WEBVTT - Spotting Wildfires with AI 0:00:15.356 --> 0:00:15.796 Pushkin. 0:00:20.956 --> 0:00:25.796 So in September of twenty twenty, there was lightning storms 0:00:25.996 --> 0:00:29.396 that started over six hundred fires over the span of 0:00:29.436 --> 0:00:33.076 a single day, and it caused a number of enormous 0:00:33.196 --> 0:00:36.956 mega fires and really overwhelmed the fire department, and smoke 0:00:37.036 --> 0:00:40.996 flooded over San Francisco and blanketed the sky, and all 0:00:41.036 --> 0:00:43.916 of us living in San Francisco woke up to a 0:00:44.196 --> 0:00:47.636 blood red, blade runner sky and the sun never rose 0:00:47.796 --> 0:00:48.236 that day. 0:00:49.076 --> 0:00:51.196 This is Sonya Kessner. She spent a lot of her 0:00:51.236 --> 0:00:55.436 career managing supply chains for companies like Nest, which makes 0:00:55.596 --> 0:00:59.876 doorbells and thermostats, and Packs which makes fancy vape pins. 0:01:00.436 --> 0:01:03.396 But the year before that day the sky turned red, 0:01:03.516 --> 0:01:06.996 she had founded a company called pano Ai. The company's 0:01:07.036 --> 0:01:10.636 initial goal was to reduce the damage caused by wildfires. 0:01:11.636 --> 0:01:15.996 After that day that the sky turned blood red, we 0:01:16.036 --> 0:01:19.556 talked to each other and decided we wanted to speed up, 0:01:19.756 --> 0:01:22.436 and we just got an outpouring of support from all 0:01:22.476 --> 0:01:25.956 of our friends and family, and everyone encouraged us go 0:01:25.996 --> 0:01:28.676 out and raise vunt your capital funding, go as quickly 0:01:28.716 --> 0:01:31.916 as you can. We need folks working on this problem. 0:01:32.276 --> 0:01:37.556 And we felt optimistic actually that this crisis would lead 0:01:37.596 --> 0:01:40.236 to urgency in the market, and we actually have seen that. 0:01:40.316 --> 0:01:43.156 So that's the silver lining of what was a very, 0:01:44.076 --> 0:01:46.556 very scary and bone chilling wake up call. 0:01:52.476 --> 0:01:54.876 I'm Jacob Goldstein and this is What's Your Problem, the 0:01:54.916 --> 0:01:57.116 show where I talk to people who are trying to 0:01:57.156 --> 0:02:01.996 make technological progress. Pano, the company Sonya founded, mounts cameras 0:02:02.036 --> 0:02:06.636 on remote mountaintop towers, then sends panoramic images from those 0:02:06.636 --> 0:02:10.196 cameras to an AI model that's trained to spot smoke 0:02:10.316 --> 0:02:13.836 from wildfires. The goal is to alert fire cruise early 0:02:13.916 --> 0:02:18.356 before the fire spreads. So far, Panel's customers include utility 0:02:18.396 --> 0:02:21.956 companies and firefighting agencies in several states across the western 0:02:22.036 --> 0:02:26.316 US and also in Australia. I should mention that I 0:02:26.356 --> 0:02:29.716 talked to Sonya last month before the fire in Maui. 0:02:30.716 --> 0:02:32.916 Sonia start a Pano because she wanted to solve a 0:02:32.916 --> 0:02:36.916 problem that goes beyond wildfires. The problem is this, how 0:02:36.916 --> 0:02:39.916 do you use data to mitigate the damage caused by 0:02:39.916 --> 0:02:40.916 climate change? 0:02:41.676 --> 0:02:44.956 I will say, even after the sky turned red, raising 0:02:45.036 --> 0:02:48.876 venture capital was not that easy. For the first year 0:02:48.956 --> 0:02:52.316 or two, the idea of using technology to adapt to 0:02:52.356 --> 0:02:57.196 climate change was still a very immature, nascent idea. There 0:02:57.276 --> 0:03:00.476 was starting to be more focus again on technologies to 0:03:00.516 --> 0:03:04.516 mitigate climate change, but when we would meet with VCS, 0:03:04.876 --> 0:03:07.316 we had to start with wait, tell me again. You're 0:03:07.316 --> 0:03:10.596 saying climate change is already here today. You're saying we 0:03:10.596 --> 0:03:12.556 should do something about it. You're saying we can do 0:03:12.636 --> 0:03:15.236 something about it. That's where we started the conversation. 