1 00:00:02,520 --> 00:00:08,720 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg's Thomas Black He 2 00:00:08,800 --> 00:00:11,200 Speaker 1: recently wrote in an opinion piece and he talked about 3 00:00:11,440 --> 00:00:14,960 Speaker 1: the hype around humanoid robots being pretty i with forecasts 4 00:00:14,960 --> 00:00:18,200 Speaker 1: of nearly one billion humanoids in service by twenty fifty, 5 00:00:18,200 --> 00:00:21,479 Speaker 1: but the reality is that most people overestimate what robots 6 00:00:21,480 --> 00:00:24,239 Speaker 1: can do at this point in their development. Got to say, though, 7 00:00:24,280 --> 00:00:26,520 Speaker 1: one company that's been using robots a lot and for 8 00:00:26,560 --> 00:00:29,800 Speaker 1: a long time and has deployed its one millionth robot 9 00:00:29,880 --> 00:00:32,600 Speaker 1: happens to be one of the world's largest marketcat companies 10 00:00:32,640 --> 00:00:35,160 Speaker 1: out there. It's also a household name. So we wanted 11 00:00:35,200 --> 00:00:37,800 Speaker 1: to check in once again with Ty Brady. He is 12 00:00:37,880 --> 00:00:42,360 Speaker 1: chief technologist at Amazon Robotics. He joins us from Amazon's 13 00:00:42,360 --> 00:00:46,160 Speaker 1: Delivering the Future twenty twenty five event from Amazon's dur three. 14 00:00:46,760 --> 00:00:52,720 Speaker 1: It's a delivery station in Millipedes, California, and so we 15 00:00:52,840 --> 00:00:54,440 Speaker 1: kind of want to find out where they are in 16 00:00:54,520 --> 00:00:58,080 Speaker 1: terms of the world of robotics. Ty, I gotta tell you, Tim, 17 00:00:58,120 --> 00:00:59,680 Speaker 1: and I love talking to you last time, so so 18 00:00:59,760 --> 00:01:02,040 Speaker 1: glad to check in with you again. First of all, 19 00:01:02,160 --> 00:01:03,920 Speaker 1: this event, where you are I mean right now, I 20 00:01:03,960 --> 00:01:07,480 Speaker 1: just see a curtain, but I'm assuming there's lots of 21 00:01:07,480 --> 00:01:10,640 Speaker 1: stuff happening at the event. Who's there, what's going on? 22 00:01:10,920 --> 00:01:11,959 Speaker 1: Tell us a little bit about it? 23 00:01:13,000 --> 00:01:14,520 Speaker 2: Yep. Well, first of all, thank you so much for 24 00:01:14,520 --> 00:01:16,160 Speaker 2: having me, and I really appreciate it, and I enjoyed 25 00:01:16,160 --> 00:01:19,280 Speaker 2: our conversation as well. It's just really great to be here. 26 00:01:19,560 --> 00:01:24,720 Speaker 2: We are fourth delivering the Future event here and there's 27 00:01:24,760 --> 00:01:26,760 Speaker 2: a bunch of press that we have here, and we 28 00:01:26,840 --> 00:01:29,480 Speaker 2: made a couple of big announcements in robotics set today. 29 00:01:30,280 --> 00:01:32,760 Speaker 1: Can you tell us about them if you have. 30 00:01:32,760 --> 00:01:39,679 Speaker 2: But the first is in our manipulation. About that we 31 00:01:39,760 --> 00:01:42,240 Speaker 2: call blue Jay, and what blue Jay? The way that 32 00:01:42,280 --> 00:01:43,840 Speaker 2: you can think of that as we can you can 33 00:01:43,920 --> 00:01:45,880 Speaker 2: take three assembly lines and put it in the same 34 00:01:45,880 --> 00:01:49,639 Speaker 2: footprint of one. What it does is help eliminate the menial, 35 00:01:49,680 --> 00:01:52,080 Speaker 2: the mundane, and the repetitive, and it could pick more 36 00:01:52,120 --> 00:01:54,520 Speaker 2: than seventy five percent of the inventory that we actually 37 00:01:54,520 --> 00:01:56,880 Speaker 2: sell in our sortable network, which is a really big deal. 38 00:01:57,040 --> 00:01:59,120 Speaker 2: I'm really proud of that. And I also say there's 39 00:01:59,120 --> 00:02:01,440 Speaker 2: just something interesting about blue Jay as well as compared 40 00:02:01,480 --> 00:02:07,160 Speaker 2: to our other bird manipulation systems, Cardinal Sparrow and Robin 41 00:02:07,520 --> 00:02:10,600 Speaker 2: it took us about three years to kind of design, deploy, 42 00:02:10,840 --> 00:02:14,160 Speaker 2: and get out to our frontline employees. We have actually 43 00:02:14,840 --> 00:02:17,440 Speaker 2: done blue Jay with the power of AI in just 44 00:02:17,560 --> 00:02:20,480 Speaker 2: over a year. So it's really the pace of innovation. 