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Spotting Wildfires with AI · What's Your Problem?

• 0:15
  Speaker 1: Pushkin.
• 0:20
  Speaker 2: So in September of twenty twenty, there was lightning storms
• 0:25
  Speaker 2: that started over six hundred fires over the span of
• 0:29
  Speaker 2: a single day, and it caused a number of enormous
• 0:33
  Speaker 2: mega fires and really overwhelmed the fire department, and smoke
• 0:37
  Speaker 2: flooded over San Francisco and blanketed the sky, and all
• 0:41
  Speaker 2: of us living in San Francisco woke up to a
• 0:44
  Speaker 2: blood red, blade runner sky and the sun never rose
• 0:47
  Speaker 2: that day.
• 0:49
  Speaker 1: This is Sonya Kessner. She spent a lot of her
• 0:51
  Speaker 1: career managing supply chains for companies like Nest, which makes
• 0:55
  Speaker 1: doorbells and thermostats, and Packs which makes fancy vape pins.
• 1:00
  Speaker 1: But the year before that day the sky turned red,
• 1:03
  Speaker 1: she had founded a company called pano Ai. The company's
• 1:07
  Speaker 1: initial goal was to reduce the damage caused by wildfires.
• 1:11
  Speaker 2: After that day that the sky turned blood red, we
• 1:16
  Speaker 2: talked to each other and decided we wanted to speed up,
• 1:19
  Speaker 2: and we just got an outpouring of support from all
• 1:22
  Speaker 2: of our friends and family, and everyone encouraged us go
• 1:25
  Speaker 2: out and raise vunt your capital funding, go as quickly
• 1:28
  Speaker 2: as you can. We need folks working on this problem.
• 1:32
  Speaker 2: And we felt optimistic actually that this crisis would lead
• 1:37
  Speaker 2: to urgency in the market, and we actually have seen that.
• 1:40
  Speaker 2: So that's the silver lining of what was a very,
• 1:44
  Speaker 2: very scary and bone chilling wake up call.
• 1:52
  Speaker 1: I'm Jacob Goldstein and this is What's Your Problem, the
• 1:54
  Speaker 1: show where I talk to people who are trying to
• 1:57
  Speaker 1: make technological progress. Pano, the company Sonya founded, mounts cameras
• 2:02
  Speaker 1: on remote mountaintop towers, then sends panoramic images from those
• 2:06
  Speaker 1: cameras to an AI model that's trained to spot smoke
• 2:10
  Speaker 1: from wildfires. The goal is to alert fire cruise early
• 2:13
  Speaker 1: before the fire spreads. So far, Panel's customers include utility
• 2:18
  Speaker 1: companies and firefighting agencies in several states across the western
• 2:22
  Speaker 1: US and also in Australia. I should mention that I
• 2:26
  Speaker 1: talked to Sonya last month before the fire in Maui.
• 2:30
  Speaker 1: Sonia start a Pano because she wanted to solve a
• 2:32
  Speaker 1: problem that goes beyond wildfires. The problem is this, how
• 2:36
  Speaker 1: do you use data to mitigate the damage caused by
• 2:39
  Speaker 1: climate change?
• 2:41
  Speaker 2: I will say, even after the sky turned red, raising
• 2:45
  Speaker 2: venture capital was not that easy. For the first year
• 2:48
  Speaker 2: or two, the idea of using technology to adapt to
• 2:52
  Speaker 2: climate change was still a very immature, nascent idea. There
• 2:57
  Speaker 2: was starting to be more focus again on technologies to
• 3:00
  Speaker 2: mitigate climate change, but when we would meet with VCS,
• 3:04
  Speaker 2: we had to start with wait, tell me again. You're
• 3:07
  Speaker 2: saying climate change is already here today. You're saying we
• 3:10
  Speaker 2: should do something about it. You're saying we can do
• 3:12
  Speaker 2: something about it. That's where we started the conversation.
