1 00:00:03,120 --> 00:00:07,480 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,720 --> 00:00:11,960 Speaker 2: I recently had a conversation that really changed the way 3 00:00:12,039 --> 00:00:15,640 Speaker 2: I think about AI, its power, and how it might 4 00:00:15,680 --> 00:00:19,599 Speaker 2: be used in our day to day interactions. It started 5 00:00:19,760 --> 00:00:22,360 Speaker 2: with a phone call to a company called Sannas. 6 00:00:22,800 --> 00:00:26,120 Speaker 3: Hello, thank you for calling Sanas Airline. My name is Mozielle. 7 00:00:26,160 --> 00:00:27,360 Speaker 3: How can I assist you today? 8 00:00:27,640 --> 00:00:31,560 Speaker 2: Hi, I'm Rebecca. I'm trying to cancel my flights to Singapore, 9 00:00:31,720 --> 00:00:32,880 Speaker 2: but I'm having problems. 10 00:00:33,040 --> 00:00:35,640 Speaker 3: Okay, I'm sorry to hear that, Rebecca, and for us 11 00:00:35,720 --> 00:00:37,720 Speaker 3: to proceed, can I have your pickup number? 12 00:00:37,760 --> 00:00:38,080 Speaker 1: Please? 13 00:00:38,440 --> 00:00:41,479 Speaker 2: It sounds like a typical conversation you might have with 14 00:00:41,600 --> 00:00:45,680 Speaker 2: a customer rep. But here's the thing. The sound of Luzille, 15 00:00:45,720 --> 00:00:49,120 Speaker 2: the person I'm speaking with, is actually being modified quite 16 00:00:49,240 --> 00:00:54,160 Speaker 2: dramatically with AI. Without the AI, here's what our conversation 17 00:00:54,440 --> 00:00:55,640 Speaker 2: would actually sound like. 18 00:00:56,320 --> 00:00:59,640 Speaker 4: Hi, Rebecca, this is Lucille and I'm from the Philippines 19 00:01:00,040 --> 00:01:02,160 Speaker 4: and this is my normal voice and accent. 20 00:01:02,480 --> 00:01:06,640 Speaker 2: Wow, raziel that is a wild transformation, both in accent 21 00:01:06,880 --> 00:01:11,119 Speaker 2: and in that clarity of that noise. Yeah right, wait, 22 00:01:11,200 --> 00:01:13,160 Speaker 2: can you turn on the app again? 23 00:01:13,800 --> 00:01:17,920 Speaker 3: Okay, that's a problem, So there you go. The Sanus 24 00:01:17,959 --> 00:01:20,840 Speaker 3: appistored on now So Hi Rebecca, nice meeting you. 25 00:01:21,080 --> 00:01:25,319 Speaker 2: Wow, that's just wild. The AI company Luzille works for, 26 00:01:25,560 --> 00:01:31,800 Speaker 2: Sanas causes technology accent translation. It says it eliminates background 27 00:01:31,800 --> 00:01:35,559 Speaker 2: noise and enhances the clarity of voice and speech while 28 00:01:35,560 --> 00:01:39,400 Speaker 2: making sure it still sounds natural. And Luzille, who runs 29 00:01:39,440 --> 00:01:43,640 Speaker 2: demos for Sanas, says the technology helps call reps when 30 00:01:43,640 --> 00:01:46,600 Speaker 2: they use it, fewer customers asked to be transferred to 31 00:01:46,640 --> 00:01:50,200 Speaker 2: a different agent that used to happen all the time 32 00:01:50,320 --> 00:01:53,600 Speaker 2: during the twelve years she worked as a customer service rep. 33 00:01:53,840 --> 00:01:58,160 Speaker 4: For example, if we answer the call, they actually look 34 00:01:58,240 --> 00:02:02,360 Speaker 4: for us represent that they right away instead of trying 35 00:02:02,480 --> 00:02:06,000 Speaker 4: us to talk to us. There's already a doubt that 36 00:02:06,160 --> 00:02:10,320 Speaker 4: if we are equipped or capable of answering their questions 37 00:02:10,560 --> 00:02:12,480 Speaker 4: or resolving their concern and queries. 