1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg P and L Podcast. I'm Pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Bramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg P M L 6 00:00:20,840 --> 00:00:30,840 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot Com. We 7 00:00:30,960 --> 00:00:33,479 Speaker 1: hear a lot about increasing tensions between some of the 8 00:00:33,479 --> 00:00:38,000 Speaker 1: biggest states with respect to air pollution controls and Washington, 9 00:00:38,159 --> 00:00:41,120 Speaker 1: d C, where President Trump has taken a more lax 10 00:00:41,360 --> 00:00:44,479 Speaker 1: stance with respect to pollution controls. I want to bring 11 00:00:44,560 --> 00:00:47,680 Speaker 1: in Mary Nichols, chair of the California Air Resources Board, 12 00:00:47,920 --> 00:00:49,760 Speaker 1: which is based in Los Angeles, but she joins us 13 00:00:49,760 --> 00:00:52,640 Speaker 1: here in our Bloomberg leven three studios UH and UH. 14 00:00:52,680 --> 00:00:57,920 Speaker 1: Mary was crucial in revealing the Volkswagen's diesel cheating scandal. 15 00:00:58,520 --> 00:01:02,080 Speaker 1: She has talked in Germany about this. She also has 16 00:01:02,160 --> 00:01:07,240 Speaker 1: taken really a hard line with respect to tightening our standards. 17 00:01:07,280 --> 00:01:10,640 Speaker 1: And I'm so glad that you're here. I'm wondering it's 18 00:01:10,680 --> 00:01:14,520 Speaker 1: California headed on a collision course with Washington, d C. 19 00:01:14,680 --> 00:01:17,760 Speaker 1: And sort of how ugly could this get well, I 20 00:01:17,800 --> 00:01:21,640 Speaker 1: hope not. We're trying not to be on a collision course. 21 00:01:21,680 --> 00:01:25,880 Speaker 1: We're trying to continue along a path that began really 22 00:01:25,920 --> 00:01:33,160 Speaker 1: many years ago, where California generally identifies a pollution problem 23 00:01:33,280 --> 00:01:39,040 Speaker 1: and helps to identify the technologies that could could solve 24 00:01:39,120 --> 00:01:41,840 Speaker 1: that problem and do it in a way that's also 25 00:01:41,880 --> 00:01:45,440 Speaker 1: good for the economy, and then usually within a period 26 00:01:45,480 --> 00:01:48,240 Speaker 1: of a few years, the federal government steps up and 27 00:01:48,280 --> 00:01:51,680 Speaker 1: adopts the same standards. That's what happened at the beginning 28 00:01:51,760 --> 00:01:56,080 Speaker 1: of the Obama administration when we locked in a national 29 00:01:56,200 --> 00:02:01,120 Speaker 1: program that combined fuel economy standards a end greenhouse gas 30 00:02:01,120 --> 00:02:04,480 Speaker 1: emission standards at the federal and the state level. So 31 00:02:04,760 --> 00:02:08,920 Speaker 1: we've been on a path working collaboratively with the federal 32 00:02:08,960 --> 00:02:13,720 Speaker 1: government UM for quite a few years now. When President 33 00:02:13,720 --> 00:02:17,680 Speaker 1: Obama came in, he immediately indicated that he wanted to 34 00:02:18,520 --> 00:02:22,320 Speaker 1: put a halt and re examine that program. And so 35 00:02:22,360 --> 00:02:25,240 Speaker 1: we're in the midst of that process right now. It's 36 00:02:25,320 --> 00:02:30,320 Speaker 1: not a foregone conclusion that the standards will change, and actually, UM, 37 00:02:30,360 --> 00:02:33,760 Speaker 1: the auto industry as a whole has indicated that they 38 00:02:33,760 --> 00:02:37,000 Speaker 1: don't want to throw out this whole program. What they're 39 00:02:37,040 --> 00:02:40,880 Speaker 1: hoping to do, in their terms, would be tweak the 40 00:02:40,960 --> 00:02:44,560 Speaker 1: standards and the enforcement provisions a little bit, but not 41 00:02:44,680 --> 00:02:49,280 Speaker 1: really halted. Now. I think part of the rationale there 42 00:02:49,400 --> 00:02:52,920 Speaker 1: is that they know that getting better fuel economy is 43 00:02:53,000 --> 00:02:55,880 Speaker 1: popular with the American public. It may not be the 44 00:02:55,960 --> 00:02:58,679 Speaker 1: most important thing that people look at when they are 45 00:02:58,720 --> 00:03:01,840 Speaker 1: making a purchase decay vision for a car or a 46 00:03:01,919 --> 00:03:05,760 Speaker 1: light truck, but people really like it that the vehicles 47 00:03:05,800 --> 00:03:08,800 Speaker 1: that are out there today are so much more efficient 48 00:03:08,800 --> 00:03:11,040 Speaker 1: than they used to be. But we're going to have 49 00:03:11,120 --> 00:03:14,480 Speaker 1: to see what this administration decides they want to do. 50 00:03:15,280 --> 00:03:21,919 Speaker 1: Mary explain to people perhaps the differences between emissions rules 51 00:03:21,960 --> 00:03:26,600 Speaker 1: and regulations in California and in other states, although there 52 00:03:26,600 --> 00:03:29,239 Speaker 1: are some states that have similar emissions and I'm wondering 53 00:03:29,560 --> 00:03:32,560 Speaker 1: if there is a push to offer a more consistent 54 00:03:32,680 --> 00:03:35,800 Speaker 1: national emissions program because each state has its own. You know, 55 00:03:35,840 --> 00:03:38,200 Speaker 1: you want to register a car, let's say, in California, 56 00:03:38,480 --> 00:03:40,720 Speaker 1: if it's older than I believe, was it sixty five 57 00:03:40,800 --> 00:03:42,840 Speaker 1: or seventy five, you don't have to go through an 58 00:03:42,840 --> 00:03:46,480 Speaker 1: emissions test. So you know, there's so many different differences 59 00:03:46,520 --> 00:03:51,120 Speaker 1: between states. What characterizes California, Well, actually there are only 60 00:03:51,200 --> 00:03:57,440 Speaker 1: two emissions standards federal and there's federal and California. Other 61 00:03:57,520 --> 00:04:00,280 Speaker 1: states can if they want to opt in to the 62 00:04:00,360 --> 00:04:04,720 Speaker 1: California program, and there are thirteen states that have done that. 63 00:04:05,200 --> 00:04:09,080 Speaker 1: So it's basically the northeastern states and the Pacific coast 64 00:04:09,120 --> 00:04:13,320 Speaker 1: states Oregon and Washington with California and then