45 00:02:20,960 --> 00:02:22,799 Speaker 3: I think one a lot of people think about Amazon 46 00:02:22,880 --> 00:02:24,320 Speaker 3: and robots, I just want to jump in because we 47 00:02:24,360 --> 00:02:25,560 Speaker 3: don't have a ton of time, but I have that 48 00:02:25,639 --> 00:02:27,120 Speaker 3: I want we'll get to some of the other announcements. 49 00:02:27,280 --> 00:02:31,000 Speaker 3: They think about the Kiva Systems robots, the big acquisition 50 00:02:31,120 --> 00:02:32,720 Speaker 3: at the time, you know, twenty twelve, that was a 51 00:02:32,720 --> 00:02:36,160 Speaker 3: big acquisition for Amazon, and they've seen pictures of the 52 00:02:36,160 --> 00:02:39,760 Speaker 3: way that those can move large loads of things across warehouses. 53 00:02:39,800 --> 00:02:41,799 Speaker 3: But if you were to go into a state of 54 00:02:41,800 --> 00:02:44,680 Speaker 3: the art Amazon warehouse today, what would you see That's 55 00:02:44,720 --> 00:02:46,600 Speaker 3: in addition to those Kiva robots. 56 00:02:47,320 --> 00:02:50,800 Speaker 2: Yeah, in addition to the world's first goods to person 57 00:02:50,919 --> 00:02:55,080 Speaker 2: fulfillment strategy, which was just a really good idea where 58 00:02:55,080 --> 00:02:57,400 Speaker 2: we have more than a million robots that we manufacture 59 00:02:57,400 --> 00:03:00,520 Speaker 2: action in Massachusetts doing that job every day. You're going 60 00:03:00,600 --> 00:03:02,880 Speaker 2: to see more of the unstructured fields, right, So you're 61 00:03:02,880 --> 00:03:05,760 Speaker 2: going to see green robot that we call Proteus that 62 00:03:05,800 --> 00:03:11,519 Speaker 2: can move big cargoes of packages to the right dock 63 00:03:11,840 --> 00:03:15,040 Speaker 2: at the right time. You're going to see many more 64 00:03:15,080 --> 00:03:18,120 Speaker 2: manipulation systems that they we have in there, eliminating the 65 00:03:18,200 --> 00:03:20,239 Speaker 2: kind of the repetitive motions that they we have. No 66 00:03:20,280 --> 00:03:22,919 Speaker 2: one wants to lift a fifty pounds box box all day. 67 00:03:23,200 --> 00:03:26,320 Speaker 2: Robin does that, Cardinal does that. Moving into some of 68 00:03:26,320 --> 00:03:32,200 Speaker 2: these proteus bound krts that we have, you're going to 69 00:03:32,240 --> 00:03:34,880 Speaker 2: see much more collaborative robotics. 70 00:03:34,960 --> 00:03:35,080 Speaker 1: Right. 71 00:03:35,160 --> 00:03:38,320 Speaker 2: So that's where we build our robotic systems to enable 72 00:03:38,440 --> 00:03:41,800 Speaker 2: people to augment what people are capable of. And we 73 00:03:41,840 --> 00:03:45,520 Speaker 2: really believe in the philosophy of people and machines working together. 