later on talking to I learned that from talking to

570
00:29:39,120 --> 00:29:41,840
Speaker 2: people at Tesla and Weymo that very similar systems had

571
00:29:41,840 --> 00:29:44,640
Speaker 2: been kind of invented. Right, we weren't talking to each other,

572
00:29:44,680 --> 00:29:46,920
Speaker 2: but we compared notes and realized that we're all built

573
00:29:46,920 --> 00:29:49,440
Speaker 2: the same thing. It's kind of a clear, clear, a

574
00:29:49,480 --> 00:29:53,560
Speaker 2: clear signal that there's an opportunity.

575
00:29:58,880 --> 00:30:02,680
Speaker 1: Similarly, Harry, for you that stint you did at Tesla

576
00:30:02,720 --> 00:30:04,920
Speaker 1: and we all know from the outside, you know what

577
00:30:04,960 --> 00:30:08,000
Speaker 1: an intense environment that is and led by you know,

578
00:30:08,080 --> 00:30:14,240
Speaker 1: a visionary engineer. Any key takeaway that's infused your approach

579
00:30:15,000 --> 00:30:16,280
Speaker 1: with Antioch.

580
00:30:16,000 --> 00:30:18,680
Speaker 4: Yeah, I mean, I think similar to what Adrianne was

581
00:30:18,720 --> 00:30:21,320
Speaker 4: talking about, it's really been I was really impressed by

582
00:30:21,320 --> 00:30:25,000
Speaker 4: the tooling inside. And I think particularly what struck me

583
00:30:26,000 --> 00:30:29,840
Speaker 4: was you know, in software we talk about shifting left

584
00:30:30,080 --> 00:30:32,400
Speaker 4: and that's used in the context of security, and it's

585
00:30:32,440 --> 00:30:34,680
Speaker 4: used in the context of testing. But essentially what that

586
00:30:34,800 --> 00:30:39,200
Speaker 4: means is as early as possible in the process of

587
00:30:39,280 --> 00:30:42,240
Speaker 4: developing something, you want to be getting a good signal

588
00:30:42,280 --> 00:30:45,760
Speaker 4: that it is doing what you think it's doing, safe, secure,

589
00:30:46,160 --> 00:30:49,320
Speaker 4: And I think Tesla had invested a huge amount in

590
00:30:49,400 --> 00:30:52,720
Speaker 4: that kind of infrastructure. So to put that concretely, you know,

591
00:30:52,760 --> 00:30:56,479
Speaker 4: when I was training a new neural network, there are

592
00:30:56,520 --> 00:30:59,080
Speaker 4: a ton of software tools that my network could be

593
00:30:59,160 --> 00:31:02,360
Speaker 4: running against me quick feedback on was this better?

594
00:31:02,720 --> 00:31:04,680
Speaker 3: Was this worse than the last thing that we had

595
00:31:04,720 --> 00:31:05,200
Speaker 3: been using.

596
00:31:05,520 --> 00:31:08,360
Speaker 4: So as a developer, I'm immediately getting that sense of okay,

597
00:31:08,480 --> 00:31:10,240
Speaker 4: like I'm doing the right thing or I'm doing the

598
00:31:10,240 --> 00:31:13,520
Speaker 4: wrong thing. And then also, you know, there were simulation

599
00:31:13,720 --> 00:31:16,040
Speaker 4: tools that were kind of built internally there as well

600
00:31:16,040 --> 00:31:18,440
Speaker 4: as kind of like the second phase of how these

601
00:31:18,440 --> 00:31:21,720
Speaker 4: things were evaluated. So some software first, then you put

602
00:31:21,720 --> 00:31:24,120
Speaker 4: it into the virtual car, see how that goes, and

603
00:31:24,200 --> 00:31:26,560
Speaker 4: only is this kind of tertiary step that you're taking

604
00:31:26,600 --> 00:31:28,760
Speaker 4: that actually out to the field. This is a bit

605
00:31:28,800 --> 00:31:31,160
Speaker 4: of an oversimplification of what it's sort of like, but

606
00:31:31,240 --> 00:31:32,760
Speaker 4: I think it's the right kind of way to think

607
00:31:32,800 --> 00:31:35,360
Speaker 4: about it, where you want to bring your evaluation as

608
00:31:35,400 --> 00:31:39,440
Speaker 4: close as possible to developers. And I think, you know,

609
00:31:39,560 --> 00:31:43,800
Speaker 4: outside of Tesla, when we talk to prospective customers, that

610
00:31:43,800 --> 00:31:46,520
Speaker 4: that kind of tooling just doesn't exist. You know, we

611
00:31:46,520 --> 00:31:49,680
Speaker 4: talked to folks in construction robotics, you know who they

612
00:31:49,720 --> 00:31:53,320
Speaker 4: train some new machine learning model for an excavator or

613
00:31:53,360 --> 00:31:56,160
Speaker 4: something like that, and they're sending it like three states

614
00:31:56,160 --> 00:31:58,719
Speaker 4: over to some guy who's actually sitting on the excavator

615
00:31:58,960 --> 00:32:01,360
Speaker 4: and he goes and digs a hole using this neural

616
00:32:01,400 --> 00:32:03,800
Speaker 4: network and then he's kind of sending that back to

617
00:32:04,040 --> 00:32:08,200
Speaker 4: to base fundamentally, and that's the evaluation loop there, and

618
00:32:08,240 --> 00:32:11,040
Speaker 4: that's so latent. So like as a developer, you make

619
00:32:11,080 --> 00:32:13,880
Speaker 4: a change, it's going to take you maybe days to

620
00:32:13,960 --> 00:32:17,880
Speaker 4: kind of get insight into you know, was that actually

621
00:32:17,920 --> 00:32:19,920
Speaker 4: the right change to make, And that's slowing you down

622
00:32:20,320 --> 00:32:23,000
Speaker 4: several orders of magnitude. And I think Tesla had done

623
00:32:23,000 --> 00:32:25,760
Speaker 4: a great job of building tooling to reduce that as

624
00:32:25,840 --> 00:32:26,640
Speaker 4: much as possible.

625
00:32:26,800 --> 00:32:30,880
Speaker 1: So both of what you're doing is speeding up development ultimately,

626
00:32:30,960 --> 00:32:34,800
Speaker 1: so hopefully you can get a prototype into a production

627
00:32:35,120 --> 00:32:40,880
Speaker 1: grade robot. Quicker Adrian. From your perspective, what sectors do

628
00:32:40,920 --> 00:32:44,160
Speaker 1: you expect to see sort of real world deployment if

629
00:32:44,280 --> 00:32:47,240
Speaker 1: robots accelerate, say in the next five to ten years.

630
00:32:47,240 --> 00:32:51,160
Speaker 1: We've got warehouses, you know, they're talking about dark warehouses

631
00:32:51,200 --> 00:32:54,440
Speaker 1: where robots are just working continuously. We've seen some really

632
00:32:54,480 --> 00:32:57,760
Speaker 1: cool stuff in agritech. The whole drone thing is a

633
00:32:57,800 --> 00:33:01,360
Speaker 1: whole other world. Any particular areas where you're saying, wow,

634
00:33:01,440 --> 00:33:03,720
Speaker 1: this is actually racing ahead. Now.