0:03:15.836 --> 0:03:18.156 Huh, that's interesting. So you're saying when you were trying 0:03:18.196 --> 0:03:20.436 to raise money when you started the company not that 0:03:20.556 --> 0:03:23.756 long ago, three four years ago, the idea of like, 0:03:23.876 --> 0:03:25.836 oh no, we live in a world where the climate 0:03:25.956 --> 0:03:28.396 already has changed, and we need to deal with that, 0:03:28.476 --> 0:03:30.236 and we need to build companies to deal with that. 0:03:30.236 --> 0:03:31.516 That was a novel idea. 0:03:32.876 --> 0:03:35.396 It was often the first time they had heard that pitch. 0:03:36.276 --> 0:03:38.676 It was the meeting with me. So I had to 0:03:38.716 --> 0:03:41.236 start there. Climate change is not something in the far 0:03:41.276 --> 0:03:44.196 distant future, but climate change is here today. I think 0:03:44.236 --> 0:03:49.076 of myself as a realist, or more of a a 0:03:49.116 --> 0:03:51.476 pessimist or a realist than others. Where you know, I 0:03:51.636 --> 0:03:53.556 work in supply chain. We're very good thinking of what 0:03:53.596 --> 0:03:56.676 can go wrong if you're willing to face the harsh 0:03:56.756 --> 0:04:00.276 reality of what might be going wrong or what is 0:04:00.316 --> 0:04:03.156 going wrong? Then you can do something about it. So 0:04:03.636 --> 0:04:08.116 it's an optimistic take on pestimism, I guess. And so 0:04:08.956 --> 0:04:11.316 I thought there would be a slew of adaptation companies 0:04:11.956 --> 0:04:15.396 and I was actually shocked that there were not, And 0:04:16.276 --> 0:04:18.596 that to me felt like, Okay, there's a void here 0:04:18.596 --> 0:04:19.516 that needs to be filled. 0:04:19.596 --> 0:04:23.676 So find of change causes lots of problems. How do 0:04:23.716 --> 0:04:27.596 you land on fires? How do you get to starting 0:04:27.636 --> 0:04:28.596 the company that you start? 0:04:30.116 --> 0:04:33.196 You know, we actually at PANO our mission is to 0:04:33.276 --> 0:04:36.436 bring more data to bear to mitigate all types of 0:04:36.516 --> 0:04:41.356 climate driven natural disasters floods, hurricanes, moth slides, But starting 0:04:41.356 --> 0:04:44.436 with wildfires is a natural place to start when you 0:04:44.476 --> 0:04:47.636 live in California and you and your friends have experienced 0:04:47.636 --> 0:04:50.356 the devastation firsthand. Your friends have lost their homes and 0:04:51.076 --> 0:04:53.916 their children have been evacuated from schools, And there was 0:04:53.996 --> 0:04:57.516 also tremendous hunger from the fire fighting community for new technology. 0:04:58.316 --> 0:05:02.516 When we went to start researching the idea of bringing 0:05:02.676 --> 0:05:08.436 technology to wildfires, there was already a huge community raising 0:05:08.476 --> 0:05:10.796 a rallying cross we need more technology as a force 0:05:10.876 --> 0:05:14.396 multiplier to tackle wildfires, and they were listing out what 0:05:14.436 --> 0:05:18.196 they wanted. We want cameras, we want drones, we want satellites, 0:05:18.236 --> 0:05:22.796 we want AI, we want mobile software. Please send us 0:05:22.796 --> 0:05:27.476 some tools. And this is exactly what I knew how 0:05:27.476 --> 0:05:29.996 to build for my career and my colleagues who come 0:05:29.996 --> 0:05:33.636 from Cisco, from Apple, from NAS from Google. This is 0:05:33.636 --> 0:05:35.956 exactly what we know how to build. But we looked 0:05:35.956 --> 0:05:37.836 around the space and there were almost no vendors. 0:05:38.476 --> 0:05:41.956 It's like a market calling out for suppliers. 0:05:42.636 --> 0:05:45.796 It really was. Yeah, I don't think it happens that often. 0:05:45.796 --> 0:05:50.076 I mean for our first product, the actual detailed features 0:05:50.116 --> 0:05:52.036 for the product were written in a report from the 0:05:52.076 --> 0:05:53.916 California Public Utilities. 0:05:53.556 --> 0:05:57.676 Basically saying, would someone build this and sell it to us? 0:05:57.996 --> 0:06:00.556 Yes? Exactly, that is exactly what happened. 