1
00:00:15,356 --> 00:00:15,796
Speaker 1: Pushkin.

2
00:00:20,276 --> 00:00:22,036
Speaker 2: I try to bring my lunch to the office, but

3
00:00:22,156 --> 00:00:24,316
Speaker 2: when I can't get my act together, which is most

4
00:00:24,316 --> 00:00:26,236
Speaker 2: of the time, I go out and get lunch at

5
00:00:26,236 --> 00:00:29,796
Speaker 2: a salad bowl place, and I pay like fourteen dollars.

6
00:00:30,316 --> 00:00:32,316
Speaker 2: This always makes me feel like a little bit of

7
00:00:32,316 --> 00:00:35,116
Speaker 2: a chump, because fourteen dollars seems like a lot to

8
00:00:35,156 --> 00:00:37,836
Speaker 2: pay for a salad. But the salad is healthy and

9
00:00:37,876 --> 00:00:40,796
Speaker 2: fast and tasty, so I buy it. There's of course,

10
00:00:40,836 --> 00:00:43,836
Speaker 2: an issue here that goes beyond my own conflicted relationship

11
00:00:43,876 --> 00:00:47,036
Speaker 2: to lunch, and that is this, a restaurant meal made

12
00:00:47,116 --> 00:00:51,156
Speaker 2: with healthy, fresh ingredients costs more than a restaurant meal

13
00:00:51,236 --> 00:00:55,476
Speaker 2: made with unhealthy processed ingredients. A salad costs more than

14
00:00:55,476 --> 00:00:58,796
Speaker 2: a burger and fries. And it would be great not

15
00:00:58,996 --> 00:01:02,156
Speaker 2: just for me, but for everybody if somebody could use

16
00:01:02,236 --> 00:01:06,476
Speaker 2: technology to make my overpriced salad a lot cheaper. So

17
00:01:06,676 --> 00:01:08,956
Speaker 2: I was pretty excited the other day when someone showed

18
00:01:08,996 --> 00:01:12,396
Speaker 2: me video of a machine that might help make that happen.

19
00:01:12,636 --> 00:01:15,716
Speaker 2: This video you sent me is password protected, which is

20
00:01:15,756 --> 00:01:18,516
Speaker 2: curious to me, Like, why is it a secret this video?

21
00:01:18,516 --> 00:01:19,956
Speaker 2: Why don't you want to show the world that your

22
00:01:19,996 --> 00:01:20,476
Speaker 2: thing works?

23
00:01:21,276 --> 00:01:24,516
Speaker 3: Because right now that version of the makeline is being

24
00:01:24,596 --> 00:01:26,796
Speaker 3: tested at the Chipotle Cultivate Test Center.

25
00:01:26,956 --> 00:01:28,836
Speaker 1: Oh and so you.

26
00:01:28,796 --> 00:01:31,556
Speaker 3: Know, we haven't officially announced anything about this.

27
00:01:31,876 --> 00:01:32,876
Speaker 1: I mean, we have.

28
00:01:32,756 --> 00:01:35,196
Speaker 3: Announced that we're working together, right, but we haven't showed

29
00:01:35,236 --> 00:01:35,916
Speaker 3: anything yet. O.

30
00:01:35,996 --> 00:01:38,476
Speaker 1: Great, we'll I should be doing that very shortly. So

31
00:01:38,596 --> 00:01:39,436
Speaker 1: this is like, which is.

32
00:01:39,396 --> 00:01:41,476
Speaker 2: Why scoop it. You can't see the video yet, but

33
00:01:41,516 --> 00:01:44,796
Speaker 2: we're gonna tell you about this video that you cannot

34
00:01:44,836 --> 00:01:52,116
Speaker 2: yet see. I'm Jacob Goldstein. This is What's Your Problem?

35
00:01:52,196 --> 00:01:54,396
Speaker 2: The show where I talk to people who are trying

36
00:01:54,436 --> 00:01:58,476
Speaker 2: to make technological progress. My guest today is Stephen Klein.

37
00:01:58,876 --> 00:02:02,036
Speaker 2: He's the co founder and CEO of Hyphen, a company

38
00:02:02,076 --> 00:02:06,716
Speaker 2: that's developing an automated make line, basically an automated way

39
00:02:06,796 --> 00:02:08,996
Speaker 2: to make the bold based meal that you get at

40
00:02:09,196 --> 00:02:12,996
Speaker 2: fast casual restaurants like Chipotle or Sweet Green or whatever.

41
00:02:13,236 --> 00:02:15,516
Speaker 2: Chipotle is an investor in Hyphen, and one of the

42
00:02:15,556 --> 00:02:18,036
Speaker 2: major projects the company is working on is an automated

43
00:02:18,076 --> 00:02:21,716
Speaker 2: system that could make online orders at thousands of Chipotle's.

44
00:02:22,516 --> 00:02:26,516
Speaker 2: Steven's problem is this, how do you make restaurant food

45
00:02:26,716 --> 00:02:28,876
Speaker 2: made from fresh ingredients cheaper?

46
00:02:30,116 --> 00:02:33,476
Speaker 3: When my co founder Daniel and I started the company,

47
00:02:33,556 --> 00:02:37,796
Speaker 3: we actually wanted to really be a vertically integrated restaurant,

48
00:02:37,796 --> 00:02:40,956
Speaker 3: meaning that we wanted to effectively make the cost of

49
00:02:41,036 --> 00:02:43,836
Speaker 3: healthy and delicious food the price of fast food. And

50
00:02:43,876 --> 00:02:46,236
Speaker 3: we wanted to be as ubiquitous as fast food, right,

51
00:02:46,276 --> 00:02:49,236
Speaker 3: And we thought that we could do that by automating

52
00:02:49,556 --> 00:02:52,196
Speaker 3: the production of the food and then having it beyond wheels.