• 3:15
  Speaker 1: Huh, that's interesting. So you're saying when you were trying
• 3:18
  Speaker 1: to raise money when you started the company not that
• 3:20
  Speaker 1: long ago, three four years ago, the idea of like,
• 3:23
  Speaker 1: oh no, we live in a world where the climate
• 3:25
  Speaker 1: already has changed, and we need to deal with that,
• 3:28
  Speaker 1: and we need to build companies to deal with that.
• 3:30
  Speaker 1: That was a novel idea.
• 3:32
  Speaker 2: It was often the first time they had heard that pitch.
• 3:36
  Speaker 2: It was the meeting with me. So I had to
• 3:38
  Speaker 2: start there. Climate change is not something in the far
• 3:41
  Speaker 2: distant future, but climate change is here today. I think
• 3:44
  Speaker 2: of myself as a realist, or more of a a
• 3:49
  Speaker 2: pessimist or a realist than others. Where you know, I
• 3:51
  Speaker 2: work in supply chain. We're very good thinking of what
• 3:53
  Speaker 2: can go wrong if you're willing to face the harsh
• 3:56
  Speaker 2: reality of what might be going wrong or what is
• 4:00
  Speaker 2: going wrong? Then you can do something about it. So
• 4:03
  Speaker 2: it's an optimistic take on pestimism, I guess. And so
• 4:08
  Speaker 2: I thought there would be a slew of adaptation companies
• 4:11
  Speaker 2: and I was actually shocked that there were not, And
• 4:16
  Speaker 2: that to me felt like, Okay, there's a void here
• 4:18
  Speaker 2: that needs to be filled.
• 4:19
  Speaker 1: So find of change causes lots of problems. How do
• 4:23
  Speaker 1: you land on fires? How do you get to starting
• 4:27
  Speaker 1: the company that you start?
• 4:30
  Speaker 2: You know, we actually at PANO our mission is to
• 4:33
  Speaker 2: bring more data to bear to mitigate all types of
• 4:36
  Speaker 2: climate driven natural disasters floods, hurricanes, moth slides, But starting
• 4:41
  Speaker 2: with wildfires is a natural place to start when you
• 4:44
  Speaker 2: live in California and you and your friends have experienced
• 4:47
  Speaker 2: the devastation firsthand. Your friends have lost their homes and
• 4:51
  Speaker 2: their children have been evacuated from schools, And there was
• 4:53
  Speaker 2: also tremendous hunger from the fire fighting community for new technology.
• 4:58
  Speaker 2: When we went to start researching the idea of bringing
• 5:02
  Speaker 2: technology to wildfires, there was already a huge community raising
• 5:08
  Speaker 2: a rallying cross we need more technology as a force
• 5:10
  Speaker 2: multiplier to tackle wildfires, and they were listing out what
• 5:14
  Speaker 2: they wanted. We want cameras, we want drones, we want satellites,
• 5:18
  Speaker 2: we want AI, we want mobile software. Please send us
• 5:22
  Speaker 2: some tools. And this is exactly what I knew how
• 5:27
  Speaker 2: to build for my career and my colleagues who come
• 5:29
  Speaker 2: from Cisco, from Apple, from NAS from Google. This is
• 5:33
  Speaker 2: exactly what we know how to build. But we looked
• 5:35
  Speaker 2: around the space and there were almost no vendors.
• 5:38
  Speaker 1: It's like a market calling out for suppliers.
• 5:42
  Speaker 2: It really was. Yeah, I don't think it happens that often.
• 5:45
  Speaker 2: I mean for our first product, the actual detailed features
• 5:50
  Speaker 2: for the product were written in a report from the
• 5:52
  Speaker 2: California Public Utilities.
• 5:53
  Speaker 1: Basically saying, would someone build this and sell it to us?
• 5:57
  Speaker 2: Yes? Exactly, that is exactly what happened.
• 6:00
  Speaker 1: So do you start the company and build that product
• 6:03
  Speaker 1: that the California whatever was asking for?