the rest 65 00:04:13,320 --> 00:04:18,039 Speaker 1: of the country having the federal standards. But the good 66 00:04:18,040 --> 00:04:20,960 Speaker 1: news at the moment is that although there are differences 67 00:04:21,040 --> 00:04:24,400 Speaker 1: in some of the features like inspection and maintenance requirements 68 00:04:24,440 --> 00:04:27,000 Speaker 1: and when you have to reregister and all that kind 69 00:04:27,000 --> 00:04:29,719 Speaker 1: of thing, when it comes to the cars that are 70 00:04:29,839 --> 00:04:35,640 Speaker 1: built by the auto manufacturers, UM, they really now build 71 00:04:35,680 --> 00:04:39,839 Speaker 1: them all to one standard. Because we and the federal 72 00:04:39,880 --> 00:04:44,599 Speaker 1: government agreed on the greenhouse gas emission standards UH and 73 00:04:44,680 --> 00:04:49,920 Speaker 1: have been pursuing a common pattern since we thought we 74 00:04:49,960 --> 00:04:55,640 Speaker 1: had locked this in until but now, because of the 75 00:04:55,760 --> 00:05:01,440 Speaker 1: Trump administration's desire to redo the so called mid term 76 00:05:01,560 --> 00:05:04,520 Speaker 1: review that was a part of that program, we're in 77 00:05:04,560 --> 00:05:07,839 Speaker 1: a holding pattern at the moment while they decide what 78 00:05:07,880 --> 00:05:10,279 Speaker 1: they want to do. Another front of this is that 79 00:05:10,360 --> 00:05:15,440 Speaker 1: California has been requiring a higher proportion of electric vehicle 80 00:05:15,960 --> 00:05:20,400 Speaker 1: UH cars sales does anyone want to buy them? Oh yes, Um, 81 00:05:20,480 --> 00:05:24,120 Speaker 1: so we now have three hundred and twenty thousand and 82 00:05:24,279 --> 00:05:28,800 Speaker 1: change electric cars on the roads in California. They're beginning 83 00:05:28,839 --> 00:05:31,680 Speaker 1: to pop up in places, and not just in San 84 00:05:31,720 --> 00:05:35,559 Speaker 1: Francisco or l A where they're very visible. Uh, And 85 00:05:35,760 --> 00:05:39,880 Speaker 1: because of that we are seeing a real increase. It 86 00:05:40,080 --> 00:05:43,560 Speaker 1: still is one of those technologies that many people think 87 00:05:43,680 --> 00:05:46,839 Speaker 1: is somehow pie in the sky. They're not really aware 88 00:05:47,000 --> 00:05:50,919 Speaker 1: of how many different models of electric vehicles are available. 89 00:05:51,320 --> 00:05:55,359 Speaker 1: But this summer we saw proliferation at county fairs of 90 00:05:55,600 --> 00:05:59,880 Speaker 1: ride and drive opportunities where hundreds of thousands of people 91 00:06:00,040 --> 00:06:03,520 Speaker 1: in total came out just to try out some of 92 00:06:03,520 --> 00:06:08,040 Speaker 1: the new electric vehicles. I wonder how much power it 93 00:06:08,080 --> 00:06:13,560 Speaker 1: gives California from a negotiating perspective that China and other 94 00:06:13,640 --> 00:06:17,440 Speaker 1: countries are so aggressively trying to promote electric vehicles, so 95 00:06:17,720 --> 00:06:20,719 Speaker 1: to be able to say, the automakers look from a 96 00:06:20,760 --> 00:06:25,160 Speaker 1: competitive advantage, you need you need to go for this. Well, 97 00:06:25,560 --> 00:06:28,520 Speaker 1: we like to think that our stance as having been 98 00:06:28,600 --> 00:06:33,400 Speaker 1: pioneers with promoting electric vehicles helped to get the companies 99 00:06:33,440 --> 00:06:36,920 Speaker 1: to the point where they made the decisions to build these. 100 00:06:37,120 --> 00:06:41,440 Speaker 1: Now very attractive models that are out there offered for sale. 101 00:06:41,839 --> 00:06:46,200 Speaker 1: But China is on its own course, and obviously a 102 00:06:46,240 --> 00:06:50,440 Speaker 1: mandate from China is going to have an enormous leveraging 103 00:06:50,480 --> 00:06:54,039 Speaker 1: effect because they're such a huge potential market out there, 104 00:06:54,440 --> 00:06:57,080 Speaker 1: and they're saying they're not going to allow any internal 105 00:06:57,080 --> 00:07:01,400 Speaker 1: combustion engines at all after twenty dirty so just a period, 106 00:07:01,480 --> 00:07:05,080 Speaker 1: don't even try that. That certainly gets the car company's 107 00:07:05,120 --> 00:07:09,120 Speaker 1: attention in a way that even California can't do. Mary, 108 00:07:09,200 --> 00:07:12,000 Speaker 1: do you and other members of the California Air Resources 109 00:07:12,080 --> 00:07:15,120 Speaker 1: Board and vision at time when we will have self 110 00:07:15,240 --> 00:07:18,760 Speaker 1: driving trucks and that truck drivers will be more like 111 00:07:18,960 --> 00:07:24,320 Speaker 1: airplane pilots with autopilot features rather than the way they 112 00:07:24,360 --> 00:07:28,680 Speaker 1: operate now. I actually think that trucks could be in 113 00:07:28,760 --> 00:07:33,240 Speaker 1: some ways the ideal place for some of the autonomous 114 00:07:33,320 --> 00:07:38,200 Speaker 1: driving features to be introduced early, because they have so 115 00:07:38,280 --> 00:07:45,040 Speaker 1: many safety features associated with them. Um, I myself least 116 00:07:44,880 --> 00:07:49,560 Speaker 1: a new car just two years ago. I hadn't bought 117 00:07:49,600 --> 00:07:52,600 Speaker 1: a new car in years. I won't bother to justify that, 118 00:07:52,680 --> 00:07:57,200 Speaker 1: but just say I'm cheap. But the fact is that 119 00:07:57,480 --> 00:08:02,600 Speaker 1: cars nowadays, you know, anything above the least expensive car 120 00:08:02,920 --> 00:08:05,760 Speaker 1: already comes with certain features that I had never heard 121 00:08:05,800 --> 00:08:08,680 Speaker 1: of before, like something that beeps at you when you 122 00:08:08,760 --> 00:08:11,480 Speaker 1: cross over the white line, and you know, a light 123 00:08:11,560 --> 00:08:13,960 Speaker 1: that flashes in their rear view mirror that tells you 124 00:08:14,000 --> 00:08:16,640 Speaker 1: when there's somebody coming too close to you. These are 125 00:08:16,680 --> 00:08:20,600 Speaker 1: things that are tremendous safety features, but if you put 126 00:08:20,640 --> 