53
00:02:52,396 --> 00:02:55,036
Speaker 3: So we built a fully robotic food truck at about

54
00:02:55,036 --> 00:02:58,356
Speaker 3: fifteen months with five people and two million dollars, and

55
00:02:58,356 --> 00:03:01,396
Speaker 3: then COVID hit about two months later, and so where

56
00:03:01,436 --> 00:03:03,676
Speaker 3: we were operating at this in the back of this

57
00:03:03,796 --> 00:03:07,676
Speaker 3: robotic food truck, you know, they were effectively ghost hounds,

58
00:03:07,836 --> 00:03:11,596
Speaker 3: you know, overnight, and so I still remember viscerally kind

59
00:03:11,636 --> 00:03:14,916
Speaker 3: of walking the streets in aerie silence, deciding like what

60
00:03:14,916 --> 00:03:18,476
Speaker 3: are we gonna do. So we effectively retooled the system

61
00:03:18,716 --> 00:03:20,236
Speaker 3: to put it in a make line, and that's what

62
00:03:20,276 --> 00:03:21,436
Speaker 3: we're seeing today in that video.

63
00:03:21,516 --> 00:03:23,196
Speaker 1: So it actually started as a food truck.

64
00:03:23,636 --> 00:03:26,676
Speaker 2: Here's what you see in that password protected video. Stephen

65
00:03:26,716 --> 00:03:29,356
Speaker 2: sent me this thing called the make line, which looks

66
00:03:29,356 --> 00:03:31,116
Speaker 2: like what you see when you walk into a Sweet

67
00:03:31,116 --> 00:03:34,676
Speaker 2: Green or a Chipotle or whatever along counter with containers

68
00:03:34,676 --> 00:03:38,036
Speaker 2: of ingredients sunk into the counter. But this make line

69
00:03:38,196 --> 00:03:41,476
Speaker 2: is different than all other make lines. Each container here

70
00:03:41,796 --> 00:03:45,036
Speaker 2: has an automated trapdoor or augur or something in the

71
00:03:45,036 --> 00:03:48,876
Speaker 2: bottom to dispense the food, and underneath the counter, like

72
00:03:48,956 --> 00:03:52,156
Speaker 2: where the cabinets would normally be, there is this automated

73
00:03:52,196 --> 00:03:55,796
Speaker 2: system where a bowl drops down into this like clamp

74
00:03:55,836 --> 00:03:58,276
Speaker 2: at the beginning of the line, and some lettuce or

75
00:03:58,316 --> 00:04:01,076
Speaker 2: whatever automatically drops into the bowl from the bin above.

76
00:04:01,436 --> 00:04:04,076
Speaker 2: Then the clamp like pivots and hands the bowl off

77
00:04:04,116 --> 00:04:06,756
Speaker 2: to another clamp a little further down the line, and

78
00:04:06,796 --> 00:04:09,516
Speaker 2: the next ingredient is dropped into the bowl, and all on,

79
00:04:09,956 --> 00:04:13,196
Speaker 2: all down the line until the salad is made, the

80
00:04:13,236 --> 00:04:15,116
Speaker 2: bowl gets to the end of the line, and then

81
00:04:15,276 --> 00:04:18,676
Speaker 2: finally this little elevator raises the bull up to the

82
00:04:18,716 --> 00:04:19,476
Speaker 2: top of the counter.

83
00:04:19,876 --> 00:04:23,436
Speaker 3: Actually, the guy that designed the elevator was designing an

84
00:04:23,516 --> 00:04:26,756
Speaker 3: elevator for one of the reads at disney Land, very

85
00:04:26,796 --> 00:04:31,356
Speaker 3: similar design about you know, ten thousand times the size.

86
00:04:31,436 --> 00:04:34,716
Speaker 3: But it was pretty funny that that was his first project.

87
00:04:34,356 --> 00:04:35,236
Speaker 1: When we kicked off.

88
00:04:35,476 --> 00:04:38,596
Speaker 2: When you watch the video, the whole process actually looks

89
00:04:38,796 --> 00:04:42,756
Speaker 2: kind of simple, which, of course it is not. Steven says,

90
00:04:42,756 --> 00:04:44,796
Speaker 2: one of the hardest parts, which you can't see at

91
00:04:44,836 --> 00:04:48,196
Speaker 2: all in the video, was getting the little trapdoor system

92
00:04:48,516 --> 00:04:51,636
Speaker 2: to allow just the right amount of every ingredient to

93
00:04:51,796 --> 00:04:55,076
Speaker 2: drop down into the bowl every time. And the details

94
00:04:55,116 --> 00:04:58,436
Speaker 2: of why that's a hard problem illuminate why fresh food

95
00:04:58,556 --> 00:05:00,836
Speaker 2: tends to be more expensive than processed food.

96
00:05:01,436 --> 00:05:06,316
Speaker 3: It's extremely difficult to dispense and properly meter and plate

97
00:05:06,556 --> 00:05:11,756
Speaker 3: ingredients because of the almost infinite permutations. Right, Let's take

98
00:05:11,796 --> 00:05:15,036
Speaker 3: kale for example. If you have kale, it will change

99
00:05:15,036 --> 00:05:18,476
Speaker 3: its permutations or characteristics depending upon how it's cut. Whether

100
00:05:18,516 --> 00:05:21,916
Speaker 3: that's sautaied, blanched of and roasted, it will change permutation.

101
00:05:22,116 --> 00:05:23,036
Speaker 1: Potentially if it's.

102
00:05:23,076 --> 00:05:26,036
Speaker 3: Sourced in Salinas you know, where we're at, you know,

103
00:05:26,196 --> 00:05:28,636
Speaker 3: or nearby where we're at, or somewhere else, it will

104
00:05:28,676 --> 00:05:31,396
Speaker 3: change depending upon the cut method, right, if you cut

105
00:05:31,396 --> 00:05:34,356
Speaker 3: it a different way. So being able to be agnostic

106
00:05:34,396 --> 00:05:38,076
Speaker 3: to the type of ingredient is extremely difficult.

107
00:05:38,516 --> 00:05:40,956
Speaker 1: So we basically have to you know, map.

108
00:05:40,716 --> 00:05:44,076
Speaker 3: All these materials and characteristics to basically better understand how

109
00:05:44,116 --> 00:05:46,276
Speaker 3: to feed that ingredient, and then it will get better

110
00:05:46,276 --> 00:05:46,716
Speaker 3: over time.