38 00:02:12,840 --> 00:02:17,520 Speaker 2: Sanas says it's AI tools quote eliminate communication barriers and 39 00:02:17,639 --> 00:02:22,239 Speaker 2: allow agents to resolve issues faster, which means shorter wait 40 00:02:22,280 --> 00:02:26,280 Speaker 2: times for customers. And Sanas is just one of the 41 00:02:26,680 --> 00:02:31,239 Speaker 2: many AI companies that are blurring the line between where 42 00:02:31,280 --> 00:02:36,000 Speaker 2: the tech starts and the human ends. And while these 43 00:02:36,040 --> 00:02:39,680 Speaker 2: tools might make things easier on customer reps. They are 44 00:02:39,760 --> 00:02:43,120 Speaker 2: a potential danger to the jobs of those working in 45 00:02:43,160 --> 00:02:47,200 Speaker 2: the customer service industry or what's known as the BPO 46 00:02:47,360 --> 00:02:50,000 Speaker 2: sector business process outsourcing. 47 00:02:50,480 --> 00:02:54,639 Speaker 1: We will see shrinking in the core of BPO work 48 00:02:55,000 --> 00:02:59,200 Speaker 1: as new AI tools get launched every month, they're bringing 49 00:02:59,240 --> 00:03:01,200 Speaker 1: in a lot more efficiency. 50 00:03:01,600 --> 00:03:05,760 Speaker 2: Bloomberg Sertha Raye covers AI in Asia from India, and 51 00:03:05,840 --> 00:03:08,600 Speaker 2: she says, if you want to see this threat up close, 52 00:03:09,200 --> 00:03:12,040 Speaker 2: the Philippines, where lou Zille is is a good place 53 00:03:12,080 --> 00:03:16,760 Speaker 2: to look. That's because it's considered the world's capital for BPOs, 54 00:03:17,080 --> 00:03:18,840 Speaker 2: particularly voice BPOs. 55 00:03:19,360 --> 00:03:24,000 Speaker 1: The industry employee is about one point seven million people 56 00:03:24,200 --> 00:03:28,560 Speaker 1: and accounts for about eight percent of Philippines GDP. 57 00:03:28,600 --> 00:03:31,960 Speaker 2: And Soesa says what's happening with the industry over in 58 00:03:31,960 --> 00:03:35,720 Speaker 2: the Philippines is being closely watched by the rest of 59 00:03:35,760 --> 00:03:36,240 Speaker 2: the world. 60 00:03:36,800 --> 00:03:41,640 Speaker 1: Entire countries. Economis experts are watching Philippines to see how 61 00:03:41,680 --> 00:03:43,880 Speaker 1: it will play out in this country of about one 62 00:03:43,920 --> 00:03:48,240 Speaker 1: hundred million people, and that could well show a signal 63 00:03:48,400 --> 00:03:51,520 Speaker 1: as to how these technologies move to other countries and 64 00:03:51,600 --> 00:03:59,520 Speaker 1: other industries and disrupt or enhance workers' lives. 65 00:04:01,880 --> 00:04:05,560 Speaker 2: Welcome to the Big Take Asia from Bloomberg News. I'm 66 00:04:05,760 --> 00:04:10,360 Speaker 2: Rebecca Chung Wilkins. Every week we take you inside some 67 00:04:10,440 --> 00:04:15,320 Speaker 2: of the world's biggest and most powerful economies and the markets, tycoons, 68 00:04:15,400 --> 00:04:20,080 Speaker 2: and businesses that drive this ever shifting region. Today on 69 00:04:20,160 --> 00:04:24,120 Speaker 2: the show, the Philippines is at the forefront of AI's 70 00:04:24,240 --> 00:04:28,240 Speaker 2: job displacement, and what happens there will say a lot 71 00:04:28,279 --> 00:04:32,600 Speaker 2: about what's ahead for white collar workers around the world. 