0:06:00.636 --> 0:06:03.076 So do you start the company and build that product 0:06:03.076 --> 0:06:05.396 that the California whatever was asking for? 0:06:06.036 --> 0:06:08.636 That is exactly what they We did. So the thing 0:06:08.676 --> 0:06:14.196 that's challenging about this business at PANO is we need 0:06:14.236 --> 0:06:18.556 to have all the capabilities of a Internet of things 0:06:18.636 --> 0:06:21.836 company like a Nest or a Fitbit. Because we design 0:06:21.876 --> 0:06:24.996 our own hardware, we manufacture in this factory back here, 0:06:25.836 --> 0:06:29.196 We design our own software, we design our own artificial 0:06:29.196 --> 0:06:33.436 intelligence algorithms. That's the same as any Internet of things, 0:06:33.996 --> 0:06:39.156 say consumer IoT gadget in your home. But on top 0:06:39.196 --> 0:06:42.116 of that, we also are a company that manufactures and 0:06:42.116 --> 0:06:46.436 deploys ruggedized equipment in remote locations, and so that makes 0:06:46.516 --> 0:06:49.516 us look more like a telecom company. We need those 0:06:49.516 --> 0:06:52.756 capabilities in house as well, because one of the things 0:06:52.756 --> 0:06:56.076 that the customers really want in our industry is a 0:06:56.076 --> 0:06:58.436 one stop shop. They want a company that just handles 0:06:58.436 --> 0:06:58.956 the whole thing. 0:06:59.156 --> 0:07:02.276 So just tell me how, just tell me how the 0:07:02.316 --> 0:07:03.676 system works, what happens. 0:07:04.876 --> 0:07:08.876 So at each tower location on a mountaintop, we deploy 0:07:08.916 --> 0:07:12.636 a PANO station which includes two ultra high definition security cameras, 0:07:13.156 --> 0:07:16.036 and these cameras are rotating three hundred and sixty degrees 0:07:16.076 --> 0:07:21.036 every minute, capturing ten frames of high resolution images, and 0:07:21.156 --> 0:07:24.236 we're uploading them to the cloud over cellular or a 0:07:24.276 --> 0:07:28.796 wired connection or starlink. And then the data goes to 0:07:28.836 --> 0:07:32.156 the cloud and it goes to two places. First, the 0:07:32.516 --> 0:07:37.196 images go through our AI algorithm, which is looking for 0:07:37.396 --> 0:07:40.876 signs of smoke, and whenever the AI thinks that's the smoke, 0:07:40.916 --> 0:07:43.396 it adds a bounding box, and those bounding boxes are 0:07:43.436 --> 0:07:46.836 then reviewed by analysts in our Pano Intelligence Center. 0:07:46.796 --> 0:07:49.596 A bounding box just mean the AI basically draws a 0:07:49.676 --> 0:07:52.636 box around what it thinks is the smoke exactly. 0:07:52.796 --> 0:07:58.556 That's exactly right, and so our Pano Intelligence Center will 0:07:58.596 --> 0:08:01.556 dismiss any false alerts, but when they see that there 0:08:01.596 --> 0:08:04.796 actually is smoke, that means it's time to trigger an alert. 0:08:05.396 --> 0:08:09.036 We arrange the panel stations so that incidents can be 0:08:09.156 --> 0:08:12.676 seen from two stations and we mark both. We have 0:08:12.716 --> 0:08:15.476 an algorithm that calculates bearing. We actually have a patent 0:08:15.516 --> 0:08:18.796 on this that allows it to be very accurate, and 0:08:18.836 --> 0:08:20.476 that creates allaw two longitude. 0:08:20.516 --> 0:08:22.596 So just to be clear, when you say bearing, it's 0:08:22.596 --> 0:08:25.796 like there's a line from each camera and you can 0:08:25.836 --> 0:08:27.596 figure out where the lines cross, so you can know 0:08:27.716 --> 0:08:30.636 exactly the latitude and longitude of the site where there 0:08:30.676 --> 0:08:33.596 is smoke. Yes, because every spot can be seen by 0:08:33.636 --> 0:08:35.636 two different stations. 