111
00:05:47,076 --> 00:05:49,196
Speaker 2: That's interesting, I mean, it makes me think about the

112
00:05:49,276 --> 00:05:52,956
Speaker 2: difference between processed food and fresh food, right, And when

113
00:05:52,996 --> 00:05:55,276
Speaker 2: you buy processed with anything that's like in the middle

114
00:05:55,276 --> 00:05:57,396
Speaker 2: of the grocery store, you know, comes in a box,

115
00:05:57,796 --> 00:06:02,156
Speaker 2: it's like become very homogenized, right, presumably so that it

116
00:06:02,196 --> 00:06:05,716
Speaker 2: can be produced at scale by machines, whereas what you

117
00:06:05,796 --> 00:06:10,316
Speaker 2: get at a Chipotle or sweet grain is very heterogeneous. Right,

118
00:06:10,356 --> 00:06:12,396
Speaker 2: as you say, not all kale stocks are the same.

119
00:06:12,916 --> 00:06:15,956
Speaker 2: And not to mention, there's like fifty different ingredients there

120
00:06:15,956 --> 00:06:18,636
Speaker 2: could be. So do you have to build or more

121
00:06:18,676 --> 00:06:21,196
Speaker 2: obviously there's more than fifty different ingredients? Do you have

122
00:06:21,276 --> 00:06:25,796
Speaker 2: to build a different dispenser for every ingredient? Like if

123
00:06:25,796 --> 00:06:28,396
Speaker 2: somebody's like, we want to do cantilina beans, right, do

124
00:06:28,396 --> 00:06:29,796
Speaker 2: you have to be like, oh shit, all we know

125
00:06:29,836 --> 00:06:31,116
Speaker 2: how to do is chick beans. We got to go

126
00:06:31,116 --> 00:06:33,316
Speaker 2: build a whole new machine for cantelini beans.

127
00:06:33,836 --> 00:06:37,076
Speaker 3: Yeah, So we have about six different feeder types or

128
00:06:37,156 --> 00:06:39,796
Speaker 3: dispenser types. We'd have one that you know, has to

129
00:06:39,836 --> 00:06:41,916
Speaker 3: deal with like guauca mole in the case of Chipotle,

130
00:06:42,516 --> 00:06:45,716
Speaker 3: or pico de gayo, which is very different than guacamole

131
00:06:45,756 --> 00:06:47,836
Speaker 3: in terms of this viscosity as well.

132
00:06:47,876 --> 00:06:50,316
Speaker 1: As it's kind of you know, the chunkier.

133
00:06:49,956 --> 00:06:52,916
Speaker 3: Ingredients, and then you have leafy greens, right, and that

134
00:06:52,916 --> 00:06:56,076
Speaker 3: that's going to you know, perform wildly arugula, et cetera.

135
00:06:56,836 --> 00:07:00,316
Speaker 3: So we yeah, we effectively have about six six dispensers

136
00:07:00,396 --> 00:07:04,836
Speaker 3: handle you know, thousands of ingredients at this point that

137
00:07:04,876 --> 00:07:07,716
Speaker 3: we've we've we've mapped and started to kind of understand

138
00:07:07,756 --> 00:07:12,076
Speaker 3: their characteristics. Greens, especially a arugula early on was was

139
00:07:12,116 --> 00:07:14,836
Speaker 3: pretty difficult to dispense. It would just start wrapping around

140
00:07:14,876 --> 00:07:18,596
Speaker 3: these these kind of paddle wheels or augurs, and you know,

141
00:07:18,676 --> 00:07:21,316
Speaker 3: I think we designed about seven hundred and fifty prototypes to.

142
00:07:21,276 --> 00:07:23,556
Speaker 1: Get to the design that we have now.

143
00:07:23,596 --> 00:07:25,836
Speaker 3: We three D print when we test, so we can

144
00:07:25,836 --> 00:07:28,556
Speaker 3: do it in about a day and then test the

145
00:07:28,596 --> 00:07:29,196
Speaker 3: next morning.

146
00:07:29,676 --> 00:07:31,676
Speaker 2: Was there a moment when you sort of solve the

147
00:07:31,756 --> 00:07:32,876
Speaker 2: leafy greens problem?

148
00:07:33,436 --> 00:07:36,396
Speaker 3: Yeah, I mean it was just through rapid iteration. There

149
00:07:36,476 --> 00:07:38,596
Speaker 3: was no one thing. It was just you know, these

150
00:07:38,636 --> 00:07:41,996
Speaker 3: micro improvements we made along the way. And then the

151
00:07:42,036 --> 00:07:43,796
Speaker 3: case of like black beans, where you get like that

152
00:07:43,876 --> 00:07:46,916
Speaker 3: pulled up juice, you know, sometimes it's pretty unpleasant.

153
00:07:47,636 --> 00:07:49,916
Speaker 1: You know, we effectively had to build.

154
00:07:49,916 --> 00:07:53,916
Speaker 3: An auger that would basically push the ingredients out while

155
00:07:54,516 --> 00:07:57,716
Speaker 3: you know, effectively letting some of that black bean juice

156
00:07:57,756 --> 00:07:59,836
Speaker 3: kind slide into almost like a gutter and a kind

157
00:07:59,836 --> 00:08:01,916
Speaker 3: of a septic tank in the back there.

158
00:08:02,116 --> 00:08:04,196
Speaker 1: And so you do get some liquid, but not not

159
00:08:04,316 --> 00:08:04,916
Speaker 1: that pool.

160
00:08:05,116 --> 00:08:08,556
Speaker 2: I like that, although not to give you on solicit advice,

161
00:08:08,596 --> 00:08:12,196
Speaker 2: I'd suggest avoiding the septic tank metaphor with the make line,

162
00:08:13,836 --> 00:08:15,036
Speaker 2: especially with the beans.

163
00:08:15,276 --> 00:08:15,516
Speaker 1: Great.

164
00:08:15,636 --> 00:08:18,556
Speaker 3: Yeah, and I'd say, like the trickiest thing that we

165
00:08:18,636 --> 00:08:22,956
Speaker 3: haven't solved yet is fand avocado. So we can do guacamoley,

166
00:08:23,116 --> 00:08:24,956
Speaker 3: we can do like you know, the goofs and mounds,

167
00:08:24,956 --> 00:08:29,636
Speaker 3: but getting that beautiful fan avocado slices like quite difficult

168
00:08:29,636 --> 00:08:29,916
Speaker 3: to do.

