1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm Pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,600 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,800 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,920 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg p m L 6 00:00:20,840 --> 00:00:27,440 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. We 7 00:00:27,480 --> 00:00:32,960 Speaker 1: are broadcasting from Bloomberg's Government Next eighteen conference in Washington, 8 00:00:33,040 --> 00:00:35,360 Speaker 1: d C. I'm Pim Fox. My co host and colleague 9 00:00:35,400 --> 00:00:41,400 Speaker 1: Lisa Abramowitz is off today. The healthcare industry and technology 10 00:00:41,440 --> 00:00:44,280 Speaker 1: where they meet is one of the expertise of Dr 11 00:00:44,360 --> 00:00:47,639 Speaker 1: Pat Connelly. She joins me now as executive vice president 12 00:00:47,640 --> 00:00:51,720 Speaker 1: of Information Technology and the chief Information Officer of the 13 00:00:51,760 --> 00:00:56,920 Speaker 1: Permanente Federation. She's also associate executive director of the Permanente 14 00:00:57,280 --> 00:01:00,240 Speaker 1: Medical Group. They are based in Oakland, but I'm happy 15 00:01:00,280 --> 00:01:02,800 Speaker 1: to say she joins me here at our Bloomberg Government 16 00:01:02,840 --> 00:01:05,480 Speaker 1: Next eighteen conference. Thank you very much for being here. 17 00:01:05,560 --> 00:01:09,119 Speaker 1: Dr Connolly tell people who may not be familiar with 18 00:01:09,240 --> 00:01:14,520 Speaker 1: the Permanent A Foundation what it is and what it does. Well, Piam, 19 00:01:14,560 --> 00:01:17,600 Speaker 1: thank you for having me um the PERMANENTI Federation is 20 00:01:17,640 --> 00:01:22,120 Speaker 1: actually the organization that links the eight PERMANENTI medical groups 21 00:01:22,240 --> 00:01:27,000 Speaker 1: in Kaiser Permanente UM and we bring together the expertise 22 00:01:27,280 --> 00:01:33,080 Speaker 1: of our physician leadership partnered with our Kaiser Foundation health 23 00:01:33,120 --> 00:01:37,800 Speaker 1: plan leaders UM and it's it's an integrated model in 24 00:01:37,840 --> 00:01:42,960 Speaker 1: which our expertise with respective care delivery UM is really 25 00:01:43,160 --> 00:01:47,320 Speaker 1: a guiding force in how we design our systems to 26 00:01:47,520 --> 00:01:51,440 Speaker 1: deliver care to the twelve million members that we serve. Now, 27 00:01:51,480 --> 00:01:54,800 Speaker 1: as part of that, you're using technology. You're going to 28 00:01:54,840 --> 00:01:56,680 Speaker 1: be using more of it in the future as our 29 00:01:56,720 --> 00:02:00,800 Speaker 1: all healthcare providers offer us a current exam ample about 30 00:02:00,800 --> 00:02:04,200 Speaker 1: how you are using algorithms to in a sense for 31 00:02:04,520 --> 00:02:08,600 Speaker 1: tell the potential for patients to have some kind of 32 00:02:08,680 --> 00:02:14,400 Speaker 1: deterioration in their condition, but to do it remotely UM. Well, 33 00:02:14,440 --> 00:02:17,040 Speaker 1: as you know, we we have a tradition of innovation 34 00:02:17,520 --> 00:02:22,360 Speaker 1: UH and UH Permanente Medicine looks carefully at evidence based 35 00:02:22,960 --> 00:02:26,919 Speaker 1: UH information to help us determine where we can offer 36 00:02:27,040 --> 00:02:32,160 Speaker 1: improvements in quality UM and with the implementation of the 37 00:02:32,160 --> 00:02:35,360 Speaker 1: e m R, we suddenly have this huge volume of 38 00:02:35,480 --> 00:02:39,400 Speaker 1: data that we can look at very differently UH and 39 00:02:39,400 --> 00:02:43,799 Speaker 1: and that's where you bring together the technology of high 40 00:02:44,000 --> 00:02:48,880 Speaker 1: UM robust analytics with large volumes of data. So we've 41 00:02:48,919 --> 00:02:52,320 Speaker 1: developed some systems, one of which we call Advanced Alert 42 00:02:52,360 --> 00:02:56,320 Speaker 1: Monitoring UM, and this has been published by Gabriel Escobar, 43 00:02:56,480 --> 00:03:01,600 Speaker 1: who is a UH Permanente Medical Group physician. And what 44 00:03:01,760 --> 00:03:04,280 Speaker 1: this does is leverage information in the e m R 45 00:03:04,919 --> 00:03:08,720 Speaker 1: with an algorithm to identify patients who are at risk 46 00:03:09,200 --> 00:03:14,240 Speaker 1: when they're hospitalized UM, at risk for a deterioration, and 47 00:03:14,320 --> 00:03:18,639 Speaker 1: we fire an alert to the care team UM so 48 00:03:18,680 --> 00:03:23,480 Speaker 1: that they can assess the patient and intervene early UM 49 00:03:23,520 --> 00:03:26,800 Speaker 1: to avoid that deterioration. UH. And we've been doing this 50 00:03:26,880 --> 00:03:32,480 Speaker 1: now for UM almost two years and have accumulated enough 51 00:03:32,560 --> 00:03:35,480 Speaker 1: evidence to demonstrate that this makes a difference, that this 52 00:03:35,600 --> 00:03:39,200 Speaker 1: is improving the care of our patients. And and with 53 00:03:39,280 --> 00:03:43,360 Speaker 1: that information then we UM integrate this algorithm and this 54 00:03:43,640 --> 00:03:48,320 Speaker 1: UM functionality into our system very broadly. Now, you're also 55 00:03:48,440 --> 00:03:53,000 Speaker 1: looking at the Internet of Things IoT as a way 56 00:03:53,040 --> 00:03:58,040 Speaker 1: to connect more directly with patients. For example, you can 57 00:03:58,080 --> 00:04:02,040 Speaker 1: have in home monitoring devices for people that are dealing 58 00:04:02,040 --> 00:04:04,920 Speaker 1: with diabetes. For example, tell us some of the things 59 00:04:04,960 --> 00:04:08,360 Speaker 1: that you see that are maybe if unless you're involved 60 00:04:08,360 --> 00:04:10,320 Speaker 1: in it, you think this is like science fiction, but 61 00:04:10,360 --> 00:04:13,600 Speaker 1: it's true to life. Well, you're right, at the pace 62 00:04:13,600 --> 00:04:17,400 Speaker 1: of technology change is outstripping in many ways our ability 63 00:04:17,440 --> 00:04:20,320 Speaker 1: to think about how um how much change this can 64 00:04:20,880 --> 00:04:24,560 Speaker 1: UM create for us. If you take the hospital for example, 65 00:04:24,920 --> 00:04:28,800 Speaker 1: the connection of devices medical devices to the e m 66 00:04:28,960 --> 00:04:33,120 Speaker 1: r UM allows us to get rid of manual entry, 67 00:04:33,640 --> 00:04:36,760 Speaker 1: which is one of those big patient safety issues where 68 00:04:36,800 --> 00:04:40,160 Speaker 1: you know mistakes can be made. UM. Now that information 69 00:04:40,320 --> 00:04:45,560 Speaker 1: is directly imported, but it's also immediately available, so you 70 00:04:45,600 --> 00:04:48,360 Speaker 1: can see how this can speed and improve the care 71 00:04:48,440 --> 00:04:51,560 Speaker 1: that we're delivering. Now you refer to an outpatient setting, 72 00:04:51,640 --> 00:04:55,520 Speaker 1: which is actually where we think the Internet of Things 73 00:04:55,640 --> 00:04:58,440 Speaker 1: really is taking us. So for the last fifty years 74 00:04:58,480 --> 00:05:02,640 Speaker 1: we've been telling people to come to us. We've created centers. 75 00:05:02,720 --> 00:05:07,440 Speaker 1: Our offices are hospitals where we concentrate equipment and expertise 76 00:05:07,560 --> 00:05:11,279 Speaker 1: to deliver care. The Internet of Things allows us to 77 00:05:11,320 --> 00:05:15,400 Speaker 1: bring care to the patient a whole different paradigm where 78 00:05:15,440 --> 00:05:19,320 Speaker 1: not only do UM are they in the security and 79 00:05:19,360 --> 00:05:23,480 Speaker 1: safety of their home a much more comfortable environment, UM, 80 00:05:23,560 --> 00:05:28,440 Speaker 1: but we're not interrupting work or school, And the real 81 00:05:28,440 --> 00:05:32,360 Speaker 1: difference is they're not passive in this process. That information 82 00:05:32,440 --> 00:05:35,360 Speaker 1: is available to the patient and to their caregivers. They 83 00:05:35,440 --> 00:05:38,719 Speaker 1: become part of the care team. And that's really the 84 00:05:38,760 --> 00:05:42,400 Speaker 1: game changer there, UM, because if we look particularly at 85 00:05:42,400 --> 00:05:46,279 Speaker 1: health and how much of disease burden is related to 86 00:05:46,400 --> 00:05:49,120 Speaker 1: choices that we all make in our lives, and we 87 00:05:49,160 --> 00:05:53,080 Speaker 1: begin to share with our patients how those choices are 88 00:05:53,120 --> 00:05:56,120 Speaker 1: impacting their care and show them how their care is progressing, 89 00:05:56,760 --> 00:05:59,960 Speaker 1: their role in improving their health is going to change. 90 00:06:00,320 --> 00:06:03,560 Speaker 1: Can you see a time when a device such as 91 00:06:03,640 --> 00:06:08,479 Speaker 1: an Apple Watch, just as an example, takes various types 92 00:06:08,760 --> 00:06:14,120 Speaker 1: of biometric information and then is able to transmit that 93 00:06:14,279 --> 00:06:18,480 Speaker 1: directly to a file of some kind that would allow 94 00:06:18,640 --> 00:06:24,240 Speaker 1: a physician or healthcare provider to gather a more holistic 95 00:06:24,320 --> 00:06:28,880 Speaker 1: picture of the patient, not just a snapshot. Right, well, 96 00:06:28,880 --> 00:06:31,640 Speaker 1: I think that's the direction that we're going right now. 97 00:06:31,680 --> 00:06:36,200 Speaker 1: Devices um, you know, such as glucometers, provide that information 98 00:06:36,240 --> 00:06:39,320 Speaker 1: and UM and it can be brought forward in a 99 00:06:39,360 --> 00:06:41,800 Speaker 1: way that you see all this data which would be 100 00:06:41,800 --> 00:06:46,479 Speaker 1: overwhelming to a care provider. Again, the power of analytics, 101 00:06:46,520 --> 00:06:50,320 Speaker 1: one can configure that data so that what the clinician 102 00:06:50,400 --> 00:06:53,719 Speaker 1: sees is an alert that something has met a threshold 103 00:06:54,400 --> 00:06:58,880 Speaker 1: UM or a trend on what you're talking about is 104 00:06:58,920 --> 00:07:02,520 Speaker 1: going from beyond a single measurement to a more holistic picture. 105 00:07:02,880 --> 00:07:06,520 Speaker 1: And you know, from a technical standpoint, absolutely that's a 106 00:07:06,560 --> 00:07:09,680 Speaker 1: possibility where we need to take a step back. Is 107 00:07:09,680 --> 00:07:11,920 Speaker 1: we need to then ask our patients how they feel 108 00:07:11,920 --> 00:07:15,440 Speaker 1: about that? UM? How you know this whole issue about 109 00:07:15,480 --> 00:07:19,040 Speaker 1: privacy my data, UM, this is me. How much do 110 00:07:19,080 --> 00:07:23,000 Speaker 1: I want you to have? UM? And how secure can 111 00:07:23,000 --> 00:07:27,080 Speaker 1: we keep it? So? UM, I think is this evolves, 112 00:07:27,160 --> 00:07:29,440 Speaker 1: there's going to be a lot of conversation that's going 113 00:07:29,480 --> 00:07:32,800 Speaker 1: to need to involve patients as well as clinicians, UM, 114 00:07:32,840 --> 00:07:34,920 Speaker 1: about how far we take this and how we use 115 00:07:35,000 --> 00:07:40,040 Speaker 1: this information A seconds, what is the feeling among docters? 116 00:07:40,320 --> 00:07:45,080 Speaker 1: Are they embracing this UM broadly? Doctors think that this 117 00:07:45,160 --> 00:07:48,400 Speaker 1: is a new future and very much so. The fear 118 00:07:48,600 --> 00:07:51,280 Speaker 1: which you've probably heard is that idea of this huge 119 00:07:51,320 --> 00:07:55,360 Speaker 1: amount of data in which very important information could be lost, 120 00:07:55,800 --> 00:07:59,440 Speaker 1: So the technology to configure it is critical to physicians. 121 00:08:00,000 --> 00:08:02,240 Speaker 1: Thank you very much for being with us. Much appreciated. 122 00:08:02,320 --> 00:08:07,280 Speaker 1: Dr Pat Connelly, Executive vice president Information Technology, Chief Information 123 00:08:07,400 --> 00:08:12,280 Speaker 1: Officer of the Permanente Federation and also Associate executive director 124 00:08:12,280 --> 00:08:15,480 Speaker 1: of the Permanente Medical Group coming up with Peter Barnes. 125 00:08:15,560 --> 00:08:20,840 Speaker 1: We've got Bloomberg Politics, policy, power and law. Thanks for listening. 