635
00:33:04,000 --> 00:33:08,440
Speaker 2: The robotics industry is really broad and there are different

636
00:33:08,600 --> 00:33:11,640
Speaker 2: sectors that are at quite different stages of maturity. So

637
00:33:11,920 --> 00:33:14,640
Speaker 2: I think it's really interesting when you're working in these

638
00:33:14,640 --> 00:33:18,120
Speaker 2: horizontal areas and getting a cross section of where people

639
00:33:18,160 --> 00:33:21,760
Speaker 2: are at and where robots are succeeding. So, for example,

640
00:33:22,280 --> 00:33:24,600
Speaker 2: the warehouse space is one where it's a little bit

641
00:33:24,640 --> 00:33:27,160
Speaker 2: further along that maturity curve, right, I mean, Amazon has

642
00:33:27,200 --> 00:33:29,959
Speaker 2: a million robots in production or more at this point.

643
00:33:30,880 --> 00:33:34,600
Speaker 2: A lot of warehouses are looking at autonomous material movement

644
00:33:34,800 --> 00:33:37,280
Speaker 2: and little mbile robots that can move things around warehouses

645
00:33:37,320 --> 00:33:41,640
Speaker 2: and things. That sector is at a point where they're

646
00:33:41,640 --> 00:33:44,240
Speaker 2: really starting to scale. Their autonomy problems are a little

647
00:33:44,240 --> 00:33:46,640
Speaker 2: bit simpler. They're not trivial, but they're simpler than like

648
00:33:46,720 --> 00:33:49,800
Speaker 2: bolding laundry or making beds or some of these things

649
00:33:49,840 --> 00:33:53,320
Speaker 2: that people are trying to do. So you're starting to

650
00:33:53,320 --> 00:33:56,120
Speaker 2: see a lot of real scale there. Similarly, the German

651
00:33:56,160 --> 00:33:59,920
Speaker 2: industry is really starting to hit scale. Self driving car

652
00:34:00,120 --> 00:34:04,640
Speaker 2: industry is slowly really starting to scale. Although it's very

653
00:34:04,840 --> 00:34:07,400
Speaker 2: very capital intensive building cars, it's compared to a lot

654
00:34:07,440 --> 00:34:09,719
Speaker 2: of the cost of the vehicles is a lot higher

655
00:34:09,719 --> 00:34:12,760
Speaker 2: than the cost of small drones or small warehouse robots.

656
00:34:13,080 --> 00:34:14,640
Speaker 2: And then at the other end of the spectrum you

657
00:34:14,719 --> 00:34:18,359
Speaker 2: have areas where there's significant amount of R and D,

658
00:34:18,840 --> 00:34:22,759
Speaker 2: you know, significant amount of investment and PC capital, and

659
00:34:23,320 --> 00:34:26,439
Speaker 2: areas like just pure robot foundation model plays where they're

660
00:34:26,440 --> 00:34:30,240
Speaker 2: trying to just build general purpose models or humanoids for example.

661
00:34:30,360 --> 00:34:33,439
Speaker 2: I think these areas we're seeing a ton of investment into.

662
00:34:33,640 --> 00:34:36,839
Speaker 2: They're further out from commercialization, right, they're still very early.

663
00:34:36,880 --> 00:34:40,480
Speaker 2: It's kind of humanoids today is where you know, I

664
00:34:40,520 --> 00:34:43,759
Speaker 2: think about where self driving was around twenty seventeen or so,

665
00:34:44,360 --> 00:34:48,040
Speaker 2: where you know, it's very promising technology. You can see

666
00:34:48,080 --> 00:34:50,040
Speaker 2: the opportunity that it's going to bring, but it's going

667
00:34:50,080 --> 00:34:52,439
Speaker 2: to take quite a while before they get to sort

668
00:34:52,440 --> 00:34:54,799
Speaker 2: of a useful enough level of ability that they're going

669
00:34:54,840 --> 00:34:58,640
Speaker 2: to go into real production US cases and be replacing

670
00:34:58,640 --> 00:35:00,640
Speaker 2: any existing people of systems.

671
00:35:00,920 --> 00:35:04,040
Speaker 1: Yeah, I mean we've seen a lot of pr attention

672
00:35:04,160 --> 00:35:08,759
Speaker 1: on optimists out of Tiesler US robotics and the you know,

673
00:35:09,200 --> 00:35:11,840
Speaker 1: robots doing backflips and all that sort of thing. Apparently.

674
00:35:12,440 --> 00:35:15,280
Speaker 2: I mean, there's an a tunnel investment happening in the space,

675
00:35:15,280 --> 00:35:17,200
Speaker 2: and there's a lot of players, a lot of Chinese

676
00:35:17,200 --> 00:35:21,000
Speaker 2: companies building humanoids, there's a number in the US. There's

677
00:35:21,040 --> 00:35:24,279
Speaker 2: a huge range of models and skills too. I mean,

678
00:35:24,280 --> 00:35:27,799
Speaker 2: you've got everything from very cheaply made sort of five

679
00:35:27,800 --> 00:35:30,560
Speaker 2: thousand dollars things that you can push over if you

680
00:35:30,600 --> 00:35:34,480
Speaker 2: accidentally bumped into them, compared to optimists and bigger, you know,

681
00:35:34,760 --> 00:35:41,120
Speaker 2: pretty very expensive advanced pieces of machinery basically at kind

682
00:35:41,160 --> 00:35:43,400
Speaker 2: of the other end of that. It'd be really interesting

683
00:35:43,400 --> 00:35:45,440
Speaker 2: to see how this plays out. I think I'm extremely

684
00:35:45,600 --> 00:35:49,920
Speaker 2: fullish long term on humanoids, but right now some of

685
00:35:49,960 --> 00:35:52,080
Speaker 2: the things that you're hearing about how soon they're arriving

686
00:35:52,239 --> 00:35:55,799
Speaker 2: are at a little inflated, shall we say.

687
00:35:56,560 --> 00:35:59,919
Speaker 1: Yeah, it's obviously a hot category for investment, and you've

688
00:36:00,080 --> 00:36:03,960
Speaker 1: both recently raised significant money at different stages.

689
00:36:04,239 --> 00:36:07,080
Speaker 2: So we just raised forty actually for Fox Love. Yeah,

690
00:36:07,120 --> 00:36:11,000
Speaker 2: we've raised about sixty million total now in the in

691
00:36:11,040 --> 00:36:14,400
Speaker 2: the foreign RP years who have been running and you

692
00:36:14,600 --> 00:36:17,319
Speaker 2: really looking to scale teams about fifty at Fox dot

693
00:36:17,400 --> 00:36:20,319
Speaker 2: Manware Hir hiring pretty aggressively.

694
00:36:19,920 --> 00:36:21,880
Speaker 1: And Harry's raised three or four million.

695
00:36:22,120 --> 00:36:22,879
Speaker 3: Yeah, that's right.