169
00:08:30,476 --> 00:08:35,196
Speaker 1: Yes, Slicing fish filets things.

170
00:08:34,956 --> 00:08:36,996
Speaker 3: Like that can be quite difficult when they're you know,

171
00:08:38,116 --> 00:08:38,996
Speaker 3: very sensitive.

172
00:08:39,356 --> 00:08:43,116
Speaker 2: So do you give up, like it's fandavocado off the table?

173
00:08:43,596 --> 00:08:47,036
Speaker 3: Yeah, there are some ingredients like fand avocado that you know,

174
00:08:47,876 --> 00:08:50,476
Speaker 3: the operator on the line, because they're just kind of

175
00:08:50,476 --> 00:08:53,156
Speaker 3: waiting for balls on the line, they can just add

176
00:08:53,316 --> 00:08:56,036
Speaker 3: It's called expo, but they basically would just add the

177
00:08:56,036 --> 00:08:59,596
Speaker 3: the avocado or those last final finishing items at the

178
00:08:59,676 --> 00:09:00,436
Speaker 3: end there.

179
00:09:00,276 --> 00:09:01,356
Speaker 1: That we can do today.

180
00:09:01,596 --> 00:09:05,036
Speaker 2: So you've been working on this for what a little

181
00:09:05,076 --> 00:09:07,636
Speaker 2: more than three years now. I know you've got an

182
00:09:07,716 --> 00:09:11,876
Speaker 2: investment from Chipotle last year and you're sort of building

183
00:09:12,076 --> 00:09:17,116
Speaker 2: a version of your line for Chipotle. Now, where is

184
00:09:17,596 --> 00:09:22,276
Speaker 2: your line your product in the world now? Like, is

185
00:09:22,316 --> 00:09:23,316
Speaker 2: it in restaurants now?

186
00:09:24,476 --> 00:09:27,596
Speaker 3: Yeah, it's it's in a few and we have a

187
00:09:27,596 --> 00:09:30,156
Speaker 3: lot actually coming in Q four of this year.

188
00:09:30,196 --> 00:09:31,956
Speaker 1: We're just kind of starting to ramp up production.

189
00:09:32,356 --> 00:09:34,276
Speaker 2: How many restaurants is your machine in now?

190
00:09:34,796 --> 00:09:36,036
Speaker 1: About a dozen? Okay?

191
00:09:36,676 --> 00:09:39,716
Speaker 2: And what's happening with Chipotle? Like, so Chipotle made an

192
00:09:39,756 --> 00:09:42,196
Speaker 2: investment in your company last year, right, So like that

193
00:09:42,276 --> 00:09:46,076
Speaker 2: seems significant? Like is that I don't know, that seems

194
00:09:46,076 --> 00:09:47,356
Speaker 2: like a big deal. And what's happening with that?

195
00:09:48,156 --> 00:09:50,956
Speaker 3: Yeah, and they're actually investing in the second time too.

196
00:09:51,156 --> 00:09:53,156
Speaker 3: We're just wrapping that up right now, so we'll have

197
00:09:53,396 --> 00:09:55,476
Speaker 3: some stuff to announce also.

198
00:09:55,996 --> 00:09:59,476
Speaker 1: And Q four of this year with Chipotle, is.

199
00:09:59,396 --> 00:10:03,076
Speaker 2: It likely that your machine will be in Chipotle next year?

200
00:10:03,516 --> 00:10:03,716
Speaker 1: Yes?

201
00:10:04,276 --> 00:10:07,156
Speaker 3: The CEO of Chipotle, you know, I think Believe has

202
00:10:07,156 --> 00:10:09,636
Speaker 3: publicly stated, you know, he wants to all this out

203
00:10:09,676 --> 00:10:11,716
Speaker 3: fleet wide, So you know, I think they have like

204
00:10:11,756 --> 00:10:13,716
Speaker 3: thirty five hundred stores today.

205
00:10:14,316 --> 00:10:16,596
Speaker 2: I mean you're saying there are thousands of Chipotlas in

206
00:10:16,596 --> 00:10:19,236
Speaker 2: the world and they want your machine and all of them.

207
00:10:19,596 --> 00:10:23,156
Speaker 3: Yes, they to have all of their digital orders. Yes,

208
00:10:23,676 --> 00:10:25,956
Speaker 3: front of house you know, will always be front of house.

209
00:10:26,356 --> 00:10:29,436
Speaker 2: Oh that's interesting. So the idea is your machine would

210
00:10:29,436 --> 00:10:31,436
Speaker 2: be in the in the back of the restaurant, and

211
00:10:31,476 --> 00:10:35,076
Speaker 2: when people order on their app, the robot your machine

212
00:10:35,076 --> 00:10:37,476
Speaker 2: would make it, and when people walk in, a person

213
00:10:37,516 --> 00:10:38,076
Speaker 2: would make it.

214
00:10:38,116 --> 00:10:40,476
Speaker 1: Is that is that the idea precisely?

215
00:10:40,676 --> 00:10:43,076
Speaker 3: And having that you know, that staff that's friendly and

216
00:10:43,076 --> 00:10:46,076
Speaker 3: providing that hospitality is super important if you're ordering in person, right,

217
00:10:46,076 --> 00:10:48,316
Speaker 3: But if you're ordering from your phone, you know, you

218
00:10:48,396 --> 00:10:51,596
Speaker 3: just want your food to be fresh, fast and you know, accurate, ideally,

219
00:10:52,356 --> 00:10:54,836
Speaker 3: and that's where we help. I mean, right now we're

220
00:10:54,916 --> 00:10:57,516
Speaker 3: really focused on just getting these lines to produce and

221
00:10:57,556 --> 00:10:59,276
Speaker 3: plate you know, perfect food every time.

