1 00:00:00,280 --> 00:00:11,400 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Bloomberg Intelligence 2 00:00:11,560 --> 00:00:13,680 Speaker 1: with Alex Steel and Paul Sweeney. 3 00:00:13,760 --> 00:00:16,959 Speaker 2: The real app performance has been in US corporate high yield. 4 00:00:17,160 --> 00:00:19,520 Speaker 3: Are the companies lean enough? Have they trimmed all the fats? 5 00:00:19,600 --> 00:00:23,200 Speaker 2: The semiconductor business is a really cyclical business. 6 00:00:22,880 --> 00:00:26,560 Speaker 1: Breaking market headlines and corporate news from across the globe. 7 00:00:26,600 --> 00:00:29,040 Speaker 3: Do investors like the M and A that we've seen? 8 00:00:29,240 --> 00:00:30,200 Speaker 4: These are two. 9 00:00:30,120 --> 00:00:32,199 Speaker 2: Big time blue chip companies. 10 00:00:32,479 --> 00:00:36,199 Speaker 5: Window between the peak and cunt changing super fast. 11 00:00:36,120 --> 00:00:41,160 Speaker 1: Bloomberg Intelligence with Alex Steele and Paul Sweeney on Bloomberg Radio. 12 00:00:42,640 --> 00:00:45,080 Speaker 2: On Today's Bloomberg Intelligence Show, we dig inside the big 13 00:00:45,120 --> 00:00:48,000 Speaker 2: business stories impacting Wall Street and the global markets. Each 14 00:00:48,040 --> 00:00:50,040 Speaker 2: and every week we provide in depth research and data 15 00:00:50,120 --> 00:00:51,920 Speaker 2: on some of the two thousand companies and one hundred 16 00:00:51,920 --> 00:00:55,160 Speaker 2: and thirty industries our analysts cover worldwide. Today, well look 17 00:00:55,160 --> 00:00:58,240 Speaker 2: at why the planemaker Boeing reported first quarter results that 18 00:00:58,360 --> 00:01:02,160 Speaker 2: exceeded Wall Street's expectation. Plus we'll discuss how one company 19 00:01:02,280 --> 00:01:07,559 Speaker 2: develops advanced technologies for recycling lithium ion batteries. But first 20 00:01:07,680 --> 00:01:09,720 Speaker 2: we would begin with some of our best conversations this 21 00:01:09,800 --> 00:01:13,039 Speaker 2: week from the New Jersey Institute of Technology. This week 22 00:01:13,120 --> 00:01:15,560 Speaker 2: co hosts Alex Steele and I, we're at NJIT where 23 00:01:15,560 --> 00:01:18,199 Speaker 2: they enroll more than thirteen thousand students and are really 24 00:01:18,240 --> 00:01:22,520 Speaker 2: some of the leaders of science and technology in the US. There, 25 00:01:22,560 --> 00:01:27,080 Speaker 2: we spoke with Wunmi Sadic NJIT, Distinguished Professor of Chemistry 26 00:01:27,080 --> 00:01:30,679 Speaker 2: and Environmental Science and founder of the Biosmart Center. She 27 00:01:30,760 --> 00:01:34,600 Speaker 2: discussed the development of nanoscience analytical sensors for measuring pain 28 00:01:34,959 --> 00:01:37,679 Speaker 2: in the human body, and I first asked Wunmi to 29 00:01:37,800 --> 00:01:40,800 Speaker 2: talk about what she's working on in the Biosmart Center. 30 00:01:41,120 --> 00:01:45,959 Speaker 6: The Biosmas Center, our goal is to look for sustainable 31 00:01:46,120 --> 00:01:51,120 Speaker 6: materials in terms of chemistry, to create technologies that will 32 00:01:51,200 --> 00:01:57,360 Speaker 6: help people. One of us technologies actually, you know, to 33 00:01:57,440 --> 00:02:03,800 Speaker 6: detect pain. Over one hundred US adults live with chronic 34 00:02:03,880 --> 00:02:11,440 Speaker 6: pain and more than ten million individuals struggle with prescription medications. 35 00:02:11,960 --> 00:02:15,320 Speaker 6: But every time you go to the hospital and the clinicians, 36 00:02:15,639 --> 00:02:19,720 Speaker 6: physicians are required to measure pain. And the only way 37 00:02:19,720 --> 00:02:25,160 Speaker 6: we do that, despite advancement, is to show you a 38 00:02:25,200 --> 00:02:26,600 Speaker 6: facial skills. 39 00:02:27,360 --> 00:02:29,360 Speaker 5: They wear on the scale, like what faces are you 40 00:02:29,440 --> 00:02:29,919 Speaker 5: right now? 41 00:02:30,080 --> 00:02:30,440 Speaker 4: Exact? 42 00:02:30,520 --> 00:02:33,040 Speaker 3: So what would your research be able to do. 43 00:02:33,400 --> 00:02:38,080 Speaker 6: So Basically, my research says pain is biochemical in nature, 44 00:02:39,240 --> 00:02:42,480 Speaker 6: and when you have chronic pain, there's a lot of inflammation. 45 00:02:43,400 --> 00:02:47,359 Speaker 6: And when there's inflammation, there are chemicals that are biochemicals 46 00:02:47,360 --> 00:02:51,400 Speaker 6: that are produced by the body. By measuring, first of all, 47 00:02:51,440 --> 00:02:55,560 Speaker 6: by knowing those biochemicals and measuring how much they are, 48 00:02:55,919 --> 00:02:59,359 Speaker 6: we can relate this to pain that people are feeling. 49 00:03:00,000 --> 00:03:04,720 Speaker 6: You don't need the subjective approach to measure pain because 50 00:03:04,760 --> 00:03:08,119 Speaker 6: if you have infants, for example, if you have elderly, 51 00:03:08,240 --> 00:03:10,680 Speaker 6: if you have people who are conscious, they're not able 52 00:03:10,720 --> 00:03:14,080 Speaker 6: to articulate their pain. And so you can actually use 53 00:03:15,320 --> 00:03:18,800 Speaker 6: our bio sensors or smart biosensors to give you the 54 00:03:18,880 --> 00:03:20,519 Speaker 6: level of pain that people are going through. 55 00:03:20,560 --> 00:03:22,280 Speaker 2: So where are you in terms of your. 56 00:03:22,200 --> 00:03:27,320 Speaker 6: Research our sensors that are being used currently? Uh you know, 57 00:03:27,919 --> 00:03:32,400 Speaker 6: you know, we have collaborators in opposite New York and 58 00:03:32,960 --> 00:03:38,360 Speaker 6: they take human blood samples and they measure the levels 59 00:03:38,400 --> 00:03:45,800 Speaker 6: of molecules called cyclopgenis too or inducible nitros oxide and taste, 60 00:03:46,360 --> 00:03:49,760 Speaker 6: and they measure the level. We combine this with artificial 61 00:03:49,880 --> 00:03:54,480 Speaker 6: intelligence to be able to give you the exact amount 62 00:03:54,560 --> 00:03:57,800 Speaker 6: of pain that people are going through. And for the 63 00:03:57,880 --> 00:04:01,640 Speaker 6: most part, we've been able to think the level that 64 00:04:01,800 --> 00:04:05,640 Speaker 6: people suggest to the level that we're majoring from. 65 00:04:05,680 --> 00:04:10,800 Speaker 5: Ours are how far away from like regular doctors and nurses. 66 00:04:10,560 --> 00:04:12,800 Speaker 3: Using it in hospitals currently? 67 00:04:12,960 --> 00:04:16,080 Speaker 6: I mean it's we've looked at close to one thousand 68 00:04:16,279 --> 00:04:21,360 Speaker 6: individuals and we're getting eighty percent accuracy in terms of 69 00:04:21,640 --> 00:04:24,200 Speaker 6: what people tell you. At the end of the day, 70 00:04:24,360 --> 00:04:28,840 Speaker 6: pain is also individualistic, right, There are aspects of pain 71 00:04:29,440 --> 00:04:32,120 Speaker 6: that you know, you know it depends on individuals. 72 00:04:32,160 --> 00:04:34,200 Speaker 3: You have pay tolerance, right exactly. 73 00:04:34,360 --> 00:04:36,520 Speaker 6: You know you have you know, you have saturation, you 74 00:04:36,600 --> 00:04:39,919 Speaker 6: can so there are so many other components that impacted. 75 00:04:40,440 --> 00:04:42,960 Speaker 6: But in terms of being able to actually test this out, 76 00:04:43,120 --> 00:04:44,240 Speaker 6: we're doing this already. 77 00:04:44,880 --> 00:04:47,359 Speaker 2: So how does doing research at a place like an 78 00:04:47,440 --> 00:04:49,440 Speaker 2: NJAI T How does that work? How do you balance 79 00:04:49,600 --> 00:04:52,400 Speaker 2: like I guess, research with teaching and all that, because 80 00:04:52,400 --> 00:04:54,679 Speaker 2: I know most professors have to deal with that across 81 00:04:54,920 --> 00:04:55,760 Speaker 2: various disciplines. 