0:08:35.436 --> 0:08:39.836 That's right. And this triangulation strategy was deployed in fire 0:08:39.916 --> 0:08:42.316 lookout towers for hundreds of years. They would use a 0:08:42.356 --> 0:08:47.756 dial and a string to draw the bearing manually in 0:08:47.796 --> 0:08:50.156 a lookout tower. So we just created the digital version 0:08:50.156 --> 0:08:52.916 of this. So we've done a human review, We've marked 0:08:52.916 --> 0:08:56.356 the fire we've created a latitude and longitude. We push 0:08:56.396 --> 0:09:00.836 out then automated notifications through text and email to all 0:09:00.956 --> 0:09:03.516 of the emergency managers who have been onboarded to the 0:09:03.516 --> 0:09:07.396 platform in that area, and they all get alerted to 0:09:07.436 --> 0:09:10.596 this incident within minutes of the fire starting, and it 0:09:10.636 --> 0:09:13.276 gives them location information and it gives them a video 0:09:13.956 --> 0:09:15.356 of the growing smoke. 0:09:15.796 --> 0:09:19.156 And I presume it's a subscription. They pay you by 0:09:19.196 --> 0:09:20.196 the season or something. 0:09:20.556 --> 0:09:25.116 Yeah, we do an annual subscription and PANO everything's included 0:09:25.156 --> 0:09:29.236 that subscription, and Panel maintains the equipment. We maintained the AI, 0:09:29.916 --> 0:09:32.356 the Panel Intelligence Center, et cetera. The customers are just 0:09:32.436 --> 0:09:34.156 getting fire intelligence as a service. 0:09:34.556 --> 0:09:37.956 So tell me about developing the AI, Like, was there 0:09:38.196 --> 0:09:40.516 some off the shelf model that you could start with 0:09:40.916 --> 0:09:43.076 or what where did you start with the I? What 0:09:43.116 --> 0:09:43.796 did you have to do? 0:09:44.716 --> 0:09:49.676 So we do use open source object detection models, like 0:09:49.796 --> 0:09:53.636 any company in modern computer vision would, but these are 0:09:53.676 --> 0:09:56.556 not specific to detecting smoke or fire. These are just 0:09:57.036 --> 0:10:00.356 models that are used to detect objects out of camera data. 0:10:00.836 --> 0:10:03.996 And then we need to train the model with images 0:10:04.036 --> 0:10:08.236 of smoke and not smoke, And it turns out that 0:10:08.316 --> 0:10:12.276 Detecting smoke is a hard computer vision problem. You know. 0:10:12.636 --> 0:10:14.636 When I first started the company, I mean, I'm a 0:10:14.676 --> 0:10:17.596 hardware manufacturing person. I thought the AI would be easy, 0:10:17.636 --> 0:10:19.836 you know, like I saw this Silicon Valley episode hot 0:10:19.876 --> 0:10:21.876 Dog not a hot Dog. I figured, you know, a 0:10:21.916 --> 0:10:24.076 couple of months, a couple of months of loading some 0:10:24.196 --> 0:10:28.236 data into you know, TensorFlow, boom boom, be done. You know, 0:10:28.276 --> 0:10:30.756 three years in we have a great model and it's 0:10:30.756 --> 0:10:34.476 still getting better every day. Smoke is a difficult thing 0:10:34.516 --> 0:10:38.556 to detect because a wildfire, smoke is very rare. It 0:10:38.596 --> 0:10:41.596 doesn't wildfires don't occur that often, but there are lots 0:10:41.636 --> 0:10:44.476 of things that look like smoke. There's cloud, there's fog, 0:10:44.596 --> 0:10:46.436 there's dust, there's barbecues. 0:10:47.116 --> 0:10:48.876 What did you have to do to make it work? 0:10:48.916 --> 0:10:51.236 It was harder than you thought. Like how did you 0:10:51.276 --> 0:10:52.916 go from it not working to it working? 0:10:54.276 --> 0:10:55.716 Well? I could tell you, but I'd have to kill 0:10:55.716 --> 0:11:00.556 you on that one. But I can't get into too 0:11:00.636 --> 0:11:02.436 much of the things we tried and what worked and 0:11:02.476 --> 0:11:04.276 what didn't work and how we how we got to 0:11:04.356 --> 0:11:06.996 our you know, end result. But you know, what I'll 0:11:06.996 --> 0:11:10.116 say is that you know the range tools have to 0:11:10.156 --> 0:11:13.516 do with the data you gather, how you labeled the data, 0:11:14.316 --> 0:11:17.516 the type of model you use, so certain model techniques, 0:11:18.756 --> 0:11:22.556 the type of not fire data that you gather as well. 0:11:22.916 --> 0:11:25.356 You want to show the model lots of instances where 0:11:25.356 --> 0:11:27.756 there's dust blowing up from the ground and where there's 0:11:28.196 --> 0:11:30.676 fog in a way that looks kind of like smoke, 0:11:30.756 --> 0:11:32.436 and all of the things we can think of that 0:11:32.476 --> 0:11:33.796 are smoke. 