• 6:06
  Speaker 2: That is exactly what they We did. So the thing
• 6:08
  Speaker 2: that's challenging about this business at PANO is we need
• 6:14
  Speaker 2: to have all the capabilities of a Internet of things
• 6:18
  Speaker 2: company like a Nest or a Fitbit. Because we design
• 6:21
  Speaker 2: our own hardware, we manufacture in this factory back here,
• 6:25
  Speaker 2: We design our own software, we design our own artificial
• 6:29
  Speaker 2: intelligence algorithms. That's the same as any Internet of things,
• 6:33
  Speaker 2: say consumer IoT gadget in your home. But on top
• 6:39
  Speaker 2: of that, we also are a company that manufactures and
• 6:42
  Speaker 2: deploys ruggedized equipment in remote locations, and so that makes
• 6:46
  Speaker 2: us look more like a telecom company. We need those
• 6:49
  Speaker 2: capabilities in house as well, because one of the things
• 6:52
  Speaker 2: that the customers really want in our industry is a
• 6:56
  Speaker 2: one stop shop. They want a company that just handles
• 6:58
  Speaker 2: the whole thing.
• 6:59
  Speaker 1: So just tell me how, just tell me how the
• 7:02
  Speaker 1: system works, what happens.
• 7:04
  Speaker 2: So at each tower location on a mountaintop, we deploy
• 7:08
  Speaker 2: a PANO station which includes two ultra high definition security cameras,
• 7:13
  Speaker 2: and these cameras are rotating three hundred and sixty degrees
• 7:16
  Speaker 2: every minute, capturing ten frames of high resolution images, and
• 7:21
  Speaker 2: we're uploading them to the cloud over cellular or a
• 7:24
  Speaker 2: wired connection or starlink. And then the data goes to
• 7:28
  Speaker 2: the cloud and it goes to two places. First, the
• 7:32
  Speaker 2: images go through our AI algorithm, which is looking for
• 7:37
  Speaker 2: signs of smoke, and whenever the AI thinks that's the smoke,
• 7:40
  Speaker 2: it adds a bounding box, and those bounding boxes are
• 7:43
  Speaker 2: then reviewed by analysts in our Pano Intelligence Center.
• 7:46
  Speaker 1: A bounding box just mean the AI basically draws a
• 7:49
  Speaker 1: box around what it thinks is the smoke exactly.
• 7:52
  Speaker 2: That's exactly right, and so our Pano Intelligence Center will
• 7:58
  Speaker 2: dismiss any false alerts, but when they see that there
• 8:01
  Speaker 2: actually is smoke, that means it's time to trigger an alert.
• 8:05
  Speaker 2: We arrange the panel stations so that incidents can be
• 8:09
  Speaker 2: seen from two stations and we mark both. We have
• 8:12
  Speaker 2: an algorithm that calculates bearing. We actually have a patent
• 8:15
  Speaker 2: on this that allows it to be very accurate, and
• 8:18
  Speaker 2: that creates allaw two longitude.
• 8:20
  Speaker 1: So just to be clear, when you say bearing, it's
• 8:22
  Speaker 1: like there's a line from each camera and you can
• 8:25
  Speaker 1: figure out where the lines cross, so you can know
• 8:27
  Speaker 1: exactly the latitude and longitude of the site where there
• 8:30
  Speaker 1: is smoke. Yes, because every spot can be seen by
• 8:33
  Speaker 1: two different stations.
• 8:35
  Speaker 2: That's right. And this triangulation strategy was deployed in fire
• 8:39
  Speaker 2: lookout towers for hundreds of years. They would use a
• 8:42
  Speaker 2: dial and a string to draw the bearing manually in
• 8:47
  Speaker 2: a lookout tower. So we just created the digital version
• 8:50
  Speaker 2: of this. So we've done a human review, We've marked
• 8:52
  Speaker 2: the fire we've created a latitude and longitude. We push
• 8:56
  Speaker 2: out then automated notifications through text and email to all
• 9:00
  Speaker 2: of the emergency managers who have been onboarded to the
• 9:03
  Speaker 2: platform in that area, and they all get alerted to
• 9:07
  Speaker 2: this incident within minutes of the fire starting, and it
• 9:10
  Speaker 2: gives them location information and it gives them a video
• 9:13
  Speaker 2: of the growing smoke.