72 00:04:37,560 --> 00:04:42,640 Speaker 2: A few decades ago, major global corporations began outsourcing a 73 00:04:42,680 --> 00:04:46,479 Speaker 2: lot of their back end work think HR accounting, auditing, 74 00:04:46,520 --> 00:04:51,920 Speaker 2: and customer service to countries with lower labor costs, and 75 00:04:51,960 --> 00:04:54,279 Speaker 2: Bloomberg's to Read the Rye says one of the top 76 00:04:54,360 --> 00:04:58,920 Speaker 2: places these tasks were outsourced to was the Philippines, and 77 00:04:59,080 --> 00:05:02,240 Speaker 2: part of the reason for that, she says, is because 78 00:05:02,279 --> 00:05:04,240 Speaker 2: of the way people there speak. 79 00:05:04,440 --> 00:05:08,760 Speaker 1: So the Philippines is really one of those countries which 80 00:05:08,800 --> 00:05:13,160 Speaker 1: is culturally very aligned with the United States, and people 81 00:05:13,360 --> 00:05:15,880 Speaker 1: speak in an accent that is much closer to the 82 00:05:15,920 --> 00:05:21,039 Speaker 1: American accent, much more than pretty much most of Asia. 83 00:05:21,760 --> 00:05:24,719 Speaker 1: So that is the reason why a lot the BPO 84 00:05:25,120 --> 00:05:29,440 Speaker 1: work has increasingly moved towards Philippines and made it really 85 00:05:29,480 --> 00:05:31,080 Speaker 1: the call center capital of. 86 00:05:31,000 --> 00:05:34,760 Speaker 2: The world, SOESA tells me. The Philippines started growing it's 87 00:05:34,839 --> 00:05:38,640 Speaker 2: back office industry in the two thousands, and today call 88 00:05:38,760 --> 00:05:42,120 Speaker 2: centers are the country's biggest source of private sector jobs. 89 00:05:42,600 --> 00:05:46,039 Speaker 2: The industry is forecast hit thirty eight billion dollars in 90 00:05:46,120 --> 00:05:50,560 Speaker 2: revenue this year, and this industry boom has created the 91 00:05:50,839 --> 00:05:55,120 Speaker 2: kind of jobs that have helped transform people's lives. 92 00:05:55,440 --> 00:05:58,840 Speaker 1: These are well tamed jobs. These are jobs where you 93 00:05:58,880 --> 00:06:03,720 Speaker 1: can actually be socially upwardly mobile. You can actually get 94 00:06:03,760 --> 00:06:07,120 Speaker 1: paid recently and make a change in your lifestyle by 95 00:06:07,160 --> 00:06:09,600 Speaker 1: a home, by a car, set up a small business 96 00:06:09,640 --> 00:06:11,400 Speaker 1: on the side, set up your family. 97 00:06:11,960 --> 00:06:14,960 Speaker 2: But Ciisa says, in the last eight months or so, 98 00:06:15,440 --> 00:06:17,880 Speaker 2: there have been big changes in these jobs that are 99 00:06:18,000 --> 00:06:22,080 Speaker 2: raising questions about whether or not they will continue to 100 00:06:22,160 --> 00:06:26,200 Speaker 2: be a stable source of income and employment for millions 101 00:06:26,240 --> 00:06:27,400 Speaker 2: of Filipinos. 102 00:06:27,800 --> 00:06:30,960 Speaker 1: The largest vPOS in the Philippines have rolled out a 103 00:06:31,040 --> 00:06:35,600 Speaker 1: variety of AI tools pretty extensively. These AI tools do 104 00:06:35,720 --> 00:06:39,800 Speaker 1: all kinds of things, such as assisting agents while they're 105 00:06:39,800 --> 00:06:44,920 Speaker 1: on life calls, training the agents, sometimes even making our 106 00:06:45,000 --> 00:06:47,239 Speaker 1: bound calls to sell something. 