0:11:33.676 --> 0:11:36.716 Like, right, right, and you know, one of the keys 0:11:36.836 --> 0:11:42.436 to developing a great AI program, which we're still continuing 0:11:42.476 --> 0:11:45.676 to build out now. And actually we just hired a 0:11:45.676 --> 0:11:48.556 new VP of Engineering who had a five hundred person 0:11:48.596 --> 0:11:52.796 team at Meta that included both machine learning and software 0:11:53.156 --> 0:11:55.396 and he has a PhD in computer vision. He spent 0:11:55.436 --> 0:11:58.036 his entire career in this field. One of the first 0:11:58.076 --> 0:12:00.396 things he did when he joined us a few months 0:12:00.436 --> 0:12:03.276 ago was to say, we really need to invest in infrastructure, 0:12:04.156 --> 0:12:08.996 not just not just running the experiments and building new 0:12:09.156 --> 0:12:11.756 versions of the model, but an entire end to end 0:12:12.276 --> 0:12:16.236 pipeline that lets you run these experiments more efficiently, gather 0:12:16.316 --> 0:12:19.476 the learnings, compare the different results, and and then iterate 0:12:19.476 --> 0:12:22.916 and iterate faster and faster. And so actually a lot 0:12:22.916 --> 0:12:25.476 of companies in the in the AI space right now 0:12:25.556 --> 0:12:29.116 are shifting to more focus on just as much focus 0:12:29.156 --> 0:12:34.716 on the infrastructure around AI development as into the experiments themselves. 0:12:34.956 --> 0:12:38.996 Is that why in Vidia stock is worth trillions of 0:12:39.036 --> 0:12:42.876 dollars all of a sudden trillion dollars. One of the reasons, Yeah, 0:12:42.956 --> 0:12:45.076 a strained at all by hardware. 0:12:45.916 --> 0:12:49.196 We're not constrained by it, because if you're willing to pay, 0:12:49.956 --> 0:12:52.316 you can get as much as you need from the 0:12:52.316 --> 0:12:55.996 cloud providers. But it is, but it is extremely expensive. 0:12:56.236 --> 0:12:58.996 It is. It is one of our is definitely one 0:12:58.996 --> 0:13:02.356 of our highest R and D expenses is the cloud 0:13:02.396 --> 0:13:06.396 computed for running experiments and then actually on an ongoing basis. 0:13:06.596 --> 0:13:09.756 I mean, we're uploading trillions of pixels and and they 0:13:09.796 --> 0:13:13.156 have to all be processed every minute, and so one 0:13:13.156 --> 0:13:16.756 of our highest expenses is running the AI on all 0:13:16.836 --> 0:13:18.196 this data all the time. 0:13:20.516 --> 0:13:23.476 In a minute. Lots of other ways that Pano might 0:13:23.596 --> 0:13:34.116 use data to mitigate the risk of disasters. Now back 0:13:34.156 --> 0:13:37.236 to the show. Earlier, you said something to the effect that, like, 0:13:37.796 --> 0:13:41.276 the big idea for the company was not about wildfires 0:13:41.316 --> 0:13:45.636 per se, but it was an idea that better data 0:13:45.916 --> 0:13:48.716 could help mitigate natural disasters. 0:13:48.796 --> 0:13:51.396 Is that right, You just gave my elevator pitch for me, 0:13:51.556 --> 0:13:53.676 Thank you very much. Now, I say, what we heard 0:13:53.716 --> 0:13:58.196 from customers who work in disaster management is that there 0:13:58.236 --> 0:14:02.556 is a paucity of data at all phases of disaster response. 0:14:02.996 --> 0:14:07.196 And those phases are the real time response phase, which 0:14:07.196 --> 0:14:08.596 is probably what most of us think of when they 0:14:08.636 --> 0:14:13.076 think of disaster management, evacuation, search and rescue, fire containment, 0:14:13.396 --> 0:14:19.076 restoring power and internet. But there's three other phases. There's recovery, mitigation, 0:14:19.436 --> 0:14:21.956 which is determining how you're going to harden your system 0:14:22.036 --> 0:14:24.436 so that the disasters aren't as damaging the next time. 0:14:24.876 --> 0:14:27.676 And then preparedness, which is planning making sure you have 0:14:27.756 --> 0:14:30.676 enough emergency blankets, making sure you know your evacuation roots, 0:14:30.676 --> 0:14:36.596 and and you have shelters prepared. And all of those 0:14:36.636 --> 0:14:39.956 phases need more data to face this growing threat. 