696
00:36:23,000 --> 00:36:24,919
Speaker 1: Icehouse Ventures involved in your rays.

697
00:36:25,160 --> 00:36:29,279
Speaker 4: Yeah, so it was primarily US based venture capitalists, but

698
00:36:29,719 --> 00:36:32,120
Speaker 4: super happy to have you know, the ice House there.

699
00:36:32,239 --> 00:36:35,719
Speaker 4: They're on board as with as with Adrian, so it's

700
00:36:35,760 --> 00:36:38,960
Speaker 4: really nice to have the New Zealand connection there, and

701
00:36:39,000 --> 00:36:41,440
Speaker 4: I think as we start to think about deploying that

702
00:36:41,520 --> 00:36:45,160
Speaker 4: it's primarily going to be deployed in the US, but

703
00:36:45,280 --> 00:36:47,040
Speaker 4: we are wanting to sort of take advantage of some

704
00:36:47,160 --> 00:36:50,120
Speaker 4: of the amazing kind of like three D artistry talent

705
00:36:50,160 --> 00:36:52,480
Speaker 4: here in New Zealand as well, So looking to kind

706
00:36:52,480 --> 00:36:53,720
Speaker 4: of maintain that connection.

707
00:36:54,160 --> 00:36:57,239
Speaker 1: And Adrian and that's a pretty decent chunk of money.

708
00:36:57,280 --> 00:37:00,719
Speaker 1: What sort of that runway? What does that enable you

709
00:37:00,840 --> 00:37:01,520
Speaker 1: now to do.

710
00:37:01,640 --> 00:37:04,319
Speaker 2: Really expanding a product team for us, where at this

711
00:37:04,480 --> 00:37:06,759
Speaker 2: point you know that you hope to get to as

712
00:37:06,760 --> 00:37:09,279
Speaker 2: a startup where you've got enough, You've demonstrated that you

713
00:37:09,320 --> 00:37:11,160
Speaker 2: have a viable product, you have product like a fit,

714
00:37:11,280 --> 00:37:14,080
Speaker 2: you have a lot of customers out there, and the

715
00:37:14,120 --> 00:37:18,040
Speaker 2: customers want more. Essentially, there's a lot, you know, there's

716
00:37:18,080 --> 00:37:20,440
Speaker 2: a lot to build here, There's an endless list of

717
00:37:20,520 --> 00:37:24,799
Speaker 2: feature requests and so really for us, we could have

718
00:37:24,880 --> 00:37:28,120
Speaker 2: kept scaling without raising money, but it would have really

719
00:37:28,200 --> 00:37:30,480
Speaker 2: slowed down our product process. Right then you're really spending

720
00:37:30,480 --> 00:37:32,880
Speaker 2: a lot more time selling than you are actually building

721
00:37:32,880 --> 00:37:36,720
Speaker 2: the product. So for us, this leads us massively expand

722
00:37:36,760 --> 00:37:39,120
Speaker 2: the engineering team and build a lot more of the

723
00:37:39,200 --> 00:37:41,359
Speaker 2: roadmap that everyone keeps asking for.

724
00:37:41,440 --> 00:37:45,480
Speaker 1: A bigger picture. We hear a lot I follow sort

725
00:37:45,480 --> 00:37:48,680
Speaker 1: of geo strategists like Peter Zion, who's informing us at

726
00:37:48,719 --> 00:37:51,719
Speaker 1: you declaring birth rate around where we're really going to

727
00:37:51,800 --> 00:37:55,200
Speaker 1: need robotics if we want to have the standard of

728
00:37:55,239 --> 00:37:57,840
Speaker 1: living that we currently do now, because literally we're not

729
00:37:57,880 --> 00:38:00,960
Speaker 1: going to have the sort of work that we've enjoyed

730
00:38:01,000 --> 00:38:03,040
Speaker 1: in the past. This is very apparent in places like

731
00:38:03,120 --> 00:38:06,800
Speaker 1: China where the birth rate has plunged. Japan has already

732
00:38:06,840 --> 00:38:12,440
Speaker 1: experienced this there massively into robotics. So yeah, interested in

733
00:38:13,760 --> 00:38:15,960
Speaker 1: your thoughts about I guess the sort of the social

734
00:38:16,000 --> 00:38:19,239
Speaker 1: contract around robotics. We're going to need more robotics. It's

735
00:38:19,239 --> 00:38:23,759
Speaker 1: a practical necessity, but we want to have good robotics.

736
00:38:23,760 --> 00:38:26,719
Speaker 1: We want to have a future of robotics that's good

737
00:38:26,760 --> 00:38:29,280
Speaker 1: for society, good for workers.

738
00:38:29,520 --> 00:38:32,600
Speaker 2: Friendly robots, Yeah, don't leave robots.

739
00:38:32,680 --> 00:38:36,640
Speaker 1: Yeah, obviously we don't want terminator robots. But in terms

740
00:38:36,680 --> 00:38:38,520
Speaker 1: of you know, the impact on the workforce and that,

741
00:38:38,640 --> 00:38:42,160
Speaker 1: do you guys think about this when you're designing, I guess, Harry,

742
00:38:42,200 --> 00:38:45,880
Speaker 1: particularly for simulation, you know this is a really valuable

743
00:38:45,880 --> 00:38:48,520
Speaker 1: thing that you're adding to that you can actually talent scale.

744
00:38:48,719 --> 00:38:51,520
Speaker 1: What's going to happen when you have hundreds or thousands

745
00:38:51,520 --> 00:38:52,480
Speaker 1: of robots out there?

746
00:38:52,800 --> 00:38:56,080
Speaker 4: Yeah, No, I think it's I think it's very forefront

747
00:38:56,280 --> 00:38:58,759
Speaker 4: and kind of how we think about a lot of

748
00:38:58,800 --> 00:39:01,360
Speaker 4: this stuff. I think we think about, you know, safety

749
00:39:02,200 --> 00:39:05,480
Speaker 4: at a couple of angles. There's, of course, the practical

750
00:39:05,640 --> 00:39:08,120
Speaker 4: safety of you don't want the robot to hurt or

751
00:39:08,160 --> 00:39:11,200
Speaker 4: to kill somebody, and so for that, the more that

752
00:39:11,239 --> 00:39:14,000
Speaker 4: you can do in terms of testing and evaluation is

753
00:39:14,040 --> 00:39:16,719
Speaker 4: really critical. But there's also safety in the sort of

754
00:39:16,719 --> 00:39:19,279
Speaker 4: more macro sense of like, you know, what does the

755
00:39:19,320 --> 00:39:22,439
Speaker 4: sort of you know, revolution of this technology coming into

756
00:39:22,440 --> 00:39:27,600
Speaker 4: society do for jobs and for the economy as a

757
00:39:27,600 --> 00:39:30,640
Speaker 4: whole and so on that you know, I think it's

758
00:39:30,680 --> 00:39:34,120
Speaker 4: going to take a sort of very carefully thought out

759
00:39:34,600 --> 00:39:38,120
Speaker 4: rollout between the private sector and also the public sector

760
00:39:38,160 --> 00:39:39,400
Speaker 4: to make sure that that's done in a way that

761
00:39:40,160 --> 00:39:44,040
Speaker 4: is safe in that sense. I think I'm particularly excited

762
00:39:44,080 --> 00:39:47,399
Speaker 4: about a couple of things. One is that it makes

763
00:39:47,440 --> 00:39:53,440
Speaker 4: industries that were previously understaffed or uneconomic a lot more robust.