107 00:06:47,520 --> 00:06:50,400 Speaker 2: One of the call centers in the middle of adapting 108 00:06:50,440 --> 00:06:55,799 Speaker 2: to this AI transition is twenty four seven. AI Serisa 109 00:06:56,080 --> 00:06:59,440 Speaker 2: was given rare access to their call center in Manila 110 00:06:59,520 --> 00:07:03,239 Speaker 2: where they using a chat GPT like tool to train 111 00:07:03,360 --> 00:07:07,400 Speaker 2: customer service agents. In the test run that Saritha saw, 112 00:07:07,839 --> 00:07:11,760 Speaker 2: the AI tool generated different scenarios and took on a 113 00:07:11,920 --> 00:07:16,200 Speaker 2: range of personas to help the human agent roleplay with 114 00:07:16,400 --> 00:07:19,240 Speaker 2: different types of callers they might get. 115 00:07:19,440 --> 00:07:24,080 Speaker 1: For example, pleasant, irate, tough, hard bargainer, or treat. The 116 00:07:24,160 --> 00:07:29,200 Speaker 1: sentiment can be tense, distressed, irritated, or calm. So for example, 117 00:07:29,360 --> 00:07:32,560 Speaker 1: somebody can choose a scenario which is a gen z 118 00:07:33,040 --> 00:07:38,600 Speaker 1: male irate churned customer or a female a millennial who 119 00:07:38,880 --> 00:07:41,280 Speaker 1: is calm but has a real problem. 120 00:07:41,840 --> 00:07:45,880 Speaker 2: What does an irate gen Zai customers sound like? 121 00:07:46,360 --> 00:07:52,080 Speaker 1: Very difficult to deal with? I can char I overheard 122 00:07:52,240 --> 00:07:55,600 Speaker 1: some of those calls and it was not easy, but 123 00:07:55,720 --> 00:07:58,960 Speaker 1: it was tremendous how calmly these agents were dealing with 124 00:07:59,160 --> 00:08:01,480 Speaker 1: really annoyed and tough customers. 125 00:08:01,520 --> 00:08:05,440 Speaker 2: At the other end, the idea, Saitha says, is to 126 00:08:05,560 --> 00:08:09,000 Speaker 2: prepare the agents to deal with as many different scenarios 127 00:08:09,200 --> 00:08:13,720 Speaker 2: and customer personalities as possible. It's also to help train 128 00:08:13,800 --> 00:08:17,600 Speaker 2: them to give the most appropriate response and Soretha says 129 00:08:18,000 --> 00:08:21,000 Speaker 2: the company told her that the kind of work the 130 00:08:21,000 --> 00:08:24,720 Speaker 2: AI is doing to train human agents would take much 131 00:08:24,800 --> 00:08:28,640 Speaker 2: longer if it were being done by an actual human trainer. 132 00:08:29,360 --> 00:08:33,280 Speaker 1: You cannot have the trainers go from pleasant to irate, 133 00:08:33,679 --> 00:08:39,080 Speaker 1: to a tough bargainer to a distressed customer or within seconds, 134 00:08:39,280 --> 00:08:42,160 Speaker 1: whereas the AI can easily do that, which is why 135 00:08:42,520 --> 00:08:45,040 Speaker 1: what used to take three times the number of days 136 00:08:45,080 --> 00:08:48,079 Speaker 1: to train an agent has now come down to about 137 00:08:48,120 --> 00:08:48,520 Speaker 1: a month. 138 00:08:48,960 --> 00:08:53,720 Speaker 2: But with productivity gains and workflow improvements come trade offs. 139 00:08:54,280 --> 00:08:57,520 Speaker 2: Soretha spoke to a few people whose jobs came under 140 00:08:57,559 --> 00:09:02,040 Speaker 2: threat from the AI revolution in the BAO industry. One 141 00:09:02,080 --> 00:09:05,160 Speaker 2: of them is forty seven year old Christopher Bautista. 