222
00:10:59,756 --> 00:11:01,836
Speaker 2: How far are you from them? Like, I guess it's

223
00:11:01,916 --> 00:11:04,236
Speaker 2: not binary when you say like every time. I mean

224
00:11:04,236 --> 00:11:06,756
Speaker 2: it's never going to be every every time, right, But

225
00:11:06,876 --> 00:11:11,996
Speaker 2: there's some percentage liability presumably where it like, if you

226
00:11:12,036 --> 00:11:16,316
Speaker 2: get above X, then it makes operational and economic sense. Right,

227
00:11:16,356 --> 00:11:18,996
Speaker 2: What is X in terms of the uptim or reliability

228
00:11:19,076 --> 00:11:20,116
Speaker 2: or whatever the metric is.

229
00:11:20,356 --> 00:11:23,116
Speaker 3: Yeah, I mean, like you want to be at ninety

230
00:11:23,156 --> 00:11:25,636
Speaker 3: nine percent up time, ideally ninety nine point nine percent

231
00:11:25,676 --> 00:11:27,556
Speaker 3: up time. In the event that you don't, you know,

232
00:11:27,596 --> 00:11:29,756
Speaker 3: it's just like when your Internet goes down or you know,

233
00:11:29,796 --> 00:11:31,356
Speaker 3: your slack goes down, right.

234
00:11:31,236 --> 00:11:32,196
Speaker 2: And what are you at now?

235
00:11:33,636 --> 00:11:34,196
Speaker 1: Ninety five?

236
00:11:34,396 --> 00:11:34,796
Speaker 2: Okay?

237
00:11:34,916 --> 00:11:37,276
Speaker 3: Going from ninety five to ninety nine is really hard,

238
00:11:37,276 --> 00:11:39,556
Speaker 3: and going from ninety nine to ninety nine point nine

239
00:11:39,676 --> 00:11:40,636
Speaker 3: is even harder.

240
00:11:41,716 --> 00:11:43,756
Speaker 2: So in a sense, the first ninety five percent, like

241
00:11:43,796 --> 00:11:46,276
Speaker 2: you've done the easy part, in the last four percent

242
00:11:46,396 --> 00:11:49,476
Speaker 2: is hard, and then the last zero point nine percent

243
00:11:49,596 --> 00:11:53,116
Speaker 2: is killer is in order it might be harder? Yeah,

244
00:11:53,236 --> 00:11:55,916
Speaker 2: And that's where that like one weird kale leaf just

245
00:11:55,916 --> 00:12:01,276
Speaker 2: screws you, right, Ah, that fand avocado, right, fand avocado.

246
00:12:01,316 --> 00:12:03,476
Speaker 2: We gave up on fand avocado and you're still at

247
00:12:03,556 --> 00:12:07,076
Speaker 2: ninety five to get So do you just have to

248
00:12:07,196 --> 00:12:10,316
Speaker 2: keep iterating to get from ninety five to ninety nine?

249
00:12:10,956 --> 00:12:13,236
Speaker 1: So that's a really great question.

250
00:12:13,316 --> 00:12:17,436
Speaker 3: So right now we are going through we're basically doing

251
00:12:17,476 --> 00:12:20,156
Speaker 3: in DFMA, so design for manufacturing assembly, and as we

252
00:12:20,196 --> 00:12:22,356
Speaker 3: do that, we start to injectionable parts, we start to

253
00:12:22,396 --> 00:12:26,116
Speaker 3: kind of get you know, production ready materials that will

254
00:12:26,236 --> 00:12:27,756
Speaker 3: drastically improve.

255
00:12:27,476 --> 00:12:29,836
Speaker 2: Going from prototype to production basically.

256
00:12:30,316 --> 00:12:32,596
Speaker 1: Yeah, we're also doing a lot of accelerated life testing.

257
00:12:32,676 --> 00:12:35,476
Speaker 1: So we actually, you know, we did this system.

258
00:12:35,916 --> 00:12:38,596
Speaker 3: We started in November on this new system, and we

259
00:12:38,796 --> 00:12:42,676
Speaker 3: you know, we launched in April, so pretty quick, right,

260
00:12:43,396 --> 00:12:46,436
Speaker 3: But you know, doing accelerated life testing, we have to

261
00:12:46,756 --> 00:12:48,756
Speaker 3: run thousands of times. So I think we have like

262
00:12:48,756 --> 00:12:52,316
Speaker 3: we've clocked like ten years on our subsystems. But through

263
00:12:52,356 --> 00:12:54,716
Speaker 3: that type of testing and knowing where the failure points

264
00:12:54,756 --> 00:12:57,316
Speaker 3: are and the hardware is how we get to that

265
00:12:57,396 --> 00:13:00,436
Speaker 3: ninety nine percent. Yeah, it's just those types of iterations, right,

266
00:13:00,876 --> 00:13:04,156
Speaker 3: and just micro improvements on the actual design itself.

267
00:13:04,756 --> 00:13:08,836
Speaker 2: So okay, So if we imagine this future where there

268
00:13:08,956 --> 00:13:14,076
Speaker 2: is a much more highly automated, fast casual sort of

269
00:13:14,196 --> 00:13:18,796
Speaker 2: restaurant universe, I mean one question is it cheaper? Right? Like,

270
00:13:18,876 --> 00:13:21,596
Speaker 2: one of my favorite things about technology is it makes

271
00:13:21,636 --> 00:13:24,636
Speaker 2: things cheaper. I try and bring my lunch to work,

272
00:13:24,636 --> 00:13:27,636
Speaker 2: but I do end up buying the fifteen dollars salad

273
00:13:28,236 --> 00:13:30,236
Speaker 2: once or twice a week and feeling like a chump

274
00:13:30,236 --> 00:13:31,956
Speaker 2: for spending that much on a salad, though it is

275
00:13:31,996 --> 00:13:34,756
Speaker 2: a good salad, Like, will it get my fifteen dollars

276
00:13:34,796 --> 00:13:37,076
Speaker 2: salad to a nine dollars salad.

277
00:13:38,236 --> 00:13:39,596
Speaker 1: Maybe even cheaper? Right?

