1 00:00:01,880 --> 00:00:04,640 Speaker 1: Hi, can I get to create your own bowl? Please? 2 00:00:05,559 --> 00:00:08,920 Speaker 1: It's lunchtime on a Tuesday in Washington, DC, and our 3 00:00:08,960 --> 00:00:12,360 Speaker 1: producer Moe Barrow walks over to Sweet Green. That's a 4 00:00:12,360 --> 00:00:15,560 Speaker 1: growing chain where the employees make salads and bowls to 5 00:00:15,720 --> 00:00:17,080 Speaker 1: order right in front of you. 6 00:00:17,360 --> 00:00:18,759 Speaker 2: Do you want everything on internet? 7 00:00:19,440 --> 00:00:19,640 Speaker 3: Um? 8 00:00:20,440 --> 00:00:21,160 Speaker 2: Let me see. 9 00:00:21,360 --> 00:00:22,600 Speaker 1: Can we go down the line? 10 00:00:23,000 --> 00:00:23,200 Speaker 2: Yeah? 11 00:00:23,280 --> 00:00:25,360 Speaker 1: That part of the appeal, of course, is that you 12 00:00:25,400 --> 00:00:27,200 Speaker 1: can choose exactly what you want. 13 00:00:28,080 --> 00:00:33,680 Speaker 2: I think I'll do spring mix, thanks, red onions, cucumbers 14 00:00:34,760 --> 00:00:36,160 Speaker 2: and cilantro is fine. 15 00:00:36,080 --> 00:00:38,519 Speaker 1: But during the lunch rush it may also mean a 16 00:00:38,560 --> 00:00:42,159 Speaker 1: bit of a weight. Salad makers can only move so fast, 17 00:00:42,360 --> 00:00:46,520 Speaker 1: especially if there's a contemplative person up ahead in line. 18 00:00:47,040 --> 00:00:50,000 Speaker 1: I'm looking at you, mo, what else? Oh? 19 00:00:50,040 --> 00:00:50,720 Speaker 2: I can get more? 20 00:00:51,080 --> 00:00:54,200 Speaker 1: But when the sails. That's one reason Sweet Green and 21 00:00:54,320 --> 00:00:58,160 Speaker 1: other fast food places are considering automation to perform some 22 00:00:58,280 --> 00:01:01,240 Speaker 1: jobs like taking orders and making food that are now 23 00:01:01,280 --> 00:01:04,560 Speaker 1: done by people. Sweet Green's potential answer is called the 24 00:01:04,800 --> 00:01:09,240 Speaker 1: Infinite Kitchen. It's a prototype salad and bowl making machine. 25 00:01:09,880 --> 00:01:13,120 Speaker 3: Sweet Green says that one huge advantage is that the 26 00:01:13,440 --> 00:01:16,880 Speaker 3: Infinite Kitchen allows them to get orders done for small 27 00:01:16,959 --> 00:01:20,600 Speaker 3: faster and second of all more accurately in. 28 00:01:20,520 --> 00:01:23,360 Speaker 1: An industry that often struggles to find enough people to 29 00:01:23,480 --> 00:01:26,520 Speaker 1: work those jobs, especially now in a tight labor market 30 00:01:26,560 --> 00:01:30,560 Speaker 1: where workers have more choices. The rise of artificial intelligence 31 00:01:30,600 --> 00:01:34,120 Speaker 1: and other automation has big names like McDonald's and Wendy's. 32 00:01:34,319 --> 00:01:35,560 Speaker 1: Giving it a closer look. 33 00:01:35,840 --> 00:01:43,720 Speaker 2: That will be sixteen thirty nine. 34 00:01:44,520 --> 00:01:48,760 Speaker 1: I'm Westksova today on the Big Take. This robot is 35 00:01:48,960 --> 00:01:59,640 Speaker 1: happy to take your order. Bloomberg Business We contributor Elizabeth 36 00:01:59,720 --> 00:02:04,160 Speaker 1: dun and Sauce Wheet Greens Infinite Kitchen Machine in action. 37 00:02:05,040 --> 00:02:11,160 Speaker 3: The Infinite Kitchen is a robotic contraption that Sweet Green 38 00:02:11,280 --> 00:02:15,160 Speaker 3: has just installed in their first new store. Sweet Green 39 00:02:15,400 --> 00:02:19,760 Speaker 3: is kind of famous for these elevated salads that are 40 00:02:19,960 --> 00:02:22,920 Speaker 3: mixed as many as fifty different ingredients in the restaurant. 41 00:02:23,240 --> 00:02:24,240 Speaker 2: They're kind of pricey. 42 00:02:24,320 --> 00:02:27,959 Speaker 3: They range from tennish to fifteen ish dollars, and they've 43 00:02:28,000 --> 00:02:31,359 Speaker 3: got about two hundred stores and they're growing. They've always 44 00:02:31,400 --> 00:02:34,200 Speaker 3: really focused on the idea that their salads are prepped 45 00:02:34,280 --> 00:02:37,359 Speaker 3: right in the store. You see people chopping ingredients, loading 46 00:02:37,400 --> 00:02:40,079 Speaker 3: them into the makelines where somebody's standing in front of 47 00:02:40,160 --> 00:02:43,799 Speaker 3: you and kind of scooping ingredients into the bowl and 48 00:02:43,960 --> 00:02:46,200 Speaker 3: you get the salad at the end. A lot of 49 00:02:46,240 --> 00:02:49,600 Speaker 3: their marketing has focused on this connection with farmers and 50 00:02:49,639 --> 00:02:53,480 Speaker 3: really the idea of freshness and health and quality. I 51 00:02:53,560 --> 00:02:55,880 Speaker 3: actually went and visited the first Sweet Green that is 52 00:02:55,919 --> 00:02:59,160 Speaker 3: equipped with this new technology, which is right outside of Chicago. 53 00:02:59,320 --> 00:03:02,000 Speaker 3: So normally, when you walk into a Sweet Green, you 54 00:03:02,080 --> 00:03:04,000 Speaker 3: walk up to a counter, there are people behind the 55 00:03:04,040 --> 00:03:07,840 Speaker 3: counter assembling these salad bowls. In this location, you walk 56 00:03:07,919 --> 00:03:10,840 Speaker 3: up to the counter, what you see is this huge, 57 00:03:11,280 --> 00:03:17,080 Speaker 3: room height contraption that is filled with tubes of salad ingredients. 58 00:03:17,160 --> 00:03:19,520 Speaker 3: It reminded me kind of like the bulk bins at 59 00:03:19,520 --> 00:03:20,160 Speaker 3: a Whole Foods. 60 00:03:20,200 --> 00:03:20,359 Speaker 1: Right. 61 00:03:20,440 --> 00:03:22,960 Speaker 3: So you've got these like clear tubes of ingredients, and 62 00:03:23,280 --> 00:03:26,280 Speaker 3: when you place your order, a little bowl kind of 63 00:03:26,440 --> 00:03:30,920 Speaker 3: whizzes out underneath and pauses underneath all of the different 64 00:03:31,000 --> 00:03:33,000 Speaker 3: tubes that need to be used. 65 00:03:32,800 --> 00:03:33,840 Speaker 2: To assemble your salad. 66 00:03:33,840 --> 00:03:37,880 Speaker 3: So like the arugola, the quinoa, the beats and gets 67 00:03:37,920 --> 00:03:40,280 Speaker 3: to the end and a Sweet Green employee picks it up, 68 00:03:40,480 --> 00:03:43,960 Speaker 3: gives it a look, maybe adds a couple of delicate ingredients, 69 00:03:44,000 --> 00:03:45,120 Speaker 3: and then hands it over to you. 70 00:03:46,360 --> 00:03:48,000 Speaker 1: And this is the first one. This is like the 71 00:03:48,000 --> 00:03:52,680 Speaker 1: prototype store that they hope will lead to replication and 72 00:03:52,760 --> 00:03:53,760 Speaker 1: across other stores. 