142 00:09:05,440 --> 00:09:08,160 Speaker 1: He's worked in the BPO industry for nearly two decades. 143 00:09:08,679 --> 00:09:12,800 Speaker 2: Christopher told Saifa that for months he'd watched as AI 144 00:09:12,920 --> 00:09:16,840 Speaker 2: took on more responsibility where he worked, The AI took 145 00:09:16,880 --> 00:09:20,240 Speaker 2: care of customers questions such as general inquiries about products, 146 00:09:20,600 --> 00:09:24,640 Speaker 2: what the problem was, and more, before routing calls to 147 00:09:24,720 --> 00:09:29,040 Speaker 2: human agents. And then last November, he and others at 148 00:09:29,040 --> 00:09:33,160 Speaker 2: the BPO company serving a multinational tech giant were put 149 00:09:33,200 --> 00:09:34,959 Speaker 2: on floating status. 150 00:09:35,600 --> 00:09:38,840 Speaker 1: Floating status means no work, no pay, but still on 151 00:09:38,880 --> 00:09:41,319 Speaker 1: the roads. So you are not jobless, but you are 152 00:09:41,360 --> 00:09:44,000 Speaker 1: not getting paid. So that went on for about four 153 00:09:44,080 --> 00:09:48,480 Speaker 1: or five months before Christopher quit the company and then 154 00:09:48,600 --> 00:09:53,720 Speaker 1: has found a job in an entirely different company. 155 00:09:54,400 --> 00:09:57,480 Speaker 2: So just how many jobs in the BPO industry are 156 00:09:57,520 --> 00:10:01,120 Speaker 2: going to come under threat because of this transition and 157 00:10:01,160 --> 00:10:04,080 Speaker 2: what will that mean for the Philippine economy which is 158 00:10:04,200 --> 00:10:09,240 Speaker 2: heavily dependent on this sector that's coming up after the break. 159 00:10:18,200 --> 00:10:20,960 Speaker 2: Over the past year, most of the major players in 160 00:10:20,960 --> 00:10:25,080 Speaker 2: the Philippines vast BPO industry have introduced some form of 161 00:10:25,160 --> 00:10:30,480 Speaker 2: AI copilot, having algorithms run alongside human operators to make 162 00:10:30,520 --> 00:10:34,480 Speaker 2: their work much more efficient, all in real time. And 163 00:10:34,559 --> 00:10:39,640 Speaker 2: Bloomberg Saretha Rai says, with these new AI tools, something 164 00:10:39,679 --> 00:10:43,359 Speaker 2: that used to give the Philippines an advantage in this industry, 165 00:10:43,520 --> 00:10:47,240 Speaker 2: its cultural closeness to America may not matter anymore. 166 00:10:47,320 --> 00:10:51,440 Speaker 1: These AI tools will make it possible for BPOs to 167 00:10:51,520 --> 00:10:55,200 Speaker 1: set up anywhere because accent will not be a problem. 168 00:10:55,720 --> 00:10:58,800 Speaker 2: So Refa says that could open doors for foreign owned 169 00:10:58,880 --> 00:11:02,200 Speaker 2: companies to move their call center operations to places in 170 00:11:02,280 --> 00:11:06,640 Speaker 2: Africa like Garana, where it's cheaper to recruit agents and 171 00:11:06,679 --> 00:11:11,080 Speaker 2: where the BPO industry is starting to expand, and that 172 00:11:11,200 --> 00:11:15,400 Speaker 2: has big implications for the Philippine economy, which has been 173 00:11:15,520 --> 00:11:17,319 Speaker 2: transformed by the sector. 