126 00:08:21,240 --> 00:08:24,520 Speaker 1: We've got Day two coming up of the Bloomberg Government 127 00:08:24,600 --> 00:08:33,320 Speaker 1: Next Conference in Washington, d C. The world's largest retailer 128 00:08:33,640 --> 00:08:36,839 Speaker 1: and foot traffic as well as same stores sales when 129 00:08:36,880 --> 00:08:39,160 Speaker 1: it comes to the holiday shopping season. Here to tell 130 00:08:39,200 --> 00:08:43,000 Speaker 1: us more about Walmart earnings and a new partnership, as 131 00:08:43,000 --> 00:08:45,280 Speaker 1: well as the effect of their acquisition of flip Card 132 00:08:45,360 --> 00:08:49,160 Speaker 1: in India is Matt Boyle, consumer reporter for Bloomberg, joining 133 00:08:49,200 --> 00:08:52,760 Speaker 1: us from our Bloomberg Interactive Broker's studio. Matt tell us 134 00:08:52,800 --> 00:08:57,800 Speaker 1: about Walmart and what investors really wanted to hear when 135 00:08:57,880 --> 00:09:01,000 Speaker 1: it comes to all of those quarterly result Well, they 136 00:09:01,000 --> 00:09:03,560 Speaker 1: probably don't want to hear about the news actually that 137 00:09:03,679 --> 00:09:07,160 Speaker 1: hit the drama surrounding their Indian e commerce unit whose 138 00:09:07,520 --> 00:09:12,280 Speaker 1: co founder was ousted or forced to resign actually um 139 00:09:12,320 --> 00:09:16,280 Speaker 1: after some allegations of sexual assault. Investors really want to 140 00:09:16,280 --> 00:09:18,959 Speaker 1: hear about the US business and are they keeping up 141 00:09:18,960 --> 00:09:21,960 Speaker 1: the momentum that they've had last quarter their second quarter 142 00:09:22,440 --> 00:09:25,760 Speaker 1: they post the best sales growth in UH in over 143 00:09:25,840 --> 00:09:30,040 Speaker 1: a decade. UM but for every quarter that Walmart does well, Um, 144 00:09:30,080 --> 00:09:32,200 Speaker 1: it's getting harder and harder for them to top that. 145 00:09:32,320 --> 00:09:34,800 Speaker 1: Of course, so they're going up against a pretty strong 146 00:09:35,360 --> 00:09:39,480 Speaker 1: UH sales growth figure last year this time, so all 147 00:09:39,520 --> 00:09:40,880 Speaker 1: eyes are going to be on them, and then not 148 00:09:40,960 --> 00:09:43,559 Speaker 1: just their sales, but it's their bottom line that's been 149 00:09:43,640 --> 00:09:46,960 Speaker 1: really a sore point for some investors. People are happy 150 00:09:47,040 --> 00:09:49,560 Speaker 1: with the sales growth, but at what cost is it 151 00:09:49,679 --> 00:09:52,280 Speaker 1: coming if their margins have really been hurt by all 152 00:09:52,320 --> 00:09:55,800 Speaker 1: the investments they're making. A broad flip card is an example. 153 00:09:55,800 --> 00:09:58,760 Speaker 1: They spent sixteen billion there and then also the investments 154 00:09:58,760 --> 00:10:01,839 Speaker 1: in the US to battle Amazon. Well, talk a little 155 00:10:01,840 --> 00:10:05,280 Speaker 1: bit more about those efforts in their battle with Amazon. 156 00:10:05,400 --> 00:10:08,800 Speaker 1: I believe that they have opened an artificial what they're 157 00:10:08,840 --> 00:10:14,040 Speaker 1: calling an artificial intelligence kiosk or area at one Walmart 158 00:10:14,120 --> 00:10:17,760 Speaker 1: in Levittown, New York. Yeah, they're trying to do they're well, 159 00:10:17,800 --> 00:10:20,960 Speaker 1: they're they're trying to make shopping easier, and they're testing 160 00:10:21,000 --> 00:10:23,400 Speaker 1: all sorts of different things. You know, Amazon and Google 161 00:10:23,440 --> 00:10:26,920 Speaker 1: aren't the only companies that can try to test things 162 00:10:27,000 --> 00:10:29,600 Speaker 1: and see if they could fail fast. Walmart is, you know, 163 00:10:29,640 --> 00:10:32,560 Speaker 1: investing billions and figuring out you know, how can we 164 00:10:32,640 --> 00:10:36,679 Speaker 1: make it easier for customers to get goods, particularly groceries, uh, 165 00:10:36,720 --> 00:10:40,080 Speaker 1: you know, into their hands. So they they're doing some 166 00:10:40,160 --> 00:10:44,480 Speaker 1: small tests also in Texas. They've got a lab going 167 00:10:44,480 --> 00:10:46,840 Speaker 1: on in Austin. So they're what they're doing is they're 168 00:10:46,920 --> 00:10:52,040 Speaker 1: rapidly hiring technology experts, data scientists, throwing them out all 169 00:10:52,080 --> 00:10:55,200 Speaker 1: these problems, saying, Okay, you know, we built the most 170 00:10:55,320 --> 00:10:59,160 Speaker 1: profitable retail concept known to man, called the supercenter. But 171 00:10:59,200 --> 00:11:02,199 Speaker 1: a supercenter is not how most people really want to 172 00:11:02,240 --> 00:11:04,400 Speaker 1: shop and not how they're going to be shopping in 173 00:11:04,720 --> 00:11:07,760 Speaker 1: the years ahead. So Walmart really wants to be seen 174 00:11:07,920 --> 00:11:11,040 Speaker 1: as a technology company. Um, it's not the first thing 175 00:11:11,080 --> 00:11:13,240 Speaker 1: you think about, of course, but that's why they're doing 176 00:11:13,559 --> 00:11:16,800 Speaker 1: all these deals and partnerships. They've got partnerships with Google, 177 00:11:17,000 --> 00:11:20,320 Speaker 1: They've got partnerships, you know, with the Japanese company Recut, 178 00:11:20,400 --> 00:11:23,120 Speaker 1: and they're they're all over the board with these partnerships, 179 00:11:23,200 --> 00:11:25,640 Speaker 1: thinking that, you know, the enemy of my enemy is 180 00:11:25,679 --> 00:11:28,880 Speaker 1: my friend, that being of course Amazon, and so they're 181 00:11:28,960 --> 00:11:31,880 Speaker 1: using this to get smarter. And today's partnership, of course 182 00:11:31,920 --> 00:11:34,840 Speaker 1: with Ford is yet another example of that. Well you've 183 00:11:34,840 --> 00:11:37,480 Speaker 1: done the segue for me, Matt, go ahead, tell everybody 184 00:11:37,520 --> 00:11:39,800 Speaker 1: about how maybe you're gonna get into an automobile. It 185 00:11:39,840 --> 00:11:42,880 Speaker 1: will be driverless or autonomous, whatever you want to describe it, 186 00:11:43,080 --> 00:11:45,040 Speaker 1: and maybe there'll be a button that you're just pushing 187 00:11:45,080 --> 00:11:47,280 Speaker 1: it will take you to Walmart. Well, yeah, there's two 188 00:11:47,280 --> 00:11:49,480 Speaker 1: elements of this. One is what they already had announced 189 00:11:49,480 --> 00:11:52,160 Speaker 1: earlier this year with Weymo, which is where yet a driver, 190 00:11:52,280 --> 00:11:56,320 Speaker 1: this car would actually transport you to the Walmart. Um. 191 00:11:56,360 --> 00:11:58,720 Speaker 1: I'm not sure if how I feel about that at 192 00:11:58,720 --> 00:12:01,960 Speaker 1: the moment, but what's been announced today with Ford is 193 00:12:02,520 --> 00:12:07,360 Speaker 1: autonomous card deliveries. Um, where the uh, the car would 194 00:12:07,400 --> 00:12:10,839 Speaker 1: come right up to your curbside to your house. Now, 195 00:12:10,840 --> 00:12:14,280 Speaker 1: there would be a driver in it. It would look autonomous. 