73 00:03:54,440 --> 00:03:57,600 Speaker 3: This is the prototype store. It's a technology that they 74 00:03:57,800 --> 00:04:01,560 Speaker 3: acquired a couple of years ago through a company called Spice, 75 00:04:01,600 --> 00:04:04,840 Speaker 3: which had two stores in Boston. So when you think 76 00:04:04,840 --> 00:04:07,240 Speaker 3: about all of the labor that goes into a Sweet 77 00:04:07,280 --> 00:04:10,720 Speaker 3: Green store, and Sweet Greens do typically have as many 78 00:04:10,720 --> 00:04:13,400 Speaker 3: as twenty people per shift to do all of that 79 00:04:13,800 --> 00:04:16,240 Speaker 3: ingredient prep and all of the salad assembly, so it's 80 00:04:16,279 --> 00:04:20,880 Speaker 3: a hugely labor intensive process. When we see automation in 81 00:04:21,400 --> 00:04:24,640 Speaker 3: fast food restaurants or fast casual restaurants, I mean, first 82 00:04:24,640 --> 00:04:27,120 Speaker 3: of all, it's very rare that there really is any 83 00:04:27,240 --> 00:04:30,040 Speaker 3: But when there is, it's usually a device that does 84 00:04:30,160 --> 00:04:34,960 Speaker 3: one very specific thing, like it'll fry tortilla chips, or 85 00:04:35,000 --> 00:04:38,400 Speaker 3: it will help dispense drinks. So these are very specific tasks. 86 00:04:38,760 --> 00:04:41,080 Speaker 3: The interesting thing to me about the Infinite Kitchen is 87 00:04:41,120 --> 00:04:44,280 Speaker 3: that it is able to replace the effort of as 88 00:04:44,320 --> 00:04:47,320 Speaker 3: many as half of the people on a Sweet Green shift. 89 00:04:47,640 --> 00:04:49,440 Speaker 3: You know, you look at this device and there's nothing 90 00:04:49,480 --> 00:04:52,520 Speaker 3: about it that is totally space age. I mean, it 91 00:04:52,520 --> 00:04:56,080 Speaker 3: seems like something that you could imagine in a manufacturing context. 92 00:04:56,400 --> 00:04:58,600 Speaker 3: There's no component of it that is just like wild 93 00:04:58,760 --> 00:05:02,680 Speaker 3: and space Age, but as an addition to their store, 94 00:05:02,839 --> 00:05:06,000 Speaker 3: you could see it being something that really changes the 95 00:05:06,080 --> 00:05:08,640 Speaker 3: dynamics of how those stores work and how many people 96 00:05:08,839 --> 00:05:10,320 Speaker 3: are needed to run them. 97 00:05:10,880 --> 00:05:12,640 Speaker 1: So you said, there's all these tubes and it's filled 98 00:05:12,640 --> 00:05:15,120 Speaker 1: with all the different ingredients. How do the ingredients get 99 00:05:15,120 --> 00:05:15,799 Speaker 1: in the tubes? 100 00:05:16,320 --> 00:05:20,240 Speaker 3: The ingredients get in the tubes by being carried up 101 00:05:20,360 --> 00:05:24,279 Speaker 3: a set of stairs. Employees walk behind the Infinite Kitchen. 102 00:05:24,320 --> 00:05:26,600 Speaker 3: They walk up a series of stairs and you end 103 00:05:26,680 --> 00:05:30,400 Speaker 3: up on this platform behind the Infinite Kitchen. I think 104 00:05:30,440 --> 00:05:31,839 Speaker 3: of it as like the bridge of a ship. It 105 00:05:31,839 --> 00:05:35,880 Speaker 3: looks like a control center. There's screens that have information 106 00:05:35,960 --> 00:05:38,720 Speaker 3: about how full each tube is, and that's fed by 107 00:05:38,760 --> 00:05:41,560 Speaker 3: sensors that are in the tubes. And there's these big 108 00:05:41,640 --> 00:05:44,160 Speaker 3: what look like sort of the lids of chest freezers 109 00:05:44,600 --> 00:05:47,040 Speaker 3: that you open up, and then there is a kind 110 00:05:47,080 --> 00:05:49,160 Speaker 3: of like a just a shoot that you can put 111 00:05:49,400 --> 00:05:51,720 Speaker 3: ingredients down. So you open up the lid of one 112 00:05:51,760 --> 00:05:54,920 Speaker 3: of these kind of chest freezery things and there's tubes 113 00:05:54,960 --> 00:05:59,760 Speaker 3: for chickpeas and cucumber. It's very user friendly. You kind 114 00:05:59,800 --> 00:06:02,159 Speaker 3: of open it up and it's clear where everything goes. 115 00:06:02,560 --> 00:06:04,920 Speaker 3: When the tube is full, the little screen will tell 116 00:06:04,920 --> 00:06:07,960 Speaker 3: you that you've added enough. A big part of what 117 00:06:08,279 --> 00:06:10,800 Speaker 3: the Spice engineers were working on in the past year 118 00:06:10,839 --> 00:06:13,719 Speaker 3: and a half after this sale to Sweet Green was 119 00:06:13,880 --> 00:06:16,600 Speaker 3: just this kind of thing making it super user friendly 120 00:06:16,720 --> 00:06:19,640 Speaker 3: for Sweet Green's hourly workforce. 121 00:06:20,160 --> 00:06:23,480 Speaker 1: So you hit upon this pretty important point, which is 122 00:06:23,520 --> 00:06:27,640 Speaker 1: that if it works, it'll let these restaurants potentially operate 123 00:06:27,760 --> 00:06:30,039 Speaker 1: with fewer employees. And that's a big deal right now, 124 00:06:30,080 --> 00:06:33,360 Speaker 1: because with a tight labor market, fast food restaurants other 125 00:06:33,400 --> 00:06:37,120 Speaker 1: retail businesses are having trouble finding workers who are willing 126 00:06:37,160 --> 00:06:40,560 Speaker 1: to work for relatively low pay, not great benefits, kind 127 00:06:40,600 --> 00:06:43,240 Speaker 1: of long hours, and not very attractive conditions. 128 00:06:43,720 --> 00:06:46,400 Speaker 3: This has really kind of always been a problem for 129 00:06:46,520 --> 00:06:50,119 Speaker 3: food service. It's always been an industry that has really 130 00:06:50,200 --> 00:06:54,520 Speaker 3: high turnover where it's difficult to attract and retain people. 131 00:06:54,920 --> 00:06:57,960 Speaker 3: That's obviously only gotten worse in the current labor situation. 