82 00:04:56,640 --> 00:05:01,760 Speaker 6: In actual fact, there's correlation because in the classroom I 83 00:05:02,360 --> 00:05:06,360 Speaker 6: teach graduate students, I teach them the fundamentals, and then 84 00:05:06,440 --> 00:05:10,240 Speaker 6: we take it further from the classroom and actually do 85 00:05:10,400 --> 00:05:12,640 Speaker 6: this in the lab, and so there is a connection 86 00:05:12,839 --> 00:05:15,239 Speaker 6: between what you do in the classroom, what you're teaching 87 00:05:15,279 --> 00:05:17,520 Speaker 6: the classroom, and what you're actually doing your love. 88 00:05:19,040 --> 00:05:22,560 Speaker 5: We talk a lot on Bloomberg here about tariff risks, 89 00:05:22,600 --> 00:05:24,080 Speaker 5: but economic risks. 90 00:05:24,480 --> 00:05:26,560 Speaker 3: About products being in short supply. 91 00:05:27,640 --> 00:05:30,120 Speaker 5: Is any of that relevant to the work that you do, Like, 92 00:05:30,160 --> 00:05:34,000 Speaker 5: are you worried about getting certain materials or products to 93 00:05:34,240 --> 00:05:36,640 Speaker 5: fund and continue moving your research along? 94 00:05:37,680 --> 00:05:41,160 Speaker 6: Suddenly we're going to be affected because, as you know, 95 00:05:41,560 --> 00:05:45,080 Speaker 6: most research at the momental funded by the federal government, 96 00:05:45,760 --> 00:05:51,360 Speaker 6: and so if there's less funding, there's less time that 97 00:05:51,600 --> 00:05:53,600 Speaker 6: will not be able to support students to be able 98 00:05:53,640 --> 00:05:57,359 Speaker 6: to do the work, and so ultimately it will impact 99 00:05:57,760 --> 00:06:00,400 Speaker 6: our research. It would impact the classroom. 100 00:06:00,320 --> 00:06:00,960 Speaker 4: And what we do. 101 00:06:01,720 --> 00:06:03,560 Speaker 2: What's the next step for you in your research? 102 00:06:03,760 --> 00:06:03,960 Speaker 4: Are you? 103 00:06:04,040 --> 00:06:08,039 Speaker 2: Are you working with a team other professors, maybe other universities. 104 00:06:08,600 --> 00:06:09,360 Speaker 2: What's your team work? 105 00:06:09,480 --> 00:06:12,640 Speaker 6: Like my team at at the moment we have six 106 00:06:12,880 --> 00:06:17,600 Speaker 6: PhD students, we have post dogs, we have clinicians that 107 00:06:17,680 --> 00:06:19,839 Speaker 6: are working with us. We have computer scientists who are 108 00:06:19,920 --> 00:06:23,680 Speaker 6: looking at the AI component of our work. So it's 109 00:06:23,760 --> 00:06:25,520 Speaker 6: a whole center activity. 110 00:06:26,120 --> 00:06:28,600 Speaker 3: How did you come to research this particular part? 111 00:06:28,720 --> 00:06:31,120 Speaker 5: I always find that really fascinating when you like narrow 112 00:06:31,160 --> 00:06:33,600 Speaker 5: it down, like the field must be so broad, right, 113 00:06:33,760 --> 00:06:35,080 Speaker 5: Like why measuring pain? 114 00:06:36,279 --> 00:06:38,400 Speaker 6: That's a very good I'm sorry, that's a very good 115 00:06:38,480 --> 00:06:42,640 Speaker 6: question because I have always developed sensors for different things. 116 00:06:42,680 --> 00:06:46,160 Speaker 6: We developed sensors or the environment. We developed sensors to 117 00:06:46,279 --> 00:06:49,400 Speaker 6: measure different things. But I had a friend whose daughter 118 00:06:50,160 --> 00:06:55,440 Speaker 6: was suffering from sickle cell and you know, and she asks, 119 00:06:55,600 --> 00:06:59,240 Speaker 6: you know a view. You know, many times she's in crisis. 120 00:07:00,200 --> 00:07:04,240 Speaker 6: Physicians scientificately, they cannot really assess what No, she's in pain. 121 00:07:04,839 --> 00:07:06,479 Speaker 3: And I thought, well, that should be easy. 122 00:07:06,520 --> 00:07:10,320 Speaker 6: As long as we can find a particular molecule, we 123 00:07:10,440 --> 00:07:11,200 Speaker 6: can measure that. 124 00:07:11,760 --> 00:07:15,440 Speaker 3: And I thought somebody should have done that. It seems 125 00:07:15,440 --> 00:07:17,840 Speaker 3: so obvious. Now I'm kidding, but we did. 126 00:07:17,960 --> 00:07:20,240 Speaker 6: We looked in literature and we'll realize it's actually no. 127 00:07:20,960 --> 00:07:24,520 Speaker 6: And this is where we studied the work fifteen years ago. 128 00:07:24,800 --> 00:07:28,840 Speaker 2: Oh thanks to one Sadi Njit, Distinguished Professor of Chemistry 129 00:07:28,880 --> 00:07:31,880 Speaker 2: and Environmental Science. We continue with some of the best 130 00:07:31,920 --> 00:07:35,440 Speaker 2: conversations from NJIT. Their co hosts Alex Steel and I 131 00:07:35,480 --> 00:07:40,080 Speaker 2: spoke with Eric Fortune NJIT, Associate Professor of Biological Sciences. 132 00:07:40,680 --> 00:07:42,960 Speaker 2: Eric discussed how he recently led a team of scientists 133 00:07:43,000 --> 00:07:45,520 Speaker 2: in a contest to see who could count the most 134 00:07:45,760 --> 00:07:49,560 Speaker 2: creatures in a square kilometer of the Amazon rainforest, and 135 00:07:49,680 --> 00:07:51,760 Speaker 2: his team walked away with a five million dollar prize 136 00:07:51,760 --> 00:07:55,679 Speaker 2: awarded by X Prize Foundation at the G twenty Social 137 00:07:55,720 --> 00:07:58,360 Speaker 2: Summit in Rio de Janeiro. I first asked Eric to 138 00:07:58,400 --> 00:08:00,760 Speaker 2: walk us through what his experience was like and how 139 00:08:00,800 --> 00:08:01,120 Speaker 2: he did it. 140 00:08:01,480 --> 00:08:05,840 Speaker 7: Well, it's a super exciting project that we were part of. 141 00:08:06,440 --> 00:08:09,760 Speaker 7: It was sponsored by this group called the X Prize, 142 00:08:10,000 --> 00:08:16,920 Speaker 7: and their goal is to incentivize fields where otherwise there 143 00:08:16,960 --> 00:08:20,920 Speaker 7: weren't sufficient finances to drive things. So they feel like 144 00:08:21,000 --> 00:08:25,520 Speaker 7: they're responsible for the current space exploration that's occurring in 145 00:08:25,600 --> 00:08:28,840 Speaker 7: the private sector because they sponsored Next Prize thirty years 146 00:08:28,840 --> 00:08:32,120 Speaker 7: ago that drove that market. So their goal with this 147 00:08:32,400 --> 00:08:36,319 Speaker 7: X Prize was to drive the same kind of development 148 00:08:36,440 --> 00:08:40,480 Speaker 7: and innovation in the area of biodiversity. So their rules 149 00:08:40,600 --> 00:08:43,679 Speaker 7: were that they would give us a few months ahead 150 00:08:43,679 --> 00:08:48,000 Speaker 7: of time, a random location in some rainforest on the planet, 151 00:08:48,240 --> 00:08:52,520 Speaker 7: give us one day to sample with only drones and 152 00:08:52,679 --> 00:08:56,120 Speaker 7: other kinds of remote sensing technologies. No human was allowed 153 00:08:56,160 --> 00:08:59,160 Speaker 7: to go into this square kilometer, and then forty eight 154 00:08:59,200 --> 00:09:02,880 Speaker 7: hours to analyze the data and provide a report about 155 00:09:02,920 --> 00:09:05,120 Speaker 7: the biodiversity that we encountered in that time. 156 00:09:05,640 --> 00:09:08,040 Speaker 2: What did you find here? Findings? What was the bio 157 00:09:08,200 --> 00:09:10,440 Speaker 2: I can't think of a more biodiverse area maybe than 158 00:09:10,480 --> 00:09:11,000 Speaker 2: a rainforest. 159 00:09:11,120 --> 00:09:15,600 Speaker 7: Well, we went to perhaps the most biodiverse place on Earth. 160 00:09:15,679 --> 00:09:19,760 Speaker 7: So this was a habitat in the Amazon rainforest. And 161 00:09:20,080 --> 00:09:23,400 Speaker 7: so we had a square kilometer just outside of Manaos 162 00:09:23,920 --> 00:09:27,160 Speaker 7: in Brazil, and so we deployed our drones and these 163 00:09:27,240 --> 00:09:30,640 Speaker 7: devices that sat on top of the rainforest canopy and 164 00:09:30,720 --> 00:09:35,319 Speaker 7: they collected insects and sound and environmental DNA, and we 165 00:09:35,440 --> 00:09:39,520 Speaker 7: were able to take like twenty seven million samples of 166 00:09:39,840 --> 00:09:44,760 Speaker 7: genetic information from the forest, identified more species of birds 167 00:09:44,800 --> 00:09:46,880 Speaker 7: that exist in all of North America in this one 168 00:09:47,840 --> 00:09:51,880 Speaker 7: one kilometer area, and then measure hundreds of thousands of 169 00:09:52,440 --> 00:09:55,439 Speaker 7: insects all in this twenty four hour period. It's really unprecedented. 