00:08:22,840 Speaker 1: them into a truck, it's going to make a huge 127 00:08:22,880 --> 00:08:25,880 Speaker 1: difference because the trucks do so much more damage. Thank 128 00:08:25,920 --> 00:08:29,000 Speaker 1: you very much. Mary Nichols, chair of the California Air 129 00:08:29,080 --> 00:08:32,320 Speaker 1: Resources Board, speaking to us here at our Bloomberg eleven 130 00:08:32,360 --> 00:08:46,560 Speaker 1: three oh Studios. This is Bloomberg. I want to turn 131 00:08:46,559 --> 00:08:49,360 Speaker 1: our attention now to the world of equal pay for 132 00:08:49,480 --> 00:08:52,360 Speaker 1: equal work. And Bob Morrits is the chairman of Price 133 00:08:52,440 --> 00:08:56,880 Speaker 1: Waterhouse Cooper's International and the Price Waterhouse Cooper's has just 134 00:08:56,920 --> 00:09:00,839 Speaker 1: put together the Heat for She Impact Sport and here 135 00:09:00,880 --> 00:09:03,400 Speaker 1: to tell us more about what it revealed. Bob more, 136 00:09:03,440 --> 00:09:05,920 Speaker 1: it's Bob. Thanks for being with us. Maybe you could 137 00:09:05,960 --> 00:09:08,720 Speaker 1: just give us some of the highlights for the results 138 00:09:08,720 --> 00:09:12,960 Speaker 1: of the annual report. Sure, I love to um first. 139 00:09:12,960 --> 00:09:15,079 Speaker 1: The Heat First SHE initiative is something that the u 140 00:09:15,280 --> 00:09:18,640 Speaker 1: N sponsors, which is a effort to get more men 141 00:09:19,760 --> 00:09:22,480 Speaker 1: uh in the business of supporting women, both at a 142 00:09:22,559 --> 00:09:25,439 Speaker 1: corporate level, at a country level, and at an educational level. 143 00:09:25,960 --> 00:09:29,040 Speaker 1: And we had ten Impact Champions from university, ten country 144 00:09:29,120 --> 00:09:32,840 Speaker 1: leaders as well as ten corporate leaders exemplifying what we're 145 00:09:32,840 --> 00:09:37,480 Speaker 1: looking for individual organizations and individuals themselves to do. So 146 00:09:37,520 --> 00:09:41,520 Speaker 1: what the report focuses on is what progress have women made. 147 00:09:42,360 --> 00:09:46,440 Speaker 1: It provides some insights and hopefully some inspiration for the 148 00:09:46,480 --> 00:09:49,360 Speaker 1: thirty organizations to ten by ten by ten in terms 149 00:09:49,440 --> 00:09:54,280 Speaker 1: of what they've done in their own organizations, countries or institutions, 150 00:09:54,360 --> 00:09:57,839 Speaker 1: and exemplifies the benefit that can come from it. So, 151 00:09:57,920 --> 00:10:00,199 Speaker 1: for example, we heard from the pres is it in 152 00:10:00,240 --> 00:10:04,559 Speaker 1: a Malawi who changed laws to allow for and eliminate 153 00:10:05,320 --> 00:10:08,240 Speaker 1: child marriages, um that put more women in the education 154 00:10:08,280 --> 00:10:11,400 Speaker 1: system that allow for them to have job opportunities. Thereafter, 155 00:10:11,480 --> 00:10:14,920 Speaker 1: we heard from university presidents in terms of what they 156 00:10:14,920 --> 00:10:18,440 Speaker 1: were doing to deal with sexual assault and discrimination. And 157 00:10:18,440 --> 00:10:20,640 Speaker 1: we heard from the corporates both in terms of what 158 00:10:20,679 --> 00:10:23,360 Speaker 1: they're doing to increase the number of women both in 159 00:10:23,480 --> 00:10:28,080 Speaker 1: leadership roles as well as to move forward with pay equity, 160 00:10:28,160 --> 00:10:30,200 Speaker 1: as well as what corporates are doing to help in 161 00:10:30,240 --> 00:10:34,240 Speaker 1: the education system to enable women to bigger opportunities, particularly 162 00:10:34,240 --> 00:10:37,000 Speaker 1: in the STEM research areas that are important to the 163 00:10:37,040 --> 00:10:40,120 Speaker 1: future of work in various countries around the world. Bob 164 00:10:40,440 --> 00:10:45,680 Speaker 1: in the US, what is the major obstacle to women 165 00:10:45,920 --> 00:10:50,640 Speaker 1: having more prominent roles and organizations? From our perspective, what 166 00:10:50,679 --> 00:10:55,280 Speaker 1: we see is it's a combination of the unconscious preferences 167 00:10:55,400 --> 00:10:59,160 Speaker 1: or biases that come out of organizations. UM. If you 168 00:10:59,240 --> 00:11:03,040 Speaker 1: think about roles, for example, UM, there's very few women 169 00:11:03,040 --> 00:11:05,320 Speaker 1: on boards today and those boards are responsible or picking 170 00:11:05,320 --> 00:11:08,320 Speaker 1: the new CEOs UM, So it's not surprising that there's 171 00:11:08,360 --> 00:11:12,280 Speaker 1: a biased um that might come from that. But nonetheless, 172 00:11:12,320 --> 00:11:14,120 Speaker 1: we've got to do more to get more women, um 173 00:11:14,200 --> 00:11:16,080 Speaker 1: at the top of the house in these board rooms. 174 00:11:16,120 --> 00:11:19,440 Speaker 1: So there's a different perspective. And when you have women 175 00:11:19,440 --> 00:11:22,319 Speaker 1: on the boards, our own study has found actually diversity 176 00:11:23,000 --> 00:11:26,079 Speaker 1: becomes an important factor in thinking about leadership succession as 177 00:11:26,080 --> 00:11:27,959 Speaker 1: the men are in the room that it's not the one, 178 00:11:28,000 --> 00:11:30,560 Speaker 1: two or three issue that's top of mind for them. UM. 179 00:11:30,760 --> 00:11:32,600 Speaker 1: The second thing I would say is that we've got 180 00:11:32,600 --> 00:11:34,880 Speaker 1: to make sure that organizations do a much better job 181 00:11:34,920 --> 00:11:38,640 Speaker 1: with their talent management, succession planning and making sure that 182 00:11:38,679 --> 00:11:42,400 Speaker 1: the women have the opportunities because the women are equally 183 00:11:42,440 --> 00:11:44,440 Speaker 1: qualified to do these roles. It's a cruel matter of 184 00:11:44,480 --> 00:11:47,800 Speaker 1: creating the opportunity for success being business unit leaders and 185 00:11:47,880 --> 00:11:50,800 Speaker 1: driving that. And this is where data becomes important. What 186 00:11:50,840 --> 00:11:53,600 Speaker 1: we have found is there's tremendous amount