132 00:06:58,040 --> 00:07:02,480 Speaker 3: The current labor market. The industry experts that I spoke 133 00:07:02,560 --> 00:07:07,240 Speaker 3: to about this said, robotic solutions like this, automation like this, 134 00:07:07,720 --> 00:07:11,800 Speaker 3: it's often less about trying to actively fire people and 135 00:07:11,880 --> 00:07:15,200 Speaker 3: it's more about or reducing your need to rehire people, 136 00:07:15,240 --> 00:07:17,800 Speaker 3: to continue to rehire and retrain people. So you're really 137 00:07:17,960 --> 00:07:20,600 Speaker 3: trying to find something that will work with a smaller 138 00:07:21,160 --> 00:07:24,400 Speaker 3: workforce because that's what you're realistically kind of able to maintain. 139 00:07:25,240 --> 00:07:27,960 Speaker 1: Ultimately, though it does result in a store that just 140 00:07:28,000 --> 00:07:30,640 Speaker 1: has fewer people having to work there because the robot 141 00:07:30,720 --> 00:07:32,640 Speaker 1: winds up doing the job of several people. 142 00:07:33,160 --> 00:07:35,640 Speaker 3: Yeah, I mean, that is definitely the intention. This is 143 00:07:35,680 --> 00:07:39,200 Speaker 3: their first pilot of Infinite Kitchen. They're going to do 144 00:07:39,280 --> 00:07:41,800 Speaker 3: another one in Boston where they take an existing Sweet 145 00:07:41,840 --> 00:07:44,640 Speaker 3: Green and they swap out the make lines and they 146 00:07:44,640 --> 00:07:47,360 Speaker 3: put in an infinite kitchen to test it in that context. 147 00:07:47,720 --> 00:07:51,200 Speaker 3: But the company has been very cautious about what they're 148 00:07:51,440 --> 00:07:54,280 Speaker 3: willing to promise in terms of labor reduction. But it 149 00:07:54,360 --> 00:07:58,280 Speaker 3: is definitely their intention to operate the store with fewer people. 150 00:08:00,120 --> 00:08:02,920 Speaker 1: Of the ideas here is to cut down on operating 151 00:08:03,000 --> 00:08:05,920 Speaker 1: costs of a business. What is the machine like this 152 00:08:06,240 --> 00:08:08,960 Speaker 1: cost itself, and how many people do you need to 153 00:08:09,000 --> 00:08:11,960 Speaker 1: maintain it and all the other things associated with switching 154 00:08:12,000 --> 00:08:13,160 Speaker 1: from a human to a robot. 155 00:08:14,000 --> 00:08:17,720 Speaker 3: So we know that they paid around fifty million dollars 156 00:08:17,760 --> 00:08:20,640 Speaker 3: for the technology in twenty twenty one. We know that 157 00:08:20,680 --> 00:08:24,080 Speaker 3: they've invested something in it between then and now. They've 158 00:08:24,080 --> 00:08:27,160 Speaker 3: got a staff of about twenty engineers that came over 159 00:08:27,360 --> 00:08:29,640 Speaker 3: from Spice with the technology, who have been kind of 160 00:08:29,680 --> 00:08:32,160 Speaker 3: tinkering with it and figuring out how to make it 161 00:08:32,200 --> 00:08:35,640 Speaker 3: work perfectly for the Sweet Green environment. What they did 162 00:08:35,679 --> 00:08:38,200 Speaker 3: tell me in terms of the cost per machine, so 163 00:08:38,240 --> 00:08:39,920 Speaker 3: what does it actually cost to get one of these 164 00:08:39,920 --> 00:08:42,240 Speaker 3: things up and running in a store, They described it 165 00:08:42,280 --> 00:08:45,280 Speaker 3: as a touch more than what it costs to build 166 00:08:45,360 --> 00:08:49,840 Speaker 3: their conventional salad make lines in the restaurants, those lines 167 00:08:49,880 --> 00:08:53,760 Speaker 3: that you see filled with ingredients behind the counter. They're 168 00:08:53,760 --> 00:08:56,800 Speaker 3: custom built. They have a lot of complex refrigeration and 169 00:08:56,840 --> 00:08:58,920 Speaker 3: heating elements in them, so they're more expensive than you 170 00:08:58,920 --> 00:09:01,280 Speaker 3: would think. There's all often two or three of those 171 00:09:01,360 --> 00:09:03,720 Speaker 3: in a store. There's usually one or two behind the 172 00:09:03,720 --> 00:09:05,880 Speaker 3: scenes making orders for digital pickup. 173 00:09:06,280 --> 00:09:07,040 Speaker 2: So when you put. 174 00:09:06,880 --> 00:09:09,800 Speaker 3: Together the whole cost of all of that equipment, I 175 00:09:09,840 --> 00:09:13,640 Speaker 3: suppose you know, the infinite kitchen is not drastically more expensive. 176 00:09:14,160 --> 00:09:17,560 Speaker 1: So have they at the same time automated the ordering 177 00:09:17,559 --> 00:09:19,160 Speaker 1: system so you don't go up to the counter and 178 00:09:19,400 --> 00:09:20,240 Speaker 1: order from a person. 179 00:09:20,960 --> 00:09:24,080 Speaker 3: The majority of Sweet Greens orders already come in via 180 00:09:24,240 --> 00:09:27,600 Speaker 3: app online, there are still a big chunk of people 181 00:09:27,640 --> 00:09:30,160 Speaker 3: who will walk up to the counter and order from 182 00:09:30,200 --> 00:09:31,400 Speaker 3: the person making the salad. 183 00:09:31,520 --> 00:09:32,880 Speaker 2: So in this new. 184 00:09:32,760 --> 00:09:36,439 Speaker 3: Pilot store, instead of a person, there is a tablet, 185 00:09:36,520 --> 00:09:38,800 Speaker 3: so they have a series of touchscreen tablets. It looks 186 00:09:38,920 --> 00:09:41,520 Speaker 3: very similar to what you'd find on the Sweet Green 187 00:09:41,559 --> 00:09:44,120 Speaker 3: app that you use on your phone. They do also 188 00:09:44,320 --> 00:09:47,480 Speaker 3: have one person kind of behind the counter. If you 189 00:09:47,480 --> 00:09:50,360 Speaker 3: are allergic to the idea of entering the information into 190 00:09:50,400 --> 00:09:53,160 Speaker 3: the touch Green yourself, you can tell somebody verbally and 191 00:09:53,200 --> 00:09:54,840 Speaker 3: they will do the same thing for you. 192 00:09:55,600 --> 00:09:57,760 Speaker 1: Am I wrong to think that this all sounds kind 193 00:09:57,760 --> 00:10:00,600 Speaker 1: of needlessly complex? Then you could just have a person 194 00:10:00,600 --> 00:10:01,320 Speaker 1: making a salad. 195 00:10:02,120 --> 00:10:06,120 Speaker 3: So when I've watched prototypes or pilots of kitchen robotics before, 196 00:10:06,559 --> 00:10:08,920 Speaker 3: they often don't seem like they're working very fast, Like 197 00:10:08,920 --> 00:10:11,040 Speaker 3: there'll be a robotic arm that's doing something and you're 198 00:10:11,080 --> 00:10:13,120 Speaker 3: just kind of thinking it feels like it would be 199 00:10:13,160 --> 00:10:14,959 Speaker 3: faster if a human arm was doing this. 200 00:10:15,400 --> 00:10:18,640 Speaker 2: In this case, the thing moves super fast. 201 00:10:18,679 --> 00:10:21,200 Speaker 3: So you can imagine at that peak lunchtime when there's 202 00:10:21,200 --> 00:10:23,320 Speaker 3: a long line of people trying to get their salads. 