170 00:09:55,520 --> 00:09:57,880 Speaker 5: So okay, so you take this, you analyze that you 171 00:09:57,920 --> 00:09:59,400 Speaker 5: have a tremendous amount of research. 172 00:09:59,679 --> 00:10:03,480 Speaker 7: Then what then what? Well, that's I think the big 173 00:10:03,600 --> 00:10:07,560 Speaker 7: problem that Exerprise is trying to identify, which is first 174 00:10:07,600 --> 00:10:09,719 Speaker 7: to develop the technology so that we can do this 175 00:10:09,880 --> 00:10:12,600 Speaker 7: kind of analysis, and then the next steps the part 176 00:10:12,640 --> 00:10:14,400 Speaker 7: that we're in now is to try and develop and 177 00:10:14,520 --> 00:10:19,160 Speaker 7: address the market for biodiversity monitoring, not only in rainforest 178 00:10:19,240 --> 00:10:23,600 Speaker 7: and critically important habitats like the Amazon Basin, but across 179 00:10:23,679 --> 00:10:24,120 Speaker 7: the planet. 180 00:10:24,720 --> 00:10:28,200 Speaker 2: So what are the next technological frontiers for monitoring. 181 00:10:29,200 --> 00:10:34,199 Speaker 7: So we've now developed and tested and proven these technologies, 182 00:10:34,240 --> 00:10:37,640 Speaker 7: so our goal now is to translate these things into businesses. 183 00:10:38,280 --> 00:10:41,679 Speaker 7: So our team alone has generated six or seven new 184 00:10:41,760 --> 00:10:45,440 Speaker 7: businesses that are each focusing on components of this biodiversity 185 00:10:45,520 --> 00:10:48,880 Speaker 7: monitoring that are entering the market at this moment. And 186 00:10:48,960 --> 00:10:51,760 Speaker 7: the other teams that we compete it with, some of 187 00:10:51,840 --> 00:10:54,880 Speaker 7: their teams are also generating these new companies, new companies 188 00:10:54,920 --> 00:10:59,840 Speaker 7: that do things like monitoring environmental DNA at a particular location. 189 00:11:00,040 --> 00:11:03,800 Speaker 7: So if you're building a power plant somewhere along an 190 00:11:03,920 --> 00:11:06,560 Speaker 7: endangered forest, you want to know what your impacts are. 191 00:11:06,679 --> 00:11:10,480 Speaker 7: You measure the environmental DNA to know what species were 192 00:11:10,520 --> 00:11:14,079 Speaker 7: there before andies what your impact is on species later. 193 00:11:14,720 --> 00:11:17,240 Speaker 2: Do sense changing winds out there in terms of funding, 194 00:11:18,040 --> 00:11:21,439 Speaker 2: terms of support for biodiversity and just environment in general. 195 00:11:21,800 --> 00:11:25,959 Speaker 7: Well, I mean it's complicated, of course with changing political winds, 196 00:11:26,040 --> 00:11:29,079 Speaker 7: but we all live on this planet and that's not changing, 197 00:11:29,120 --> 00:11:31,120 Speaker 7: and I think anyone of our age and I don't 198 00:11:31,160 --> 00:11:34,240 Speaker 7: mean to say anything about how old any of us are, 199 00:11:34,400 --> 00:11:39,160 Speaker 7: but it's inescapable that during your lifetime you have observed 200 00:11:39,240 --> 00:11:43,839 Speaker 7: changes in climate and in biodiversity that occurs, and so 201 00:11:45,320 --> 00:11:48,079 Speaker 7: whether we like it or not, this is something that 202 00:11:48,200 --> 00:11:49,959 Speaker 7: we're going to have to deal with. The question I 203 00:11:50,000 --> 00:11:52,400 Speaker 7: think from a business perspective, of course, is what's the 204 00:11:52,480 --> 00:11:55,160 Speaker 7: time horizon of that? Is it one year, ten years, 205 00:11:55,400 --> 00:11:58,120 Speaker 7: one hundred years? And that's a complicated thing that I 206 00:11:58,240 --> 00:11:59,559 Speaker 7: am not equipped to answer. 207 00:12:00,600 --> 00:12:01,600 Speaker 3: What's next for you guys? 208 00:12:02,240 --> 00:12:06,320 Speaker 7: So I'm personally. I've started a company that came out 209 00:12:06,360 --> 00:12:10,200 Speaker 7: of this X Prize competition, and so we have our 210 00:12:10,240 --> 00:12:14,360 Speaker 7: first order, and so I'm busy building things, building these 211 00:12:14,440 --> 00:12:17,079 Speaker 7: high tech devices that are deployable into these kinds of 212 00:12:17,160 --> 00:12:20,320 Speaker 7: habitats that collect this kind of data. And we see 213 00:12:20,360 --> 00:12:23,079 Speaker 7: that as at least on a small scale, a sustainable 214 00:12:23,120 --> 00:12:26,520 Speaker 7: business for quite quite some time. Anyone who owns land 215 00:12:26,559 --> 00:12:30,400 Speaker 7: and is interested in in the biodiversity there, starting with 216 00:12:30,559 --> 00:12:34,360 Speaker 7: like national parks or local and city parks, or any 217 00:12:34,400 --> 00:12:37,679 Speaker 7: other business that have large landing holdings, they're going to 218 00:12:37,800 --> 00:12:42,520 Speaker 7: need over time devices like this to answer regulatory and 219 00:12:43,320 --> 00:12:47,280 Speaker 7: their customers demands about biodiversity. 220 00:12:47,000 --> 00:12:49,840 Speaker 2: Our thanks to air Fortune and j It Associate Professor 221 00:12:49,880 --> 00:12:52,760 Speaker 2: of Biological Sciences. Coming up will break down what it 222 00:12:52,880 --> 00:12:55,480 Speaker 2: means to stimulate the brain and the benefits that come 223 00:12:55,559 --> 00:12:58,560 Speaker 2: with it. You're listening to Bloomberg Intelligence on Bloomberg Radio, 224 00:12:58,800 --> 00:13:01,280 Speaker 2: providing end up research and data on two thousand companies 225 00:13:01,320 --> 00:13:03,840 Speaker 2: and one hundred and thirty industries. You can access Bloomberg 226 00:13:03,880 --> 00:13:06,640 Speaker 2: Intelligence via Bigo on the terminal. I'm Paul Sweeney. 227 00:13:06,960 --> 00:13:08,200 Speaker 4: This is Bloomberg. 228 00:13:13,640 --> 00:13:17,320 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 229 00:13:17,440 --> 00:13:20,480 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 230 00:13:20,520 --> 00:13:23,800 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 231 00:13:23,880 --> 00:13:27,000 Speaker 1: you get your podcasts, or watch us live on YouTube. 232 00:13:28,160 --> 00:13:30,959 Speaker 2: We continue with some of our best conversations this week 233 00:13:31,040 --> 00:13:33,840 Speaker 2: from the New Jersey Institute of Technology co hosts Alex 234 00:13:33,840 --> 00:13:36,520 Speaker 2: Steele and Irat and Jit, where they enrolled more than 235 00:13:36,559 --> 00:13:39,320 Speaker 2: thirteen thousand students and are really some of the leaders 236 00:13:39,400 --> 00:13:42,679 Speaker 2: of science and technology in the US. There, we spoke 237 00:13:42,760 --> 00:13:47,000 Speaker 2: with chaw yon Njit, alumnus, co founder and CEO Princeton 238 00:13:47,200 --> 00:13:50,800 Speaker 2: New Energy also known as PNE. He discussed how P 239 00:13:50,960 --> 00:13:55,400 Speaker 2: and E develops advanced technologies for recycling lithium ion batteries. 240 00:13:55,440 --> 00:13:57,360 Speaker 2: I first asked Chow to talk about his company and 241 00:13:57,440 --> 00:13:58,280 Speaker 2: what they're trying to do. 242 00:13:59,040 --> 00:14:01,920 Speaker 4: So President Edit, we have a great technology and the 243 00:14:02,040 --> 00:14:06,040 Speaker 4: using plasma to recycle LiTi ion battery with much lower 244 00:14:06,120 --> 00:14:10,680 Speaker 4: cost roughly forty fifty percent lower than the traditional recycling 245 00:14:10,720 --> 00:14:14,319 Speaker 4: technology and also much more cleaner compared with the traditional 246 00:14:14,400 --> 00:14:18,920 Speaker 4: lead as a leaching process. So that's why recycling technology 247 00:14:19,080 --> 00:14:22,840 Speaker 4: we need in the US is cleaner and cheaper. So 248 00:14:23,000 --> 00:14:26,040 Speaker 4: talk about the supply chain for the battery. The biggest 249 00:14:26,160 --> 00:14:28,720 Speaker 4: problem for the US right now is that the EV 250 00:14:28,880 --> 00:14:32,080 Speaker 4: is still too expensive, so how we can reduce the 251 00:14:32,280 --> 00:14:35,400 Speaker 4: cost for the EV is important. So there is a 252 00:14:35,600 --> 00:14:37,920 Speaker 4: more than half of the costs inside the battery, which 253 00:14:38,000 --> 00:14:41,440 Speaker 4: is they call the cathode active materials. So the direct 254 00:14:41,480 --> 00:14:46,200 Speaker 4: recycling our technology is to direct extract those cathode active 255 00:14:46,280 --> 00:14:49,880 Speaker 4: materials outside from the old batteries that you can reuse. 