of data now 187 00:11:53,840 --> 00:11:57,240 Speaker 1: demonstrating where policies need to change, where perhaps leaders might 188 00:11:57,280 --> 00:12:00,520 Speaker 1: have those unconscious or conscious preferences or biases. And the 189 00:12:00,600 --> 00:12:03,880 Speaker 1: data can allow for organizations to pinpoint what changes you 190 00:12:03,920 --> 00:12:06,160 Speaker 1: need to make it kind an organization and where the 191 00:12:06,160 --> 00:12:09,160 Speaker 1: efforts should be pinpointed for hopefully turning the actions into 192 00:12:09,200 --> 00:12:12,080 Speaker 1: better results than better outcomes. You know, maybe I'm just 193 00:12:12,160 --> 00:12:15,880 Speaker 1: revealing my bias here, but in my experience, a lot 194 00:12:15,880 --> 00:12:18,280 Speaker 1: of it does come down to childcare and if the 195 00:12:18,320 --> 00:12:22,480 Speaker 1: woman is the primary caretaker, which usually that is the assumption, 196 00:12:22,960 --> 00:12:26,440 Speaker 1: she is going to prefer to have a lighter schedule 197 00:12:26,440 --> 00:12:29,000 Speaker 1: and a less prominent rule in order to take care 198 00:12:29,040 --> 00:12:31,000 Speaker 1: of her family. Sometimes it's not even a preference, it's 199 00:12:31,000 --> 00:12:33,679 Speaker 1: a it's a mandatory kind of reality of life. So, 200 00:12:33,800 --> 00:12:35,679 Speaker 1: I mean, what what what what What did you say 201 00:12:35,679 --> 00:12:38,960 Speaker 1: to that? Yeah, it's it's definitely one of a few 202 00:12:39,000 --> 00:12:41,360 Speaker 1: issues that actually have to be dealt with at the 203 00:12:41,400 --> 00:12:44,000 Speaker 1: corporate level UM. And here's what we see organization is doing. So, 204 00:12:44,080 --> 00:12:47,240 Speaker 1: for example, with us at PwC, we put in a 205 00:12:47,280 --> 00:12:50,240 Speaker 1: number of different policy changes. So give you another stat 206 00:12:50,280 --> 00:12:53,640 Speaker 1: which is when women leave from alternity to leave out 207 00:12:53,640 --> 00:12:55,120 Speaker 1: of side and of mind more than one year and 208 00:12:55,160 --> 00:12:57,960 Speaker 1: they come back into the system because they're out of 209 00:12:57,960 --> 00:13:01,560 Speaker 1: side of the mind, there's automatically a decrease in their 210 00:13:01,600 --> 00:13:05,559 Speaker 1: reading and assessment of their performance. Why is that. It's 211 00:13:05,559 --> 00:13:07,320 Speaker 1: probably because they were out of sight, out of mind 212 00:13:07,360 --> 00:13:09,000 Speaker 1: for a period of time, or there might be some 213 00:13:09,400 --> 00:13:12,079 Speaker 1: unconscious behavior for it. So we as a P two 214 00:13:12,080 --> 00:13:15,559 Speaker 1: BC organization change the way in which we do evaluations 215 00:13:15,679 --> 00:13:19,200 Speaker 1: for women that have left on alternatively UM and giving 216 00:13:19,240 --> 00:13:21,440 Speaker 1: them a two year period where we're saying, hey, listen, 217 00:13:21,480 --> 00:13:24,160 Speaker 1: if they come back into doing good work, their assessments 218 00:13:24,160 --> 00:13:27,080 Speaker 1: should not change. The second thing we've done is provide 219 00:13:27,120 --> 00:13:31,160 Speaker 1: more opportunity for flexibility. We've got more women that are 220 00:13:31,160 --> 00:13:33,960 Speaker 1: being promoted at a senior level that have flexible schedules 221 00:13:33,960 --> 00:13:35,560 Speaker 1: and we need to role model them and put them 222 00:13:35,559 --> 00:13:38,520 Speaker 1: in places more accepting. And third, we're actually putting in 223 00:13:38,559 --> 00:13:41,480 Speaker 1: more support care UM to make sure that there's an 224 00:13:41,520 --> 00:13:46,080 Speaker 1: opportunity for them to leverage tools UM and other methodologies 225 00:13:46,120 --> 00:13:48,160 Speaker 1: as well as other support groups to deal with the 226 00:13:48,240 --> 00:13:50,440 Speaker 1: child care issues. So there's a big opportunity, but it 227 00:13:50,520 --> 00:13:53,440 Speaker 1: causes the management teams to look at that data and 228 00:13:53,480 --> 00:13:56,520 Speaker 1: then take the appropriate actions necessary to address that data. 229 00:13:57,040 --> 00:13:59,160 Speaker 1: What you do with women is much different than what 230 00:13:59,200 --> 00:14:01,680 Speaker 1: you need to do to overcome some of the challenges 231 00:14:01,679 --> 00:14:05,040 Speaker 1: with minorities, for example, men or women. UM, So, I 232 00:14:05,080 --> 00:14:07,240 Speaker 1: think that's where people have got to be very tailored. 233 00:14:07,240 --> 00:14:10,360 Speaker 1: There's no one silver bullet or one size. SIT's all well, well, Bob, 234 00:14:10,360 --> 00:14:13,800 Speaker 1: I mean I understand all these initiatives, programs and you know, 235 00:14:14,040 --> 00:14:17,839 Speaker 1: management training efforts, but why don't you just pay people 236 00:14:17,880 --> 00:14:20,720 Speaker 1: more money? I mean, why do this? What's the incentive? 237 00:14:20,760 --> 00:14:23,320 Speaker 1: I mean it may sound great and it sounds fair, 238 00:14:23,720 --> 00:14:26,080 Speaker 1: but if you're a manager who isn't going to get 239 00:14:26,080 --> 00:14:31,000 Speaker 1: any financial bonus or any financial incentive to, let's say, 240 00:14:31,120 --> 00:14:34,040 Speaker 1: hire women or make women more prominent in the organization, 241 00:14:35,040 --> 00:14:38,600 Speaker 1: why wouldn't you just use the incentive of pay people 242 00:14:38,640 --> 00:14:40,960 Speaker 1: more for doing whatever it is you want them to do. 243 00:14:42,120 --> 00:14:45,120 Speaker 1: So we'll take your your point in sort of flip 244 00:14:45,160 --> 00:14:47,880 Speaker 1: it a little bit um. There's organizations now that are 245 00:14:47,960 --> 00:14:50,480 Speaker 1: much better, and we made a change about three years ago. 246 00:14:50,600 --> 00:14:53,080 Speaker 1: Was one of our commitments to He for She, which 247 00:14:53,160 --> 00:14:55,920 Speaker 1: was to put a diversity index in place. That caused 248 00:14:55,960 --> 00:14:58,400 Speaker 1: us to get very specific with our leadership teams around 249 00:14:58,400 --> 00:15:03,000 Speaker 1: expectations were now there's risk and reward to their compensation model. 