74 00:03:45,840 --> 00:03:48,040 Speaker 2: How can we build a tool set that enables our 75 00:03:48,080 --> 00:03:51,840 Speaker 2: employees to do their job not only more efficiently, but 76 00:03:51,880 --> 00:03:53,960 Speaker 2: also with better safety in mind? 77 00:03:54,000 --> 00:03:56,000 Speaker 1: All right, So there's this robot arm called blue Jay. 78 00:03:56,040 --> 00:03:57,720 Speaker 1: You just talked about it. You know, you guys are 79 00:03:57,840 --> 00:04:01,560 Speaker 1: using this AI agent called the Iluna, and then you're 80 00:04:01,800 --> 00:04:04,920 Speaker 1: also working with augmented reality glasses to be worn by 81 00:04:05,000 --> 00:04:08,280 Speaker 1: drivers and delivery trucks in the field. There's like so 82 00:04:08,400 --> 00:04:11,440 Speaker 1: much going on. Step back for a moment, because you 83 00:04:11,520 --> 00:04:13,680 Speaker 1: guys have a lot of data. You look at what 84 00:04:13,720 --> 00:04:17,480 Speaker 1: you're doing. I'm just curious investors who are thinking about 85 00:04:17,480 --> 00:04:19,280 Speaker 1: Amazon and what you are doing. How do they think 86 00:04:19,279 --> 00:04:22,280 Speaker 1: about the long term, like ROI return on investment when 87 00:04:22,320 --> 00:04:25,200 Speaker 1: it comes to the investments you guys make in robots. 88 00:04:25,480 --> 00:04:29,240 Speaker 1: Is the goal about labor efficiency, through put speed or 89 00:04:29,279 --> 00:04:33,880 Speaker 1: is it margin expansion kind of tie across your fulfillment operations? 90 00:04:33,880 --> 00:04:34,400 Speaker 1: What is it? 91 00:04:35,920 --> 00:04:39,720 Speaker 2: Yeah? Well, definitely efficiency. We think about efficiencies and how 92 00:04:39,720 --> 00:04:42,680 Speaker 2: can we gain efficiency through all the chain of our 93 00:04:42,680 --> 00:04:47,919 Speaker 2: fulfillment processes for sure, And you mentioned data and data 94 00:04:48,000 --> 00:04:51,000 Speaker 2: is the fuel for AI systems. Data has allowed us 95 00:04:51,040 --> 00:04:55,360 Speaker 2: to bring think of the body of being our robotics, 96 00:04:55,400 --> 00:04:57,960 Speaker 2: but bring the mind to robotics, allowing it to be 97 00:04:57,960 --> 00:05:01,200 Speaker 2: more adaptable, more fluid. You can almost think of this 98 00:05:01,279 --> 00:05:04,360 Speaker 2: as the ability to pour our robotics systems into any 99 00:05:04,440 --> 00:05:08,680 Speaker 2: size building, any scale of building, to amplify what our 100 00:05:08,720 --> 00:05:11,160 Speaker 2: employees are already doing. We want to give them an 101 00:05:11,160 --> 00:05:13,520 Speaker 2: amazing tool set, and we see that when we do that, 102 00:05:13,720 --> 00:05:16,400 Speaker 2: we're more productive. Right when you do robotics, Right when 103 00:05:16,400 --> 00:05:18,520 Speaker 2: you do collab of robotics where you need both people 104 00:05:18,560 --> 00:05:21,080 Speaker 2: and machines doing what they do best and they do 105 00:05:21,240 --> 00:05:24,640 Speaker 2: different things better, That it allows you to be more productive, 106 00:05:24,640 --> 00:05:27,200 Speaker 2: and when you're more productive, that allows you to invest 107 00:05:27,320 --> 00:05:30,320 Speaker 2: more in people. We've skilled more than seven hundred thousand 108 00:05:30,360 --> 00:05:33,520 Speaker 2: of our employees, which is a great stat And also 109 00:05:33,600 --> 00:05:36,240 Speaker 2: in our robotics, we've expanded from the Kiva days, We've 110 00:05:36,279 --> 00:05:39,640 Speaker 2: expanded from just a movement solution to now movement and 111 00:05:39,680 --> 00:05:44,200 Speaker 2: mobility and sortation and storage and perception systems, packing systems 112 00:05:44,360 --> 00:05:47,720 Speaker 2: that have really changed the game for our customers. That's 113 00:05:47,760 --> 00:05:49,320 Speaker 2: a big deal to us. What ty. 114 00:05:50,760 --> 00:05:52,880 Speaker 1: Well, you need less workers and I got to bring 115 00:05:52,960 --> 00:05:55,360 Speaker 1: it up. You know, The Times had a story out. 116 00:05:56,680 --> 00:06:00,000 Speaker 1: They