764
00:39:53,800 --> 00:39:56,280
Speaker 4: And so that's everything from how do you think about

765
00:39:56,680 --> 00:40:00,359
Speaker 4: scalably sorting recycling Like it's really depressing when you look

766
00:40:00,360 --> 00:40:02,680
Speaker 4: at those sorts of metrics of all the stuff you

767
00:40:02,680 --> 00:40:04,920
Speaker 4: put in the recycling, but how much is actually getting

768
00:40:04,960 --> 00:40:07,920
Speaker 4: recycled versus thrown out because it's just so expensive and

769
00:40:07,960 --> 00:40:11,879
Speaker 4: difficult to sort that stuff through to how we care

770
00:40:12,000 --> 00:40:15,560
Speaker 4: for people with sort of like medical conditions or older

771
00:40:15,560 --> 00:40:19,600
Speaker 4: people industries where we're really struggling to get the sort

772
00:40:19,640 --> 00:40:22,360
Speaker 4: of volume in the workforce that we need there. But

773
00:40:22,480 --> 00:40:25,279
Speaker 4: also I think you know, over the last fifty or

774
00:40:25,320 --> 00:40:28,439
Speaker 4: so years in New Zealand and also in the US

775
00:40:28,480 --> 00:40:30,960
Speaker 4: where Adrian and I are based, we've seen a lot

776
00:40:31,000 --> 00:40:35,319
Speaker 4: of jobs and industries moving overseas for cost reasons, and

777
00:40:35,360 --> 00:40:38,360
Speaker 4: that's really been I think quite a sort of a

778
00:40:38,480 --> 00:40:42,279
Speaker 4: difficult trend from a social standpoint. It's leading to this

779
00:40:42,360 --> 00:40:45,440
Speaker 4: eroding of the middle class in our countries, and I

780
00:40:45,480 --> 00:40:47,000
Speaker 4: think we're kind of seeing a little bit of that

781
00:40:47,120 --> 00:40:50,319
Speaker 4: play into the political environments as well in a way

782
00:40:50,320 --> 00:40:53,200
Speaker 4: that that's not great. And I think that autonomy kind

783
00:40:53,239 --> 00:40:55,480
Speaker 4: of gives us the opportunity to move some more of

784
00:40:55,520 --> 00:40:59,920
Speaker 4: those primary industries back home, things like manufacturing, scaling agric

785
00:41:00,000 --> 00:41:01,279
Speaker 4: culture again that sort of stuff.

786
00:41:01,360 --> 00:41:03,960
Speaker 2: Yeah, this is one of the big sort of definitely

787
00:41:04,000 --> 00:41:06,640
Speaker 2: one of the big tailwinds behind the robotics at the

788
00:41:06,680 --> 00:41:11,080
Speaker 2: moment is macroeconomic and geopolitical factors. You've got a lot

789
00:41:11,120 --> 00:41:15,160
Speaker 2: of this push to resure manufacturing things like that, You've

790
00:41:15,200 --> 00:41:17,840
Speaker 2: got a lot of, as you mentioned, kind of aging workforce,

791
00:41:17,920 --> 00:41:20,120
Speaker 2: and so you have these these driving factors that are

792
00:41:20,800 --> 00:41:25,640
Speaker 2: encouraging the use of more automation, more autonomy. I think

793
00:41:25,719 --> 00:41:30,279
Speaker 2: that when any new piece of technology comes out, there's

794
00:41:30,320 --> 00:41:32,240
Speaker 2: always a group of people who are sort of saying,

795
00:41:32,440 --> 00:41:34,640
Speaker 2: this time it's different, and you know, this is going

796
00:41:34,719 --> 00:41:38,239
Speaker 2: to be the one piece of technology that suddenly gives

797
00:41:38,280 --> 00:41:41,719
Speaker 2: humans normal purpose in life. And people have been saying,

798
00:41:41,760 --> 00:41:43,719
Speaker 2: you mean, you go back to the early nineteen hundreds, right,

799
00:41:43,760 --> 00:41:45,600
Speaker 2: you had like more than fifty percent of the population

800
00:41:45,800 --> 00:41:48,680
Speaker 2: and employed in agriculture, and now there numbers like two percent.

801
00:41:49,840 --> 00:41:51,799
Speaker 2: You go back and there are people, you know, as

802
00:41:51,840 --> 00:41:54,279
Speaker 2: absolutely freaking out about every step along the way every

803
00:41:54,280 --> 00:41:56,120
Speaker 2: piece of technology. People are like, oh my god, like

804
00:41:56,160 --> 00:41:58,200
Speaker 2: nobody's going to have a job anymore. That has never

805
00:41:58,280 --> 00:42:01,920
Speaker 2: been true. I fundamentally do not believe that it's going

806
00:42:01,960 --> 00:42:04,040
Speaker 2: to be true with robotics, there were a long long

807
00:42:04,080 --> 00:42:07,520
Speaker 2: way from the robots being able to manufacture and maintain themselves.

808
00:42:08,680 --> 00:42:12,040
Speaker 2: And so in the meantime, like you know, the opportunities

809
00:42:12,040 --> 00:42:13,960
Speaker 2: out there in the world are constantly changing. We have

810
00:42:14,080 --> 00:42:18,879
Speaker 2: many jobs today that didn't exist one or two decades ago.

811
00:42:19,640 --> 00:42:22,320
Speaker 2: Jobs are currently changed, They are always changing. It's always

812
00:42:22,400 --> 00:42:27,920
Speaker 2: kind of opportunities for things like training and that. But yeah, overall,

813
00:42:28,040 --> 00:42:32,520
Speaker 2: I mean it's technology has historically been a benefits for society.

814
00:42:32,719 --> 00:42:36,040
Speaker 1: Yeah, that's every wave of technology has created more jobs

815
00:42:36,080 --> 00:42:40,239
Speaker 1: than it has destroyed. I think that's proven to be true.

816
00:42:40,440 --> 00:42:43,759
Speaker 1: But you know, autonomous vehicles is a good example of

817
00:42:43,800 --> 00:42:47,360
Speaker 1: where California in particular was very proactive around regulation to

818
00:42:47,400 --> 00:42:51,280
Speaker 1: allow all of those companies to test in real world

819
00:42:51,360 --> 00:42:54,400
Speaker 1: environments and that accelerated the technology. When it comes to

820
00:42:54,440 --> 00:42:59,799
Speaker 1: something like humanoid robots moving in unstructured sort of environments,

821
00:43:00,520 --> 00:43:02,279
Speaker 1: is there going to need to be a lot of

822
00:43:02,480 --> 00:43:06,840
Speaker 1: changes happening in the US around regulation and even I

823
00:43:06,840 --> 00:43:10,400
Speaker 1: guess labored dynamics. You've got strong unions in some industries

824
00:43:10,520 --> 00:43:13,319
Speaker 1: over there, is there a lot of non tech sort

825
00:43:13,320 --> 00:43:15,640
Speaker 1: of blockers that are going to have to be removed

826
00:43:15,800 --> 00:43:19,920
Speaker 1: to really allow the number of robots that are deployed

827
00:43:19,920 --> 00:43:21,320
Speaker 1: to really increase the difference.