250 00:15:03,120 --> 00:15:05,640 Speaker 1: To your point, right, how do they actually lead? By example, 251 00:15:06,040 --> 00:15:07,960 Speaker 1: let'son sent them. But now we can pinpoint with the 252 00:15:08,040 --> 00:15:10,680 Speaker 1: right data where people are not doing what's needed and 253 00:15:10,720 --> 00:15:13,520 Speaker 1: therefore there is a negative or a negative implication of 254 00:15:13,560 --> 00:15:16,600 Speaker 1: their compensation. So you do have the data to say 255 00:15:16,600 --> 00:15:19,960 Speaker 1: who's doing things quantitatively as well as get a sense qualitative. 256 00:15:20,000 --> 00:15:24,600 Speaker 1: With some feedback processes, you can adjust accordingly um compensation 257 00:15:24,640 --> 00:15:27,760 Speaker 1: for your leadership team if you're very specific on accountability 258 00:15:27,800 --> 00:15:31,120 Speaker 1: and you hold them accountable very specifically in terms of 259 00:15:31,160 --> 00:15:33,240 Speaker 1: your assessment on their performance as well as in the 260 00:15:33,280 --> 00:15:36,600 Speaker 1: compensation that they received. Bob Maritz, thank you so much 261 00:15:36,600 --> 00:15:40,560 Speaker 1: for joining us. He's chairman of Price Waterhouse Cooper's International Limited, 262 00:15:40,920 --> 00:15:44,000 Speaker 1: which is based in New York, and they just put 263 00:15:44,000 --> 00:15:47,920 Speaker 1: out this Heat for Sheet Impact Report, a lengthy look 264 00:15:48,080 --> 00:15:52,160 Speaker 1: at what the obstacles are to getting more women and 265 00:15:52,480 --> 00:15:56,560 Speaker 1: UH and a broader diversity of employees into positions of 266 00:15:56,600 --> 00:16:12,200 Speaker 1: power and earning more well. There has been an escalating 267 00:16:12,400 --> 00:16:16,040 Speaker 1: war of words and threats between North Korea and the 268 00:16:16,120 --> 00:16:19,120 Speaker 1: United States. The latest is in North Korea is threatening 269 00:16:19,120 --> 00:16:22,080 Speaker 1: to test a powerful nuclear weapon over the Pacific Ocean 270 00:16:22,080 --> 00:16:27,359 Speaker 1: in response to President Donald Trump's threats and increased sanctions 271 00:16:27,480 --> 00:16:30,360 Speaker 1: on the country. To give us a sense of what's 272 00:16:30,400 --> 00:16:34,440 Speaker 1: at stake here, how much more this exacerbates the tensions here. 273 00:16:34,480 --> 00:16:37,040 Speaker 1: I want to bring in Scott Seeman uh. He is 274 00:16:37,120 --> 00:16:40,880 Speaker 1: the director for Asia at Eurasia Group, which is based 275 00:16:40,880 --> 00:16:42,760 Speaker 1: in Washington, d C. Scott, thank you so much for 276 00:16:42,840 --> 00:16:46,560 Speaker 1: joining us. So this seems alarming. I'm wondering at what 277 00:16:46,640 --> 00:16:52,200 Speaker 1: point does this escalation of words bleed over into escalation 278 00:16:52,240 --> 00:16:58,200 Speaker 1: and actual physical combat. So we still attach a pretty 279 00:16:58,200 --> 00:17:03,480 Speaker 1: low probability to the risk of an actual military conflict UM. 280 00:17:03,560 --> 00:17:07,040 Speaker 1: And that's talking about a wide range of scenarios, everything 281 00:17:07,160 --> 00:17:12,680 Speaker 1: from uh, someone starting intentionally an attack to an accident 282 00:17:12,720 --> 00:17:16,719 Speaker 1: or miscalculation kind of getting out of control and pushing 283 00:17:16,720 --> 00:17:19,760 Speaker 1: in a direction of a larger conflict UM. So we're 284 00:17:19,760 --> 00:17:23,240 Speaker 1: not you know, we're not overly concerned at this point 285 00:17:23,480 --> 00:17:28,000 Speaker 1: that this rhetoric that we're seeing is really escalating the 286 00:17:28,119 --> 00:17:34,040 Speaker 1: chances of something going awry. But certainly the threat to 287 00:17:34,359 --> 00:17:38,399 Speaker 1: send a missile someplace over the Pacific and detonate it UH, 288 00:17:38,440 --> 00:17:40,920 Speaker 1: that adds a new element that the US and other 289 00:17:40,920 --> 00:17:44,000 Speaker 1: countries are going to have to take into consideration. Scott. 290 00:17:44,440 --> 00:17:47,480 Speaker 1: In the past month, North Koreans have launched two missiles 291 00:17:47,520 --> 00:17:51,480 Speaker 1: over Japan. They tested a sixth and powerful nuclear device. 292 00:17:51,560 --> 00:17:55,120 Speaker 1: They described it as a hydrogen bomb, and that follows 293 00:17:55,240 --> 00:18:00,399 Speaker 1: two successful test launches of intercontinental ballistic missiles in July. 294 00:18:01,000 --> 00:18:04,280 Speaker 1: If they try to put both of those technologies together. 295 00:18:04,840 --> 00:18:07,840 Speaker 1: What could the United States or its allies in Asia 296 00:18:07,960 --> 00:18:11,080 Speaker 1: do if indeed they tried to launch and test a 297 00:18:11,200 --> 00:18:16,440 Speaker 1: hydrogen weapon in the atmosphere over the Pacific Ocean. So 298 00:18:16,560 --> 00:18:19,520 Speaker 1: the range of options everything from some sort of a 299 00:18:19,520 --> 00:18:23,359 Speaker 1: surgical strike UH to try to eliminate some of the 300 00:18:23,359 --> 00:18:29,359 Speaker 1: capabilities that preemptively. I don't think preemptively is you know, 301 00:18:29,440 --> 00:18:33,600 Speaker 1: unless there was intelligence that said that the North Koreans 302 00:18:33,600 --> 00:18:36,200 Speaker 1: were fueling up a rocket that was going to threaten 303 00:18:36,960 --> 00:18:39,639 Speaker 1: the United States or an ally with an actual UH 304 00:18:40,080 --> 00:18:43,119 Speaker 1: nuclear weapon. No, I don't think a preemptive strike is 305 00:18:43,160 --> 00:18:47,239 Speaker 1: probably in the cards, but we'll we'll have to be 306 00:18:47,280 --> 00:18:51,640 Speaker 1: watching to see what kind of preparations the satellites give 307 00:18:51,720 --> 00:18:54,680 Speaker 1: us an indication that the North Koreans are doing, and 308 00:18:54,920 --> 00:18:58,399 