0:14:40.156 --> 0:14:43.916 To be clear, you're not just talking about wildfires here, right. 0:14:44.076 --> 0:14:46.716 Is there a next kind of disaster you're thinking over, 0:14:46.796 --> 0:14:49.556 a next phase of disaster. You're thinking over both, like 0:14:50.676 --> 0:14:52.516 what do you want to do next? 0:14:52.636 --> 0:14:57.276 So we are customers often started out as wildfire mitigation 0:14:57.436 --> 0:15:01.436 teams and then we're asked to add mudslides and flooding 0:15:01.876 --> 0:15:08.196 and other disasters to their remit because extreme heat, extreme cold, 0:15:08.676 --> 0:15:11.716 for example, our power utility customers, all of these disasters 0:15:11.756 --> 0:15:16.476 are incredibly disruptive to their power grid, and so we've 0:15:16.516 --> 0:15:21.956 been asked to explore our building situational awareness tools that 0:15:21.996 --> 0:15:25.036 can help them make better decisions both in the real 0:15:25.076 --> 0:15:27.436 time heat of the moment when they're trying to restore power, 0:15:27.476 --> 0:15:32.796 restore internet after a hurricane, for example, or after the fact, 0:15:32.956 --> 0:15:37.956 when they're trying to analyze their entire asset map and 0:15:38.076 --> 0:15:41.716 decide on how to deploy billions of dollars into hardening 0:15:41.716 --> 0:15:45.116 their system. The flood maps are one hundred years out 0:15:45.116 --> 0:15:48.516 of date. How are they going to decide which region 0:15:48.556 --> 0:15:52.156 of their territory should they bury power lines first or second. 0:15:53.556 --> 0:15:57.276 There's going to be trillions of dollars deployed over the 0:15:57.316 --> 0:16:02.076 next couple of decades to help humanity prepare for climate change, 0:16:02.116 --> 0:16:06.076 to help us harden our cities, harden our transportation infrastructure, 0:16:06.436 --> 0:16:11.956 harden our power grids. Where's the data to inform those 0:16:12.036 --> 0:16:15.076 that trillion dollars of spend? These customers are realizing that 0:16:15.156 --> 0:16:16.556 this data gap is a problem. 0:16:16.916 --> 0:16:25.116 Somehow, As you were talking about about disaster management as 0:16:25.156 --> 0:16:29.076 a data problem. I was thinking of what you were 0:16:29.076 --> 0:16:31.676 saying earlier about supply chains and supply chains as sort 0:16:31.716 --> 0:16:34.956 of a worldview, and I couldn't quite nail the link. 0:16:34.996 --> 0:16:39.116 I couldn't quite articulate the connection. But can you do 0:16:39.156 --> 0:16:40.836 you feel a connection between those? 0:16:42.276 --> 0:16:46.316 Actually you're getting at something which I didn't share. To 0:16:46.356 --> 0:16:48.636 your question of why I wanted to found this company, 0:16:49.556 --> 0:16:53.596 I had been in terms of thinking about getting into 0:16:53.636 --> 0:16:56.876 the field of disaster management, something I had been thinking 0:16:56.876 --> 0:17:00.036 about for many years because every time I would hear 0:17:00.076 --> 0:17:04.156 on the news about a disaster and I would envision 0:17:04.236 --> 0:17:06.396 the skill that it would take to go cope with 0:17:06.396 --> 0:17:09.276 this disaster. It actually reminded me a lot of supply chain. 0:17:09.156 --> 0:17:10.916 Manufacturing in what way. 0:17:11.716 --> 0:17:15.076 So when you're running a supply chain, well, nobody notices you. 0:17:15.436 --> 0:17:19.756 Everything just shows up on time, beautifully, and and it's 0:17:19.796 --> 0:17:23.236 it's invisible. So you need to make very very meticulous 0:17:23.276 --> 0:17:25.796 plans of exactly what you're going to build and all 0:17:25.876 --> 0:17:30.756 the necessary pieces that need to go into place to 0:17:30.836 --> 0:17:34.076 make sure that you can manufacture that product at the 0:17:34.116 --> 0:17:36.996 quantities you need on time and get it to where 0:17:36.996 --> 0:17:40.836 it needs to go. So you're planning meticulously