174 00:11:17,600 --> 00:11:21,000 Speaker 1: Some ten twelve years ago, money law was a different city. 175 00:11:21,320 --> 00:11:26,400 Speaker 1: Now most of these slick sky scrapers, these luxurious homes, 176 00:11:26,640 --> 00:11:29,880 Speaker 1: these big malls, all of this has been majorly on 177 00:11:30,040 --> 00:11:32,280 Speaker 1: encounter BPO industries boom. 178 00:11:32,720 --> 00:11:35,920 Speaker 2: But now one estimate says that up to three hundred 179 00:11:36,040 --> 00:11:39,480 Speaker 2: thousand contact center jobs could be lost in the Philippines 180 00:11:39,480 --> 00:11:41,920 Speaker 2: to AI in the next five years. 181 00:11:42,240 --> 00:11:45,480 Speaker 1: There is a recognition that you know that there is 182 00:11:45,640 --> 00:11:48,560 Speaker 1: change coming, that there will be job losses, they will 183 00:11:48,559 --> 00:11:51,959 Speaker 1: be less hiring, and you do not see the kind 184 00:11:52,000 --> 00:11:56,600 Speaker 1: of frenzy that used to be the hallmark of the 185 00:11:56,800 --> 00:11:59,280 Speaker 1: BPO industry even a decade ago. 186 00:12:00,600 --> 00:12:03,960 Speaker 2: Now, Soaretha says, some of the executives in the industry 187 00:12:04,080 --> 00:12:07,400 Speaker 2: she spoke to don't see the changes as all about 188 00:12:07,520 --> 00:12:11,960 Speaker 2: job losses. They say AI will create different types of roles, 189 00:12:12,200 --> 00:12:16,400 Speaker 2: jobs like training algorithms or curating data. As for the 190 00:12:16,400 --> 00:12:20,440 Speaker 2: Philippine government, who had been banking on the BPO industry 191 00:12:20,480 --> 00:12:24,960 Speaker 2: to help propel its economy, we are Soaretha how they've 192 00:12:25,040 --> 00:12:28,839 Speaker 2: responded to the growing presence of AI in the industry. 193 00:12:29,080 --> 00:12:34,200 Speaker 1: There is a recognition that AI can really upend the industry. 194 00:12:34,640 --> 00:12:42,239 Speaker 1: The government has been talking about reskilling and training their workforce, 195 00:12:42,760 --> 00:12:46,480 Speaker 1: but there is very little yet on the ground that 196 00:12:46,559 --> 00:12:51,040 Speaker 1: I see in terms of real skilling initiators or training 197 00:12:51,080 --> 00:12:54,600 Speaker 1: initiatives that the government has initiated. 198 00:12:55,520 --> 00:12:59,120 Speaker 2: Last month, the government launched an AI research center aimed 199 00:12:59,120 --> 00:13:02,720 Speaker 2: at helping her the Philippines into a regional front runner 200 00:13:02,920 --> 00:13:06,520 Speaker 2: in the AI space, But so Resa says the government 201 00:13:06,559 --> 00:13:09,240 Speaker 2: has yet to put a figure on how much it's 202 00:13:09,240 --> 00:13:10,040 Speaker 2: planning to spend. 203 00:13:10,440 --> 00:13:13,880 Speaker 1: There is no real dollars set aside for retraining. 204 00:13:15,000 --> 00:13:19,760 Speaker 2: I suppose every technological revolution has ultimately led to some 205 00:13:20,240 --> 00:13:23,800 Speaker 2: job cuts, and I wonder if this is any different, 206 00:13:24,360 --> 00:13:27,479 Speaker 2: or is this just another one of those key technological 207 00:13:27,559 --> 00:13:28,920 Speaker 2: turning points in history. 208 00:13:29,480 --> 00:13:33,960 Speaker 1: In my coverage of the technology industry, I've covered a 209 00:13:34,040 --> 00:13:38,480 Speaker 1: variety of disruptions, the latter part of the Internet disruption, 210 00:13:39,000 --> 00:13:43,439 Speaker 1: the mobile disruption, or the cloud disruption, all of these disruptions. 