828
00:43:21,400 --> 00:43:23,959
Speaker 2: The big difference distinction I would draw with self driving

829
00:43:24,040 --> 00:43:26,800
Speaker 2: vehicles is the fundamentally putting a lot of other people's

830
00:43:26,880 --> 00:43:29,440
Speaker 2: lives at risk with them. And so when you think

831
00:43:29,480 --> 00:43:33,040
Speaker 2: about robots that are being deployed into manufacturing facilities or

832
00:43:33,080 --> 00:43:35,600
Speaker 2: things like this, these are controlled environments, right, They know

833
00:43:35,680 --> 00:43:38,200
Speaker 2: every single person that's checking in and out, They train

834
00:43:38,280 --> 00:43:43,200
Speaker 2: those people, and so it's not you know, regulation needs

835
00:43:43,200 --> 00:43:46,200
Speaker 2: to step in when you're sort of putting other people's laser. Certainly,

836
00:43:46,280 --> 00:43:50,520
Speaker 2: unions are concerned, but again that's really a concern between

837
00:43:50,520 --> 00:43:54,480
Speaker 2: these companies and the unions and the technology that they're

838
00:43:54,520 --> 00:43:58,759
Speaker 2: looking to deploy. But I don't see certainly and some

839
00:43:58,800 --> 00:44:01,600
Speaker 2: of these things like manufactur during and logistics and things,

840
00:44:01,640 --> 00:44:06,400
Speaker 2: regulation being a huge, you know, kind of block or anything.

841
00:44:06,440 --> 00:44:08,400
Speaker 2: Certainly in the way that it was, and I'm not

842
00:44:08,440 --> 00:44:11,480
Speaker 2: even saying regulation was a blocker for self driving, but

843
00:44:11,520 --> 00:44:12,879
Speaker 2: it was certainly a fact that there was a lot

844
00:44:12,880 --> 00:44:16,600
Speaker 2: of education that needed to be done across the industry.

845
00:44:17,239 --> 00:44:20,000
Speaker 2: In the US, it's quite lucky that there's a lot

846
00:44:20,000 --> 00:44:22,680
Speaker 2: of different states and we could let these kind of

847
00:44:22,719 --> 00:44:24,440
Speaker 2: approaches play out and see how I worked in some

848
00:44:24,480 --> 00:44:28,440
Speaker 2: states that were willing to be more more pro testing.

849
00:44:28,840 --> 00:44:31,200
Speaker 2: But that is findamentally something where you're driving out in

850
00:44:31,239 --> 00:44:34,080
Speaker 2: public roads and you're putting public lives at risk, which

851
00:44:34,200 --> 00:44:37,080
Speaker 2: which those people crossing the street or with their kids

852
00:44:37,080 --> 00:44:40,440
Speaker 2: playing on the street didn't sign up for.

853
00:44:40,719 --> 00:44:43,000
Speaker 4: Yeah, I think that distinction is spot on, and I

854
00:44:43,000 --> 00:44:46,640
Speaker 4: think there's also sort of a symbiosis between you know,

855
00:44:46,880 --> 00:44:50,359
Speaker 4: regulations enacted in response to some sort of thing going wrong,

856
00:44:50,440 --> 00:44:52,880
Speaker 4: and obviously things go wrong with cars all the time,

857
00:44:52,960 --> 00:44:56,400
Speaker 4: so we have a sort of well defined regulatory framework

858
00:44:56,480 --> 00:44:58,840
Speaker 4: generally speaking for how we kind of think about mitigating

859
00:44:58,840 --> 00:45:01,920
Speaker 4: and managing and license for those sorts of risks. And

860
00:45:01,960 --> 00:45:05,520
Speaker 4: so I think I totally agree with Adrian that I

861
00:45:05,600 --> 00:45:07,840
Speaker 4: don't sort of see that happening in a lot of

862
00:45:07,880 --> 00:45:12,320
Speaker 4: these other industries where we're kind of seeing the adoption

863
00:45:12,360 --> 00:45:15,440
Speaker 4: of autonomy. But I think it's also critical that we

864
00:45:15,480 --> 00:45:17,319
Speaker 4: sort of take it upon ourselves to make sure that

865
00:45:17,320 --> 00:45:20,359
Speaker 4: that rollout is safe less things go wrong, and then

866
00:45:20,360 --> 00:45:22,400
Speaker 4: you kind of start to see more of that response

867
00:45:22,440 --> 00:45:23,360
Speaker 4: come in there as well.

868
00:45:23,480 --> 00:45:25,799
Speaker 1: Harry, you know, if we played this episode back in

869
00:45:25,920 --> 00:45:29,560
Speaker 1: twenty thirty five, what do you think when it comes

870
00:45:29,600 --> 00:45:32,799
Speaker 1: to robotics, we're going to see that was sort of inevitable.

871
00:45:33,360 --> 00:45:36,600
Speaker 1: But also conversely and interest in your views as well,

872
00:45:36,640 --> 00:45:39,120
Speaker 1: Adrian on stuff that gets a lot of hype, but

873
00:45:39,320 --> 00:45:41,359
Speaker 1: it's just going to be so much slower to roll out.

874
00:45:41,480 --> 00:45:44,880
Speaker 2: Everything takes a lot longer to roll out than people realize,

875
00:45:45,200 --> 00:45:49,560
Speaker 2: especially or more so in the physical world. So everyone,

876
00:45:49,920 --> 00:45:51,719
Speaker 2: there's a lot of talk right now about the chat

877
00:45:51,800 --> 00:45:55,040
Speaker 2: GPT moment for robots. Basically right, we hit this point,

878
00:45:55,360 --> 00:45:57,839
Speaker 2: as Harry said, back in twenty twenty three where chat

879
00:45:57,920 --> 00:46:00,879
Speaker 2: Gibt came out and it changed everything. Literally the day

880
00:46:00,960 --> 00:46:03,200
Speaker 2: came out, I spent like twenty four hours gillingto my computer,

881
00:46:03,320 --> 00:46:05,239
Speaker 2: like chatting to this thing and just being like, what

882
00:46:05,280 --> 00:46:07,279
Speaker 2: the hell did they just invent? But then this is

883
00:46:07,280 --> 00:46:10,520
Speaker 2: a purely digital product, right, Like scaling it was challenging

884
00:46:10,600 --> 00:46:14,160
Speaker 2: and throwing together data centers and you know, nuclear actors

885
00:46:14,160 --> 00:46:16,080
Speaker 2: and things as fast as possible to scale it. But

886
00:46:16,200 --> 00:46:19,960
Speaker 2: like digital things are very much a lot lot easier

887
00:46:20,000 --> 00:46:24,799
Speaker 2: to scale in the physical world. It just you know,

888
00:46:25,080 --> 00:46:28,920
Speaker 2: there are real challenges manufacturing and operating. My example here

889
00:46:28,960 --> 00:46:31,240
Speaker 2: would be Weimo, right, like what the constraint on Weimo

890
00:46:31,360 --> 00:46:34,239
Speaker 2: scaling now is not the autonomy SEFWMO has software that

891
00:46:34,280 --> 00:46:36,560
Speaker 2: can drive around cities, that can drive on freeways. Now,

892
00:46:38,560 --> 00:46:41,160
Speaker 2: even if Wemo, even if even if you made Wave

893
00:46:41,200 --> 00:46:43,760
Speaker 2: the magic one I said, Weymo's driving is absolutely perfect

894
00:46:43,800 --> 00:46:45,760
Speaker 2: and and available and it never makes a single mistake.