256 00:14:50,480 --> 00:14:52,000 Speaker 4: And at the same time, we do not want to 257 00:14:52,080 --> 00:14:55,320 Speaker 4: produce a lot of waste. So in the traditional way, 258 00:14:55,480 --> 00:14:58,920 Speaker 4: using the software acid you leaching all the metals and 259 00:14:59,080 --> 00:15:00,880 Speaker 4: know as any you can do a lot of the 260 00:15:00,960 --> 00:15:04,040 Speaker 4: sodium software and we don't have the place to dumb 261 00:15:04,120 --> 00:15:06,320 Speaker 4: them right now, so that's why we need the great 262 00:15:06,400 --> 00:15:09,240 Speaker 4: technology to do that and which is a much lower cost. 263 00:15:09,480 --> 00:15:10,400 Speaker 4: So that's what we are doing. 264 00:15:10,680 --> 00:15:12,920 Speaker 3: So let's go to the cathoin part first. So you're 265 00:15:12,960 --> 00:15:15,720 Speaker 3: doing that for cheaper than competitors. 266 00:15:15,480 --> 00:15:19,560 Speaker 4: How so Yeah, because of the traditional way, you need 267 00:15:19,680 --> 00:15:23,440 Speaker 4: to break the old batteries to downb to the element. 268 00:15:23,960 --> 00:15:28,040 Speaker 4: So using the acid, so we don't destroy the cathode materials, 269 00:15:28,120 --> 00:15:31,440 Speaker 4: we just fix them reuse them. So that's how we 270 00:15:31,560 --> 00:15:34,479 Speaker 4: reduce the cost and using our plasma technology. 271 00:15:35,440 --> 00:15:39,960 Speaker 2: So where are we with just battery technology and recycling, 272 00:15:40,000 --> 00:15:42,800 Speaker 2: I mean, are there more advances to go here? Because 273 00:15:42,800 --> 00:15:46,960 Speaker 2: it feels like that's such a key part of electric vehicles, 274 00:15:47,080 --> 00:15:48,760 Speaker 2: just electric power going forward. 275 00:15:49,360 --> 00:15:53,480 Speaker 4: Yeah, so it's not only for the EVA, but also 276 00:15:53,600 --> 00:15:57,320 Speaker 4: like the ANDRE storage batteries. Yes, as the big the 277 00:15:57,440 --> 00:16:01,480 Speaker 4: storage system. So traditional tech tchnology we're trying to build 278 00:16:01,520 --> 00:16:04,880 Speaker 4: in the US, but it's very expensive and the processing 279 00:16:04,960 --> 00:16:07,560 Speaker 4: costs is also very expensive. So that's why in the 280 00:16:07,720 --> 00:16:10,440 Speaker 4: US we're try to scaling up our technology. So the 281 00:16:10,520 --> 00:16:14,040 Speaker 4: company was founded in twenty nineteen and we have technology 282 00:16:14,400 --> 00:16:17,000 Speaker 4: and after that we have the large space lab in 283 00:16:17,080 --> 00:16:19,800 Speaker 4: New Jersey which is a close to Princeton. And also 284 00:16:19,840 --> 00:16:22,640 Speaker 4: we have a build up pilot production line which is 285 00:16:22,680 --> 00:16:25,280 Speaker 4: about three four years ago right now is upruning about 286 00:16:25,320 --> 00:16:29,440 Speaker 4: two years which is in Dallas, Texas and starting from 287 00:16:29,560 --> 00:16:32,280 Speaker 4: last year we are building the first commercial scale the 288 00:16:32,320 --> 00:16:35,560 Speaker 4: production line in South Karina and Chester County. So in 289 00:16:35,640 --> 00:16:38,280 Speaker 4: this one we are able to recycle five thousand towns 290 00:16:38,320 --> 00:16:40,760 Speaker 4: as a phase one and we target to expand to 291 00:16:40,880 --> 00:16:43,920 Speaker 4: thirty thousand towns as end to recycle the batteries, do 292 00:16:43,920 --> 00:16:44,800 Speaker 4: you have to have end. 293 00:16:44,760 --> 00:16:47,480 Speaker 5: Buyers that will contract that material for you to feel 294 00:16:47,520 --> 00:16:49,760 Speaker 5: confident putting in that kind of capex. 295 00:16:49,920 --> 00:16:52,360 Speaker 4: Yes, we need that and do you have that? We 296 00:16:52,720 --> 00:16:55,400 Speaker 4: do have the feed stock provider which give us the 297 00:16:55,520 --> 00:16:58,920 Speaker 4: WETE batteries and it were comeing from like a sale 298 00:16:58,960 --> 00:17:02,800 Speaker 4: manufacturers who make the batteries there are manufacturing scrap, so 299 00:17:02,920 --> 00:17:05,199 Speaker 4: we do have a contract with them to recycle their 300 00:17:05,680 --> 00:17:09,200 Speaker 4: manufacturing scrap. We do have a contract with auto Ems 301 00:17:09,359 --> 00:17:13,040 Speaker 4: and also the Junkyard players who have a lot of 302 00:17:13,119 --> 00:17:15,439 Speaker 4: waste batteries, so we also have a contract for. 303 00:17:15,520 --> 00:17:16,800 Speaker 3: That one who's buying them now. 304 00:17:17,080 --> 00:17:20,719 Speaker 4: So currently we are selling to the leaching companies who 305 00:17:20,880 --> 00:17:23,600 Speaker 4: need those batteries to continue to get the medals for 306 00:17:23,720 --> 00:17:24,560 Speaker 4: the later usage. 307 00:17:25,480 --> 00:17:27,560 Speaker 2: How are you funding your company? I'm a former banker, 308 00:17:27,600 --> 00:17:29,600 Speaker 2: so I always think about the money. How are you 309 00:17:29,640 --> 00:17:30,399 Speaker 2: funding this company? 310 00:17:30,680 --> 00:17:33,840 Speaker 4: That's a very important part. So we close the two 311 00:17:33,920 --> 00:17:36,760 Speaker 4: rounds of the investment. We call a cias run and 312 00:17:36,840 --> 00:17:40,439 Speaker 4: a RAND. So we have a private investors who interest 313 00:17:40,520 --> 00:17:43,800 Speaker 4: with US investor US and supporting US, and those the 314 00:17:43,920 --> 00:17:47,800 Speaker 4: investors some of the finishing investors AHOW Strategy investor so. 315 00:17:48,640 --> 00:17:50,800 Speaker 4: And on top of this we get a big support 316 00:17:50,880 --> 00:17:54,119 Speaker 4: from the Department Energy in the past six years, starting 317 00:17:54,200 --> 00:17:57,000 Speaker 4: from like a smaller grand spr later on we have 318 00:17:57,080 --> 00:17:59,680 Speaker 4: a larger grant, so we got a rough about twenty 319 00:17:59,720 --> 00:18:00,560 Speaker 4: minute dollars. 320 00:18:00,400 --> 00:18:03,520 Speaker 3: Pouring us will What is your level of confidence that 321 00:18:03,640 --> 00:18:04,280 Speaker 3: that continues. 322 00:18:05,600 --> 00:18:09,280 Speaker 4: I think for the United States critical minerals are very important, 323 00:18:09,880 --> 00:18:12,000 Speaker 4: so we don't have so many minds in the US. 324 00:18:12,960 --> 00:18:16,719 Speaker 4: What we need is how we can leverage those waste 325 00:18:16,840 --> 00:18:19,119 Speaker 4: stuff and how to reuse them. So that's why I 326 00:18:19,200 --> 00:18:23,400 Speaker 4: think recycling technology is a critical for US to secure 327 00:18:23,440 --> 00:18:27,040 Speaker 4: the critical minerals and this will link to the US 328 00:18:27,280 --> 00:18:31,600 Speaker 4: energy security. So I think for our technology is very 329 00:18:31,640 --> 00:18:35,000 Speaker 4: critical for the United States for the materials what we 330 00:18:35,200 --> 00:18:37,960 Speaker 4: need and also for the batteries what we're going to build. 331 00:18:38,480 --> 00:18:41,359 Speaker 4: So that's what we need, and just give you a 332 00:18:41,400 --> 00:18:44,919 Speaker 4: little bit numbers. So currently us don't produce any castle 333 00:18:44,960 --> 00:18:48,640 Speaker 4: the materials, so all the materials we import from outside, 334 00:18:48,760 --> 00:18:53,080 Speaker 4: so directly recycling, we use the waste batteries and produce 335 00:18:53,119 --> 00:18:56,280 Speaker 4: the castle the materials to make new batteries and that's 336 00:18:56,359 --> 00:18:59,600 Speaker 4: content more than half of the value inside the little 337 00:18:59,640 --> 00:19:03,600 Speaker 4: I M. So how important is That's why we believe 338 00:19:03,720 --> 00:19:07,320 Speaker 4: the grant will continue to support this critical minerals research 339 00:19:07,800 --> 00:19:10,159 Speaker 4: and also the support our energy security. 