Speaker 1: certainly there will be a whole range of options that 309 00:18:58,480 --> 00:19:01,720 Speaker 1: military leaders here in the US and elsewhere we'll have 310 00:19:01,760 --> 00:19:04,359 Speaker 1: to sit down and start thinking about. Now. You know, 311 00:19:04,600 --> 00:19:08,080 Speaker 1: I have to wonder what the reaction is within North 312 00:19:08,160 --> 00:19:12,960 Speaker 1: Korea among the common folk, because these increased sanctions are 313 00:19:13,000 --> 00:19:17,320 Speaker 1: only going to exacerbate food shortages that have been brought 314 00:19:17,359 --> 00:19:19,440 Speaker 1: about by the worst drought since two thousand and one 315 00:19:19,560 --> 00:19:24,000 Speaker 1: in the country. Is there any chance of rising political 316 00:19:24,000 --> 00:19:27,920 Speaker 1: tensions within North Korea? I doubt it. I think we've 317 00:19:27,920 --> 00:19:32,960 Speaker 1: been hoping for that for decades and and it hasn't happened. 318 00:19:33,520 --> 00:19:37,200 Speaker 1: I think the normal people, the people who are outside 319 00:19:37,200 --> 00:19:41,000 Speaker 1: of Kongyang, who are not part of the government elite. Uh, 320 00:19:41,040 --> 00:19:45,359 Speaker 1: they're simply trying to get through uh, you know, every day. UM. 321 00:19:45,400 --> 00:19:47,679 Speaker 1: I don't think that the economy is in a state 322 00:19:47,800 --> 00:19:51,840 Speaker 1: right now that we're looking at starvation famine on the 323 00:19:51,880 --> 00:19:55,680 Speaker 1: scale that we saw under Kim Jong UN's father, for example. 324 00:19:55,720 --> 00:19:58,680 Speaker 1: The economy seems to be performing relatively well, so they've 325 00:19:58,680 --> 00:20:01,159 Speaker 1: got quite a bit of a cushion. Uh. So I 326 00:20:01,200 --> 00:20:03,159 Speaker 1: think the chances of some sort of a you know, 327 00:20:03,200 --> 00:20:07,920 Speaker 1: a political crisis or or an uprising, uh would you know, 328 00:20:08,000 --> 00:20:10,720 Speaker 1: it's extremely remote. And again we've been hoping for that 329 00:20:10,960 --> 00:20:14,240 Speaker 1: for decades and it just hasn't materialized, you know, Scott. 330 00:20:14,560 --> 00:20:19,359 Speaker 1: Despite some people's lamenting the increasing war of words between 331 00:20:19,440 --> 00:20:22,639 Speaker 1: Kim Jong un and President Trump, some others say that 332 00:20:22,760 --> 00:20:24,960 Speaker 1: actually this is just highlighting a problem that has been 333 00:20:24,960 --> 00:20:28,359 Speaker 1: going on for a long time, that the situation is 334 00:20:28,359 --> 00:20:31,399 Speaker 1: coming to a head, not because of the words, but 335 00:20:31,480 --> 00:20:34,080 Speaker 1: because of what's been behind it and what's been sort 336 00:20:34,119 --> 00:20:37,520 Speaker 1: of left for future presidents to deal with. Do you 337 00:20:37,560 --> 00:20:40,000 Speaker 1: agree with that or do you think that that there 338 00:20:40,040 --> 00:20:42,840 Speaker 1: has been some kind of material escalation since the beginning 339 00:20:42,880 --> 00:20:47,359 Speaker 1: of the year. So I hate to go into assessing 340 00:20:47,400 --> 00:20:51,679 Speaker 1: whether previous governments in the US or elsewhere could have 341 00:20:51,720 --> 00:20:54,760 Speaker 1: done more, should have done more. Um, I think you 342 00:20:54,800 --> 00:20:59,040 Speaker 1: can always say that probably more effort in time could 343 00:20:59,040 --> 00:21:02,119 Speaker 1: have been put into ensuring that we don't get to 344 00:21:02,160 --> 00:21:05,200 Speaker 1: the point that we're at right now. But that's where 345 00:21:05,200 --> 00:21:10,640 Speaker 1: we are, and certainly the pace of development around these 346 00:21:10,680 --> 00:21:14,840 Speaker 1: weapons that we've been seeing has greatly accelerated. People are 347 00:21:14,840 --> 00:21:19,320 Speaker 1: talking about this being a breakout, meaning that they have 348 00:21:19,600 --> 00:21:25,080 Speaker 1: really moved quickly towards eventually getting the technology that they 349 00:21:25,119 --> 00:21:29,359 Speaker 1: require for a viable I CBM. So, uh, this is 350 00:21:29,480 --> 00:21:32,720 Speaker 1: moving all in the wrong direction. Um. The flip side 351 00:21:32,720 --> 00:21:34,639 Speaker 1: of that, of course, is that one part of the 352 00:21:34,720 --> 00:21:37,679 Speaker 1: strategy to deal with North Korea is to ensure that 353 00:21:37,800 --> 00:21:40,720 Speaker 1: this becomes a bigger crisis for China, hoping that that 354 00:21:40,760 --> 00:21:43,399 Speaker 1: will motivate Beijing to do more as well. Well, we 355 00:21:43,440 --> 00:21:45,679 Speaker 1: know that the Chinese have just forbidden their banks to 356 00:21:45,720 --> 00:21:49,359 Speaker 1: do business transact business with companies in the North Korea. 357 00:21:49,400 --> 00:21:52,080 Speaker 1: That was announced earlier today. But just quickly, Scott, what 358 00:21:52,080 --> 00:21:55,040 Speaker 1: do you think about the the sort of fear that 359 00:21:55,080 --> 00:21:58,840 Speaker 1: people are not expressing. I'm looking at the Korean stock index. 360 00:21:59,080 --> 00:22:01,080 Speaker 1: It is of more than seven in team percent year 361 00:22:01,119 --> 00:22:03,520 Speaker 1: to date, and it doesn't seem as if investors care. 362 00:22:03,680 --> 00:22:07,520 Speaker 1: Give you about seconds, Yeah, no, we we've seen the 363 00:22:07,560 --> 00:22:10,680 Speaker 1: markets really kind of shrug off a lot of this. 364 00:22:11,160 --> 00:22:14,040 Speaker 1: Part of it is probably because this is so this 365 00:22:14,119 --> 00:22:17,439 Speaker 1: is such a common occurrence, But I think the market 366 00:22:17,480 --> 00:22:20,960 Speaker 1: is probably needing to spend a little more time looking 367 00:22:21,000 --> 00:22:24,440 Speaker 1: at this issue. Well, I guess that's a diplomatic way 368 00:22:24,600 --> 00:22:27,160 Speaker 1: saying no one's afraid, but it might not be bad 369 00:22:27,200 --> 00:22:29,239 Speaker 1: to look over your shoulder at least a little bit. 370 00:22:29,280 --> 00:22:31,440 