895
00:46:46,160 --> 00:46:48,680
Speaker 2: It would still be an enormous challenge to roll that

896
00:46:48,760 --> 00:46:52,680
Speaker 2: out to you know, enormous capital expense and almost operational

897
00:46:52,719 --> 00:46:54,680
Speaker 2: challenge of rolling that out to millions and millions of

898
00:46:54,760 --> 00:46:57,880
Speaker 2: vehicles across one hundred cities, to have cleaning crews that

899
00:46:57,920 --> 00:47:00,879
Speaker 2: are servicing those vehicles every night, because that's not automated yet.

900
00:47:01,960 --> 00:47:04,920
Speaker 2: Just you know, the logistical challenge of rolling out millions

901
00:47:04,920 --> 00:47:08,200
Speaker 2: of anything is difficult. And so I think the thing

902
00:47:08,239 --> 00:47:09,920
Speaker 2: that we're going to see over the next few years

903
00:47:09,920 --> 00:47:13,360
Speaker 2: of Eurotics system incredible breakthroughs made from a technology perspective,

904
00:47:13,360 --> 00:47:15,840
Speaker 2: and they're going to see robots solving things that previously

905
00:47:15,880 --> 00:47:18,799
Speaker 2: were way way out of reach of robots. But that

906
00:47:18,800 --> 00:47:20,680
Speaker 2: doesn't mean that they're going to be deployed by the

907
00:47:20,719 --> 00:47:23,600
Speaker 2: millions anytime in the next few years, right, it's going

908
00:47:23,640 --> 00:47:26,439
Speaker 2: to take That's the thing that by twenty thirty five,

909
00:47:26,520 --> 00:47:29,040
Speaker 2: we will absolutely be into those numbers and we'll start

910
00:47:29,040 --> 00:47:31,960
Speaker 2: seeing millions and then tens of millions and hundreds of

911
00:47:31,960 --> 00:47:34,880
Speaker 2: millions of robots. We have billions of smartphones today, so

912
00:47:34,920 --> 00:47:37,720
Speaker 2: certainly like humanity knows how to manufacture billions of things,

913
00:47:38,120 --> 00:47:40,279
Speaker 2: but it takes a while to like ramp up those

914
00:47:40,520 --> 00:47:43,200
Speaker 2: production chains and to even like operate that many of anything.

915
00:47:43,560 --> 00:47:47,200
Speaker 4: I completely agree with that. I also think that I

916
00:47:47,200 --> 00:47:51,520
Speaker 4: think a little bit about morphology and ownership, and so

917
00:47:51,640 --> 00:47:54,319
Speaker 4: I guess what I mean by that is, you know,

918
00:47:54,360 --> 00:47:56,880
Speaker 4: I think right now there's a ton of excitement, particularly

919
00:47:56,920 --> 00:48:00,640
Speaker 4: around humanoid form factors, and I think this also excitement

920
00:48:00,680 --> 00:48:03,440
Speaker 4: around this idea that like we might all have humanoids

921
00:48:03,800 --> 00:48:05,960
Speaker 4: in our homes kind of performing all these tasks, we

922
00:48:06,000 --> 00:48:10,279
Speaker 4: sort of own these these sorts of machines. I think

923
00:48:10,280 --> 00:48:12,000
Speaker 4: it's early days. I think it's hard to say with

924
00:48:12,000 --> 00:48:15,920
Speaker 4: with with too much conviction, but generally speaking, I think

925
00:48:16,120 --> 00:48:18,200
Speaker 4: my view is that there's going to be a ton

926
00:48:18,239 --> 00:48:20,359
Speaker 4: of autonomy out there. I think most of it is

927
00:48:20,440 --> 00:48:23,040
Speaker 4: not going to look human, even in our kind of

928
00:48:23,080 --> 00:48:28,240
Speaker 4: home environments. So smart vacuum cleaners, you know, smart lawn mowers,

929
00:48:28,320 --> 00:48:30,960
Speaker 4: all of these sorts of things that that kind of exist.

930
00:48:31,600 --> 00:48:33,880
Speaker 4: I think, you know, there are a ton of returns

931
00:48:33,920 --> 00:48:36,919
Speaker 4: to specialization. We've kind of seen that since Henry Ford

932
00:48:37,000 --> 00:48:40,680
Speaker 4: kind of invented there the production line, and so I

933
00:48:40,719 --> 00:48:43,840
Speaker 4: think as these starts of these types of things that

934
00:48:43,880 --> 00:48:45,720
Speaker 4: have start to roll out in the world, my bed

935
00:48:45,760 --> 00:48:48,720
Speaker 4: is that that's probably what we're going to see. First's

936
00:48:48,800 --> 00:48:53,320
Speaker 4: like a lot more point solutions solving very very specific problems.

937
00:48:53,320 --> 00:48:54,480
Speaker 3: And they may not all be.

938
00:48:54,520 --> 00:48:56,719
Speaker 4: Owned by us at our homes in the way that

939
00:48:56,719 --> 00:48:58,799
Speaker 4: we kind of expect. They might also be owned by

940
00:48:59,120 --> 00:49:01,680
Speaker 4: companies who are us in these things to you know,

941
00:49:01,760 --> 00:49:04,240
Speaker 4: like a few you know, have a gardener or something

942
00:49:04,320 --> 00:49:07,520
Speaker 4: like that. Maybe they're kind of bringing a smart lawn

943
00:49:07,520 --> 00:49:09,120
Speaker 4: mower to you, or a smart hedge trim or to

944
00:49:09,160 --> 00:49:12,239
Speaker 4: you or something like that, rather than us necessarily all

945
00:49:12,280 --> 00:49:16,480
Speaker 4: having all of these different robots in our homes there.