340 00:19:10,560 --> 00:19:12,840 Speaker 2: So you get your masters and your PhD here. 341 00:19:12,800 --> 00:19:15,800 Speaker 4: Right, that's right in chemistry department. That sounds fun. 342 00:19:16,840 --> 00:19:18,240 Speaker 3: How was your experience here? 343 00:19:18,920 --> 00:19:24,360 Speaker 4: It's awesome. I really enjoyed the research here. So basically 344 00:19:24,840 --> 00:19:29,440 Speaker 4: it's built up my very strong research and engineering foundation. 345 00:19:30,040 --> 00:19:32,680 Speaker 4: So I think that's would be very critical because once 346 00:19:32,760 --> 00:19:36,960 Speaker 4: you move into the next step, so doing research basically 347 00:19:37,119 --> 00:19:38,920 Speaker 4: finished pH no one's going to teach you how to 348 00:19:39,040 --> 00:19:42,639 Speaker 4: do it. You have very strong the experience how to 349 00:19:43,000 --> 00:19:47,240 Speaker 4: design your research, how to set up everything, and then 350 00:19:47,920 --> 00:19:50,640 Speaker 4: after research, how to write a paper and the publications, 351 00:19:51,040 --> 00:19:54,960 Speaker 4: and more important, how to find the research topics, write 352 00:19:55,000 --> 00:19:57,640 Speaker 4: the proposals to get a grant. So yeah, we got 353 00:19:57,720 --> 00:19:59,600 Speaker 4: I get a pretty good foundation here. 354 00:20:00,200 --> 00:20:04,280 Speaker 2: Thanks to cell John nj alumnus, co founder and CEO 355 00:20:04,480 --> 00:20:06,919 Speaker 2: of Princeton New Energy. We continue with some of our 356 00:20:06,920 --> 00:20:10,560 Speaker 2: best conversations from NJIT. Their co host Alex deel and 357 00:20:10,600 --> 00:20:14,400 Speaker 2: I spoke with Alisa kelly Onemi, Assistant Professor of biomedical 358 00:20:14,520 --> 00:20:18,000 Speaker 2: Engineering at NJIT. She discussed what it means to stimulate 359 00:20:18,080 --> 00:20:20,399 Speaker 2: the brain and the benefits that come with it. I 360 00:20:20,440 --> 00:20:23,200 Speaker 2: first asked Elisa what kind of research she's focusing on 361 00:20:23,359 --> 00:20:23,800 Speaker 2: these days. 362 00:20:24,200 --> 00:20:28,440 Speaker 8: The biggest question my research is trying to understand how 363 00:20:28,600 --> 00:20:33,280 Speaker 8: to modulate the brain safely and precisely. So we already 364 00:20:33,400 --> 00:20:38,080 Speaker 8: know that several brain disorders have like abnormal brain activities, 365 00:20:39,160 --> 00:20:39,879 Speaker 8: but we don't know. 366 00:20:40,720 --> 00:20:41,680 Speaker 3: What causes them. 367 00:20:42,359 --> 00:20:45,680 Speaker 8: I'm kind of like, how can we normalize them? And 368 00:20:45,800 --> 00:20:48,760 Speaker 8: that's where prain stimulation comes from. So prain stimulation is 369 00:20:48,800 --> 00:20:51,240 Speaker 8: a method where we can actually modulate the brain safely. 370 00:20:51,480 --> 00:20:54,080 Speaker 5: Modulate the brain does I mean like fix it or 371 00:20:54,400 --> 00:20:56,240 Speaker 5: change the brain waves or what does that mean? 372 00:20:56,640 --> 00:20:58,240 Speaker 3: So basically it's kind. 373 00:20:58,080 --> 00:20:59,920 Speaker 8: Of like the radio, So like it with the radio, 374 00:21:00,040 --> 00:21:02,760 Speaker 8: you can find two things. So with this one, we 375 00:21:02,840 --> 00:21:07,480 Speaker 8: are applying these like small energy pulses to the brain 376 00:21:07,600 --> 00:21:11,919 Speaker 8: that are totally safe, and these energy pulses are able 377 00:21:12,080 --> 00:21:13,800 Speaker 8: to change your brain activity. 378 00:21:15,320 --> 00:21:15,600 Speaker 4: Wow. 379 00:21:15,840 --> 00:21:18,879 Speaker 2: So give us like a typical example of kind of 380 00:21:19,000 --> 00:21:21,399 Speaker 2: what you're trying to do with a patient who may 381 00:21:21,440 --> 00:21:23,080 Speaker 2: have some brain issues. What's an example? 382 00:21:23,800 --> 00:21:29,280 Speaker 8: Yeah, So, well, for example, considering medications. So medications are 383 00:21:29,359 --> 00:21:33,480 Speaker 8: life saving for many individuals, but the challenge is that, 384 00:21:33,680 --> 00:21:37,800 Speaker 8: like some people get side effects, some people don't just 385 00:21:38,320 --> 00:21:43,080 Speaker 8: like tolerate them, some people just don't get like any response, 386 00:21:43,359 --> 00:21:45,240 Speaker 8: and obviously that's a problem because then we don't have 387 00:21:45,320 --> 00:21:47,800 Speaker 8: any treatments for those. So what I'm trying to do 388 00:21:47,960 --> 00:21:50,760 Speaker 8: with my research is kind of like help those individuals 389 00:21:50,760 --> 00:21:54,159 Speaker 8: who don't get help from the pharmaceuticals. So with these 390 00:21:54,240 --> 00:21:58,080 Speaker 8: brain simulation methods, we kind of like fill that gap 391 00:21:58,280 --> 00:22:01,439 Speaker 8: and try to help them. So we try to develop 392 00:22:01,600 --> 00:22:04,879 Speaker 8: methods that we could kind of like a whatever problem 393 00:22:04,960 --> 00:22:09,200 Speaker 8: they have in their brain, we could elevate their symptoms. 394 00:22:09,400 --> 00:22:11,679 Speaker 3: And then in that case it's sort of customized per 395 00:22:11,760 --> 00:22:15,160 Speaker 3: person to do that. So I mean that's amazing, that's 396 00:22:15,240 --> 00:22:16,280 Speaker 3: like a life saving thing. 397 00:22:16,280 --> 00:22:18,320 Speaker 5: You say it's totally safe, but you say electric magnetic 398 00:22:18,400 --> 00:22:19,400 Speaker 5: pulses in your brain. 399 00:22:19,240 --> 00:22:21,040 Speaker 3: And you're like, WHOA, I don't know. That sounds scary. 400 00:22:22,359 --> 00:22:23,720 Speaker 3: Give me the pitch for why it's safe. 401 00:22:24,680 --> 00:22:28,159 Speaker 8: So so basically with h this these path is we 402 00:22:28,240 --> 00:22:31,640 Speaker 8: can just reach the surface of the brain and then 403 00:22:32,280 --> 00:22:36,080 Speaker 8: like your brain is already naturally electrical. So what we're 404 00:22:36,119 --> 00:22:38,960 Speaker 8: basically doing is that we just like a initiate the 405 00:22:39,080 --> 00:22:41,920 Speaker 8: activity that you would be initiating yourself as well, but 406 00:22:42,040 --> 00:22:46,160 Speaker 8: we just do it externally and then whatever was supposed 407 00:22:46,200 --> 00:22:48,320 Speaker 8: to happen in your brain will happen. So it's kind 408 00:22:48,320 --> 00:22:51,320 Speaker 8: of like we just initiate the domino. 409 00:22:51,000 --> 00:22:52,000 Speaker 3: Effects, so to speak. 410 00:22:52,560 --> 00:22:55,600 Speaker 2: Where are you in your research now in terms of 411 00:22:55,680 --> 00:22:58,040 Speaker 2: maybe getting at some point two practical applications. 412 00:22:59,560 --> 00:23:02,359 Speaker 8: So my LAP is rather new, So I've been an 413 00:23:02,480 --> 00:23:05,520 Speaker 8: NHT only like two and a half years, so I 414 00:23:05,560 --> 00:23:07,639 Speaker 8: would say that we're still at the kind of like 415 00:23:07,760 --> 00:23:12,879 Speaker 8: the first steps. But we already have some industry collaborations, 416 00:23:12,960 --> 00:23:16,359 Speaker 8: so we've worked with so there's a for example, this 417 00:23:16,480 --> 00:23:20,440 Speaker 8: program and SFI coorse so that that's a program where 418 00:23:20,440 --> 00:23:23,080 Speaker 8: we collaborate with industry and then kind of like a 419 00:23:24,359 --> 00:23:27,680 Speaker 8: try to kind of like get an idea of where 420 00:23:27,800 --> 00:23:30,560 Speaker 8: we could help with our research. So I've had a 421 00:23:30,640 --> 00:23:34,359 Speaker 8: couple of student teams done that and then but basically, 422 00:23:34,520 --> 00:23:37,520 Speaker 8: like everything that we do, the end goal is to 423 00:23:37,680 --> 00:23:41,520 Speaker 8: help patients so somehow, because I mean, this is electricity, 424 00:23:41,640 --> 00:23:44,520 Speaker 8: so obviously like that's where the engineering comes from. But 425 00:23:44,720 --> 00:23:47,320 Speaker 8: like in addition, obviously we have to understand other feels 426 00:23:47,400 --> 00:23:49,000 Speaker 8: like neuroscience and clinical things. 