talked about interviews and a cash of internal struts 117 00:06:00,360 --> 00:06:04,360 Speaker 1: documents that they saw that it reveals that your execs 118 00:06:04,839 --> 00:06:06,960 Speaker 1: think that the company's on the cusp of its next 119 00:06:06,960 --> 00:06:10,040 Speaker 1: big workplace shift. I'm reading from the Times and replacing 120 00:06:10,040 --> 00:06:12,640 Speaker 1: more than half a million jobs with robots, so you know, 121 00:06:12,760 --> 00:06:14,839 Speaker 1: we know things change. I'm not in a horse and 122 00:06:14,839 --> 00:06:18,080 Speaker 1: buggy anymore. I don't make things piecemeal. I get it. 123 00:06:18,920 --> 00:06:21,039 Speaker 1: I don't get tons of faxes and I don't get 124 00:06:21,120 --> 00:06:25,240 Speaker 1: tons of pieces of physical mail. Thank god. Things change. 125 00:06:25,279 --> 00:06:28,800 Speaker 1: Having said that, is Amazon, do you guys believe that 126 00:06:28,839 --> 00:06:31,520 Speaker 1: you're on the cusp of a workplace shift and that 127 00:06:31,600 --> 00:06:35,480 Speaker 1: you won't need as many workers because the advancements in robotics. 128 00:06:36,600 --> 00:06:38,839 Speaker 2: Well, there's no doubt that things change. I mean, and 129 00:06:38,880 --> 00:06:41,240 Speaker 2: we're actually really proud of that day. And Amazon is 130 00:06:41,240 --> 00:06:45,120 Speaker 2: that we avoid stasis at all costs. We change and 131 00:06:45,160 --> 00:06:49,520 Speaker 2: we adapt and the nature of tasks definitely changed. There's 132 00:06:49,560 --> 00:06:53,400 Speaker 2: no doubt about that. We are laser focused on efficiencies 133 00:06:53,400 --> 00:06:57,200 Speaker 2: inside of our buildings. But when it comes to that article, 134 00:06:57,440 --> 00:07:00,279 Speaker 2: that article was speculating ten years out right, that's a 135 00:07:00,320 --> 00:07:03,800 Speaker 2: ten year speculation, and it's it's hard to say what's 136 00:07:03,839 --> 00:07:05,479 Speaker 2: going to happen in the next ten years. But I 137 00:07:05,520 --> 00:07:07,599 Speaker 2: can tell you what happened in the last ten years, 138 00:07:07,880 --> 00:07:10,680 Speaker 2: the last ten years, which is when we seriously invested 139 00:07:10,840 --> 00:07:13,840 Speaker 2: in robotics, that we created hundreds of thousands of new 140 00:07:13,920 --> 00:07:17,200 Speaker 2: jobs and new job types. And there's been no employer 141 00:07:17,440 --> 00:07:21,160 Speaker 2: in the United States that has employed more people than 142 00:07:21,240 --> 00:07:24,960 Speaker 2: Amazon in ten years. I mean, that's and that's the 143 00:07:25,080 --> 00:07:29,600 Speaker 2: power of efficiencies and building your robots in way that's 144 00:07:29,640 --> 00:07:33,200 Speaker 2: applied and real that actually augments the human potential. 145 00:07:33,280 --> 00:07:34,880 Speaker 1: All Right, my brother say, I'm like a dog with 146 00:07:34,920 --> 00:07:37,840 Speaker 1: a bone. Wait, so does that mean ten years out. 147 00:07:38,040 --> 00:07:41,080 Speaker 1: It could be less jobs or we just don't know, 148 00:07:41,280 --> 00:07:42,960 Speaker 1: or you don't think that's the case. 149 00:07:43,200 --> 00:07:45,680 Speaker 2: Well you have to think about I mean, there's jobs 150 00:07:45,720 --> 00:07:48,720 Speaker 2: and there's tasks, right, so we of course we're changing 151 00:07:48,760 --> 00:07:51,600 Speaker 2: the nature of tasks. Like I'm very bullish on eliminating 152 00:07:51,680 --> 00:07:55,559 Speaker 2: every menial, mundane and repetitive job out there. Nobody wants 153 00:07:55,600 --> 00:07:57,360 Speaker 2: to do that. So we were going to change those 154 00:07:57,400 --> 00:08:01,400 