• 9:15
  Speaker 1: And I presume it's a subscription. They pay you by
• 9:19
  Speaker 1: the season or something.
• 9:20
  Speaker 2: Yeah, we do an annual subscription and PANO everything's included
• 9:25
  Speaker 2: that subscription, and Panel maintains the equipment. We maintained the AI,
• 9:29
  Speaker 2: the Panel Intelligence Center, et cetera. The customers are just
• 9:32
  Speaker 2: getting fire intelligence as a service.
• 9:34
  Speaker 1: So tell me about developing the AI, Like, was there
• 9:38
  Speaker 1: some off the shelf model that you could start with
• 9:40
  Speaker 1: or what where did you start with the I? What
• 9:43
  Speaker 1: did you have to do?
• 9:44
  Speaker 2: So we do use open source object detection models, like
• 9:49
  Speaker 2: any company in modern computer vision would, but these are
• 9:53
  Speaker 2: not specific to detecting smoke or fire. These are just
• 9:57
  Speaker 2: models that are used to detect objects out of camera data.
• 10:00
  Speaker 2: And then we need to train the model with images
• 10:04
  Speaker 2: of smoke and not smoke, And it turns out that
• 10:08
  Speaker 2: Detecting smoke is a hard computer vision problem. You know.
• 10:12
  Speaker 2: When I first started the company, I mean, I'm a
• 10:14
  Speaker 2: hardware manufacturing person. I thought the AI would be easy,
• 10:17
  Speaker 2: you know, like I saw this Silicon Valley episode hot
• 10:19
  Speaker 2: Dog not a hot Dog. I figured, you know, a
• 10:21
  Speaker 2: couple of months, a couple of months of loading some
• 10:24
  Speaker 2: data into you know, TensorFlow, boom boom, be done. You know,
• 10:28
  Speaker 2: three years in we have a great model and it's
• 10:30
  Speaker 2: still getting better every day. Smoke is a difficult thing
• 10:34
  Speaker 2: to detect because a wildfire, smoke is very rare. It
• 10:38
  Speaker 2: doesn't wildfires don't occur that often, but there are lots
• 10:41
  Speaker 2: of things that look like smoke. There's cloud, there's fog,
• 10:44
  Speaker 2: there's dust, there's barbecues.
• 10:47
  Speaker 1: What did you have to do to make it work?
• 10:48
  Speaker 1: It was harder than you thought. Like how did you
• 10:51
  Speaker 1: go from it not working to it working?
• 10:54
  Speaker 2: Well? I could tell you, but I'd have to kill
• 10:55
  Speaker 2: you on that one. But I can't get into too
• 11:00
  Speaker 2: much of the things we tried and what worked and
• 11:02
  Speaker 2: what didn't work and how we how we got to
• 11:04
  Speaker 2: our you know, end result. But you know, what I'll
• 11:06
  Speaker 2: say is that you know the range tools have to
• 11:10
  Speaker 2: do with the data you gather, how you labeled the data,
• 11:14
  Speaker 2: the type of model you use, so certain model techniques,
• 11:18
  Speaker 2: the type of not fire data that you gather as well.
• 11:22
  Speaker 1: You want to show the model lots of instances where
• 11:25
  Speaker 1: there's dust blowing up from the ground and where there's
• 11:28
  Speaker 1: fog in a way that looks kind of like smoke,
• 11:30
  Speaker 1: and all of the things we can think of that
• 11:32
  Speaker 1: are smoke.