427 00:23:49,040 --> 00:23:50,399 Speaker 3: But like a from my. 428 00:23:50,560 --> 00:23:53,440 Speaker 8: Labs perspective, we're trying to kind of like provide the 429 00:23:53,520 --> 00:23:56,520 Speaker 8: engineering perspective. So what do you need to do or 430 00:23:56,560 --> 00:23:59,760 Speaker 8: what can we do through an engineer's perspective to to 431 00:24:00,200 --> 00:24:02,080 Speaker 8: model like kind of like improve. 432 00:24:01,800 --> 00:24:05,600 Speaker 5: These methods so this could become you could commercialize what 433 00:24:05,720 --> 00:24:06,120 Speaker 5: you're doing. 434 00:24:06,320 --> 00:24:11,639 Speaker 8: So this this technology is already commercialized, Okay. So so 435 00:24:11,760 --> 00:24:15,480 Speaker 8: basically this was invented about thirty years ago. So there 436 00:24:15,520 --> 00:24:18,960 Speaker 8: are several companies I believe currently there's like thirty in 437 00:24:19,040 --> 00:24:22,000 Speaker 8: different companies that are developing these these methods and there 438 00:24:22,040 --> 00:24:25,720 Speaker 8: are FDA approved at treatments. So why we still need 439 00:24:26,200 --> 00:24:28,720 Speaker 8: like our research is because like we have this problem 440 00:24:28,840 --> 00:24:32,640 Speaker 8: that like we know that this works, but we don't 441 00:24:32,680 --> 00:24:36,320 Speaker 8: really understand the interaction between the brain and the electricity 442 00:24:36,400 --> 00:24:38,960 Speaker 8: that well, so okay, we know that it works in 443 00:24:39,040 --> 00:24:41,480 Speaker 8: this one individual, but then like how do we modify 444 00:24:41,560 --> 00:24:44,960 Speaker 8: to the second individual. That's the mystery. So we're trying 445 00:24:45,000 --> 00:24:47,200 Speaker 8: to kind of like find find out that what is 446 00:24:47,280 --> 00:24:49,080 Speaker 8: the like what do we have to do, like what 447 00:24:49,160 --> 00:24:52,240 Speaker 8: do we have to change? So currently it's FDA approved 448 00:24:52,440 --> 00:24:57,760 Speaker 8: things like depression OCDS, so obsessive compulsive disorder and micrants 449 00:24:57,800 --> 00:25:01,840 Speaker 8: with ours. But everything's like one size with all. So 450 00:25:02,480 --> 00:25:07,439 Speaker 8: if you have like let's say, like your tenetics somehow different, 451 00:25:07,600 --> 00:25:10,440 Speaker 8: it's like it might not work for you. But then 452 00:25:10,800 --> 00:25:13,320 Speaker 8: currently we don't really know why and what should we. 453 00:25:13,440 --> 00:25:17,280 Speaker 2: Do our Thanks toy, Lisa Kayaleo and Yemi Assistant Professor 454 00:25:17,359 --> 00:25:20,840 Speaker 2: of Biomedical Engineering at NJIT. Coming up on the program, 455 00:25:21,119 --> 00:25:23,280 Speaker 2: we'll break down how one startup is trying to develop 456 00:25:23,480 --> 00:25:27,560 Speaker 2: virtual reality solutions for treating vision disorders. You're listening to 457 00:25:27,560 --> 00:25:30,720 Speaker 2: the Bloomberg Intelligence on Bloomberg Radio, providing in depth research 458 00:25:30,800 --> 00:25:32,720 Speaker 2: and data on two thousand companies and one hundred and 459 00:25:32,760 --> 00:25:35,639 Speaker 2: thirty industries. You can access Bloomberg Intelligence VI A B. 460 00:25:35,880 --> 00:25:36,919 Speaker 2: I go on the terminal. 461 00:25:37,160 --> 00:25:37,919 Speaker 4: I'm Paul Sweeney. 462 00:25:38,200 --> 00:25:39,280 Speaker 2: This is Bloomberg. 463 00:25:46,480 --> 00:25:50,280 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch the program 464 00:25:50,480 --> 00:25:53,399 Speaker 1: live weekdays at ten a m. Eastern on Applecarplay and 465 00:25:53,480 --> 00:25:56,440 Speaker 1: Android Auto with the Bloomberg Business app. You can also 466 00:25:56,600 --> 00:25:59,960 Speaker 1: listen live on Amazon Alexa from our flagship New York's 467 00:26:00,800 --> 00:26:03,440 Speaker 1: Just Say Alexa Play Bloomberg. Eleven thirty. 468 00:26:04,560 --> 00:26:07,000 Speaker 2: We continue with some of our best conversations this week 469 00:26:07,040 --> 00:26:09,920 Speaker 2: from the New Jersey Institute of Technology. This week, co 470 00:26:10,000 --> 00:26:12,359 Speaker 2: host Alex Steele and I were at NJIT, where they 471 00:26:12,440 --> 00:26:15,000 Speaker 2: enroll more than thirteen thousand students and are really some 472 00:26:15,160 --> 00:26:18,640 Speaker 2: of the leaders of science and technology in the US. There, 473 00:26:18,680 --> 00:26:22,119 Speaker 2: we spoke with Tara Alvarez, and JIT Distinguished Professor of 474 00:26:22,200 --> 00:26:25,520 Speaker 2: Biomedical Engineering. She's also the founder of the startup Oculo 475 00:26:25,880 --> 00:26:29,320 Speaker 2: Motor Technologies. Tara discussed how the company tries to develop 476 00:26:29,480 --> 00:26:33,520 Speaker 2: virtual reality solutions for optometrists to use in diagnosing and 477 00:26:33,600 --> 00:26:37,120 Speaker 2: treating vision disorders. I first asked Tara about the work 478 00:26:37,240 --> 00:26:39,959 Speaker 2: she's doing and the types of vision disorders she's looking at. 479 00:26:40,480 --> 00:26:42,959 Speaker 9: Glasses is what most people think of when they think 480 00:26:43,000 --> 00:26:46,760 Speaker 9: about an eye disorder, and if you can imagine, it's 481 00:26:46,920 --> 00:26:50,639 Speaker 9: very difficult to know what clear vision looks like. Unless 482 00:26:50,680 --> 00:26:54,200 Speaker 9: you've been fitted for your first pair of glasses. My 483 00:26:54,440 --> 00:26:58,040 Speaker 9: expertise is in how the brain brings visual information into 484 00:26:58,119 --> 00:27:00,760 Speaker 9: the brain, which is the idea of using the eyes 485 00:27:00,800 --> 00:27:04,120 Speaker 9: as a team to get the information into the brain. 486 00:27:04,520 --> 00:27:06,960 Speaker 9: And if you don't do that well, you might not 487 00:27:07,080 --> 00:27:10,200 Speaker 9: even realize you have it, but it can result in 488 00:27:10,480 --> 00:27:13,359 Speaker 9: problems when doing near work such as reading, working on 489 00:27:13,440 --> 00:27:18,280 Speaker 9: your phone, working on computers, and vision therapy works quite 490 00:27:18,400 --> 00:27:22,159 Speaker 9: well for this condition known as convergence insufficiency, which is 491 00:27:22,280 --> 00:27:25,120 Speaker 9: the inability of the eyes to work well as a team. 492 00:27:28,560 --> 00:27:29,200 Speaker 9: How do you fix that? 493 00:27:29,400 --> 00:27:31,280 Speaker 3: I guess or how do you find it? And then 494 00:27:31,280 --> 00:27:31,920 Speaker 3: how do you fix it? 495 00:27:32,240 --> 00:27:36,080 Speaker 9: Great questions. So vision therapy, which is basically like a 496 00:27:36,200 --> 00:27:40,120 Speaker 9: form of physical or occupational therapy for your eyes, strengthens 497 00:27:40,200 --> 00:27:43,399 Speaker 9: the eye muscles and the communication between the brain and 498 00:27:43,680 --> 00:27:47,119 Speaker 9: the eyes. My work has been funded mostly through the 499 00:27:47,240 --> 00:27:50,720 Speaker 9: National Institutes of Health, which is very critical in funding 500 00:27:51,119 --> 00:27:55,040 Speaker 9: research that has direct impact to our society. You can 501 00:27:55,160 --> 00:27:58,159 Speaker 9: find this by going to an eye doctor, so an 502 00:27:58,200 --> 00:28:01,080 Speaker 9: optometrist or an ophthalmologist and they can do an exam. 503 00:28:02,000 --> 00:28:04,280 Speaker 9: But most people don't even know that they have it, 504 00:28:04,400 --> 00:28:07,000 Speaker 9: so they don't even realize that this is a problem. 505 00:28:07,160 --> 00:28:10,560 Speaker 9: So typical problems people can have as they get headaches 506 00:28:10,600 --> 00:28:13,560 Speaker 9: while reading, they feel like they read slowly, they get 507 00:28:13,640 --> 00:28:16,600 Speaker 9: blurry vision, double vision, and it takes them much longer. 508 00:28:16,760 --> 00:28:19,800 Speaker 9: So it's not that they have a cognitive or a 509 00:28:20,359 --> 00:28:23,240 Speaker 9: problem in learning, it's that they're struggling to get the 510 00:28:23,359 --> 00:28:25,280 Speaker 9: visual information into the brain. 