203 00:10:23,360 --> 00:10:25,679 Speaker 3: I mean, I can imagine it's speeding up the flow 204 00:10:25,720 --> 00:10:29,800 Speaker 3: of things. I think that accuracy point is a pretty 205 00:10:29,800 --> 00:10:32,840 Speaker 3: big one. There's no reason why the machine should make 206 00:10:32,880 --> 00:10:35,400 Speaker 3: an error, so it should bring their accuracy way up. 207 00:10:35,840 --> 00:10:38,240 Speaker 2: And the sort of chaos. 208 00:10:37,760 --> 00:10:40,560 Speaker 3: Of twenty people moving around a store and trying to 209 00:10:40,559 --> 00:10:42,960 Speaker 3: get these ingredients where they need to go and getting 210 00:10:43,000 --> 00:10:45,280 Speaker 3: everything into bowls, I mean, it's real. I think it's 211 00:10:45,320 --> 00:10:49,240 Speaker 3: a very chaotic food production environment, so it doesn't feel 212 00:10:49,280 --> 00:10:50,440 Speaker 3: completely crazy to me. 213 00:10:52,040 --> 00:10:55,800 Speaker 1: After the break, can a fast food plays be run 214 00:10:56,240 --> 00:11:08,360 Speaker 1: entirely by robots? Lizzie, you said something before the break 215 00:11:08,400 --> 00:11:10,880 Speaker 1: that I think is interesting that when you look at 216 00:11:11,000 --> 00:11:15,160 Speaker 1: other attempts at making these automated robots to do fast food, 217 00:11:15,320 --> 00:11:18,440 Speaker 1: they sometimes look like they're not working very well. And 218 00:11:18,480 --> 00:11:20,200 Speaker 1: in your story you write there is kind of like 219 00:11:20,240 --> 00:11:23,800 Speaker 1: a long, sad history of trying to build robots to 220 00:11:23,880 --> 00:11:25,280 Speaker 1: do the things that people do. 221 00:11:25,960 --> 00:11:29,400 Speaker 3: There have been a lot of food service robots that 222 00:11:29,440 --> 00:11:32,040 Speaker 3: have tried and failed. There are a bunch that are 223 00:11:32,120 --> 00:11:34,480 Speaker 3: being tried and have not failed yet but don't seem 224 00:11:34,800 --> 00:11:40,560 Speaker 3: hugely promising. There's a zillion pizza robots. There's Zoom Pizza. 225 00:11:40,640 --> 00:11:43,720 Speaker 3: It had like two hundred million dollars worth of funding 226 00:11:43,720 --> 00:11:45,600 Speaker 3: from SoftBank, and the idea was it was going to 227 00:11:45,600 --> 00:11:48,719 Speaker 3: be this like robotic pizza assembly line where the robotics 228 00:11:48,760 --> 00:11:50,600 Speaker 3: were in the back of a pizza truck and it 229 00:11:50,640 --> 00:11:53,720 Speaker 3: was going to cook the pizza while the truck drove around, 230 00:11:53,800 --> 00:11:55,640 Speaker 3: and so that you got to a house and. 231 00:11:55,480 --> 00:11:57,080 Speaker 2: The pizza was like perfectly cooked. 232 00:11:57,400 --> 00:11:59,880 Speaker 1: So you'd order the pizza for delivery, and instead of 233 00:12:00,040 --> 00:12:01,800 Speaker 1: making the pizza and hopping in the car and trying 234 00:12:01,800 --> 00:12:03,000 Speaker 1: to get it to you while it's hot, it would 235 00:12:03,000 --> 00:12:05,520 Speaker 1: actually be made while it was on its way to you. 236 00:12:06,280 --> 00:12:08,079 Speaker 3: Yeah, And it turns out there are a bunch of 237 00:12:08,160 --> 00:12:10,440 Speaker 3: issues with trying to cook a pizza in an eight 238 00:12:10,520 --> 00:12:13,440 Speaker 3: hundred degree of an on a van that roves around, 239 00:12:13,960 --> 00:12:18,640 Speaker 3: so that whole operation is defunct. There's one called Stellar Pizza, 240 00:12:19,080 --> 00:12:22,800 Speaker 3: and that's a group of former SpaceX engineers who are 241 00:12:22,800 --> 00:12:26,079 Speaker 3: doing something kind of similar, except the truck is stationary, 242 00:12:26,120 --> 00:12:28,080 Speaker 3: so they're not trying to actually cook as they drive. 243 00:12:28,720 --> 00:12:30,720 Speaker 3: They seem to be having a little more success, but 244 00:12:30,760 --> 00:12:34,040 Speaker 3: they still only have one truck that they're piloting the 245 00:12:34,080 --> 00:12:38,200 Speaker 3: last time I checked. And then there's like pizza vending machines. 246 00:12:38,240 --> 00:12:40,800 Speaker 3: There's pizza assembly lines, so pizza is a big one. 247 00:12:41,200 --> 00:12:45,600 Speaker 3: There are some robotic arm prototypes that can do different 248 00:12:45,600 --> 00:12:49,600 Speaker 3: things like make smoothies or make salads. Really, the biggest 249 00:12:49,640 --> 00:12:52,400 Speaker 3: company in the space is a company called Miso Robotics. 250 00:12:52,800 --> 00:12:57,079 Speaker 3: They had a robot named Flippy, and Flippy flipped hamburgers. 251 00:12:57,520 --> 00:13:00,360 Speaker 3: Flippy wasn't so good at his job, so now there's 252 00:13:00,360 --> 00:13:05,959 Speaker 3: Flippy two, and Flippy two makes French fries. So that's 253 00:13:06,160 --> 00:13:09,000 Speaker 3: kind of being piloted at a couple dozen white castles 254 00:13:09,040 --> 00:13:11,720 Speaker 3: in a few other places. But basically all of these 255 00:13:11,840 --> 00:13:14,920 Speaker 3: efforts are in there very very early stages. There's nothing 256 00:13:14,920 --> 00:13:17,520 Speaker 3: that's like threatening to take over all of the fast 257 00:13:17,520 --> 00:13:22,720 Speaker 3: food industry. There's really two different areas of a fast 258 00:13:22,720 --> 00:13:25,440 Speaker 3: food restaurant operation. There's what you call the front of house, 259 00:13:25,480 --> 00:13:27,960 Speaker 3: which is the ordering, and then there's the back of house, 260 00:13:27,960 --> 00:13:31,840 Speaker 3: which is the kitchen. So the ordering piece of this 261 00:13:32,040 --> 00:13:35,680 Speaker 3: pie really has had a lot of automation. So we 262 00:13:35,760 --> 00:13:38,760 Speaker 3: have smartphones now that we are often using to order 263 00:13:38,800 --> 00:13:42,600 Speaker 3: through apps. You can order online. There's kiosks and restaurants, 264 00:13:42,679 --> 00:13:46,520 Speaker 3: so the ordering component of the fast food restaurant that 265 00:13:46,559 --> 00:13:51,920 Speaker 3: really has been dramatically changed through automation, but most of 266 00:13:51,960 --> 00:13:54,960 Speaker 3: the labor in a fast food restaurant is still the 267 00:13:55,040 --> 00:13:57,160 Speaker 3: cooking part of the operation. It's still in the kitchen, 268 00:13:57,480 --> 00:14:00,600 Speaker 3: and that's an area where there has been very little 269 00:14:00,760 --> 00:14:04,160 Speaker 3: meaningful automation. But you know, you see these headlines about 270 00:14:04,320 --> 00:14:08,240 Speaker 3: a fully automated McDonald's or a taco bell that's touchless, 271 00:14:08,240 --> 00:14:10,120 Speaker 3: and they're really just talking about the ordering. 