946
00:49:16,560 --> 00:49:19,840
Speaker 1: Yeah, it's interesting. I've just been traveling recently in Europe

947
00:49:19,840 --> 00:49:22,960
Speaker 1: in the US and just you know, just seeing industrial

948
00:49:23,239 --> 00:49:29,200
Speaker 1: vacuum cleaner robots starting to wander around airports, seeing robots

949
00:49:29,200 --> 00:49:32,440
Speaker 1: picking up plates from around restaurants. So it's it's just

950
00:49:32,480 --> 00:49:35,480
Speaker 1: becoming and diners are just ignoring them. As they zoom around,

951
00:49:35,560 --> 00:49:38,200
Speaker 1: you know. So it's it's interesting how just that sort

952
00:49:38,239 --> 00:49:40,319
Speaker 1: of stuff was a novelty just a few years ago.

953
00:49:40,440 --> 00:49:43,879
Speaker 1: Now it's just part of the fabric of everyday life,

954
00:49:43,880 --> 00:49:45,920
Speaker 1: and that will that will eventually be the same for

955
00:49:46,000 --> 00:49:46,839
Speaker 1: humanoids as well.

956
00:49:46,920 --> 00:49:47,480
Speaker 3: Totally crue.

957
00:49:47,600 --> 00:49:50,759
Speaker 2: Yeah, the expectation has changed very quickly, right yeah, I

958
00:49:50,760 --> 00:49:53,719
Speaker 2: mean it's just a complete non event now seeing a

959
00:49:53,760 --> 00:49:56,279
Speaker 2: way mow. I mean you go for a walk around

960
00:49:56,320 --> 00:49:59,080
Speaker 2: San Francisco, I mean just you probably see ten waymos

961
00:49:59,120 --> 00:50:01,520
Speaker 2: over the space of three minutes. It's they're just everywhere,

962
00:50:01,560 --> 00:50:04,040
Speaker 2: and so it's a car with no one driving. It

963
00:50:04,080 --> 00:50:06,880
Speaker 2: is now not even worth looking out from your phone

964
00:50:06,960 --> 00:50:11,080
Speaker 2: for and that can change in the span in five years. Right,

965
00:50:11,160 --> 00:50:14,719
Speaker 2: so we'll definitely agreeing with Harry. We'll see some of

966
00:50:14,760 --> 00:50:16,880
Speaker 2: these use cases. We see a lot of robots to

967
00:50:16,920 --> 00:50:21,240
Speaker 2: plod largely you know, initially probably point solutions, and within

968
00:50:21,280 --> 00:50:22,880
Speaker 2: the span of a year or two, people are going

969
00:50:22,920 --> 00:50:25,279
Speaker 2: from thinking it's normal to just completely ignoring it and

970
00:50:25,320 --> 00:50:26,120
Speaker 2: moving on with their life.

971
00:50:26,160 --> 00:50:29,600
Speaker 4: I was shocked by you know, writing in these way

972
00:50:29,640 --> 00:50:33,080
Speaker 4: more cars and also in like Tesla and cruise powered

973
00:50:33,160 --> 00:50:36,680
Speaker 4: kind of vehicles too, Like I think you sort of

974
00:50:36,719 --> 00:50:39,960
Speaker 4: assume that it's going to be this like crazy experience

975
00:50:39,960 --> 00:50:41,480
Speaker 4: when you're inside this thing, and it's going to be

976
00:50:41,520 --> 00:50:44,040
Speaker 4: this constant sense of novelty. But I was just struck

977
00:50:44,040 --> 00:50:46,560
Speaker 4: by her. After about forty five seconds, I was completely

978
00:50:46,600 --> 00:50:48,640
Speaker 4: bored of the fact that I was in an autonomous vehicle.

979
00:50:48,840 --> 00:50:50,919
Speaker 4: I was back on my phone, you know, tapping away

980
00:50:50,960 --> 00:50:54,520
Speaker 4: on Reddit or whatever it was. I think that sort

981
00:50:54,560 --> 00:50:57,799
Speaker 4: of normalcy. Yeah, I think I completely agree with that point.

982
00:50:58,040 --> 00:51:00,560
Speaker 1: I had exactly the same experience. Have been to San

983
00:51:00,600 --> 00:51:02,600
Speaker 1: Francisco two years in a row, so I've had about

984
00:51:02,760 --> 00:51:06,880
Speaker 1: maybe a dozen rides and waymos and it's just second

985
00:51:06,960 --> 00:51:10,000
Speaker 1: nature now, So you know that that is the way

986
00:51:10,040 --> 00:51:12,359
Speaker 1: it is going to go. Just finally, guys, you know

987
00:51:13,320 --> 00:51:19,160
Speaker 1: you've found your way into physical AI and robotics via

988
00:51:19,480 --> 00:51:22,440
Speaker 1: you know, a really interesting journey through software development and

989
00:51:22,680 --> 00:51:26,440
Speaker 1: the like. If you know, if a young Kiwi engineer

990
00:51:26,719 --> 00:51:28,120
Speaker 1: came to you and said, I really want to get

991
00:51:28,120 --> 00:51:32,440
Speaker 1: into robotics, what's the pathway that you would recommend. Not

992
00:51:32,520 --> 00:51:34,160
Speaker 1: a lot of that going on in New Zealand. We

993
00:51:34,200 --> 00:51:37,759
Speaker 1: do have a couple of interesting companies, but would you

994
00:51:37,760 --> 00:51:40,080
Speaker 1: would your recommendation be follow that path that you did,

995
00:51:40,120 --> 00:51:42,800
Speaker 1: try and get to a larger market as soon as possible,

996
00:51:42,840 --> 00:51:47,200
Speaker 1: and try and work for one of those great companies

997
00:51:47,239 --> 00:51:49,680
Speaker 1: that employs a lot of robotics engineers.

998
00:51:49,880 --> 00:51:54,440
Speaker 2: I would say that even putting aside of robotics, if

999
00:51:54,440 --> 00:51:58,160
Speaker 2: you're if you're young and ambitious and trying to break

1000
00:51:58,200 --> 00:52:01,719
Speaker 2: into anything, you need to get to working at a

1001
00:52:01,880 --> 00:52:06,319
Speaker 2: high growth, a high growth tech company that is at

1002
00:52:06,320 --> 00:52:09,160
Speaker 2: the front of their industry. And so that's definitely true

1003
00:52:09,200 --> 00:52:11,879
Speaker 2: in robotics, it's true elsewhere, and I really, I really

1004
00:52:12,040 --> 00:52:15,360
Speaker 2: encourage people to if there are, like you said, a

1005
00:52:15,360 --> 00:52:18,239
Speaker 2: few companies in New Zealand that you can get to,

1006
00:52:18,400 --> 00:52:21,040
Speaker 2: but you need to be at a company that's growing quickly.

1007
00:52:21,680 --> 00:52:24,600
Speaker 2: You know, this is like ideally that's more than doubling

1008
00:52:24,840 --> 00:52:27,400
Speaker 2: year of a year of their revenue or their headcount

1009
00:52:28,280 --> 00:52:30,200
Speaker 2: that is growing fast, and you can get a front

1010
00:52:30,239 --> 00:52:33,319
Speaker 2: receipt to that. So there are very few companies like

1011
00:52:33,360 --> 00:52:35,160
Speaker 2: that in New Zealand. There are a lot in the States.