• 11:33
  Speaker 2: Like, right, right, and you know, one of the keys
• 11:36
  Speaker 2: to developing a great AI program, which we're still continuing
• 11:42
  Speaker 2: to build out now. And actually we just hired a
• 11:45
  Speaker 2: new VP of Engineering who had a five hundred person
• 11:48
  Speaker 2: team at Meta that included both machine learning and software
• 11:53
  Speaker 2: and he has a PhD in computer vision. He spent
• 11:55
  Speaker 2: his entire career in this field. One of the first
• 11:58
  Speaker 2: things he did when he joined us a few months
• 12:00
  Speaker 2: ago was to say, we really need to invest in infrastructure,
• 12:04
  Speaker 2: not just not just running the experiments and building new
• 12:09
  Speaker 2: versions of the model, but an entire end to end
• 12:12
  Speaker 2: pipeline that lets you run these experiments more efficiently, gather
• 12:16
  Speaker 2: the learnings, compare the different results, and and then iterate
• 12:19
  Speaker 2: and iterate faster and faster. And so actually a lot
• 12:22
  Speaker 2: of companies in the in the AI space right now
• 12:25
  Speaker 2: are shifting to more focus on just as much focus
• 12:29
  Speaker 2: on the infrastructure around AI development as into the experiments themselves.
• 12:34
  Speaker 1: Is that why in Vidia stock is worth trillions of
• 12:39
  Speaker 1: dollars all of a sudden trillion dollars. One of the reasons, Yeah,
• 12:42
  Speaker 1: a strained at all by hardware.
• 12:45
  Speaker 2: We're not constrained by it, because if you're willing to pay,
• 12:49
  Speaker 2: you can get as much as you need from the
• 12:52
  Speaker 2: cloud providers. But it is, but it is extremely expensive.
• 12:56
  Speaker 2: It is. It is one of our is definitely one
• 12:58
  Speaker 2: of our highest R and D expenses is the cloud
• 13:02
  Speaker 2: computed for running experiments and then actually on an ongoing basis.
• 13:06
  Speaker 2: I mean, we're uploading trillions of pixels and and they
• 13:09
  Speaker 2: have to all be processed every minute, and so one
• 13:13
  Speaker 2: of our highest expenses is running the AI on all
• 13:16
  Speaker 2: this data all the time.
• 13:20
  Speaker 1: In a minute. Lots of other ways that Pano might
• 13:23
  Speaker 1: use data to mitigate the risk of disasters. Now back
• 13:34
  Speaker 1: to the show. Earlier, you said something to the effect that, like,
• 13:37
  Speaker 1: the big idea for the company was not about wildfires
• 13:41
  Speaker 1: per se, but it was an idea that better data
• 13:45
  Speaker 1: could help mitigate natural disasters.
• 13:48
  Speaker 2: Is that right, You just gave my elevator pitch for me,
• 13:51
  Speaker 2: Thank you very much. Now, I say, what we heard
• 13:53
  Speaker 2: from customers who work in disaster management is that there
• 13:58
  Speaker 2: is a paucity of data at all phases of disaster response.
• 14:02
  Speaker 2: And those phases are the real time response phase, which
• 14:07
  Speaker 2: is probably what most of us think of when they
• 14:08
  Speaker 2: think of disaster management, evacuation, search and rescue, fire containment,
• 14:13
  Speaker 2: restoring power and internet. But there's three other phases. There's recovery, mitigation,
• 14:19
  Speaker 2: which is determining how you're going to harden your system
• 14:22
  Speaker 2: so that the disasters aren't as damaging the next time.
• 14:24
  Speaker 2: And then preparedness, which is planning making sure you have
• 14:27
  Speaker 2: enough emergency blankets, making sure you know your evacuation roots,
• 14:30
  Speaker 2: and and you have shelters prepared. And all of those
• 14:36
  Speaker 2: phases need more data to face this growing threat.
• 14:40
  Speaker 1: To be clear, you're not just talking about wildfires here, right.
• 14:44
  Speaker 1: Is there a next kind of disaster you're thinking over,
• 14:46
  Speaker 1: a next phase of disaster. You're thinking over both, like
• 14:50
  Speaker 1: what do you want to do next?