196 00:12:14,480 --> 00:12:16,520 Speaker 1: It would give the appearance that there's nobody there, but 197 00:12:16,559 --> 00:12:19,320 Speaker 1: there actually would be a human in the car, but 198 00:12:19,520 --> 00:12:23,240 Speaker 1: using voice prompts and commands, uh, the person would come 199 00:12:23,240 --> 00:12:26,480 Speaker 1: out of their house, pop the trunk, get their groceries, 200 00:12:26,880 --> 00:12:29,080 Speaker 1: tell the car in some fashion that the you know, 201 00:12:29,120 --> 00:12:31,679 Speaker 1: the delivery is done, and it would go on its way. 202 00:12:31,720 --> 00:12:34,960 Speaker 1: I mean, walmartist is experimenting with all sorts of different 203 00:12:34,960 --> 00:12:39,800 Speaker 1: ways to get online orders to its customers. They've even 204 00:12:39,840 --> 00:12:44,720 Speaker 1: tried having their own employees deliver orders on their way 205 00:12:44,760 --> 00:12:48,559 Speaker 1: home from work. I can just see now an automated 206 00:12:48,679 --> 00:12:53,480 Speaker 1: sling shot that just throws your entire grocery order through 207 00:12:53,520 --> 00:12:57,000 Speaker 1: the window of the automobile that lands precisely on your 208 00:12:57,040 --> 00:13:00,079 Speaker 1: doorstep as the old wet newspaper used to do. This. 209 00:13:00,400 --> 00:13:03,400 Speaker 1: Is any have they costed out, like what advantage this 210 00:13:03,440 --> 00:13:07,880 Speaker 1: would give them beating Amazon? Or is this just throw 211 00:13:07,920 --> 00:13:11,520 Speaker 1: a lot sticks? They're not. Yeah, it's it's let's see 212 00:13:11,559 --> 00:13:14,920 Speaker 1: what sticks. What is really most economical for Walmart is 213 00:13:14,960 --> 00:13:17,679 Speaker 1: for you to still get in your car and go 214 00:13:17,840 --> 00:13:20,520 Speaker 1: to Walmart. Um. They're trying to make that easier, of 215 00:13:20,559 --> 00:13:23,760 Speaker 1: course by having curbside pick up, where you will just 216 00:13:23,840 --> 00:13:25,920 Speaker 1: drive up to the parking lot and a nice, friendly 217 00:13:25,960 --> 00:13:29,160 Speaker 1: person will come out and put the your grocery order 218 00:13:29,200 --> 00:13:32,640 Speaker 1: in your trunk. But this is Walmart's big advantage against 219 00:13:32,720 --> 00:13:35,760 Speaker 1: Amazon right now. I don't mean delivery. I'm talking about 220 00:13:35,800 --> 00:13:38,920 Speaker 1: their food business that they need to keep their lead 221 00:13:38,960 --> 00:13:42,600 Speaker 1: in the US foods sector. Um, they have over market 222 00:13:42,640 --> 00:13:45,080 Speaker 1: share in the US grocery business. This is nearly a 223 00:13:45,080 --> 00:13:48,720 Speaker 1: one trillion dollar market and it's one that Amazon, for 224 00:13:48,760 --> 00:13:51,920 Speaker 1: all its prowess and for all it's smarts still has 225 00:13:51,960 --> 00:13:54,040 Speaker 1: not really figured out. And that's of course why they 226 00:13:54,160 --> 00:13:56,880 Speaker 1: know why they bought Whole Foods last year in an 227 00:13:56,880 --> 00:14:00,800 Speaker 1: attempt to get serious about food. But Walmart needs to 228 00:14:00,800 --> 00:14:04,200 Speaker 1: know they need to keep your weekly grocery shop. It's reliable, 229 00:14:04,240 --> 00:14:07,000 Speaker 1: it's profitable, and so they're coming up with just about 230 00:14:07,040 --> 00:14:09,440 Speaker 1: any way they can do it, and that includes you know, 231 00:14:09,880 --> 00:14:12,440 Speaker 1: a car driver, this car coming to your house to 232 00:14:12,559 --> 00:14:15,360 Speaker 1: drop off your avocados. I'm waiting for Matt Boyle to 233 00:14:15,440 --> 00:14:19,040 Speaker 1: drop off my avocados. Matt Boyle, consumer reporter for Bloomberg. 234 00:14:19,120 --> 00:14:21,760 Speaker 1: Thank you for joining us in our Bloomberg Interactive Broker 235 00:14:21,840 --> 00:14:29,360 Speaker 1: studios talking about Walmart shares are down about one broadcasting 236 00:14:29,400 --> 00:14:34,400 Speaker 1: live from Bloomberg's Government Next conference in Washington, d C. 237 00:14:35,080 --> 00:14:37,680 Speaker 1: And we've heard a lot about connected cities, and here 238 00:14:37,680 --> 00:14:40,080 Speaker 1: to help us understand what that term means and what 239 00:14:40,120 --> 00:14:42,440 Speaker 1: it can do for the lives of citizens in those 240 00:14:42,440 --> 00:14:46,040 Speaker 1: cities is Ben Levine. He is the executive director of 241 00:14:46,200 --> 00:14:49,680 Speaker 1: Metro Lab Network. This is a national network of forty 242 00:14:49,760 --> 00:14:53,320 Speaker 1: cities and fifty universities, and he's here to tell us more. Ben, 243 00:14:53,320 --> 00:14:55,480 Speaker 1: thank you very much for being with us. What is 244 00:14:55,840 --> 00:14:59,040 Speaker 1: metro lab network. Well, first of all, PIM, thank you 245 00:14:59,080 --> 00:15:02,320 Speaker 1: for having me. Metro Lab is a collaborative of cities 246 00:15:02,360 --> 00:15:05,640 Speaker 1: and universities and the goal is to take academic research 247 00:15:05,680 --> 00:15:08,320 Speaker 1: that is happening in universities and figure out a way 248 00:15:08,360 --> 00:15:11,760 Speaker 1: of translating that to to the local government policy process, 249 00:15:11,800 --> 00:15:14,800 Speaker 1: to think about how new knowledge that's created, whether it's 250 00:15:14,800 --> 00:15:18,640 Speaker 1: related to data science or engineering or social science, how 251 00:15:18,680 --> 00:15:21,240 Speaker 1: that can have a positive impact on the lives of 252 00:15:21,400 --> 00:15:24,600 Speaker 1: residents of cities and communities. What would be an example 253 00:15:25,040 --> 00:15:28,320 Speaker 1: of such an application is that things like cameras that 254 00:15:28,360 --> 00:15:31,440 Speaker 1: are then linked to databases. What would be an example, 255 00:15:31,600 --> 00:15:34,080 Speaker 1: So you brought up cameras, I'll give you an example. 