Speaker 2: tasks one hundred percent. But we can again, as history 155 00:08:01,400 --> 00:08:04,440 Speaker 2: has shown, we continue to create jobs, right with the 156 00:08:04,440 --> 00:08:07,240 Speaker 2: goal of two things. Can you have it all? Can 157 00:08:07,280 --> 00:08:10,040 Speaker 2: you be more productive which means more efficiency, and can 158 00:08:10,080 --> 00:08:12,520 Speaker 2: you also create a safer environment for employees? And we're 159 00:08:12,560 --> 00:08:15,360 Speaker 2: actually doing both. That's what history has shown. In the 160 00:08:15,400 --> 00:08:18,280 Speaker 2: last ten years. We have created better than thirty percent 161 00:08:18,840 --> 00:08:23,240 Speaker 2: reduction in our overall recordable injury rate over the last 162 00:08:23,280 --> 00:08:27,960 Speaker 2: five years because of our robotics and also much more efficient. 163 00:08:27,960 --> 00:08:31,800 Speaker 2: For example, our latest generation filment center in shreport is 164 00:08:31,840 --> 00:08:37,000 Speaker 2: twenty five percent more efficient the order. So you can 165 00:08:37,080 --> 00:08:39,080 Speaker 2: have it all, but you have to build your robotics 166 00:08:39,080 --> 00:08:41,000 Speaker 2: in the right way that empowers people. 167 00:08:41,520 --> 00:08:45,000 Speaker 3: We're speaking with Ty Brady, chief technologist at Amazon Robotics. 168 00:08:45,240 --> 00:08:48,360 Speaker 3: When we think about hiring for these one off or 169 00:08:48,440 --> 00:08:52,520 Speaker 3: seasonal events like the holiday season, our Amazon Prime Day. 170 00:08:53,080 --> 00:08:56,440 Speaker 3: I'm wondering how you're putting pencil to paper right now 171 00:08:56,440 --> 00:08:59,200 Speaker 3: and thinking about those numbers and to what extent automation 172 00:08:59,600 --> 00:09:03,720 Speaker 3: has read deduced Amazon's dependency on seasonal or hourly labor 173 00:09:03,800 --> 00:09:06,400 Speaker 3: for holidays, for Prime Day. What does that look like 174 00:09:06,480 --> 00:09:08,280 Speaker 3: or what will that look like this year next year? 175 00:09:09,520 --> 00:09:13,440 Speaker 2: Well, it's the same philosophy of empowering employees, whether they're 176 00:09:13,480 --> 00:09:16,440 Speaker 2: temporary or the full time employees with the world's best 177 00:09:16,440 --> 00:09:18,920 Speaker 2: machines that help them do their job. It's the same philosophy. 178 00:09:19,000 --> 00:09:20,640 Speaker 2: I'm really proud of the fact that this year we're 179 00:09:20,640 --> 00:09:23,040 Speaker 2: going to offer more than two hundred and fifty thousand 180 00:09:23,520 --> 00:09:26,480 Speaker 2: temporary jobs for our employees to come in during the 181 00:09:26,520 --> 00:09:29,199 Speaker 2: holiday season. Those are good paying jobs. I'm really happy 182 00:09:29,240 --> 00:09:31,720 Speaker 2: about that, really pleased with that. And I'm really pleased 183 00:09:31,760 --> 00:09:33,840 Speaker 2: with the work that our women and men have done 184 00:09:34,040 --> 00:09:37,520 Speaker 2: in the robotics field designing the pioneering these new physical 185 00:09:37,520 --> 00:09:40,000 Speaker 2: AI systems that help them do their jobs better. 186 00:09:41,360 --> 00:09:43,760 Speaker 1: I got to say, one thing that I've been thinking 187 00:09:43,800 --> 00:09:47,520 Speaker 1: a lot about is what's going on overseas, whether it's China, 188 00:09:47,559 --> 00:09:51,000 Speaker 1: whether it's Japan. Bloomberg has done some reporting about service 189 00:09:51,000 --> 00:09:53,840 Speaker 1: industries in Japan because they have a labor shortage that 190 00:09:53,880 --> 00:09:57,520 Speaker 1: they are leading increasingly on robotics. Have you been over 191 00:09:57,520 --> 00:09:59,679 Speaker 1: there and looking at what they're doing, and I'm just 192 00:09:59,720 --> 00:10:04,160 Speaker 1: cures if you're seeing some things that are pretty impressive 193 00:10:04,200 --> 00:10:08,560 Speaker 1: and that the US as a creator of things or 194 00:10:08,880 --> 00:10:12,040 Speaker 1: its role in the robotics industry, has to keep a 195 00:10:12,080 --> 00:10:13,680 Speaker 1: watch on what's going on in other countries. 