272 00:14:10,679 --> 00:14:13,640 Speaker 1: And the new automated Sweet Green that you've been talking 273 00:14:13,640 --> 00:14:16,760 Speaker 1: about just recently opened its doors. Do you have any 274 00:14:16,760 --> 00:14:18,079 Speaker 1: idea how business has been. 275 00:14:18,720 --> 00:14:20,200 Speaker 2: So it's in Naperville, Illinois. 276 00:14:20,680 --> 00:14:22,880 Speaker 3: I am not local to the area, so I haven't 277 00:14:22,880 --> 00:14:24,720 Speaker 3: been able to be back to see, but I did 278 00:14:24,800 --> 00:14:29,960 Speaker 3: see some Instagram reels of lines out the door on 279 00:14:30,160 --> 00:14:33,160 Speaker 3: opening day. People are intrigued. I think there's probably a 280 00:14:33,160 --> 00:14:35,640 Speaker 3: lot of interest in the technology and seeing exactly what 281 00:14:35,640 --> 00:14:37,720 Speaker 3: it is and how it works, and then it will 282 00:14:37,800 --> 00:14:40,720 Speaker 3: kind of remain to be seen how people feel about 283 00:14:40,720 --> 00:14:41,480 Speaker 3: it in the long run. 284 00:14:41,960 --> 00:14:43,720 Speaker 1: You got a chance when you were there to kind 285 00:14:43,720 --> 00:14:45,960 Speaker 1: of see it in action. How'd you feel about it? 286 00:14:46,520 --> 00:14:50,680 Speaker 3: I arrived with the skeptics mindset, because you know, I'm 287 00:14:50,720 --> 00:14:53,640 Speaker 3: a food reporter. I care a lot about food and restaurants, 288 00:14:53,680 --> 00:14:55,640 Speaker 3: and I think that a big part of food is 289 00:14:55,680 --> 00:14:58,160 Speaker 3: about connection and it's about people. And I still believe 290 00:14:58,200 --> 00:15:01,400 Speaker 3: that's true, but it in this context. You know, when 291 00:15:01,440 --> 00:15:04,320 Speaker 3: I walk into a Sweet Green, what I'm really there 292 00:15:04,360 --> 00:15:07,840 Speaker 3: to do is get a healthy, fast meal where I 293 00:15:07,840 --> 00:15:10,720 Speaker 3: feel the ingredients are high quality. And the fact that 294 00:15:10,760 --> 00:15:13,680 Speaker 3: it's being physically assembled by a person. I mean, that's 295 00:15:13,720 --> 00:15:16,960 Speaker 3: not something that makes a huge difference to me. When 296 00:15:17,000 --> 00:15:21,160 Speaker 3: people think about automation, and specifically about robotics, oftentimes the 297 00:15:21,200 --> 00:15:24,720 Speaker 3: first place their minds go is about job losses and 298 00:15:24,840 --> 00:15:27,880 Speaker 3: replacing humans, and they're being sort of a mass exodus 299 00:15:28,080 --> 00:15:31,640 Speaker 3: of jobs. When we actually look at the trend historically 300 00:15:32,160 --> 00:15:35,680 Speaker 3: from nineteen ninety to today, the number of food service 301 00:15:35,760 --> 00:15:39,520 Speaker 3: jobs has almost doubled. Think about everything that's happened in 302 00:15:39,600 --> 00:15:43,280 Speaker 3: terms of food service automation and kiosks and online ordering, 303 00:15:43,400 --> 00:15:46,760 Speaker 3: and then innovations like automatic espresso machines and all these 304 00:15:46,760 --> 00:15:49,760 Speaker 3: little things that kind of help businesses run more efficiently. 305 00:15:50,280 --> 00:15:51,760 Speaker 2: It really suggests that. 306 00:15:51,800 --> 00:15:55,000 Speaker 3: The overall trend is not going to be towards jobs 307 00:15:55,080 --> 00:15:56,880 Speaker 3: in the sector being reduced. 308 00:15:57,960 --> 00:16:02,040 Speaker 1: So you cover this industry, do you think that automation 309 00:16:02,160 --> 00:16:05,800 Speaker 1: eventually starts taking over more and more functions, and that 310 00:16:06,200 --> 00:16:08,240 Speaker 1: we're going to be seeing a lot more robots at 311 00:16:08,240 --> 00:16:09,520 Speaker 1: the fast food places we go to. 312 00:16:10,360 --> 00:16:14,880 Speaker 3: Probably eventually robotics and automation will play a bigger role 313 00:16:15,240 --> 00:16:17,920 Speaker 3: in food service. I think this is probably going to 314 00:16:18,000 --> 00:16:23,600 Speaker 3: happen relatively slowly. Sweet Green and businesses like it. Any 315 00:16:23,600 --> 00:16:26,200 Speaker 3: place that makes a bowl or a salad, they have 316 00:16:26,320 --> 00:16:30,880 Speaker 3: some aspects of their menus that make them particularly suited 317 00:16:30,920 --> 00:16:33,000 Speaker 3: for this kind of automation, Like you can have an 318 00:16:33,040 --> 00:16:36,840 Speaker 3: infinite kitchen robot that makes every item on the menu. 319 00:16:37,520 --> 00:16:42,280 Speaker 3: Most fast casual and fast food restaurants, their menus aren't 320 00:16:42,360 --> 00:16:46,160 Speaker 3: like that. There are dozens of different discrete tasks that 321 00:16:46,200 --> 00:16:48,680 Speaker 3: need to be done to create order, from flipping a 322 00:16:48,720 --> 00:16:52,560 Speaker 3: hamburger to assembling the hamburger and pouring's fountain drinks and 323 00:16:52,600 --> 00:16:56,520 Speaker 3: frying off fries. I don't see a future where five 324 00:16:56,600 --> 00:16:59,240 Speaker 3: years from now we're walking into a McDonald's or a 325 00:16:59,280 --> 00:17:02,120 Speaker 3: Wendy's or Taco Bell and it's just a whole bunch 326 00:17:02,120 --> 00:17:04,480 Speaker 3: of machines in the back. But I do think in 327 00:17:04,520 --> 00:17:06,800 Speaker 3: the fullness of time, I guess anything is possible. 