256 00:15:34,440 --> 00:15:37,520 Speaker 1: The University of Texas at Austin is working on a 257 00:15:37,640 --> 00:15:41,760 Speaker 1: video analytics approach that uses computer vision and machine learning 258 00:15:41,760 --> 00:15:45,440 Speaker 1: to understand how humans are moving around their cities and ultimately, 259 00:15:45,520 --> 00:15:47,600 Speaker 1: and you can think about that as you know, someone 260 00:15:47,680 --> 00:15:50,000 Speaker 1: crosses the street with a stroller or on a bicycle 261 00:15:50,120 --> 00:15:52,160 Speaker 1: or in a car, and a computer is able to 262 00:15:52,240 --> 00:15:57,360 Speaker 1: understand the characteristics of that person's maybe their age, because 263 00:15:57,400 --> 00:16:01,320 Speaker 1: maybe they're walking uh with a with a particular characteristic 264 00:16:01,360 --> 00:16:03,480 Speaker 1: that would define someone's older, or maybe someone's walking with 265 00:16:03,520 --> 00:16:06,280 Speaker 1: a stroller, maybe a car is driving in a manner 266 00:16:06,320 --> 00:16:09,640 Speaker 1: that's unsafe or getting into a near collision. A computer 267 00:16:09,680 --> 00:16:13,200 Speaker 1: can understand how those how those dynamics are playing out, 268 00:16:13,400 --> 00:16:17,000 Speaker 1: and can can ultimately funnel back into a policy making process, 269 00:16:17,480 --> 00:16:20,400 Speaker 1: because now the city understands how to make urban planning 270 00:16:20,440 --> 00:16:24,560 Speaker 1: decisions that can create safer streets for everybody. You have 271 00:16:24,800 --> 00:16:28,040 Speaker 1: a background, You've advised the Department of the Treasury. You've 272 00:16:28,080 --> 00:16:30,640 Speaker 1: also had a background working in finance with Morgan Stanley, 273 00:16:30,640 --> 00:16:33,840 Speaker 1: where you're interacted with a lot of state and municipal governments. 274 00:16:34,280 --> 00:16:36,880 Speaker 1: What have you found in terms of the level of 275 00:16:36,960 --> 00:16:42,160 Speaker 1: technology that currently exists in many cities. Well, I think, 276 00:16:43,040 --> 00:16:46,040 Speaker 1: like the entire economy, I think the world is becoming 277 00:16:46,080 --> 00:16:50,160 Speaker 1: more technology and data oriented. I think that it's probably 278 00:16:50,200 --> 00:16:55,080 Speaker 1: true that maybe cities lag the leading engineering or leading 279 00:16:55,600 --> 00:16:58,320 Speaker 1: companies in the world just because they aren't able to 280 00:16:58,440 --> 00:17:03,160 Speaker 1: hire the UH either because of salaries or because of workforce, 281 00:17:03,280 --> 00:17:05,760 Speaker 1: or because of the way they're resourced and budgets. I 282 00:17:05,800 --> 00:17:08,720 Speaker 1: think that they're not able to necessarily look like UH 283 00:17:08,720 --> 00:17:11,960 Speaker 1: in Amazon's say. But that's where metro lab comes in, right, 284 00:17:12,359 --> 00:17:13,959 Speaker 1: that's where that's where I think we're trying to make 285 00:17:14,000 --> 00:17:16,000 Speaker 1: a difference. I think that there is not a perfect 286 00:17:16,080 --> 00:17:20,160 Speaker 1: solution to taking local governments and turning them into uh 287 00:17:20,160 --> 00:17:23,159 Speaker 1: into entities that are that are running on sort of 288 00:17:23,240 --> 00:17:26,560 Speaker 1: the absolute latest technology on a dime. I think that, 289 00:17:26,880 --> 00:17:28,440 Speaker 1: And and by the way, I think there's good reason 290 00:17:28,480 --> 00:17:31,000 Speaker 1: for that. I think that that as as governments we 291 00:17:31,000 --> 00:17:33,320 Speaker 1: should be skeptical of technology. We should be careful not 292 00:17:33,400 --> 00:17:37,840 Speaker 1: to adopt uh certain video analytics approaches that may introduce 293 00:17:37,920 --> 00:17:41,200 Speaker 1: questions about civil rights or may introduce questions about privacy. 294 00:17:41,240 --> 00:17:44,640 Speaker 1: I think that there's actually an obligation for for governments 295 00:17:44,640 --> 00:17:46,439 Speaker 1: to be more measured in the way that they approach 296 00:17:46,520 --> 00:17:49,040 Speaker 1: these technologies. But we do need to figure out a 297 00:17:49,040 --> 00:17:53,040 Speaker 1: way of embracing what's out there and and and trying 298 00:17:53,080 --> 00:17:56,200 Speaker 1: to aim that at at the goals that that governments have. Now. 299 00:17:56,200 --> 00:17:58,160 Speaker 1: Of course, we have been reporting, along with many other 300 00:17:58,200 --> 00:18:01,880 Speaker 1: news organizations about the wild fires that are hitting and 301 00:18:02,119 --> 00:18:08,440 Speaker 1: are really taxing the strength of California's fired preparedness and 302 00:18:08,480 --> 00:18:14,560 Speaker 1: causing great devastation. Metro Lab can help to figure out 303 00:18:14,640 --> 00:18:17,200 Speaker 1: how to prevent or at least mitigate those kinds of things. 