Speaker 1: Thanks very much, Scott Seaman, he is the director of 371 00:22:31,480 --> 00:22:34,199 Speaker 1: Asia for the Eurasia Group, giving us his thoughts on 372 00:22:34,960 --> 00:22:38,440 Speaker 1: the turmoil that exists on the Korean peninsula. As I said, 373 00:22:38,520 --> 00:22:41,440 Speaker 1: the South Korean stock index, the costpy up more than 374 00:22:41,480 --> 00:22:55,439 Speaker 1: seven and a half percent so far. It is a 375 00:22:55,480 --> 00:22:57,520 Speaker 1: bad day for grub hub today. It is a very 376 00:22:57,520 --> 00:23:00,600 Speaker 1: bad day because Amazon is looking at their business model 377 00:23:00,640 --> 00:23:03,119 Speaker 1: and saying we can do that too. To tell us 378 00:23:03,160 --> 00:23:06,520 Speaker 1: more is Craig Giamana. He's consumer reporter for Bloomberg News, 379 00:23:06,720 --> 00:23:10,240 Speaker 1: and he joins us now Amazon dot Com is about 380 00:23:10,359 --> 00:23:13,879 Speaker 1: to get into the or deeper into the food delivery business. 381 00:23:13,960 --> 00:23:16,000 Speaker 1: What's going on here, like give us a label? End? Yeah, 382 00:23:16,000 --> 00:23:18,560 Speaker 1: So they have, um, they've had Amazon restaurants for a while. 383 00:23:18,600 --> 00:23:21,400 Speaker 1: I think they started that in Seattle. You know, it's 384 00:23:21,440 --> 00:23:23,600 Speaker 1: it's still pretty small, and I don't think it's quite 385 00:23:23,600 --> 00:23:26,280 Speaker 1: crossed over into you know, mainstream peel and they haven't 386 00:23:26,280 --> 00:23:29,080 Speaker 1: really attracted many national chains. Is the big thing. So 387 00:23:29,520 --> 00:23:31,200 Speaker 1: the news today is that they're partnering with a company 388 00:23:31,240 --> 00:23:34,560 Speaker 1: called Olo, which provides digital order and pay solutions for 389 00:23:34,600 --> 00:23:38,000 Speaker 1: about two restaurant brands that have forty tho locations, and 390 00:23:38,040 --> 00:23:40,280 Speaker 1: they're going to basically make it easy for all of 391 00:23:40,320 --> 00:23:42,480 Speaker 1: their customers to work with Amazon. So, yes, this is 392 00:23:42,520 --> 00:23:46,040 Speaker 1: Amazon making a big push into the restaurant delivery space, 393 00:23:46,040 --> 00:23:47,760 Speaker 1: which has gotten more and more popular it seems like 394 00:23:47,800 --> 00:23:50,080 Speaker 1: every year. Hey Craig, does this mean that Amazon gets 395 00:23:50,080 --> 00:23:53,719 Speaker 1: an exclusive with those customers, No, it doesn't. What it 396 00:23:53,760 --> 00:23:56,040 Speaker 1: means basically is that if you are an OLO customer, 397 00:23:56,359 --> 00:23:59,359 Speaker 1: you can easily tap into Amazon restaurants. So the feeling 398 00:23:59,440 --> 00:24:02,600 Speaker 1: is that places like shake Shack, Chipotle's catering business that 399 00:24:03,119 --> 00:24:05,199 Speaker 1: sort of they'll flip the switch and turn this on. 400 00:24:05,280 --> 00:24:07,040 Speaker 1: But also what it does this is a little bit 401 00:24:07,080 --> 00:24:09,720 Speaker 1: in the weeds, but you know, there's Grubhub, there's Seamless. 402 00:24:09,960 --> 00:24:13,000 Speaker 1: All of those businesses basically provide these restaurants with a 403 00:24:13,040 --> 00:24:14,520 Speaker 1: tablet that they have to take to the back of 404 00:24:14,560 --> 00:24:16,919 Speaker 1: the house. What all I was saying is sign up 405 00:24:16,960 --> 00:24:19,240 Speaker 1: with us and we'll integrate all that directly into your 406 00:24:19,240 --> 00:24:21,600 Speaker 1: POS system, your sales system. So it's supposed to make 407 00:24:21,600 --> 00:24:23,920 Speaker 1: it much easier for these restaurants to handle these orders 408 00:24:23,960 --> 00:24:27,240 Speaker 1: in connecting it to your point of sales system. Can 409 00:24:27,280 --> 00:24:31,119 Speaker 1: you envision a time when this is also integrated into 410 00:24:31,160 --> 00:24:33,520 Speaker 1: the back end, so it's not just the point of sale, 411 00:24:33,560 --> 00:24:39,600 Speaker 1: but eventually integrated into the inventory control system, your supply chain. 412 00:24:39,680 --> 00:24:42,480 Speaker 1: Because I was noting, for example, that Amazon, you know, 413 00:24:42,640 --> 00:24:44,719 Speaker 1: is looking to get into a lot of other businesses, 414 00:24:44,840 --> 00:24:47,199 Speaker 1: and you know, being in the restaurants supply or the 415 00:24:47,200 --> 00:24:50,440 Speaker 1: food supply business now that you have whole food might 416 00:24:50,480 --> 00:24:52,560 Speaker 1: be something that they're thinking about. I mean, I don't 417 00:24:52,560 --> 00:24:54,680 Speaker 1: think there's any question that they're thinking about that. That's 418 00:24:54,680 --> 00:24:56,840 Speaker 1: what they do, right, There's a supply chain company. And 419 00:24:56,880 --> 00:24:59,119 Speaker 1: the other thing that they do is incredible prowess with 420 00:24:59,200 --> 00:25:02,000 Speaker 1: customer data. So that's why every time there's an Amazon 421 00:25:02,040 --> 00:25:04,280 Speaker 1: press release, we see the stocks of Kroger go down, 422 00:25:04,440 --> 00:25:07,720 Speaker 1: Campbell General Mills. Today we saw grub Hub go down. 423 00:25:07,760 --> 00:25:10,320 Speaker 1: So this is Amazon going hard after the one point 424 00:25:10,320 --> 00:25:12,880 Speaker 1: five trillion dollar market for food. Half of that is grocery, 425 00:25:12,880 --> 00:25:15,560 Speaker 1: half of that is restaurant. Roughly speaking, we know they're 426 00:25:15,560 --> 00:25:18,000 Speaker 1: going after grocery hard with Whole foods now here, they 427 00:25:18,000 --> 00:25:20,639 Speaker 1: come for the restaurants I'm trying to figure out. I mean, 428 00:25:20,640 --> 00:25:24,320 Speaker 1: it's this just from a system's perspective that Amazon is 429 00:25:24,320 --> 00:25:27,360 Speaker 1: trying to streamline things for for restaurants or are they 430 00:25:27,400 --> 00:25:32,520 Speaker 1: also providing the delivery service force they they provide the delivery, so, 431 00:25:32,560 --> 00:25:34,480 Speaker 