328 00:17:07,840 --> 00:17:09,680 Speaker 1: Liz, thanks so much for coming on the. 329 00:17:09,600 --> 00:17:13,280 Speaker 2: Show, Thanks so much for having me when. 330 00:17:13,040 --> 00:17:16,160 Speaker 1: We come back. Where workers and the shortage of them 331 00:17:16,400 --> 00:17:28,400 Speaker 1: fit into this puzzle? As we all know, companies are 332 00:17:28,400 --> 00:17:31,520 Speaker 1: having trouble finding people willing to take fast food and 333 00:17:31,720 --> 00:17:35,200 Speaker 1: some other service industry jobs. The pay and benefits and 334 00:17:35,240 --> 00:17:38,720 Speaker 1: the hours are often not great. At the same time, though, 335 00:17:39,000 --> 00:17:41,560 Speaker 1: some people who do work in the service industry fear 336 00:17:41,600 --> 00:17:45,439 Speaker 1: that robots are coming for their jobs. But are they really? 337 00:17:46,000 --> 00:17:49,280 Speaker 1: Danielliser Tory Cortina knows a thing or two about this. 338 00:17:49,400 --> 00:17:53,520 Speaker 1: She covers the restaurant industry for Bloomberg. Danielle will we 339 00:17:53,520 --> 00:17:56,040 Speaker 1: hear a lot about how the fast food industry is 340 00:17:56,160 --> 00:18:01,240 Speaker 1: struggling to recruit enough employees to fill all these restaurants. 341 00:18:01,440 --> 00:18:03,320 Speaker 1: Can you just kind of paint us a picture of 342 00:18:03,359 --> 00:18:06,959 Speaker 1: this nine hundred billion dollar food service industry. What's it 343 00:18:07,040 --> 00:18:09,399 Speaker 1: like right now in terms of recruiting and trying to 344 00:18:09,520 --> 00:18:10,600 Speaker 1: keep the employees they have? 345 00:18:11,440 --> 00:18:15,159 Speaker 4: Several chains have said recently that their employee turnover, so 346 00:18:15,160 --> 00:18:17,200 Speaker 4: the number of people who leave has actually declined a 347 00:18:17,240 --> 00:18:20,480 Speaker 4: little bit from last year, and they've achieved that by improving, 348 00:18:20,560 --> 00:18:23,240 Speaker 4: you know, their training, and they've also race wages. The 349 00:18:23,320 --> 00:18:25,600 Speaker 4: economy has also softened like a little bit, and so 350 00:18:25,680 --> 00:18:28,160 Speaker 4: now the share of Americans that are in the workforce 351 00:18:28,480 --> 00:18:31,480 Speaker 4: has been edging up a little bit. Now, with the 352 00:18:31,560 --> 00:18:34,520 Speaker 4: not so positive part, we have US government data that 353 00:18:34,600 --> 00:18:37,440 Speaker 4: showed that as of March, there were about one point 354 00:18:37,480 --> 00:18:41,640 Speaker 4: three million job openings in the restaurant on accommodations sector. 355 00:18:41,840 --> 00:18:45,359 Speaker 4: So that's like a broader industry, but that's higher than 356 00:18:45,359 --> 00:18:48,679 Speaker 4: the roughly eight hundred and seventy five thousand positions that 357 00:18:48,720 --> 00:18:49,400 Speaker 4: were open in. 358 00:18:49,359 --> 00:18:50,800 Speaker 2: Twenty nineteen on average. 359 00:18:51,160 --> 00:18:53,600 Speaker 4: So even though the situation has been improving, there's still 360 00:18:53,600 --> 00:18:56,320 Speaker 4: that gap when it comes to restaurant employment. 361 00:18:57,000 --> 00:19:01,000 Speaker 1: So why is it that restaurants and fast foods have 362 00:19:01,240 --> 00:19:04,320 Speaker 1: been so hard hit? Why are they struggling to recruit 363 00:19:04,520 --> 00:19:05,120 Speaker 1: and keep. 364 00:19:04,920 --> 00:19:09,000 Speaker 4: Pable so there's more choice, for one, and second, I mean, 365 00:19:09,080 --> 00:19:11,920 Speaker 4: restaurant work has always been tough. You know, pay can 366 00:19:11,960 --> 00:19:15,040 Speaker 4: be lower than in other industries. The jobs are difficult. 367 00:19:15,119 --> 00:19:16,879 Speaker 4: You know, people spend a lot of time on their feet, 368 00:19:16,960 --> 00:19:20,159 Speaker 4: working in hot kitchens, dealing with the public directly, and 369 00:19:20,200 --> 00:19:23,120 Speaker 4: you know customers sometimes have specific demands that are hard 370 00:19:23,160 --> 00:19:25,520 Speaker 4: to meet. Those are some of the factors that also 371 00:19:25,600 --> 00:19:29,120 Speaker 4: contribute to making it harder to get people to join 372 00:19:29,160 --> 00:19:32,200 Speaker 4: restaurants and stick. And also the schedules can be very unpredictable. 373 00:19:32,320 --> 00:19:36,560 Speaker 4: Unlike other industries, restaurants have increased pay over the past 374 00:19:36,600 --> 00:19:39,119 Speaker 4: couple of years because they know that they're competing for 375 00:19:39,160 --> 00:19:40,960 Speaker 4: a bunch of different workers. And so just to give 376 00:19:40,960 --> 00:19:44,040 Speaker 4: you a little bit of data, in the US, on 377 00:19:44,240 --> 00:19:47,240 Speaker 4: average for workers in limited service restaurants, so you know, 378 00:19:47,320 --> 00:19:52,000 Speaker 4: places like McDonald's, the hourly wages were in March around 379 00:19:52,000 --> 00:19:56,160 Speaker 4: fifteen thirty four. That's actually up almost five percent from 380 00:19:56,200 --> 00:19:59,200 Speaker 4: a year ago and twenty two percent from two years ago. 381 00:19:59,320 --> 00:20:02,440 Speaker 4: So restaurants have increased wages, and even then, it's still 382 00:20:02,480 --> 00:20:03,240 Speaker 4: hard to find people. 383 00:20:05,600 --> 00:20:09,000 Speaker 1: And so here we are talking about restaurants starting to 384 00:20:09,080 --> 00:20:13,959 Speaker 1: try to use more automation. How much of that is 385 00:20:14,080 --> 00:20:17,879 Speaker 1: about trying to make up for this gap between the 386 00:20:17,960 --> 00:20:19,560 Speaker 1: number of people they need to work at the restaurants 387 00:20:19,560 --> 00:20:21,520 Speaker 1: and the number of people they're able to find to work 388 00:20:21,560 --> 00:20:22,320 Speaker 1: at their restaurants. 389 00:20:23,040 --> 00:20:25,600 Speaker 4: It's part of it, you know. I had conversations with 390 00:20:25,640 --> 00:20:31,800 Speaker 4: people across the restaurant industry, from restaurants to consultants to economists, 391 00:20:31,880 --> 00:20:33,760 Speaker 4: just to give you a little bit of context. White Castle, 392 00:20:34,080 --> 00:20:37,440 Speaker 4: which makes burgers, which they call Slighters, has a robot 393 00:20:37,480 --> 00:20:40,239 Speaker 4: called Flippy that can make fried items, so things like 394 00:20:40,480 --> 00:20:43,480 Speaker 4: chicken rings, like potato fries, and they've put these robots 395 00:20:43,480 --> 00:20:46,439 Speaker 4: in about twelve of their three hundred and fifty restaurants. 