304 00:18:17,200 --> 00:18:19,440 Speaker 1: In a certain way. Right, So I'll give an example 305 00:18:19,440 --> 00:18:21,399 Speaker 1: of a project that I think you're referring to that 306 00:18:21,920 --> 00:18:26,840 Speaker 1: UM that was done in partnership between UCSD, University of California, 307 00:18:26,840 --> 00:18:29,440 Speaker 1: San Diego, and San Diego County. And there's a project 308 00:18:29,440 --> 00:18:33,680 Speaker 1: that originally started as sort of bringing connectivity, so bringing uh, 309 00:18:33,720 --> 00:18:37,359 Speaker 1: the ability for rural broadband or sorry, rural fire stations 310 00:18:37,400 --> 00:18:41,520 Speaker 1: to communicate with broadband technology, and and that approach is 311 00:18:41,520 --> 00:18:45,119 Speaker 1: actually being used now to do video analytics to understand 312 00:18:45,359 --> 00:18:48,040 Speaker 1: how fires are are starting, when they're starting and respond 313 00:18:48,080 --> 00:18:50,560 Speaker 1: more quickly. Now, it's not a panacea. If I knew 314 00:18:50,560 --> 00:18:52,240 Speaker 1: the answer of how to address the issue, I wouldn't 315 00:18:52,240 --> 00:18:55,000 Speaker 1: be here with you today. I'd be over there helping 316 00:18:55,040 --> 00:18:56,919 Speaker 1: address the issue. And but I do think there's an 317 00:18:56,920 --> 00:19:00,199 Speaker 1: opportunity for researchers, as they've done it, you see SC 318 00:19:00,400 --> 00:19:03,280 Speaker 1: to sit down with with local authorities who are in 319 00:19:03,320 --> 00:19:06,320 Speaker 1: state authorities who are working on addressing a life threatening 320 00:19:06,320 --> 00:19:10,160 Speaker 1: issue and and and really figure out what's out there. 321 00:19:10,160 --> 00:19:12,360 Speaker 1: Because if we don't, if we don't embrace that tool, 322 00:19:12,400 --> 00:19:14,680 Speaker 1: I'm not sure what what other options we have. If 323 00:19:14,760 --> 00:19:18,120 Speaker 1: you are a state or local official listening to this 324 00:19:18,240 --> 00:19:22,200 Speaker 1: and hearing about metro lab. What do you want them 325 00:19:22,200 --> 00:19:26,040 Speaker 1: to take away if they're not already experiencing this kind 326 00:19:26,040 --> 00:19:30,080 Speaker 1: of benefit from technology. Yeah, I think that a couple 327 00:19:30,119 --> 00:19:33,680 Speaker 1: of lessons. I think one is, uh, think about how 328 00:19:33,720 --> 00:19:36,200 Speaker 1: you can use your data as a strategic asset. I 329 00:19:36,200 --> 00:19:38,880 Speaker 1: think there's a lot of uh. There's a sexy term 330 00:19:38,960 --> 00:19:40,800 Speaker 1: smart cities out there, and you'll hear a lot of 331 00:19:41,440 --> 00:19:43,399 Speaker 1: excitement about this idea of smart cities. I think at 332 00:19:43,400 --> 00:19:46,439 Speaker 1: the end of the day, smart cities is about integrating 333 00:19:46,560 --> 00:19:51,000 Speaker 1: data in responsible ways that can bring together that that 334 00:19:51,080 --> 00:19:54,879 Speaker 1: can bring together agencies, bring together public policy processes that 335 00:19:54,920 --> 00:19:58,720 Speaker 1: allow governments to make more informed decisions about about their activities. 336 00:19:58,840 --> 00:20:00,439 Speaker 1: See and I thought you were going to say, just 337 00:20:00,600 --> 00:20:04,919 Speaker 1: send Ben Levine an email at metro Lab Network in Washington, 338 00:20:05,040 --> 00:20:07,400 Speaker 1: d C. That is that is another approach. You can 339 00:20:07,480 --> 00:20:10,240 Speaker 1: visit www dot metro leb network dot org and get 340 00:20:10,280 --> 00:20:12,439 Speaker 1: in touch with us. Thanks very much, Ben Levine. He 341 00:20:12,520 --> 00:20:16,880 Speaker 1: is the executive director of metro Lab Network in Washington, 342 00:20:16,960 --> 00:20:20,439 Speaker 1: D C. We're broadcasting live from Bloomberg Government's Next eighteen 343 00:20:20,520 --> 00:20:28,240 Speaker 1: conference at the foothills of the Allegheny Mountains. Lies one 344 00:20:28,320 --> 00:20:33,720 Speaker 1: particular location, Buck Hannan, West Virginia. Joining me now is 345 00:20:33,800 --> 00:20:36,320 Speaker 1: Rob Hinton. He is the chairman of the West Virginia 346 00:20:36,560 --> 00:20:40,280 Speaker 1: Broadband Council, but he's also the executive director of the 347 00:20:40,359 --> 00:20:44,840 Speaker 1: Upshurk County Development Authority in buck Hannan, West Virginia, and 348 00:20:44,880 --> 00:20:46,800 Speaker 1: he joins me now. Rob Hinton, thank you very much 349 00:20:46,840 --> 00:20:49,400 Speaker 1: for being here. Thank you tell people a little bit 350 00:20:49,440 --> 00:20:53,560 Speaker 1: about your background and the work that you have done 351 00:20:53,840 --> 00:20:58,600 Speaker 1: to add to the economy of your local region. It's 352 00:20:58,640 --> 00:21:01,000 Speaker 1: not just something right now having to do with broadband, 353 00:21:01,000 --> 00:21:04,960 Speaker 1: although that's it, but you've done a lot for your area. Right. 354 00:21:05,000 --> 00:21:07,280 Speaker 1: So the my background is I used to be an 355 00:21:07,359 --> 00:21:09,480 Speaker 1: entrepreneur um. And what I mean I used to be 356 00:21:09,680 --> 00:21:12,640 Speaker 1: entrepreneur is that I was in the private sector, right. Um. 357 00:21:12,720 --> 00:21:14,320 Speaker 1: So as a casualty of the two thousand and eight 358 00:21:14,560 --> 00:21:18,080 Speaker 1: financial crisis and uh ended up in Buchanan, West Virginia. 359 00:21:18,520 --> 00:21:21,360 Speaker 1: I met my wife, we decided to stay there, um 360 00:21:21,480 --> 00:21:25,199 Speaker 1: and decided to create some change. And so, taking the 361 00:21:25,280 --> 00:21:29,560 Speaker 1: entrepreneurial attitude that that I haven't grained, um, we said, 362 00:21:29,600 --> 00:21:31,280 Speaker 1: you know, look, let's uh let's see how we can 363 00:21:31,320 --> 00:21:35,080 Speaker 1: expand the opportunities here. Uh. One thing we lacked was connectivity, 364 00:21:35,440 --> 00:21:39,359 Speaker 1: so we had to improve broadband connectivity. We UH UH 365 00:21:39,800 --> 00:21:42,159 Speaker 1: enticed the company to come there and then run fiber 366 00:21:42,240 --> 00:21:44,960 Speaker 1: to become a gig city. We also we have a 367 00:21:45,040 --> 00:21:48,240 Speaker 1: large county, so we had to expand wireless broadband to 368 00:21:48,240 --> 00:21:51,520 Speaker 1: connect the entire county. We also needed to leverage the 369 00:21:51,560 --> 00:21:55,400 Speaker 1: assets at our at our local university UH Business Department, 370 00:21:55,720 --> 00:22:01,280 Speaker 1: UH the entrepreneur expansion of entrepreneurial curriculum them. We're constructing 371 00:22:01,280 --> 00:22:05,320 Speaker 1: an innovation center that will mimic a very small scales 372 00:22:05,800 --> 00:22:08,919 Speaker 1: type of innovation center that mimics the Cambridge Innovation Center 373 00:22:08,960 --> 00:22:11,600 Speaker 1: that UH started up in Boston and m I T 374 00:22:11,760 --> 00:22:15,320 Speaker 1: S Campus UM. So we're we're trying to create UH. 375 00:22:15,440 --> 00:22:17,639 Speaker 1: We're trying to keep up the pace with the changing 376 00:22:17,680 --> 00:22:22,000 Speaker 1: technology and the changing changing ways of how employment takes place. 