• 14:52
  Speaker 2: So we are customers often started out as wildfire mitigation
• 14:57
  Speaker 2: teams and then we're asked to add mudslides and flooding
• 15:01
  Speaker 2: and other disasters to their remit because extreme heat, extreme cold,
• 15:08
  Speaker 2: for example, our power utility customers, all of these disasters
• 15:11
  Speaker 2: are incredibly disruptive to their power grid, and so we've
• 15:16
  Speaker 2: been asked to explore our building situational awareness tools that
• 15:21
  Speaker 2: can help them make better decisions both in the real
• 15:25
  Speaker 2: time heat of the moment when they're trying to restore power,
• 15:27
  Speaker 2: restore internet after a hurricane, for example, or after the fact,
• 15:32
  Speaker 2: when they're trying to analyze their entire asset map and
• 15:38
  Speaker 2: decide on how to deploy billions of dollars into hardening
• 15:41
  Speaker 2: their system. The flood maps are one hundred years out
• 15:45
  Speaker 2: of date. How are they going to decide which region
• 15:48
  Speaker 2: of their territory should they bury power lines first or second.
• 15:53
  Speaker 2: There's going to be trillions of dollars deployed over the
• 15:57
  Speaker 2: next couple of decades to help humanity prepare for climate change,
• 16:02
  Speaker 2: to help us harden our cities, harden our transportation infrastructure,
• 16:06
  Speaker 2: harden our power grids. Where's the data to inform those
• 16:12
  Speaker 2: that trillion dollars of spend? These customers are realizing that
• 16:15
  Speaker 2: this data gap is a problem.
• 16:16
  Speaker 1: Somehow, As you were talking about about disaster management as
• 16:25
  Speaker 1: a data problem. I was thinking of what you were
• 16:29
  Speaker 1: saying earlier about supply chains and supply chains as sort
• 16:31
  Speaker 1: of a worldview, and I couldn't quite nail the link.
• 16:34
  Speaker 1: I couldn't quite articulate the connection. But can you do
• 16:39
  Speaker 1: you feel a connection between those?
• 16:42
  Speaker 2: Actually you're getting at something which I didn't share. To
• 16:46
  Speaker 2: your question of why I wanted to found this company,
• 16:49
  Speaker 2: I had been in terms of thinking about getting into
• 16:53
  Speaker 2: the field of disaster management, something I had been thinking
• 16:56
  Speaker 2: about for many years because every time I would hear
• 17:00
  Speaker 2: on the news about a disaster and I would envision
• 17:04
  Speaker 2: the skill that it would take to go cope with
• 17:06
  Speaker 2: this disaster. It actually reminded me a lot of supply chain.
• 17:09
  Speaker 1: Manufacturing in what way.
• 17:11
  Speaker 2: So when you're running a supply chain, well, nobody notices you.
• 17:15
  Speaker 2: Everything just shows up on time, beautifully, and and it's
• 17:19
  Speaker 2: it's invisible. So you need to make very very meticulous
• 17:23
  Speaker 2: plans of exactly what you're going to build and all
• 17:25
  Speaker 2: the necessary pieces that need to go into place to
• 17:30
  Speaker 2: make sure that you can manufacture that product at the
• 17:34
  Speaker 2: quantities you need on time and get it to where
• 17:36
  Speaker 2: it needs to go. So you're planning meticulously and then
• 17:41
  Speaker 2: all of your plans never goes planned. You you still
• 17:47
  Speaker 2: all your well laid plans still result in fire drill,
• 17:53
  Speaker 2: fire drill, fire drill, fire drill, just a disaster disaster
• 17:55
  Speaker 2: every single day. I mean it's a super high adrenaline job.