1: I mean, delivery has become more and more important for 432 00:25:34,520 --> 00:25:36,960 Speaker 1: these restaurants, and McDonald's resisted it for years. There was 433 00:25:37,000 --> 00:25:39,399 Speaker 1: concerns that the fries wouldn't hold up, that the food 434 00:25:39,720 --> 00:25:41,600 Speaker 1: would arrive, it would take twenty minutes, it would be 435 00:25:41,640 --> 00:25:44,120 Speaker 1: too long. McDonald's now is signed up with Uber Eats, 436 00:25:44,119 --> 00:25:45,800 Speaker 1: which has become more and more popular. You can get 437 00:25:45,880 --> 00:25:49,280 Speaker 1: McDonald's delivered and I think thirty stores. This is Amazon 438 00:25:50,080 --> 00:25:52,960 Speaker 1: basically getting access to OL those customers, which is a 439 00:25:52,960 --> 00:25:55,360 Speaker 1: lot of restaurants, and then you'll basically put that order 440 00:25:55,400 --> 00:25:57,399 Speaker 1: through the OLO system and then Amazon will deliver it. 441 00:25:57,480 --> 00:26:00,320 Speaker 1: So you know, Amazon wants frequency, they want they want 442 00:26:00,320 --> 00:26:03,800 Speaker 1: to be wherever customers are and wherever people buy things often, 443 00:26:03,840 --> 00:26:06,360 Speaker 1: so they want you go into that website. You want 444 00:26:06,359 --> 00:26:08,240 Speaker 1: to order Chili's, or you want to order shakeshack or 445 00:26:08,280 --> 00:26:10,040 Speaker 1: something like that, do it on Amazon, and while you're there, 446 00:26:10,359 --> 00:26:12,600 Speaker 1: you know, buy a DVD player, buy some clothes, buy 447 00:26:12,600 --> 00:26:14,680 Speaker 1: some whole foods products, whatever it is. Well, but I'm 448 00:26:14,680 --> 00:26:19,000 Speaker 1: trying to understand what competitive advantage Amazon would have with 449 00:26:19,280 --> 00:26:21,879 Speaker 1: the actual delivery. I mean, I understand from this from 450 00:26:21,920 --> 00:26:24,880 Speaker 1: the computer systems, but but a lot of these restaurants 451 00:26:24,920 --> 00:26:28,959 Speaker 1: have forces and Grubhub and Seamless have entire you know, 452 00:26:29,320 --> 00:26:32,080 Speaker 1: work staffs devoted to this. That's right, that's right, and 453 00:26:32,080 --> 00:26:34,560 Speaker 1: so it's very expensive to hire delivery drivers. You're absolutely right. 454 00:26:34,560 --> 00:26:36,960 Speaker 1: So as far as the economics of sending a driver 455 00:26:37,080 --> 00:26:39,040 Speaker 1: to pick up in order at a restaurant and then 456 00:26:39,040 --> 00:26:41,360 Speaker 1: bringing out to somebody's house, the economics of that are 457 00:26:41,400 --> 00:26:44,040 Speaker 1: not great. I think what Amazon sees here again is 458 00:26:44,600 --> 00:26:47,040 Speaker 1: this is a growing piece of business and they want 459 00:26:47,040 --> 00:26:48,800 Speaker 1: to be in there. So, as we know, they're willing 460 00:26:48,840 --> 00:26:50,920 Speaker 1: to take losses on businesses if they think that that's 461 00:26:50,920 --> 00:26:53,320 Speaker 1: the thing to do. So I think your question is 462 00:26:53,400 --> 00:26:56,320 Speaker 1: right that the economics aren't great, but the frequency and 463 00:26:56,359 --> 00:26:58,239 Speaker 1: sort of the loyalty and the ability to kind of 464 00:26:58,280 --> 00:27:00,800 Speaker 1: just be the place that people go to buy things, 465 00:27:00,840 --> 00:27:02,760 Speaker 1: I think is what they're looking at. Well, just to 466 00:27:02,840 --> 00:27:06,680 Speaker 1: follow up on that, does it become a potential location 467 00:27:06,720 --> 00:27:09,760 Speaker 1: for Amazon? Every restaurant that is part of this olo 468 00:27:10,600 --> 00:27:15,160 Speaker 1: project or family, then they can become a pickup location 469 00:27:15,240 --> 00:27:17,480 Speaker 1: for other Amazon products, right, So we I mean we've 470 00:27:17,480 --> 00:27:19,480 Speaker 1: seen the Amazon lockers pop up in the Whole Foods. 471 00:27:19,480 --> 00:27:21,800 Speaker 1: I mean that happened like the day after the deal closed. 472 00:27:21,800 --> 00:27:24,199 Speaker 1: So you know, they haven't said that. I mean, but 473 00:27:24,359 --> 00:27:26,640 Speaker 1: I don't think that that's a stretch to think that 474 00:27:26,640 --> 00:27:28,560 Speaker 1: that's could be where this is going. Again, this this 475 00:27:28,600 --> 00:27:31,920 Speaker 1: isn't an exclusive arrangement, so it's not like these old 476 00:27:31,920 --> 00:27:34,560 Speaker 1: customers are going to only deal with Amazon. But again, 477 00:27:34,560 --> 00:27:36,080 Speaker 1: that's where all this is headed. I mean, if if 478 00:27:36,080 --> 00:27:37,960 Speaker 1: you're gonna be if you're gonna go to drive to 479 00:27:38,000 --> 00:27:39,960 Speaker 1: the Chilis to pick up your burger and your meal, 480 00:27:40,240 --> 00:27:42,840 Speaker 1: sure there's an Amazon locker there and you grab your package. 481 00:27:42,840 --> 00:27:44,760 Speaker 1: I mean that that's not a stretch, No, but it 482 00:27:44,800 --> 00:27:47,359 Speaker 1: does really kind of highlight how the changing nature of 483 00:27:47,359 --> 00:27:50,040 Speaker 1: the whole supply chain, particularly you're gonna be able to 484 00:27:50,040 --> 00:27:52,040 Speaker 1: save money on gasoline if they're going to do all 485 00:27:52,040 --> 00:27:54,439 Speaker 1: the delivery for you. Thanks very much for coming in. 486 00:27:54,520 --> 00:27:56,959 Speaker 1: Thank you appreciate it. The Craig Giammona. He is our 487 00:27:57,000 --> 00:28:01,760 Speaker 1: consumer reporter for Bloomberg. Thanks for listening. To the Bloomberg 488 00:28:01,840 --> 00:28:04,480 Speaker 1: P and L podcast. You can subscribe and listen to 489 00:28:04,520 --> 00:28:09,040 Speaker 1: interviews at Apple Podcasts, SoundCloud, or whatever podcast platform you prefer. 490 00:28:09,440 --> 00:28:13,040 Speaker 1: I'm pim Fox. I'm on Twitter at pim Fox. I'm 491 00:28:13,040 --> 00:28:16,359 Speaker 1: on Twitter at Lisa Abramo wits one. Before the podcast, 492 00:28:16,400 --> 00:28:19,000 Speaker 1: you can always catch us worldwide on Bloomberg Radio