196 00:10:14,880 --> 00:10:17,960 Speaker 2: Yeah, I think it's easy, especially in robotics, to get 197 00:10:17,960 --> 00:10:21,800 Speaker 2: distracted about, you know, what other people are doing. I 198 00:10:21,800 --> 00:10:24,320 Speaker 2: think anybody can make a YouTube video. I think anybody 199 00:10:24,320 --> 00:10:28,400 Speaker 2: can kind of you pop in and overflate something. But 200 00:10:28,480 --> 00:10:30,520 Speaker 2: if you come into our world, our world is all 201 00:10:30,559 --> 00:10:38,560 Speaker 2: about application. Our world is the reality, all right. 202 00:10:38,559 --> 00:10:42,400 Speaker 1: We're talking with Ty Brady, chief technologist robotics at Amazon. 203 00:10:43,400 --> 00:10:46,640 Speaker 1: Of course our shot FROs. That's the world of technology. 204 00:10:46,640 --> 00:10:49,240 Speaker 1: We're talking a lot about technology. Stuff happens, and we're 205 00:10:49,240 --> 00:10:51,640 Speaker 1: hoping we can get tied back just to finish up 206 00:10:51,640 --> 00:10:52,359 Speaker 1: this conversation. 207 00:10:52,760 --> 00:10:54,600 Speaker 3: Maybe the robots are using all the Wi Fi. 208 00:10:56,480 --> 00:10:58,319 Speaker 1: Robots, Like, I don't like what it is that is 209 00:10:58,360 --> 00:11:00,960 Speaker 1: a connectivity thing though, Well, fees into the past, right 210 00:11:01,000 --> 00:11:01,240 Speaker 1: like you. 211 00:11:01,760 --> 00:11:03,000 Speaker 3: All passion about resources? 212 00:11:03,080 --> 00:11:05,480 Speaker 1: Yeah, charge these puppies. Can't just plug them in right 213 00:11:05,559 --> 00:11:06,200 Speaker 1: if there? I don't know. 214 00:11:06,520 --> 00:11:07,520 Speaker 3: We need recharging too. 215 00:11:07,800 --> 00:11:11,440 Speaker 1: Okay, yeah, it's called food and sleep. Yeah, for Tim, 216 00:11:11,480 --> 00:11:13,360 Speaker 1: it's called food, food and sleep. 217 00:11:13,600 --> 00:11:14,480 Speaker 3: You know what I will say? 218 00:11:14,679 --> 00:11:16,319 Speaker 1: Do we have tie back? Can we grab them for 219 00:11:16,400 --> 00:11:21,520 Speaker 1: twenty seconds? Tye, twenty seconds forgive us your final thoughts here? 220 00:11:21,960 --> 00:11:22,679 Speaker 2: Sorry about that. 221 00:11:22,840 --> 00:11:24,480 Speaker 1: No, it's okay, let it go. 222 00:11:25,040 --> 00:11:25,680 Speaker 2: I'm ready for you. 223 00:11:25,880 --> 00:11:28,480 Speaker 1: Okay, twenty seconds, just final thoughts for our audience because 224 00:11:28,480 --> 00:11:29,040 Speaker 1: we got to run. 225 00:11:31,800 --> 00:11:35,280 Speaker 2: So final thoughts are is that robotics, when done the 226 00:11:35,360 --> 00:11:37,840 Speaker 2: right way, when you reframe your relationship with machines, you 227 00:11:37,840 --> 00:11:41,960 Speaker 2: can enable more productivity, create greater efficiencies, and create a 228 00:11:41,960 --> 00:11:45,760 Speaker 2: more safer environment for employees. And this collaborative mindset that 229 00:11:45,760 --> 00:11:47,720 Speaker 2: we've had, that we've done for the last ten years 230 00:11:47,960 --> 00:11:49,600 Speaker 2: really does make the day. 231 00:11:49,720 --> 00:11:52,400 Speaker 1: Ty Brady over at Amazon, so appreciate it.