396 00:20:46,880 --> 00:20:49,959 Speaker 4: White Castle is open twenty for seven. So what they 397 00:20:50,000 --> 00:20:52,000 Speaker 4: told us is that having the robot in some of 398 00:20:52,000 --> 00:20:55,359 Speaker 4: these restaurants means that they're never understaffed, especially in the 399 00:20:55,440 --> 00:20:58,480 Speaker 4: late night shift, which is hard to staff. So that's 400 00:20:58,520 --> 00:21:02,760 Speaker 4: one point that restaurants are making. That being said, labor's 401 00:21:02,760 --> 00:21:05,360 Speaker 4: not the whole story. We talked to a restaurant consultant 402 00:21:05,400 --> 00:21:07,720 Speaker 4: called Kriz Berley who's at PAIN and he said that 403 00:21:07,720 --> 00:21:09,760 Speaker 4: it's not even the main reason, at least in his view. 404 00:21:10,359 --> 00:21:13,119 Speaker 4: A big factor is also that the way that people 405 00:21:13,160 --> 00:21:16,200 Speaker 4: acts as restaurants really changed a lot during the pandemic, 406 00:21:16,680 --> 00:21:19,240 Speaker 4: and so there are things like ordering ahead, going to 407 00:21:19,280 --> 00:21:21,960 Speaker 4: a drive through, and broadly just getting more food on 408 00:21:22,000 --> 00:21:24,399 Speaker 4: the go that became so much more popular. And so 409 00:21:25,160 --> 00:21:28,280 Speaker 4: Beerley said that a lot of the automation that's happening 410 00:21:28,280 --> 00:21:31,320 Speaker 4: in the restaurants is around like apps and other systems 411 00:21:31,359 --> 00:21:34,880 Speaker 4: that allow restaurants to deliver on those new consumer expectations. 412 00:21:34,920 --> 00:21:37,440 Speaker 4: A little bit more smoothly, you know, just to put 413 00:21:37,440 --> 00:21:39,800 Speaker 4: it in the words of Wendy's. We spoke to on 414 00:21:39,960 --> 00:21:42,720 Speaker 4: executive there and they told us that basically, quick service 415 00:21:42,760 --> 00:21:46,959 Speaker 4: restaurants when based on three factors accuracy, convenience, and speed. 416 00:21:47,600 --> 00:21:50,800 Speaker 4: So they're looking to automate functions that help them improve 417 00:21:50,960 --> 00:21:53,879 Speaker 4: on any of those points, and so enter things like 418 00:21:54,520 --> 00:21:57,600 Speaker 4: AI in the drive through with a chatbot that communicates 419 00:21:57,600 --> 00:21:59,000 Speaker 4: with people so they can order that way. 420 00:22:00,000 --> 00:22:02,439 Speaker 1: How does that actually work? What are they investing in? 421 00:22:03,119 --> 00:22:06,360 Speaker 4: Wendy's is doing a pilot, and that pilot actually has 422 00:22:06,400 --> 00:22:09,160 Speaker 4: not started, so it's starting in June of this year. 423 00:22:09,200 --> 00:22:11,639 Speaker 4: And so what they've told us is that basically, you know, 424 00:22:11,680 --> 00:22:13,560 Speaker 4: you pull up to the drive through and there's a 425 00:22:13,640 --> 00:22:16,040 Speaker 4: robotic voice, which is going to be female, that's going 426 00:22:16,080 --> 00:22:21,360 Speaker 4: to take your order. And supposedly that robot will understand 427 00:22:21,640 --> 00:22:25,400 Speaker 4: the orders even if they're not placed exactly like they're 428 00:22:25,400 --> 00:22:29,199 Speaker 4: listed on the menu. So, for example, Wendy's has milkshakes 429 00:22:29,200 --> 00:22:32,240 Speaker 4: that they called Frosty's, So even if you order a milkshake, 430 00:22:32,359 --> 00:22:35,040 Speaker 4: it will understand that it's a Frosty, right, That's how 431 00:22:35,119 --> 00:22:38,159 Speaker 4: it's supposed to work. And so after that you know, 432 00:22:38,160 --> 00:22:41,159 Speaker 4: there is a menu board in which you can see 433 00:22:41,240 --> 00:22:46,080 Speaker 4: whether what you ordered is reflected on the text. One 434 00:22:46,119 --> 00:22:49,560 Speaker 4: important point is that at least for this pilot, which 435 00:22:49,560 --> 00:22:52,240 Speaker 4: will be in one store in Ohio, there will be 436 00:22:52,280 --> 00:22:55,680 Speaker 4: a person monitoring the AI just in case there are 437 00:22:55,720 --> 00:22:58,800 Speaker 4: any issues or if someone just wants to talk to 438 00:22:58,840 --> 00:22:59,280 Speaker 4: a person. 439 00:23:00,600 --> 00:23:03,399 Speaker 1: What do we know about how customers feel about this. 440 00:23:03,520 --> 00:23:06,720 Speaker 1: Do they want to interact with AI and robots or 441 00:23:06,800 --> 00:23:09,359 Speaker 1: do they like having that person standing behind the counter 442 00:23:09,440 --> 00:23:10,960 Speaker 1: taking their roder at the cash register. 443 00:23:11,760 --> 00:23:13,840 Speaker 4: It can be a little bit early to know, but 444 00:23:13,920 --> 00:23:16,600 Speaker 4: we have a couple of data points. So White Castle, 445 00:23:16,760 --> 00:23:19,880 Speaker 4: in addition to Flippy, the actual robot that makes right items, 446 00:23:20,200 --> 00:23:24,520 Speaker 4: does have an AI chap book called Julia, which can 447 00:23:24,640 --> 00:23:27,880 Speaker 4: take orders. And so what they told us is that 448 00:23:28,040 --> 00:23:31,240 Speaker 4: ninety percent of the customers that have used the AI 449 00:23:31,560 --> 00:23:36,159 Speaker 4: actually complete the order without going and asking for a person. 