and then 0:17:41.756 --> 0:17:47.116 all of your plans never goes planned. You you still 0:17:47.956 --> 0:17:53.116 all your well laid plans still result in fire drill, 0:17:53.156 --> 0:17:55.756 fire drill, fire drill, fire drill, just a disaster disaster 0:17:55.916 --> 0:17:58.116 every single day. I mean it's a super high adrenaline job. 0:17:58.876 --> 0:18:02.956 Where where there's a labor strike, there's a pandemic, there's 0:18:03.036 --> 0:18:07.516 a a you know, something's held up in customs, the 0:18:07.556 --> 0:18:11.236 supplier got confused and made the wrong color. Just disaster 0:18:11.316 --> 0:18:14.996 after disaster after disaster is what happens in supply chain, 0:18:15.356 --> 0:18:17.356 and you need to recover and think on your feet 0:18:17.516 --> 0:18:21.716 and figure out how to resolve that issue. And at 0:18:21.716 --> 0:18:25.236 the end of the day, things result in calm and 0:18:25.276 --> 0:18:29.076 stability and you save the day. And and you know, 0:18:29.196 --> 0:18:32.476 disaster management I think has a lot of similarities where 0:18:32.716 --> 0:18:37.916 emergency managers they spend the off season meticulously planning for 0:18:38.076 --> 0:18:41.916 how they're going to harden their system, like building fire 0:18:41.956 --> 0:18:44.836 brakes and safety zones, how they're going to be prepared, 0:18:44.956 --> 0:18:49.196 like communicating evacuation plans and rehearsing and having drills and 0:18:50.156 --> 0:18:54.276 ordering emergency blankets. And still when the fire comes, they 0:18:54.316 --> 0:18:57.836 need to react in real time and make snap spurt 0:18:57.876 --> 0:19:00.876 the moment decisions, and so I think I have coming 0:19:00.876 --> 0:19:02.476 from supply chain, I have a lot of empathy for 0:19:02.516 --> 0:19:06.316 our customers emergency management, and I can imagine how the 0:19:06.316 --> 0:19:08.676 more data that they can have, the better they can 0:19:08.676 --> 0:19:12.036 make their decisions. Because being in supply chain. Data is key. 0:19:12.396 --> 0:19:15.196 You know what, when I was leading supply chain organizations, 0:19:15.716 --> 0:19:21.516 my goal was to surface data on every on as 0:19:21.596 --> 0:19:23.596 far upstream and the supply chain as I could go, 0:19:23.916 --> 0:19:26.556 as early as possible. And if I could look, if 0:19:26.596 --> 0:19:29.836 I could look, you know, twelve weeks upstream into the 0:19:29.876 --> 0:19:32.716 supply chain and I saw, oh, they had a labor strike, 0:19:33.116 --> 0:19:35.476 I know there's going to be a problem, you know, 0:19:35.636 --> 0:19:37.396 coming at me twelve weeks from now. But when I 0:19:37.396 --> 0:19:39.876 have twelve weeks to react to it, it's much easier 0:19:39.916 --> 0:19:41.916 for me to solve that problem that if I only 0:19:41.956 --> 0:19:45.236 find out about that labor strike when that part just 0:19:45.276 --> 0:19:48.196 doesn't show up. Data is critical to running supply chain 0:19:48.276 --> 0:19:51.916 and emergency managers share the same thing. The more data 0:19:51.956 --> 0:19:56.436 they have, the more they can respond efficiently and safely. 0:19:57.556 --> 0:20:00.476 There are some similarities. You're right, It's good. There is 0:20:00.516 --> 0:20:01.796 something that ties it all together. 0:20:05.436 --> 0:20:07.556 We'll be back in a minute with the lightning round, 0:20:07.556 --> 0:20:17.116 then back to the show, So we do a bunch 0:20:17.156 --> 0:20:19.236 of questions at the end of the show. I didn't 0:20:19.476 --> 0:20:23.156 realize when I wrote those questions for this interview how 0:20:23.236 --> 0:20:25.276 much we talk about supply chains in the main part 0:20:25.276 --> 0:20:27.076 of the interview. I wrote a bunch of supply chain 0:20:27.116 --> 0:20:29.396 lightning around questions for you. Oh that's okay, Okay, let's 0:20:29.396 --> 0:20:30.196 do them anyways. 0:20:30.396 --> 0:20:32.276 Yeah, my favorite talking that's great. 0:20:33.516 --> 0:20:35.596 What do you love about supply chains? 