377 00:22:22,560 --> 00:22:26,040 Speaker 1: You also seem extremely good at raising money, because I'm 378 00:22:26,080 --> 00:22:29,479 Speaker 1: just looking through some of your accomplishments. Here. Three million 379 00:22:29,520 --> 00:22:32,800 Speaker 1: dollars raised from the U s D A Rural Utilities 380 00:22:32,960 --> 00:22:38,480 Speaker 1: Service Community connect funds, UH, more money raised from power grants, 381 00:22:38,680 --> 00:22:41,879 Speaker 1: opportunity zones you have to put together. I want you 382 00:22:41,920 --> 00:22:45,040 Speaker 1: to talk about the mix of opportunities that you have 383 00:22:45,160 --> 00:22:48,840 Speaker 1: to put together in order to make something happen well, 384 00:22:48,920 --> 00:22:51,480 Speaker 1: the assets of the area, so the incumbent assets that 385 00:22:51,480 --> 00:22:54,720 Speaker 1: that each area has will define what you're able to 386 00:22:54,720 --> 00:22:56,399 Speaker 1: do and what your ability is to do. And then 387 00:22:56,440 --> 00:22:58,439 Speaker 1: you have then you have to define projects. You have 388 00:22:58,520 --> 00:23:01,440 Speaker 1: to identify what kind of project will enhance this area, 389 00:23:01,440 --> 00:23:03,960 Speaker 1: what kind of projects will entice companies to locate there, 390 00:23:04,000 --> 00:23:07,080 Speaker 1: what kind of projects will UH spur economic development and 391 00:23:07,119 --> 00:23:10,720 Speaker 1: be a catalyst for private UH investment. Once you have 392 00:23:10,760 --> 00:23:12,880 Speaker 1: the projects, UH, then you can go out and start 393 00:23:12,880 --> 00:23:15,679 Speaker 1: identifying where the funding is. UH. In the private sector, 394 00:23:15,720 --> 00:23:18,200 Speaker 1: you have a lot of different options to go after funding, right, 395 00:23:18,200 --> 00:23:21,400 Speaker 1: you have banks, you have a capital equity UM, angel investors, 396 00:23:21,400 --> 00:23:24,200 Speaker 1: what have you on the In the public sector, UM, 397 00:23:24,240 --> 00:23:27,800 Speaker 1: it's very defined, UM and it is very rigorous UH 398 00:23:27,920 --> 00:23:30,200 Speaker 1: to go after capital in the public sector. From a 399 00:23:30,240 --> 00:23:32,440 Speaker 1: standpoint of our U s H that was a seven 400 00:23:32,560 --> 00:23:34,800 Speaker 1: hundred page proposal that we put together for U s 401 00:23:34,880 --> 00:23:37,840 Speaker 1: d A in order to expand Broadman over a thousand 402 00:23:37,840 --> 00:23:41,200 Speaker 1: square miles UM. OUR power funding was about two d 403 00:23:41,359 --> 00:23:44,840 Speaker 1: fifty page application to UH receive the largest share of 404 00:23:44,880 --> 00:23:48,960 Speaker 1: pro or power funding too to a small community. UM. 405 00:23:49,000 --> 00:23:50,600 Speaker 1: You know our A m l our a m L 406 00:23:50,680 --> 00:23:53,359 Speaker 1: was the largest UH largest amount that we raised was 407 00:23:53,359 --> 00:23:56,080 Speaker 1: about sixteen million, and what that is is abandoned mineland 408 00:23:56,080 --> 00:23:59,840 Speaker 1: money so used for economic development. In this pilot case 409 00:24:00,480 --> 00:24:04,240 Speaker 1: that was actually the shortest application was just about fifty pages. So, um, 410 00:24:04,280 --> 00:24:06,160 Speaker 1: I mean just putting all that stuff together, I mean, 411 00:24:06,280 --> 00:24:08,320 Speaker 1: it's it's it's it's what you have to do if 412 00:24:08,359 --> 00:24:11,200 Speaker 1: you want to achieve the funding, if you want to 413 00:24:11,200 --> 00:24:12,800 Speaker 1: go out to the funding, you have to make the 414 00:24:12,800 --> 00:24:16,359 Speaker 1: commitment to do that. Based on your experience, can you 415 00:24:16,520 --> 00:24:20,280 Speaker 1: compare or even offer up some of the things that 416 00:24:20,400 --> 00:24:24,720 Speaker 1: you have learned working in the public sector now compared 417 00:24:24,760 --> 00:24:27,720 Speaker 1: to the private sector. You just mentioned the size of 418 00:24:27,760 --> 00:24:31,200 Speaker 1: the applications, that must have been a big change. Are 419 00:24:31,200 --> 00:24:32,960 Speaker 1: there are other things that you have learned so that 420 00:24:33,000 --> 00:24:36,600 Speaker 1: people can become educated about this process. Um, you know, 421 00:24:36,960 --> 00:24:40,480 Speaker 1: A big thing to leveraging federal funds and bringing them 422 00:24:40,520 --> 00:24:45,320 Speaker 1: into rural, unser underserved communities is developing relationships with the 423 00:24:45,359 --> 00:24:52,119 Speaker 1: agencies that have the funding. Um, you know, the actual people, right, correct? Correct? 424 00:24:52,119 --> 00:24:54,520 Speaker 1: So the the actual agencies. Agencies usually have reps in 425 00:24:54,560 --> 00:24:56,600 Speaker 1: each state, and they usually have a delegate in in 426 00:24:56,600 --> 00:24:59,439 Speaker 1: in in the federal UH in d c UM. So 427 00:24:59,520 --> 00:25:04,240 Speaker 1: working with your federal delegation, senators, Congress representatives, UM, you 428 00:25:04,320 --> 00:25:08,240 Speaker 1: can achieve the relationship UM you speak about your project. 429 00:25:08,440 --> 00:25:10,639 Speaker 1: Never submit. One of the things I learned is do 430 00:25:10,680 --> 00:25:14,359 Speaker 1: not submit an application or proposal blind. Make sure the 431 00:25:14,359 --> 00:25:17,720 Speaker 1: agency is aware of the project that you have in mind. UH. 432 00:25:17,960 --> 00:25:20,600 Speaker 1: Get buy in or consensus that this is a good project. 