511 00:28:25,720 --> 00:28:28,199 Speaker 2: How common is this affliction or this. 512 00:28:28,440 --> 00:28:31,720 Speaker 9: Issue, So depending on how you do, the diagnosis is 513 00:28:31,800 --> 00:28:35,000 Speaker 9: present in between four and twelve percent, so you can 514 00:28:35,040 --> 00:28:37,680 Speaker 9: say roughly eight percent of the population. 515 00:28:39,240 --> 00:28:42,080 Speaker 5: You mentioned the funding. What's your level of confidence that 516 00:28:42,200 --> 00:28:44,360 Speaker 5: funding for this kind of study will stay. 517 00:28:45,680 --> 00:28:49,920 Speaker 9: I'm unclear right now. So right now we have I'm 518 00:28:50,000 --> 00:28:54,040 Speaker 9: on my second randomized clinical trial where we're concentrating on 519 00:28:54,520 --> 00:29:00,120 Speaker 9: concussions because we have the CDC released in December of 520 00:29:00,200 --> 00:29:04,320 Speaker 9: twenty four that concussion costs is about forty billion dollars 521 00:29:04,400 --> 00:29:08,400 Speaker 9: a year, and if you have had a concussion, especially 522 00:29:08,560 --> 00:29:13,480 Speaker 9: multiple concussions, you can develop persistent postconcussive symptoms. And out 523 00:29:13,560 --> 00:29:17,360 Speaker 9: of that population, about half of them have this convergence 524 00:29:17,400 --> 00:29:20,280 Speaker 9: and sufficiency, which is that teeming problem of the eyes. 525 00:29:21,080 --> 00:29:25,000 Speaker 9: So it is quite common, it's very impactful. My program 526 00:29:25,120 --> 00:29:28,200 Speaker 9: officer at the National Eye Institute within the National Institutes 527 00:29:28,240 --> 00:29:32,240 Speaker 9: of Health is extremely excited about our work, and in 528 00:29:32,320 --> 00:29:34,920 Speaker 9: the past administration, I would have much more confidence that 529 00:29:35,000 --> 00:29:38,160 Speaker 9: we would have funding to continue this very important work, 530 00:29:38,240 --> 00:29:40,400 Speaker 9: but it is something I have a lot of concerns 531 00:29:40,400 --> 00:29:41,040 Speaker 9: about right now. 532 00:29:42,120 --> 00:29:44,280 Speaker 2: How often do you get funded or how often do 533 00:29:44,360 --> 00:29:47,840 Speaker 2: most researchers get fund Is this an annual thing? 534 00:29:48,440 --> 00:29:52,160 Speaker 9: So typically you get what's called an ro one, which 535 00:29:52,240 --> 00:29:55,360 Speaker 9: is five years of funding, and you are reviewed every 536 00:29:55,600 --> 00:29:59,520 Speaker 9: year and typically with a randomized clinical trial, which is 537 00:29:59,560 --> 00:30:04,280 Speaker 9: what I'm leading. That's done in collaboration with Children's Hospital 538 00:30:04,320 --> 00:30:09,320 Speaker 9: Philadelphia as well as Rutgers chop Yess and Rutgers University. 539 00:30:10,040 --> 00:30:13,040 Speaker 9: It takes time because this is a rehabilitation and it's 540 00:30:13,080 --> 00:30:16,600 Speaker 9: a longitudinal study, and it's also done with Saless University 541 00:30:16,640 --> 00:30:21,640 Speaker 9: of Drexel, so it's not something that happens overnight. It 542 00:30:21,760 --> 00:30:25,280 Speaker 9: takes time to acquire this data. But it's really critical 543 00:30:25,400 --> 00:30:27,959 Speaker 9: because the knowledge that I'm gaining from this study has 544 00:30:28,040 --> 00:30:31,720 Speaker 9: been patented, where MNGT holds the patents, and that led 545 00:30:31,800 --> 00:30:35,600 Speaker 9: to our startup company, Ocular Motor Technologies. And the key 546 00:30:35,680 --> 00:30:38,440 Speaker 9: reason I became a biomedical engineer is I want to 547 00:30:38,520 --> 00:30:43,520 Speaker 9: have a positive impact on others, specifically in the healthcare sector. 548 00:30:43,960 --> 00:30:48,640 Speaker 9: And it's my children that actually inspired the core technology 549 00:30:48,800 --> 00:30:51,720 Speaker 9: of our company, which is the idea of trying to 550 00:30:51,840 --> 00:30:55,440 Speaker 9: do the therapy that works very well but is incredibly boring. 551 00:30:55,880 --> 00:30:58,040 Speaker 9: So if you can put the therapy in a virtual 552 00:30:58,080 --> 00:31:01,360 Speaker 9: reality headset and make it in to a game. If 553 00:31:01,400 --> 00:31:04,200 Speaker 9: you have a child in mine or almost all grown now, 554 00:31:04,720 --> 00:31:07,200 Speaker 9: but it's not difficult to get a kid to play 555 00:31:07,200 --> 00:31:09,840 Speaker 9: a VR game. And in essence, we are sugar coating 556 00:31:09,920 --> 00:31:13,360 Speaker 9: the therapy and they think they're having fun, but in actuality, 557 00:31:13,520 --> 00:31:16,360 Speaker 9: it's sugar coating a ton of science to get those 558 00:31:16,480 --> 00:31:18,200 Speaker 9: eyes to work better together. 559 00:31:18,520 --> 00:31:21,280 Speaker 3: It's like when I put kale in the oven. 560 00:31:21,680 --> 00:31:22,040 Speaker 10: Correct. 561 00:31:22,200 --> 00:31:24,400 Speaker 3: Yeah, it's a lot of lines exactly. 562 00:31:25,560 --> 00:31:29,240 Speaker 5: So what is the exit strategy for the startup and 563 00:31:29,400 --> 00:31:31,400 Speaker 5: can do get outside funding at the same time. 564 00:31:31,880 --> 00:31:35,920 Speaker 9: So we have been funded through the NSF through SBIR, 565 00:31:36,120 --> 00:31:39,320 Speaker 9: which is the Small Business Investigator grants. We've had both 566 00:31:39,360 --> 00:31:42,640 Speaker 9: Phase one and Phase two, and we also participated in 567 00:31:42,960 --> 00:31:48,040 Speaker 9: an nng T iCore program, and we did a national 568 00:31:48,160 --> 00:31:52,240 Speaker 9: version of iCore, which is basically teaching professors how to 569 00:31:52,600 --> 00:31:56,400 Speaker 9: create and translate their science out of the lab and 570 00:31:56,560 --> 00:31:58,120 Speaker 9: to have a positive impact. 571 00:31:58,360 --> 00:32:02,960 Speaker 2: Our thanks to Alvaros and Distinguished Professor of Biomedical Engineering. 572 00:32:03,320 --> 00:32:06,200 Speaker 2: We move next to earnings from the planemaker Boeing. This week, 573 00:32:06,240 --> 00:32:09,480 Speaker 2: Boeing reported first quarter results that exceeded Wall Street expectations, 574 00:32:09,680 --> 00:32:12,000 Speaker 2: and the company said it's ramping up jet production, aiming 575 00:32:12,040 --> 00:32:15,520 Speaker 2: to raise output of its seven thirty seven Max jetliner. 576 00:32:15,880 --> 00:32:19,760 Speaker 2: Boeing CEO Kelly Ordberg says this would help generate cash 577 00:32:19,840 --> 00:32:23,120 Speaker 2: that's been depleted by a recent strike and manufacturing crises. 578 00:32:23,320 --> 00:32:25,640 Speaker 2: For more guests, Isabelle and I were joined by George 579 00:32:25,640 --> 00:32:29,560 Speaker 2: ferguson Bloomberg Intelligence senior Aerospace, Defense and Airlines analysts. We 580 00:32:29,680 --> 00:32:32,480 Speaker 2: first asked George what his takeaways were from Boeing's first 581 00:32:32,560 --> 00:32:33,520 Speaker 2: quarter results. 582 00:32:33,960 --> 00:32:35,680 Speaker 11: Yeah, I mean, I think the biggest issue there was 583 00:32:36,040 --> 00:32:41,680 Speaker 11: a cash generation came in a billion dollars better than expectations. 584 00:32:42,080 --> 00:32:43,760 Speaker 10: I mean, I kind of get the sense that Boeing 585 00:32:43,880 --> 00:32:44,840 Speaker 10: management has sort of. 586 00:32:46,600 --> 00:32:50,240 Speaker 11: Give us some pretty conservative estimates for cash generation for 587 00:32:50,360 --> 00:32:52,680 Speaker 11: the year. I think they showed it in the first 588 00:32:52,760 --> 00:32:56,760 Speaker 11: quarter coming out pretty strong. I think there's a real 589 00:32:56,880 --> 00:33:02,240 Speaker 11: good potential that Boeing could be sort of cash flat, 590 00:33:02,400 --> 00:33:04,800 Speaker 11: meaning no usage for the year or maybe even a 591 00:33:04,840 --> 00:33:05,600 Speaker 11: bit of generation. 