1012
00:52:35,239 --> 00:52:37,040
Speaker 2: It's quite hard to get a visa for the States,

1013
00:52:37,080 --> 00:52:39,480
Speaker 2: but if you care about it, make it happen. There

1014
00:52:39,480 --> 00:52:45,000
Speaker 2: are ways within robotics specifically. The one thing I will

1015
00:52:45,000 --> 00:52:48,600
Speaker 2: add is that people underestimate how broad the field of

1016
00:52:48,719 --> 00:52:51,880
Speaker 2: robotics is, and so at every robotics company, like you

1017
00:52:51,880 --> 00:52:55,320
Speaker 2: don't need a robotics PhD. Yeah, there are mechanical engineers,

1018
00:52:55,360 --> 00:52:57,799
Speaker 2: there are electric engineers, there are AI engineers, there are

1019
00:52:57,920 --> 00:53:01,160
Speaker 2: data engineers. There are front and engine is building interfaces

1020
00:53:01,200 --> 00:53:04,760
Speaker 2: for the things. There are legal teams, there are operations teams,

1021
00:53:04,840 --> 00:53:06,920
Speaker 2: there are product managers. You know, there are so many

1022
00:53:07,000 --> 00:53:10,799
Speaker 2: roles at these companies, and so I think people that

1023
00:53:10,880 --> 00:53:13,000
Speaker 2: I talk to sometimes get too hung up on thinking

1024
00:53:13,320 --> 00:53:16,080
Speaker 2: I wish I had studied robotics in college. It doesn't matter.

1025
00:53:16,120 --> 00:53:18,359
Speaker 2: I didn't study robotics in college. Now I've been doing

1026
00:53:18,440 --> 00:53:21,480
Speaker 2: it for ten years and I know a lot. But

1027
00:53:21,840 --> 00:53:23,759
Speaker 2: you know, find an entry point at those companies where

1028
00:53:23,760 --> 00:53:25,839
Speaker 2: the skill set that you do have, these companies are

1029
00:53:25,880 --> 00:53:29,560
Speaker 2: hiring for dozens or hundreds of different roles. That would

1030
00:53:29,600 --> 00:53:31,640
Speaker 2: be my main kind of robotic specific point.

1031
00:53:31,680 --> 00:53:31,879
Speaker 5: Yeah.

1032
00:53:31,880 --> 00:53:33,120
Speaker 4: I don't have too much to add to that because

1033
00:53:33,120 --> 00:53:35,960
Speaker 4: I think that's a great answer. But generally speaking, I

1034
00:53:36,000 --> 00:53:40,399
Speaker 4: think you want to see what great looks like up

1035
00:53:40,440 --> 00:53:44,759
Speaker 4: close and personal as much as possible. By growth companies

1036
00:53:45,239 --> 00:53:48,040
Speaker 4: are exactly one fantastic way to do that. But I

1037
00:53:48,040 --> 00:53:51,080
Speaker 4: think at every step in the journey, right, whether it

1038
00:53:51,120 --> 00:53:54,240
Speaker 4: is the kinds of classes that you take, the things

1039
00:53:54,239 --> 00:53:57,319
Speaker 4: that you're learning, who you're kind of spending your time with,

1040
00:53:57,719 --> 00:54:01,520
Speaker 4: the sort of bigger, more competitive, scarier the rumors that

1041
00:54:01,560 --> 00:54:04,720
Speaker 4: you put yourself in, that kind of thing compounds very quickly,

1042
00:54:05,840 --> 00:54:08,640
Speaker 4: and so that would kind of be my two cents.

1043
00:54:08,840 --> 00:54:11,080
Speaker 2: Yeah, if you're if you're if you're fifteen in a

1044
00:54:11,120 --> 00:54:13,520
Speaker 2: high school, follow Harry's path, not mine, and try and

1045
00:54:13,560 --> 00:54:17,839
Speaker 2: get into Stanford, because well massive university was great, but

1046
00:54:17,880 --> 00:54:20,200
Speaker 2: it did not you know, this is coming back to

1047
00:54:20,239 --> 00:54:22,319
Speaker 2: Harry's point about put yourself in the room with the

1048
00:54:22,360 --> 00:54:23,440
Speaker 2: smartest people in the world.

1049
00:54:23,560 --> 00:54:26,440
Speaker 1: Great advice. Well, thanks so much, guys. It's been a

1050
00:54:26,480 --> 00:54:33,040
Speaker 1: fascinating conversation. You're working an incredible dynamic area of tech development.

1051
00:54:33,160 --> 00:54:36,360
Speaker 1: Good luck for things, and twenty twenty six is going

1052
00:54:36,400 --> 00:54:39,200
Speaker 1: to be a big year. I think for AI continue

1053
00:54:39,200 --> 00:54:41,279
Speaker 1: to be a big year. Twenty twenty five was, but

1054
00:54:41,520 --> 00:54:45,160
Speaker 1: marrying that with some of the you know, the advances

1055
00:54:45,239 --> 00:54:47,440
Speaker 1: in robotics is going to be really interesting to watch.

1056
00:54:47,480 --> 00:54:50,799
Speaker 1: So we'll keep a close eye on Fox, Glove and Antioch.

1057
00:54:50,960 --> 00:54:53,680
Speaker 1: And thanks so much for coming onto Business of Tech.

1058
00:54:53,800 --> 00:55:06,120
Speaker 5: Thanks Peter, thanks very much for having us. Fantastic So

1059
00:55:06,200 --> 00:55:06,560
Speaker 5: there you go.

1060
00:55:06,680 --> 00:55:10,600
Speaker 1: Really decent conversation with Harry Malsop from Antioch and Adrian

1061
00:55:10,680 --> 00:55:14,880
Speaker 1: McNeil from Foxglove two. Kiwis quietly building this scaffolding that

1062
00:55:14,920 --> 00:55:18,480
Speaker 1: will let robots move into the mainstream. Take away from

1063
00:55:18,520 --> 00:55:22,200
Speaker 1: me from this conversation, it's just how central data simulation

1064
00:55:22,640 --> 00:55:28,120
Speaker 1: and observability to deploying robots safely, From simulating thousands of

1065
00:55:28,239 --> 00:55:32,080
Speaker 1: edge cases and software to building the data flywheels that

1066
00:55:32,200 --> 00:55:35,520
Speaker 1: show you not just what a robot did, but what

1067
00:55:35,560 --> 00:55:38,160
Speaker 1: it thought it was doing. So I'll leave it there.

1068
00:55:38,160 --> 00:55:41,200
Speaker 1: It's been a longer episode. Thanks so much for listening.

1069
00:55:41,360 --> 00:55:45,680
Speaker 1: Thanks to two degrees for sponsoring the Business off Tech show.

1070
00:55:45,719 --> 00:55:48,120
Speaker 1: Notes are on the Business Desk website. Just go to

1071
00:55:48,160 --> 00:55:50,920
Speaker 1: Businessdesk dot co dot nz and I'll see you back

1072
00:55:50,960 --> 00:55:54,279
Speaker 1: here next week with another episode of the Business of Tech.

1073
00:55:54,560 --> 00:56:00,600
Speaker 1: Catch you then,