433 00:25:20,800 --> 00:25:23,480 Speaker 1: Because at the same time, those agencies want to invest 434 00:25:23,800 --> 00:25:26,200 Speaker 1: in projects, and so they have to be sold on 435 00:25:26,240 --> 00:25:28,560 Speaker 1: the project. And this gives you an opportunity to kind 436 00:25:28,560 --> 00:25:30,360 Speaker 1: of vet it a little bit and see what kind 437 00:25:30,359 --> 00:25:32,440 Speaker 1: of a response you'll get from the agencies and did 438 00:25:32,440 --> 00:25:34,520 Speaker 1: they then have to go and sell it to someone 439 00:25:34,560 --> 00:25:37,560 Speaker 1: else as well, because it's probably some committee or some 440 00:25:37,760 --> 00:25:40,439 Speaker 1: group that has to weigh in on this. Right. For instance, 441 00:25:40,440 --> 00:25:42,520 Speaker 1: if you look at U, S E. D A money UM, 442 00:25:42,640 --> 00:25:44,920 Speaker 1: you have a state rep UM. They're kind of the 443 00:25:45,080 --> 00:25:48,919 Speaker 1: the initial layer of vetting UM whether they like it 444 00:25:49,040 --> 00:25:51,600 Speaker 1: or not. UH. They're going to have to approve or 445 00:25:51,640 --> 00:25:54,320 Speaker 1: disapprove of the of the project before they can submit 446 00:25:54,359 --> 00:25:56,960 Speaker 1: it to their committee that represents usually six or seven 447 00:25:57,000 --> 00:26:00,640 Speaker 1: states collectively. They look and review those applications Gradham score 448 00:26:00,760 --> 00:26:03,200 Speaker 1: m UH and then decide on on what projects get 449 00:26:03,200 --> 00:26:08,640 Speaker 1: funded where. Tell us more in detail about Buckhannon and 450 00:26:08,680 --> 00:26:11,840 Speaker 1: what kinds of things you'd like to see develop in 451 00:26:11,880 --> 00:26:14,800 Speaker 1: the future. Sure, So, Boccan is a very unique area. 452 00:26:15,119 --> 00:26:18,800 Speaker 1: We're just south of the the FBI Biometric Center in 453 00:26:18,800 --> 00:26:22,000 Speaker 1: in Bridgeport and Clarksburgh. However, we're a rural area of 454 00:26:22,119 --> 00:26:25,600 Speaker 1: the city's about six thousand people, counties about twenty five thousand. 455 00:26:26,040 --> 00:26:28,919 Speaker 1: Uh We do have a small university there, West Vinia Wesleyan. 456 00:26:29,480 --> 00:26:31,919 Speaker 1: We also have a hospital as well, So I mean 457 00:26:31,960 --> 00:26:34,600 Speaker 1: we have medical and educational. One of the things that 458 00:26:34,640 --> 00:26:38,119 Speaker 1: we want to create is a hub for entrepreneurial activity. 459 00:26:38,480 --> 00:26:41,600 Speaker 1: Uh So. We have undred and ruled students at Westernia 460 00:26:41,640 --> 00:26:45,800 Speaker 1: Wesleyan UM. The best potential workforce, right, but the best 461 00:26:45,800 --> 00:26:48,320 Speaker 1: time to start a company is, you know, in your 462 00:26:48,359 --> 00:26:51,240 Speaker 1: early twenties. Uh So, it's a it's about getting these 463 00:26:51,240 --> 00:26:53,600 Speaker 1: individuals not only at the college level, but us at 464 00:26:53,600 --> 00:26:56,520 Speaker 1: the K through eighth grade through twelfth grade level, getting 465 00:26:56,520 --> 00:26:59,240 Speaker 1: them exposed to the the idea that they can be 466 00:26:59,240 --> 00:27:04,760 Speaker 1: an entrepreneur. So we're putting together programs and computer science, UM, coding, robotics, 467 00:27:04,840 --> 00:27:06,879 Speaker 1: things like that we're gonna have workshops and seminars that 468 00:27:06,880 --> 00:27:10,840 Speaker 1: we're gonna be exposing these individuals to these skills, these trades. Uh, 469 00:27:10,880 --> 00:27:13,640 Speaker 1: maybe they create a business at the end, they may 470 00:27:13,840 --> 00:27:17,880 Speaker 1: be able to be more marketable in a telework type environment. 471 00:27:18,160 --> 00:27:23,080 Speaker 1: And just quickly, if there are other local or municipal 472 00:27:23,520 --> 00:27:27,440 Speaker 1: entities that are hearing you and hearing your story, how 473 00:27:27,480 --> 00:27:30,040 Speaker 1: do they connect with you to learn from your experience? 474 00:27:30,040 --> 00:27:33,280 Speaker 1: Can they get in touch with you directly? Sure? Um, 475 00:27:33,320 --> 00:27:35,520 Speaker 1: I'm sure that. Uh, I don't know if I want 476 00:27:35,520 --> 00:27:37,600 Speaker 1: to give my phone No, no, no, I wouldn't, But 477 00:27:37,680 --> 00:27:39,919 Speaker 1: I mean, in other words, they can contact your organizations 478 00:27:40,040 --> 00:27:42,560 Speaker 1: can contact they can kind of walk you can say 479 00:27:42,560 --> 00:27:45,560 Speaker 1: this was our experience. Broadband dot West Virginia dot gov 480 00:27:45,920 --> 00:27:48,960 Speaker 1: is is our our council website. My contact information is 481 00:27:49,000 --> 00:27:51,679 Speaker 1: on there. Um, you know, feel free to contact me 482 00:27:52,119 --> 00:27:54,920 Speaker 1: if you if you'd like to chat. All right, well done, 483 00:27:55,040 --> 00:27:58,399 Speaker 1: and um, what's next for you? Where? Where? Give you 484 00:27:58,440 --> 00:28:02,320 Speaker 1: ten seconds? What what next? Raising money for? Right now? 485 00:28:02,320 --> 00:28:05,840 Speaker 1: We're carrying out the projects that we did raise, spending 486 00:28:05,880 --> 00:28:08,400 Speaker 1: the money. We're spending the money. You know, the hardest 487 00:28:08,400 --> 00:28:11,320 Speaker 1: thing about it is execution. So we want to perfect execution. 488 00:28:11,600 --> 00:28:15,399 Speaker 1: Thanks very much for being graded execution Here Rob Hinton. 489 00:28:15,560 --> 00:28:18,520 Speaker 1: He is the chairman of the West Virginia Broadband Council, 490 00:28:18,800 --> 00:28:24,199 Speaker 1: also executive director of the Upshur County Development Authority in Buckhannon, 491 00:28:24,560 --> 00:28:29,840 Speaker 1: West Virginia. We're broadcasting from Bloomberg's Government Next conference in Washington, 492 00:28:30,160 --> 00:28:34,159 Speaker 1: d C. Thanks for listening to the Bloomberg P and 493 00:28:34,240 --> 00:28:37,280 Speaker 1: L podcast. You can subscribe and listen to interviews at 494 00:28:37,320 --> 00:28:41,800 Speaker 1: Apple Podcasts, SoundCloud, or whatever podcast platform you prefer. I'm 495 00:28:41,800 --> 00:28:45,240 Speaker 1: pim Fox. I'm on Twitter at pim Fox. I'm on 496 00:28:45,280 --> 00:28:48,719 Speaker 1: Twitter at Lisa abramowits one before the podcast. You can 497 00:28:48,760 --> 00:28:51,120 Speaker 1: always catch us worldwide on Bloomberg Radio.