592 00:33:06,360 --> 00:33:10,520 Speaker 10: What we've heard is the tariff effects are. 593 00:33:10,120 --> 00:33:13,920 Speaker 11: Pretty manageable outside of China. China is a bit of 594 00:33:13,960 --> 00:33:16,160 Speaker 11: a challenge. They know that, but again a lot of 595 00:33:16,200 --> 00:33:19,320 Speaker 11: the backlog is not Chinese airplanes. A lot of that's 596 00:33:19,400 --> 00:33:22,600 Speaker 11: been the Chinese haven't placed many orders, and you know 597 00:33:22,720 --> 00:33:24,840 Speaker 11: boe has been sort of busy getting deliveries out to 598 00:33:24,920 --> 00:33:27,600 Speaker 11: them to get some of those airplanes they've already built 599 00:33:27,640 --> 00:33:31,400 Speaker 11: for them off the balance sheet. So China the major 600 00:33:31,440 --> 00:33:36,200 Speaker 11: issue again, not big though, and the rest of tariff 601 00:33:36,240 --> 00:33:39,920 Speaker 11: world sounds like where there's occasions that they have to pay, 602 00:33:39,960 --> 00:33:43,200 Speaker 11: they're paying, they can claw back some of those costs 603 00:33:43,320 --> 00:33:47,480 Speaker 11: from the administration. So it sounds like I would say 604 00:33:47,560 --> 00:33:51,680 Speaker 11: things are continued to be on track for a recovery, 605 00:33:51,720 --> 00:33:53,360 Speaker 11: for a strong recovery, hopefully this year. 606 00:33:53,760 --> 00:33:55,760 Speaker 12: Yes, and Boeing less earned a profit in mid twenty 607 00:33:55,800 --> 00:33:58,400 Speaker 12: twenty one, and it's definitely coming off. It's worse the year, 608 00:33:58,520 --> 00:34:01,880 Speaker 12: and it's century long history. We have the cee Okay 609 00:34:02,160 --> 00:34:05,240 Speaker 12: ord break saying at twenty twenty five is the turnaround? 610 00:34:05,320 --> 00:34:07,880 Speaker 12: You what is going to do differently? 611 00:34:09,400 --> 00:34:11,600 Speaker 10: Well, I mean I think they're going to deliver airplanes. Right. 612 00:34:11,680 --> 00:34:13,680 Speaker 10: So that's that's the biggest challenge. 613 00:34:13,680 --> 00:34:18,120 Speaker 11: When you're an aircraft manufacturer and you stop delivering airplanes 614 00:34:18,120 --> 00:34:21,080 Speaker 11: and you have quality problems, that's why they stop delivering. 615 00:34:21,640 --> 00:34:24,279 Speaker 10: You're just not going to generate cash. They've really been 616 00:34:24,320 --> 00:34:26,480 Speaker 10: trying to keep. 617 00:34:26,400 --> 00:34:30,920 Speaker 11: The supply chain I would say warm by buying components 618 00:34:31,680 --> 00:34:34,480 Speaker 11: from the supply chain, and that's why they've seen inventories 619 00:34:34,520 --> 00:34:35,040 Speaker 11: balloon to. 620 00:34:35,160 --> 00:34:37,840 Speaker 10: Like eighty seven billion dollars. 621 00:34:38,080 --> 00:34:40,080 Speaker 11: So, I mean a lot of the turnaround is build 622 00:34:40,080 --> 00:34:44,759 Speaker 11: those airplanes with existing inventory. Means the cash generation for 623 00:34:44,880 --> 00:34:47,920 Speaker 11: the airplanes they build and deliver ought to be higher 624 00:34:48,040 --> 00:34:52,399 Speaker 11: than historically. Use that money to pay do on debt, 625 00:34:52,640 --> 00:34:55,160 Speaker 11: keep the balance sheet, or heal the balance sheet. 626 00:34:55,600 --> 00:34:56,919 Speaker 10: That's the recovery plane. 627 00:34:57,320 --> 00:34:57,600 Speaker 4: George. 628 00:34:57,640 --> 00:34:59,320 Speaker 2: I know, if I'm talking to you and reading your research, 629 00:34:59,680 --> 00:35:02,759 Speaker 2: the CA story hinges in large part on getting those 630 00:35:02,800 --> 00:35:05,760 Speaker 2: seven three sevens out the door. Talk to us about 631 00:35:06,080 --> 00:35:09,239 Speaker 2: where production is today and where do you think it's 632 00:35:09,280 --> 00:35:10,160 Speaker 2: going to go in the future. 633 00:35:10,800 --> 00:35:14,239 Speaker 11: Yeah, So they said that they were the factory was 634 00:35:14,239 --> 00:35:19,040 Speaker 11: building at thirty low thirties number of aircraft per month. 635 00:35:19,120 --> 00:35:20,719 Speaker 11: You know, we've kind of been tracking it. I think 636 00:35:20,760 --> 00:35:23,680 Speaker 11: we saw high twenty, so probably Kelly's got maybe just 637 00:35:23,760 --> 00:35:27,520 Speaker 11: a more current number on that. I think that they'll 638 00:35:28,480 --> 00:35:31,320 Speaker 11: they'll get up to the thirty eight limit. This is 639 00:35:31,320 --> 00:35:34,080 Speaker 11: all in the seven thirty seven that the FA is 640 00:35:34,120 --> 00:35:37,360 Speaker 11: put in place for them this year, and go past that. 641 00:35:37,440 --> 00:35:40,040 Speaker 11: I think they'll get an FA approval for that, and 642 00:35:40,120 --> 00:35:42,080 Speaker 11: probably at the back half of the year, we're kind 643 00:35:42,120 --> 00:35:46,000 Speaker 11: of looking for them to be forty ish and so 644 00:35:46,200 --> 00:35:48,440 Speaker 11: again that you know, the more the more you use 645 00:35:48,480 --> 00:35:51,680 Speaker 11: the factory, the more overhead gets absorbed over over a 646 00:35:51,760 --> 00:35:55,080 Speaker 11: larger number of airplanes. The more profitable you are, the 647 00:35:55,200 --> 00:35:57,720 Speaker 11: more cash you're going to generate. Part of that story, 648 00:35:58,280 --> 00:35:58,840 Speaker 11: can you talk to. 649 00:35:58,880 --> 00:36:01,560 Speaker 12: Us a bit more about how much the tariff headwind 650 00:36:01,880 --> 00:36:04,920 Speaker 12: will affect the company's top line or bottom line, especially 651 00:36:05,000 --> 00:36:10,520 Speaker 12: with this really heated tit for tat it seems with China, well. 652 00:36:10,440 --> 00:36:15,080 Speaker 11: So again, China has really become much less of an 653 00:36:15,120 --> 00:36:19,000 Speaker 11: issue for Boeing. The Chinese have placed sixteen orders. 654 00:36:18,719 --> 00:36:20,840 Speaker 10: This decade for airplanes. 655 00:36:20,880 --> 00:36:24,520 Speaker 11: There's some four hundred orders on the Boeing books. Still, 656 00:36:24,640 --> 00:36:28,600 Speaker 11: that's of a backlog that's six thousand large. Kelly Orberger 657 00:36:28,719 --> 00:36:31,719 Speaker 11: is talking on the call that you know he's prepared to. 658 00:36:32,080 --> 00:36:35,000 Speaker 11: He was planning and delivering forty to fifty into China 659 00:36:35,080 --> 00:36:37,919 Speaker 11: this year, so not a lot of airplanes. That would 660 00:36:37,960 --> 00:36:40,520 Speaker 11: be mostly seven thirty sevens and that's out of an 661 00:36:40,560 --> 00:36:43,320 Speaker 11: expected build of maybe four hundred and seven thirty sevens 662 00:36:43,800 --> 00:36:45,239 Speaker 11: or so this year. So you can already see the 663 00:36:45,320 --> 00:36:47,799 Speaker 11: sizes and that large. And he's ready to go out 664 00:36:47,920 --> 00:36:50,600 Speaker 11: and he's going to talk to the customers, see what 665 00:36:50,640 --> 00:36:52,000 Speaker 11: they want to do, and he's ready to go out 666 00:36:52,320 --> 00:36:54,600 Speaker 11: and remarket those airplanes. I think we've already seen Air 667 00:36:54,680 --> 00:36:57,520 Speaker 11: India raise their hand say hey, we'd take some airplanes. 668 00:36:57,560 --> 00:36:59,959 Speaker 11: And there's other folks around the world that just haven't 669 00:37:00,080 --> 00:37:02,600 Speaker 11: got the deliveries they wanted. They're ready to take airplanes too. 670 00:37:02,760 --> 00:37:05,280 Speaker 11: So on the top line, I just don't see China 671 00:37:05,400 --> 00:37:07,719 Speaker 11: impacting things that much. 672 00:37:08,080 --> 00:37:10,880 Speaker 2: Our Thanks to George First and Bloomberg Intelligence senior Aerospace, 673 00:37:11,040 --> 00:37:12,720 Speaker 2: defense and airlines analysts. 674 00:37:13,360 --> 00:37:18,000 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apple, Spotify, 675 00:37:18,200 --> 00:37:21,680 Speaker 1: and anywhere else you get your podcasts. Listen live each 676 00:37:21,719 --> 00:37:25,160 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 677 00:37:25,600 --> 00:37:29,080 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 678 00:37:29,560 --> 00:37:32,440 Speaker 1: You can also Watch US live every weekday on YouTube 679 00:37:32,880 --> 00:37:35,080 Speaker 1: and always on the Bloomberg terminal