278
00:13:40,116 --> 00:13:42,876
Speaker 3: Because again, you know, the two primary drivers of that

279
00:13:42,916 --> 00:13:45,836
Speaker 3: price is the food, the quality of the foods, and

280
00:13:45,876 --> 00:13:48,956
Speaker 3: then labor, right is this second. And so if you

281
00:13:48,996 --> 00:13:51,876
Speaker 3: can make the marginal cost of producing food near zero,

282
00:13:52,476 --> 00:13:55,796
Speaker 3: you know, you can drastically reduce that price, right, and

283
00:13:55,916 --> 00:13:58,436
Speaker 3: you could get that you know, nine dollars five dollars salad.

284
00:13:58,636 --> 00:13:58,916
Speaker 1: Five.

285
00:13:58,996 --> 00:14:02,076
Speaker 2: I like that, You're going for five five dollars salad. Really,

286
00:14:02,396 --> 00:14:03,196
Speaker 2: that's big for me.

287
00:14:03,716 --> 00:14:04,156
Speaker 1: You like that?

288
00:14:08,556 --> 00:14:10,436
Speaker 2: Now it's time for an ad that It'll be back

289
00:14:10,476 --> 00:14:22,796
Speaker 2: in a minute. Okay, the ad is over. Back to Stephen.

290
00:14:23,236 --> 00:14:26,716
Speaker 2: Stephen has this long term dream for Hyphen. He wants

291
00:14:26,716 --> 00:14:29,156
Speaker 2: the company to open up its own make lines in

292
00:14:29,236 --> 00:14:32,196
Speaker 2: cities around the country, and then he wants to let

293
00:14:32,436 --> 00:14:37,276
Speaker 2: anyone who wants to to create their own basically virtual restaurant.

294
00:14:37,476 --> 00:14:41,236
Speaker 2: So whatever I could go online, I could design Goldstein bowls,

295
00:14:41,676 --> 00:14:44,436
Speaker 2: sell them for nine dollars a bowl, list them on

296
00:14:44,596 --> 00:14:47,916
Speaker 2: Uber eats and seamless, so I would look like a restaurant,

297
00:14:48,116 --> 00:14:51,076
Speaker 2: but Hyphen would be doing the actual food part. They'd

298
00:14:51,076 --> 00:14:54,036
Speaker 2: be buying the ingredients making the bowls, and they would

299
00:14:54,076 --> 00:14:56,716
Speaker 2: just charge me a fee for each bowl that I sold.

300
00:14:57,236 --> 00:14:59,156
Speaker 2: So that is a universe where you have a sort

301
00:14:59,156 --> 00:15:05,236
Speaker 2: of automated ghost or virtual kitchen. Right, Like, there's no

302
00:15:06,476 --> 00:15:08,836
Speaker 2: restaurant as we think of it in that universe. There's

303
00:15:08,876 --> 00:15:12,076
Speaker 2: not like a sign outside or whatever, but it exists

304
00:15:12,156 --> 00:15:14,116
Speaker 2: on your phone, and it can make any number of

305
00:15:14,156 --> 00:15:18,556
Speaker 2: kinds of food, presumably cheaply and quickly.

306
00:15:19,276 --> 00:15:22,556
Speaker 3: I mean, that is what excites me. My sister in law, Sathara,

307
00:15:22,636 --> 00:15:24,636
Speaker 3: she's in Brooklyn. You know, she's always wanted to start

308
00:15:24,676 --> 00:15:27,556
Speaker 3: her own restaurant. I always just imagine her, you know,

309
00:15:27,636 --> 00:15:31,076
Speaker 3: in her apartment, you know, developing a recipe on HERKMS

310
00:15:31,076 --> 00:15:34,076
Speaker 3: on our Hyphen Kitchen management system, taste testing it on

311
00:15:34,076 --> 00:15:37,196
Speaker 3: a nearby make line, and then launching her brand on

312
00:15:37,276 --> 00:15:39,676
Speaker 3: door Dash or uber eats, right and getting it fulfilled

313
00:15:39,676 --> 00:15:42,476
Speaker 3: that way. Why Hyphen you know, can scale when she's ready.

314
00:15:42,796 --> 00:15:44,836
Speaker 3: So I kind of actually see it now. I think

315
00:15:44,836 --> 00:15:47,156
Speaker 3: about it kind of like you know what happened in

316
00:15:47,196 --> 00:15:50,036
Speaker 3: the beer industry where you had you know, Budweiser and

317
00:15:50,076 --> 00:15:52,716
Speaker 3: Anaheuser Bush and then you had all these craft beers

318
00:15:52,796 --> 00:15:55,836
Speaker 3: right and from Portland, Oregon, so IPAs and things like that.

319
00:15:56,196 --> 00:15:58,196
Speaker 3: I see a similar movement happen where you go from

320
00:15:58,196 --> 00:16:01,596
Speaker 3: overly processed food like McDonald's to you know, more of

321
00:16:01,636 --> 00:16:05,476
Speaker 3: the artisanal meals that are you know, local to you.

322
00:16:05,676 --> 00:16:06,036
Speaker 1: Huh.

323
00:16:06,316 --> 00:16:08,596
Speaker 2: And then if it's like the beer industry, all those

324
00:16:08,756 --> 00:16:11,836
Speaker 2: artists and all craft of bowl makers will get acquired

325
00:16:11,916 --> 00:16:15,636
Speaker 2: by Chipotle, right, just like all the craft beer maker

326
00:16:15,636 --> 00:16:17,196
Speaker 2: it's got acquired by AB and BEV.

327
00:16:18,236 --> 00:16:18,916
Speaker 1: That is true.

328
00:16:18,956 --> 00:16:21,436
Speaker 2: That could happen, and so that happens in this new

329
00:16:21,516 --> 00:16:25,356
Speaker 2: universe because the startup costs are lower, essentially, because the

330
00:16:25,356 --> 00:16:27,756
Speaker 2: startup costs can be very low where you don't even

331
00:16:27,756 --> 00:16:29,436
Speaker 2: have to start a restaurant. You just go to you

332
00:16:29,556 --> 00:16:31,756
Speaker 2: and say I want to start selling balls, and you're like, okay,

333
00:16:31,756 --> 00:16:33,356
Speaker 2: you're not selling very many, so we're going to charge

334
00:16:33,356 --> 00:16:35,716
Speaker 2: you three dollars a bowl. And I'm like, whatever, that's fun.