• 17:58
  Speaker 2: Where where there's a labor strike, there's a pandemic, there's
• 18:03
  Speaker 2: a a you know, something's held up in customs, the
• 18:07
  Speaker 2: supplier got confused and made the wrong color. Just disaster
• 18:11
  Speaker 2: after disaster after disaster is what happens in supply chain,
• 18:15
  Speaker 2: and you need to recover and think on your feet
• 18:17
  Speaker 2: and figure out how to resolve that issue. And at
• 18:21
  Speaker 2: the end of the day, things result in calm and
• 18:25
  Speaker 2: stability and you save the day. And and you know,
• 18:29
  Speaker 2: disaster management I think has a lot of similarities where
• 18:32
  Speaker 2: emergency managers they spend the off season meticulously planning for
• 18:38
  Speaker 2: how they're going to harden their system, like building fire
• 18:41
  Speaker 2: brakes and safety zones, how they're going to be prepared,
• 18:44
  Speaker 2: like communicating evacuation plans and rehearsing and having drills and
• 18:50
  Speaker 2: ordering emergency blankets. And still when the fire comes, they
• 18:54
  Speaker 2: need to react in real time and make snap spurt
• 18:57
  Speaker 2: the moment decisions, and so I think I have coming
• 19:00
  Speaker 2: from supply chain, I have a lot of empathy for
• 19:02
  Speaker 2: our customers emergency management, and I can imagine how the
• 19:06
  Speaker 2: more data that they can have, the better they can
• 19:08
  Speaker 2: make their decisions. Because being in supply chain. Data is key.
• 19:12
  Speaker 2: You know what, when I was leading supply chain organizations,
• 19:15
  Speaker 2: my goal was to surface data on every on as
• 19:21
  Speaker 2: far upstream and the supply chain as I could go,
• 19:23
  Speaker 2: as early as possible. And if I could look, if
• 19:26
  Speaker 2: I could look, you know, twelve weeks upstream into the
• 19:29
  Speaker 2: supply chain and I saw, oh, they had a labor strike,
• 19:33
  Speaker 2: I know there's going to be a problem, you know,
• 19:35
  Speaker 2: coming at me twelve weeks from now. But when I
• 19:37
  Speaker 2: have twelve weeks to react to it, it's much easier
• 19:39
  Speaker 2: for me to solve that problem that if I only
• 19:41
  Speaker 2: find out about that labor strike when that part just
• 19:45
  Speaker 2: doesn't show up. Data is critical to running supply chain
• 19:48
  Speaker 2: and emergency managers share the same thing. The more data
• 19:51
  Speaker 2: they have, the more they can respond efficiently and safely.
• 19:57
  Speaker 2: There are some similarities. You're right, It's good. There is
• 20:00
  Speaker 2: something that ties it all together.
• 20:05
  Speaker 1: We'll be back in a minute with the lightning round,
• 20:07
  Speaker 1: then back to the show, So we do a bunch
• 20:17
  Speaker 1: of questions at the end of the show. I didn't
• 20:19
  Speaker 1: realize when I wrote those questions for this interview how
• 20:23
  Speaker 1: much we talk about supply chains in the main part
• 20:25
  Speaker 1: of the interview. I wrote a bunch of supply chain
• 20:27
  Speaker 1: lightning around questions for you. Oh that's okay, Okay, let's
• 20:29
  Speaker 1: do them anyways.
• 20:30
  Speaker 2: Yeah, my favorite talking that's great.
• 20:33
  Speaker 1: What do you love about supply chains?
• 20:37
  Speaker 2: I think what I love about the supply chain is
• 20:39
  Speaker 2: both the planning part and the and the adrenaline part
• 20:44
  Speaker 2: of having to to have the diving catches in the moment.
• 20:48
  Speaker 1: Diving catch is a great metaphor. Who doesn't love making
• 20:50
  Speaker 1: the diving catch? Yeah? What was the difference between managing
• 20:56
  Speaker 1: the supply chain for high tech vight pens at packs
• 21:00
  Speaker 1: and managing the supply chain for fancy thermostats or cameras
• 21:05
  Speaker 1: at nest?
