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