335
00:16:35,716 --> 00:16:37,476
Speaker 2: I'm just sitting here at my computer. And if nobody

336
00:16:37,516 --> 00:16:39,876
Speaker 2: it's like, you're like the Zazzle Shop, but for salad

337
00:16:39,916 --> 00:16:41,276
Speaker 2: bowls exactly.

338
00:16:41,276 --> 00:16:42,716
Speaker 3: And if we're doing that for so many people where

339
00:16:42,796 --> 00:16:45,076
Speaker 3: they can do three or four bowls and they you know,

340
00:16:45,316 --> 00:16:47,836
Speaker 3: it's again are like maybe a better example is like

341
00:16:47,876 --> 00:16:50,116
Speaker 3: a Spotify or square space, right, Like they're designing their

342
00:16:50,156 --> 00:16:53,036
Speaker 3: menu on the website, but it's effectively that easy to run.

343
00:16:53,116 --> 00:16:55,276
Speaker 3: They're not making the food, they're not driving the food,

344
00:16:55,316 --> 00:16:57,956
Speaker 3: they're not you know, having to buy real estate, right,

345
00:16:57,996 --> 00:16:59,956
Speaker 3: they can do it all from their own dormer.

346
00:16:59,636 --> 00:17:03,276
Speaker 2: Apartment in that universe. The sort of ghost kitchen restaurant,

347
00:17:03,316 --> 00:17:06,516
Speaker 2: it's really a marketing question, right, Like, I mean, sure

348
00:17:06,516 --> 00:17:08,116
Speaker 2: you want the food to taste good, but it's like

349
00:17:08,116 --> 00:17:09,996
Speaker 2: a fixed set of ingreded against the Food's not going

350
00:17:10,036 --> 00:17:11,916
Speaker 2: to be that different. What's going to be different is

351
00:17:11,996 --> 00:17:15,316
Speaker 2: like whatever can you get everybody on TikTok to buy

352
00:17:15,436 --> 00:17:16,916
Speaker 2: the brand salad with your name on it?

353
00:17:16,996 --> 00:17:17,236
Speaker 1: Right?

354
00:17:18,396 --> 00:17:21,276
Speaker 3: Yeah, it's like TikTok becomes like QVC meets the Food

355
00:17:21,276 --> 00:17:23,916
Speaker 3: Network or something right where people are like you know,

356
00:17:23,996 --> 00:17:25,916
Speaker 3: selling their meals on TikTok.

357
00:17:26,396 --> 00:17:31,756
Speaker 2: And then what do you see happening with traditional restaurants

358
00:17:32,116 --> 00:17:34,916
Speaker 2: where you you know, sit down and a waiter comes

359
00:17:34,956 --> 00:17:38,436
Speaker 2: to your table and you order food from that person

360
00:17:39,756 --> 00:17:42,076
Speaker 2: and there's a chef in the back cooking the food. Like,

361
00:17:42,116 --> 00:17:43,396
Speaker 2: what's going to happen with that.

362
00:17:45,156 --> 00:17:47,356
Speaker 3: Yeah, I think it's going to be bifurcated. It kind

363
00:17:47,356 --> 00:17:50,396
Speaker 3: of already is like the food experience are eating is

364
00:17:50,476 --> 00:17:52,756
Speaker 3: quite bifurcated, right, You have there are times when you

365
00:17:52,796 --> 00:17:54,676
Speaker 3: want to have these types of conversations, got to a

366
00:17:54,676 --> 00:17:57,476
Speaker 3: fancy dinner, drink wine, have a som kind of you know,

367
00:17:57,636 --> 00:18:00,476
Speaker 3: tell you about that selection, and you know, really really

368
00:18:00,516 --> 00:18:02,396
Speaker 3: experience the food and enjoy the conversation.

369
00:18:02,756 --> 00:18:03,796
Speaker 1: And then there's times when you.

370
00:18:03,756 --> 00:18:06,076
Speaker 3: Just did you get to your desk, or like your

371
00:18:06,156 --> 00:18:08,156
Speaker 3: kids are at home and you know the house is

372
00:18:08,156 --> 00:18:10,996
Speaker 3: busy and you just need food like fuel. Are you

373
00:18:11,116 --> 00:18:12,996
Speaker 3: love food and you don't want it to be fuel,

374
00:18:13,076 --> 00:18:15,196
Speaker 3: but like you just don't have the bandwidth at the time.

375
00:18:15,676 --> 00:18:18,756
Speaker 3: Are the money? I think that's where you know automation

376
00:18:18,876 --> 00:18:19,316
Speaker 3: can help.

377
00:18:19,676 --> 00:18:22,036
Speaker 2: And so do you think that like classic restaurant isn't

378
00:18:22,036 --> 00:18:24,116
Speaker 2: going to be any different than it is today? You

379
00:18:24,156 --> 00:18:25,036
Speaker 2: think it'll be the same.

380
00:18:25,276 --> 00:18:25,756
Speaker 1: I don't think it.

381
00:18:25,756 --> 00:18:29,196
Speaker 3: Will change much because it's just like coffee, right, It's

382
00:18:29,276 --> 00:18:31,276
Speaker 3: kind of built around this kind of artisanal kind of

383
00:18:31,316 --> 00:18:33,796
Speaker 3: craft experience and it's really conduit to culture.

384
00:18:33,876 --> 00:18:35,916
Speaker 1: Right, So you have a lot of folks that are

385
00:18:35,916 --> 00:18:37,356
Speaker 1: going there for more than just the food.

386
00:18:37,396 --> 00:18:39,596
Speaker 3: They're going there for the hospitality, and they're going for

387
00:18:39,636 --> 00:18:42,676
Speaker 3: that entire experience. I don't think you can automate that

388
00:18:42,756 --> 00:18:46,236
Speaker 3: experience ever, So I don't see that changing much at all.

389
00:18:46,596 --> 00:18:49,476
Speaker 3: But again, the rest, the other side, the other fifty percent,

390
00:18:50,556 --> 00:18:51,676
Speaker 3: is going to change massively.