0:20:37.436 --> 0:20:39.156 I think what I love about the supply chain is 0:20:39.196 --> 0:20:44.356 both the planning part and the and the adrenaline part 0:20:44.596 --> 0:20:47.956 of having to to have the diving catches in the moment. 0:20:48.116 --> 0:20:50.836 Diving catch is a great metaphor. Who doesn't love making 0:20:50.836 --> 0:20:56.356 the diving catch? Yeah? What was the difference between managing 0:20:56.636 --> 0:21:00.476 the supply chain for high tech vight pens at packs 0:21:00.676 --> 0:21:05.596 and managing the supply chain for fancy thermostats or cameras 0:21:05.716 --> 0:21:06.316 at nest? 0:21:10.476 --> 0:21:13.676 Those supply chains are pretty similar, to be honest. The 0:21:13.756 --> 0:21:18.316 reason we get so many awesome coal gadgets so quickly 0:21:18.356 --> 0:21:22.276 into the market is that the consumer electronics industry has 0:21:22.276 --> 0:21:25.956 built a really mature supply chain that's made up of 0:21:25.996 --> 0:21:29.956 building blocks that can be rearranged into very different products. 0:21:29.956 --> 0:21:32.996 So the building blocks of both of those products are 0:21:33.836 --> 0:21:37.316 surface mount technology, which is how you make the printed 0:21:37.356 --> 0:21:40.396 circuit boards, and then the other building blocks are mechanical 0:21:40.796 --> 0:21:45.956 components that go around those electronics, so plastic injection molded parts, 0:21:46.396 --> 0:21:51.396 metal formed parts. Our supply chain here PANO is radically different. 0:21:51.436 --> 0:21:54.276 However, it's interesting to think of when you describe it, 0:21:54.316 --> 0:21:58.156 how many things we get are just circuit boards with 0:21:58.236 --> 0:22:03.556 plastic wrapped around them, right, So many things yep yep. 0:22:05.436 --> 0:22:07.476 By the way, I don't want it to seem that simple, 0:22:07.476 --> 0:22:11.676 because my husband is a leads hardware engineering and at 0:22:11.716 --> 0:22:15.316 a startup, and he would say, it's not that simple. 0:22:15.556 --> 0:22:20.636 Fair. So, now you work in the world of natural disasters, 0:22:20.676 --> 0:22:23.676 and I'm curious, are you a prepper? 0:22:24.676 --> 0:22:27.396 Oh, that is a great question. You know, we do 0:22:27.516 --> 0:22:32.196 have our earthquake kit. We do have finally filled up 0:22:32.236 --> 0:22:35.996 our water jugs. I said to my husband, like, it 0:22:36.036 --> 0:22:38.916 will just be too embarrassing if I founded an emergency 0:22:38.956 --> 0:22:42.556 management company and then we die because we didn't have 0:22:42.756 --> 0:22:44.596 our jugs of water in the earthquake. 0:22:45.556 --> 0:22:49.436 Vanity and shame as a motivator to not die works right, Why. 0:22:49.276 --> 0:22:53.276 Not Honestly, it's really emergency preparedness is really important. 0:22:53.356 --> 0:22:56.036 If everything goes well, what problem will you be trying 0:22:56.036 --> 0:22:57.556 to solve in five years. 0:22:57.916 --> 0:23:02.036 I think the response phase is low hanging fruit, and 0:23:02.116 --> 0:23:04.516 we have a tool that really helps the response phase. 0:23:04.796 --> 0:23:07.476 But mitigation is just as important, and we need to 0:23:07.916 --> 0:23:10.396 We need to think through through how many more helicopters 0:23:10.476 --> 0:23:12.756 do we need, Where do we need to bury power lines, 0:23:12.956 --> 0:23:15.996 what fuel breaks do we need to cut? What are 0:23:16.036 --> 0:23:19.156 ways that we can harden our cities and the infrastructure 0:23:19.196 --> 0:23:22.276 against wildfires? But I also would like to help solve 0:23:22.276 --> 0:23:25.556 problems related to other disasters, like how do we restore 0:23:25.716 --> 0:23:30.796 power and internet faster after a disaster? Right? And can 0:23:30.876 --> 0:23:34.516 data help in that solution? So I'd really love to 0:23:34.516 --> 0:23:38.036 be working on that problem, and I'd also really love 0:23:38.076 --> 0:23:43.876 to be helping inform policy makers around rebuilding efforts. Build 0:23:43.916 --> 0:23:47.516