211 00:13:43,520 --> 00:13:49,040 Speaker 1: But this is different. This is a technology that could, 212 00:13:49,640 --> 00:13:53,280 Speaker 1: in fact Rebecca, what you're doing and what I'm doing. 213 00:13:53,559 --> 00:13:58,280 Speaker 1: I keep looking over my shoulder to see what different 214 00:13:58,600 --> 00:14:02,000 Speaker 1: technologies are doing in terms of writing and in terms 215 00:14:02,040 --> 00:14:05,680 Speaker 1: of journalism. I know that there are AI anchors, now 216 00:14:05,800 --> 00:14:09,400 Speaker 1: there are AI podcasters. What does that mean for your 217 00:14:09,480 --> 00:14:13,200 Speaker 1: job and mind? There's always that little bit of niggling 218 00:14:13,240 --> 00:14:15,280 Speaker 1: anxiety at the back of my head as I look 219 00:14:15,320 --> 00:14:17,880 Speaker 1: at this technology and I've never felt that before. 220 00:14:19,720 --> 00:14:24,920 Speaker 2: Maybe it will be a cheerful Rebecca British accented AI 221 00:14:25,120 --> 00:14:32,560 Speaker 2: Avata podcast host. It definitely feels like this story, perhaps 222 00:14:32,600 --> 00:14:34,640 Speaker 2: more than some of the other stories that we've brought 223 00:14:34,680 --> 00:14:36,520 Speaker 2: it on, we have a little bit more skin in 224 00:14:36,560 --> 00:14:42,600 Speaker 2: the game here. I agree to that point. I wonder 225 00:14:42,840 --> 00:14:46,760 Speaker 2: does what happens with AI in the Philippines affect the 226 00:14:46,800 --> 00:14:47,720 Speaker 2: rest of the world. 227 00:14:48,320 --> 00:14:53,720 Speaker 1: I think the world over governments are challenged with how 228 00:14:53,760 --> 00:14:58,080 Speaker 1: to deal with what is called a job displacement tools 229 00:14:59,160 --> 00:15:03,360 Speaker 1: that this VIA is bringing in. There is an awareness 230 00:15:03,400 --> 00:15:07,640 Speaker 1: that this is happening, but governments around the world are 231 00:15:07,720 --> 00:15:12,360 Speaker 1: doing really very little to deal with it. So this 232 00:15:12,440 --> 00:15:15,760 Speaker 1: is a bullet train that is really moving very fast, 233 00:15:15,960 --> 00:15:18,840 Speaker 1: and does the government have the speed to catch up? 234 00:15:19,080 --> 00:15:23,320 Speaker 1: That is a question that I would leave open ended. 235 00:15:26,480 --> 00:15:30,320 Speaker 2: This is the big take Asia from Bloomberg News. I'm 236 00:15:30,480 --> 00:15:35,880 Speaker 2: Rebecca Cheung Wilkins. This episode was produced by Naomi Young 237 00:15:35,960 --> 00:15:40,040 Speaker 2: Young and Alex Sugura, who also mixed it. It was 238 00:15:40,200 --> 00:15:43,600 Speaker 2: edited by Caitlin Kenny and Emily Cappan. It was fact 239 00:15:43,720 --> 00:15:48,040 Speaker 2: checked by Alex Sugura. Our senior editor is Elizabeth Ponso, 240 00:15:48,520 --> 00:15:52,640 Speaker 2: Nicole Beemstabor is our executive producer, and Sage Bauman is 241 00:15:52,680 --> 00:15:56,360 Speaker 2: Bloomberg's head of podcasts. If you like our show, please 242 00:15:56,440 --> 00:15:59,040 Speaker 2: leave us a review wherever you listen to podcasts, or 243 00:15:59,120 --> 00:16:02,720 Speaker 2: tell your friends it makes a difference. Thank you and 244 00:16:02,880 --> 00:16:03,800 Speaker 2: see you next time.