391
00:18:55,236 --> 00:18:57,316
Speaker 2: We'll be back in a minute with the lightning round.

392
00:19:08,396 --> 00:19:13,396
Speaker 2: Back to the show. Okay, let's do the lightning round. Okay,

393
00:19:13,716 --> 00:19:16,636
Speaker 2: do you cook? Oh yeah, what's a go to weeknight

394
00:19:16,676 --> 00:19:17,756
Speaker 2: dinner that you cook.

395
00:19:19,436 --> 00:19:21,716
Speaker 1: It's gonna sound silly. We love salads.

396
00:19:21,916 --> 00:19:25,316
Speaker 3: My salad that I love it's kind of like a

397
00:19:25,316 --> 00:19:27,476
Speaker 3: caesar salad, but it's it's it's kind of got a twist.

398
00:19:27,996 --> 00:19:30,436
Speaker 3: We borrow the recipe from John and Vinnie's, which is

399
00:19:30,476 --> 00:19:33,116
Speaker 3: a pizza pizza company out here in La where it's

400
00:19:33,196 --> 00:19:35,116
Speaker 3: kind of a spicy caesar. So you got a lot

401
00:19:35,156 --> 00:19:37,716
Speaker 3: of uh, you know, spice and zing to it and

402
00:19:37,756 --> 00:19:40,196
Speaker 3: some some breadcrumbs on top. I'm a pretty simple guy

403
00:19:40,236 --> 00:19:42,556
Speaker 3: when it comes to my salads, though.

404
00:19:42,676 --> 00:19:46,076
Speaker 2: What's something you think should not be automated.

405
00:19:46,556 --> 00:19:50,676
Speaker 3: Any guest interaction, like any anytime, even concierge at a

406
00:19:50,676 --> 00:19:53,836
Speaker 3: hotel like which could be automated. You know with a iPad.

407
00:19:53,876 --> 00:19:56,636
Speaker 3: I think it's like anytime that you're interacting with a guest,

408
00:19:56,916 --> 00:20:01,516
Speaker 3: you can't take that magic away or you've lost your

409
00:20:01,556 --> 00:20:04,516
Speaker 3: value proposition. So I'd say like that front of house

410
00:20:04,596 --> 00:20:07,876
Speaker 3: or you know, front facing roles or types of tasks

411
00:20:07,916 --> 00:20:09,076
Speaker 3: would be terrible to automate.

412
00:20:09,676 --> 00:20:13,556
Speaker 2: Yeah, long term, you ran a frozen yogurt chop in college,

413
00:20:13,596 --> 00:20:16,476
Speaker 2: so I got a couple questions about that first one

414
00:20:16,516 --> 00:20:17,356
Speaker 2: flavor or.

415
00:20:17,356 --> 00:20:20,836
Speaker 1: Swirl swirl Come on, it's more fun.

416
00:20:20,916 --> 00:20:23,596
Speaker 2: Swirls a little confusing to because you end up getting

417
00:20:23,756 --> 00:20:25,396
Speaker 2: like it's not like you're eating the one and you're

418
00:20:25,436 --> 00:20:27,396
Speaker 2: eating the other. You're kind of eating both the all

419
00:20:27,396 --> 00:20:27,796
Speaker 2: the time.

420
00:20:28,676 --> 00:20:31,436
Speaker 1: I like a little spice, a little little interest in

421
00:20:31,476 --> 00:20:31,876
Speaker 1: my life.

422
00:20:32,076 --> 00:20:33,436
Speaker 2: Worse stopping.

423
00:20:34,436 --> 00:20:35,436
Speaker 1: Chocolate sprinkles.

424
00:20:35,756 --> 00:20:38,036
Speaker 2: Yeah, I think I agree. My kids like them, but

425
00:20:38,076 --> 00:20:42,436
Speaker 2: I think I don't even get what's going on. How

426
00:20:42,476 --> 00:20:45,196
Speaker 2: will you know it's time to do something else for work?

427
00:20:45,636 --> 00:20:49,636
Speaker 3: You know, I'm pretty persistent, but just like if I

428
00:20:49,716 --> 00:20:51,876
Speaker 3: bang my head and I'm not moving forward, and how

429
00:20:51,876 --> 00:20:54,356
Speaker 3: I I eating for it? You know, like measuring everything

430
00:20:54,396 --> 00:20:56,036
Speaker 3: and kind of seeing how it's progressing. You know, it's

431
00:20:56,036 --> 00:20:58,916
Speaker 3: it's pretty demotivating and that's usually a good time. You know,

432
00:20:58,956 --> 00:21:01,116
Speaker 3: if you're not waking up in the shower and thinking about,

433
00:21:01,356 --> 00:21:02,916
Speaker 3: you know, what you're working on for the day, that's

434
00:21:02,996 --> 00:21:05,716
Speaker 3: usually a good signal too, because usually when you have

435
00:21:05,756 --> 00:21:08,676
Speaker 3: something like Hipeenner, you know, an idea that really compels you,

436
00:21:08,716 --> 00:21:12,036
Speaker 3: like it's occupies your brain, share your brain space, and

437
00:21:12,076 --> 00:21:13,236
Speaker 3: you can't get it out of your mind.

438
00:21:13,396 --> 00:21:16,196
Speaker 1: When you lose that feeling or that thought, probably tend

439
00:21:16,196 --> 00:21:16,516
Speaker 1: to leave.

440
00:21:21,836 --> 00:21:24,956
Speaker 2: Stephen Klein is the co founder and CEO of hyphen

441
00:21:25,836 --> 00:21:29,316
Speaker 2: Today's show was edited by Sarah Nix, produced by Edith Russolo,

442
00:21:29,436 --> 00:21:32,796
Speaker 2: and engineered by Amanda k Wong. You can email us

443
00:21:32,876 --> 00:21:36,356
Speaker 2: at problem at pushkin dot fm. I try to read

444
00:21:36,396 --> 00:21:38,916
Speaker 2: every email I'd love to hear from you. I'm Jacob

445
00:21:38,916 --> 00:21:41,516
Speaker 2: Goldstein and we'll be back next week with another episode

446
00:21:41,516 --> 00:21:47,116
Speaker 2: of What's Your Problem