450 00:23:36,520 --> 00:23:37,919 Speaker 4: So that's a good data point, I mean, and they 451 00:23:37,960 --> 00:23:41,080 Speaker 4: see it as sort of like customer acceptance for that 452 00:23:41,160 --> 00:23:43,520 Speaker 4: type of product. At the same time, there was a 453 00:23:43,560 --> 00:23:46,280 Speaker 4: survey by a research firm called in Touch Insight that 454 00:23:46,359 --> 00:23:48,560 Speaker 4: found that around half of the people that they pulled 455 00:23:48,600 --> 00:23:51,160 Speaker 4: took issue with the technology for one reason or the other, 456 00:23:51,600 --> 00:23:54,480 Speaker 4: one of them being the lack of human interaction. At 457 00:23:54,480 --> 00:23:58,400 Speaker 4: this same time, not everyone who had an opinion had 458 00:23:58,400 --> 00:24:02,200 Speaker 4: actually tested the AI, so that just muddies the picture 459 00:24:02,600 --> 00:24:06,520 Speaker 4: a little bit. Other technologies that are public facing in 460 00:24:06,600 --> 00:24:08,959 Speaker 4: the restaurant space, like you know, the big touch screens 461 00:24:09,000 --> 00:24:11,359 Speaker 4: that you might see when you walk into McDonald's so 462 00:24:11,400 --> 00:24:14,560 Speaker 4: you can place an order on your own and pay 463 00:24:14,600 --> 00:24:17,840 Speaker 4: without having to necessarily interact with a person. Intoch Insight, 464 00:24:17,920 --> 00:24:20,960 Speaker 4: the same research firm found that seventy percent of the 465 00:24:20,960 --> 00:24:24,320 Speaker 4: people that they polled have interacted with one such service, 466 00:24:24,320 --> 00:24:27,439 Speaker 4: you know, those like self checkout chios. Though many people 467 00:24:27,640 --> 00:24:30,280 Speaker 4: did prefer talking to a person if given the choice, 468 00:24:30,520 --> 00:24:34,919 Speaker 4: and older customers had a much more stronger preference for 469 00:24:35,000 --> 00:24:37,440 Speaker 4: interacting with a human than younger customers did. 470 00:24:38,320 --> 00:24:41,439 Speaker 1: If we do start to see more technology doing the 471 00:24:41,520 --> 00:24:44,480 Speaker 1: jobs that people are currently doing, now, do you think 472 00:24:44,600 --> 00:24:47,560 Speaker 1: ultimately that means we're going to start seeing fewer people 473 00:24:47,840 --> 00:24:51,280 Speaker 1: in restaurants and more automation. 474 00:24:52,359 --> 00:24:55,000 Speaker 4: One important point to keep in mind is that it's 475 00:24:55,000 --> 00:24:58,040 Speaker 4: still early days. A lot of restaurants are trying AI 476 00:24:58,080 --> 00:24:59,880 Speaker 4: and to drive through, but it's not like it's wide 477 00:25:00,119 --> 00:25:03,680 Speaker 4: spread across the entire industry. Yet in that regard, I 478 00:25:03,760 --> 00:25:05,720 Speaker 4: think we still have to wait and. 479 00:25:05,640 --> 00:25:06,360 Speaker 2: See a little bit. 480 00:25:07,040 --> 00:25:10,520 Speaker 4: The broader question of whether there will be fewer jobs 481 00:25:10,520 --> 00:25:12,879 Speaker 4: in the restaurant industry depends on who you ask, and 482 00:25:12,960 --> 00:25:15,800 Speaker 4: so what I have found this time around is that restaurants, 483 00:25:15,800 --> 00:25:17,640 Speaker 4: you know, told me again and again that they're not 484 00:25:17,800 --> 00:25:20,680 Speaker 4: looking to reduce the size of their labor force. They 485 00:25:20,720 --> 00:25:24,199 Speaker 4: make the point that currently they don't have enough people 486 00:25:24,400 --> 00:25:26,679 Speaker 4: to work at their restaurants, and what they're looking to 487 00:25:26,760 --> 00:25:31,359 Speaker 4: do is hopefully free up people to do other tasks. So, 488 00:25:31,400 --> 00:25:36,120 Speaker 4: for example, Wendy's told us that instead of having someone 489 00:25:36,440 --> 00:25:40,360 Speaker 4: scrambling to take orders, what they want is to reallocate 490 00:25:40,400 --> 00:25:42,840 Speaker 4: people to do other tests, like, for example, checking the 491 00:25:42,920 --> 00:25:45,359 Speaker 4: accuracy of your order to make sure that what you 492 00:25:45,400 --> 00:25:48,920 Speaker 4: get handed is actually what you ordered. At the same time, 493 00:25:49,000 --> 00:25:52,560 Speaker 4: I also spoke with Simon Johnson, who's an economist at MIT. 494 00:25:53,080 --> 00:25:55,880 Speaker 4: He did say that there's uncertainty about what automation will 495 00:25:55,920 --> 00:25:59,320 Speaker 4: mean for employment. In his view, it will eventually result 496 00:25:59,359 --> 00:26:03,000 Speaker 4: in a reduced need for labor. What happens next really 497 00:26:03,040 --> 00:26:07,800 Speaker 4: depends on what shape automation takes, and so from his perspective. 498 00:26:07,840 --> 00:26:09,560 Speaker 4: What he said is that the type of automation that's 499 00:26:09,560 --> 00:26:12,879 Speaker 4: happening now kind of across the economy is primarily designed 500 00:26:12,880 --> 00:26:16,320 Speaker 4: at replicating tasks that humans can do without necessarily adding 501 00:26:16,440 --> 00:26:19,159 Speaker 4: new tasks, and so he said that it's possible the 502 00:26:19,280 --> 00:26:21,520 Speaker 4: new tasks will emerge, which is kind of what restaurants 503 00:26:21,560 --> 00:26:24,040 Speaker 4: are saying that the annual to reallocate staff to do 504 00:26:24,119 --> 00:26:28,240 Speaker 4: other tasks. But in Johnson's view, the technology is just 505 00:26:28,320 --> 00:26:33,200 Speaker 4: advancing so fast that more jobs could be lost faster 506 00:26:33,680 --> 00:26:36,960 Speaker 4: without there being in enough time for new tasks to emerge. 507 00:26:37,560 --> 00:26:41,679 Speaker 1: Daniella, thanks for speaking with me today anytime. Thanks for 508 00:26:41,760 --> 00:26:43,520 Speaker 1: listening to us here at The Big Take. It's a 509 00:26:43,600 --> 00:26:47,600 Speaker 1: daily podcast from Bloomberg and iHeartRadio. For more shows from iHeartRadio, 510 00:26:47,720 --> 00:26:51,240 Speaker 1: visit the iHeartRadio app, Apple Podcasts, or wherever you listen, 511 00:26:51,560 --> 00:26:53,919 Speaker 1: and we'd love to hear from you. Email us questions 512 00:26:54,000 --> 00:26:57,520 Speaker 1: or comments to Big Take at Bloomberg dot net. The 513 00:26:57,600 --> 00:27:00,960 Speaker 1: supervising producer of The Big Take is Vicky Galina. Our 514 00:27:01,040 --> 00:27:05,200 Speaker 1: senior producer is Catherine Fink. Frederica Romanello is our producer. 515 00:27:05,520 --> 00:27:09,880 Speaker 1: Our associate producer is Zeneb Sidiki, with additional production support 516 00:27:10,000 --> 00:27:14,320 Speaker 1: from Moberrow. Filde Garcia is our engineer. Our original music 517 00:27:14,440 --> 00:27:17,760 Speaker 1: was composed by Leo Sidrin. I'm West Kasova. We'll be 518 00:27:17,800 --> 00:27:19,720 Speaker 1: back tomorrow with another big take.