• 21:10
  Speaker 2: Those supply chains are pretty similar, to be honest. The
• 21:13
  Speaker 2: reason we get so many awesome coal gadgets so quickly
• 21:18
  Speaker 2: into the market is that the consumer electronics industry has
• 21:22
  Speaker 2: built a really mature supply chain that's made up of
• 21:25
  Speaker 2: building blocks that can be rearranged into very different products.
• 21:29
  Speaker 2: So the building blocks of both of those products are
• 21:33
  Speaker 2: surface mount technology, which is how you make the printed
• 21:37
  Speaker 2: circuit boards, and then the other building blocks are mechanical
• 21:40
  Speaker 2: components that go around those electronics, so plastic injection molded parts,
• 21:46
  Speaker 2: metal formed parts. Our supply chain here PANO is radically different.
• 21:51
  Speaker 1: However, it's interesting to think of when you describe it,
• 21:54
  Speaker 1: how many things we get are just circuit boards with
• 21:58
  Speaker 1: plastic wrapped around them, right, So many things yep yep.
• 22:05
  Speaker 2: By the way, I don't want it to seem that simple,
• 22:07
  Speaker 2: because my husband is a leads hardware engineering and at
• 22:11
  Speaker 2: a startup, and he would say, it's not that simple.
• 22:15
  Speaker 1: Fair. So, now you work in the world of natural disasters,
• 22:20
  Speaker 1: and I'm curious, are you a prepper?
• 22:24
  Speaker 2: Oh, that is a great question. You know, we do
• 22:27
  Speaker 2: have our earthquake kit. We do have finally filled up
• 22:32
  Speaker 2: our water jugs. I said to my husband, like, it
• 22:36
  Speaker 2: will just be too embarrassing if I founded an emergency
• 22:38
  Speaker 2: management company and then we die because we didn't have
• 22:42
  Speaker 2: our jugs of water in the earthquake.
• 22:45
  Speaker 1: Vanity and shame as a motivator to not die works right, Why.
• 22:49
  Speaker 2: Not Honestly, it's really emergency preparedness is really important.
• 22:53
  Speaker 1: If everything goes well, what problem will you be trying
• 22:56
  Speaker 1: to solve in five years.
• 22:57
  Speaker 2: I think the response phase is low hanging fruit, and
• 23:02
  Speaker 2: we have a tool that really helps the response phase.
• 23:04
  Speaker 2: But mitigation is just as important, and we need to
• 23:07
  Speaker 2: We need to think through through how many more helicopters
• 23:10
  Speaker 2: do we need, Where do we need to bury power lines,
• 23:12
  Speaker 2: what fuel breaks do we need to cut? What are
• 23:16
  Speaker 2: ways that we can harden our cities and the infrastructure
• 23:19
  Speaker 2: against wildfires? But I also would like to help solve
• 23:22
  Speaker 2: problems related to other disasters, like how do we restore
• 23:25
  Speaker 2: power and internet faster after a disaster? Right? And can
• 23:30
  Speaker 2: data help in that solution? So I'd really love to
• 23:34
  Speaker 2: be working on that problem, and I'd also really love
• 23:38
  Speaker 2: to be helping inform policy makers around rebuilding efforts. Build
• 23:43
  Speaker 2: back better is an expression used in Washington. You can't
• 23:47
  Speaker 2: build back better if you don't have data.
• 23:55
  Speaker 1: Sonya Kassner is the founder and CEO of PANO. Today's
• 23:59
  Speaker 1: show was produced by Gabriel Hunter Chang and Edith Russolo.
• 24:03
  Speaker 1: It was edited by Sarah Nix and engineered by Amanda
• 24:06
  Speaker 1: k Wong. You can email us at problem at Pushkin
• 24:10
  Speaker 1: dot fm. You can find me on Twitter at Jacob Goldstein.
• 24:14
  Speaker 1: I'm Jacob Goldstein and we'll be back next week with
• 24:16
  Speaker 1: another episode of What's Your Problem.