Turning Mitigation into Action: FortressFire’s Wildfire Modeling Insights

In the wake of the devastating wildfires in Los Angeles, Season 2 of Building Potential focuses on wildfire risk. These episodes explore what recent events reveal, what the industry still needs to learn, and how we can apply these lessons to build a more resilient insurance ecosystem—and a more resilient society.

In Episode 3 of the Building Potential – Wildfires Special, John Wall, the Founder and CTO at FortressFire, and Michael O'Dell, the Head of Data Science and Machine Learning at FortressFire, join Archipelago’s Founder & Chairman, Hemant Shah, to discuss:

• Modeling for mitigation – How FortressFire’s structure-first approach focuses on protecting properties by modeling how and why buildings ignite—not just predicting wildfire spread.
• Turning risk reduction into reality – Why modeling isn’t the endpoint, but the starting point for real-world action, from ground-level mitigations to long-term monitoring.
• Community-scale resilience – How a new mindset around cluster mitigation could reshape insurance strategies and build collective wildfire protection.

Watch, listen, or read along to the full episode below:

Hemant:

Welcome to Building Potential where we explore emerging challenges and new opportunities across the property risk management and insurance ecosystem. I'm Hemant Shah, your host, and we've kicked off season two with a series of conversations about wildfire risk. In our first episode, we discussed an engineer's perspective reflecting on recent events. In our second we discussed the market's perspective. Today we're going to discuss a modeler's perspective, more specifically how you can model the impacts and the benefits of wildfire mitigation to reduce the risk. To do so, I'm joined today by John Wall and Mike Adell of FortressFire. John is Founder and CTO of FortressFire and has over 25 years of experience creating impact with technology-driven solutions including several marketplace service platforms. Mike is a physics-trained data- scientist, and, full disclosure, is a former classmate of mine at Stanford University. John, Mike, thank you for joining me today to have this conversation. I really appreciate your time.

John:

Thanks for having us on. We appreciate the opportunity.

Hemant:

Been looking forward to this conversation guys. John, let's get started by just telling me a bit about FortressFire and your motivation to start the company.

John:

Good question. So we've actually been around since 2019 and we grew out of a fire retardant company. So myself and two of the other founders of the company were having a discussion around 66 different air bases where they fly fire retardant out of, and just the logistical challenges of preseason, getting the inventory right and then really how you manage the logistics to make sure that you can make it to fires and have enough supplies. And this is always a game played at the beginning of the season and it's always a challenge because you can't always pick where fires are going to be. And somehow that discussion drifted into some work that Jack Cohen did for the Forest Service all the way back in 1994. And he had developed this idea and it ended up turning into something called a structural ignition assessment model. And his whole thesis was is if you can protect a structure prior to a wildfire showing up, you're just not going to have a loss period.

And he did a lot of seminal work on can you do proper physics modeling of a structure and energy interaction to actually understand what it's going to take to light a building on fire. And at that moment I think the light bulb went off for all of us and we realized that response models aren't enough. There's been amazing work done on response models, whether it's from fire departments or Cal Fire or the US Forest Service, but the fact is you can't always get to where a home's going to catch on fire in time. And if the home's ready ahead of time and is not going to burn, then you can just prevent a loss at the beginning of this. And we kind of took that challenge on. It ended up being a lot harder than we thought it was going to be. We spent a good solid three years just getting our models all working and then doing a lot of back-testing. But five years later, here we are and we've got a tool we absolutely believe can not only predict if a structure is going to catch on fire, but we've now got the ability to determine exactly what you need to do to have it not burn when a wildfire shows up. So that's how we got here.

Hemant:

Fascinating. I'm looking forward to double-clicking on several of those teams, but it sounds like the origin and your backgrounds were on the logistics and operations research of responding to the fire and then you had the light bulb moment where let's see if we can, even if we can't respond, we can stop the loss from occurring at the structure level. Fantastic. So tell me a bit more, who do you work with? I mean I imagine this kind of modeling is applicable to multiple stakeholders in this value chain. Do you work with insurers? Do you work with owners? Do you work with service providers? Who are your customers?

John:

Yes, to all the above. The one thing I've learned, and we will probably talk about this a lot today, is there's no one thing that you with a wildfire problem, there's a lot of amazing community work done. Napa's a great example of an area where they've done just amazing work at the community level, but everything is additive when you get into wildfires, whether it's the fire department response or air response from federal resources or whether it's community efforts to reduce fuels and preparedness for wildfires all the way down to structures. And what we found out with our business is we do work with insurance carriers, we work with reinsurers, we work with brokers, we have to work with service providers as well because you have to mitigate homes. And then I think most importantly we're working with property owners and one of the things that we started this really because we realized we wanted to save structures at the end of the day when we talked to homeowners, they can't get insurance anymore. So this whole thing tied together to a much bigger ecosystem.

Hemant:

Well, that's what got my attention when I first met you guys last fall at EPIC's Wildfire Summit aptly located in the WUI, the wildland urban interface up in wine country in northern California. What really struck me about you guys is unlike many modelers who focus just on the quantification of the risk, you guys work across the ecosystem across the value chain and you actually work with your customers to not only identify the risk but to identify how you can reduce the risk and then you actually partner with them to implement the practical measures to close that last mile of gap and get the risk down, which is really unusual and I think quite compelling. Can you share maybe to bring this to life, since we met up in wine country, I know you work with several wineries. Do you mind sharing an example of how you work with, let's say an owner, the actual winery owner to deliver value to them across this ecosystem?

John:

Sure, and I think Napa – the whole Napa area. All of wine country's just one of the more, I would say regions of the country that's ready for this. Napa is very focused on firewise communities. There have been a lot of devastating fires up there that have impacted wineries. So, you always have to have somebody willing to take a look at this type of stuff. So the environment, Napa I would say is one of the better areas and we've had a lot of success there. We've got 30 wineries now and almost all these wineries are sitting in areas just like you said, they're in a ee, they're in a higher risk fire area. So everybody we talk to is already paying attention to this and trying to do a lot of work. Where we jump in is our whole model. We have this thing built called a property ignition model, starts at a structure and we work outwards. So we're not a hazard model, we're not a CAT model–

Hemant:

It's not hazard in it's kind of vault structure out.

John:

It's vulnerability. Exactly. So we start, it is an energy interaction model to be an engineer on this is when you look at it, I love the book Fahrenheit 451 because it gives you an exact point for a piece of paper catches on fire. It's 451 degrees Fahrenheit. We do that at a little bit larger scale. We take terrain fuel structures, and we do that at wineries, and we look at all the fuels around it and all the structures around it, and frankly, we light them on fire virtually and find out what that energy model looks like. That allows us to identify places of risk on structures, whether it's ember or convective, flame touch or radiant energy. And then, where we have excess energy, or we have risks of buildings catching on fire, we either reduce the fuels or make the building more resilient with the property owner at the end of the day.

Then we balance that out and then we come up with a solution for them. So if a fire happens, they're not going to lose their structures. And these are wineries that really care about their inventory and they really care about their presence in the community. So like I said, I think we've got 30 of them up now. And what we found out though is not just saving these wineries. We've been working and you were there at the wine summit, we've been working with brokers and insurance carriers. So a lot of these folks were either being placed on the fair plan, they couldn't get insurance or they were getting massive rate increases. So by tying all this stuff together, working with the insurers, we've either been able to, number one, get 'em insurance at the right amount of coverage. In a lot of cases we've prevented rate increases and we've had a few cases now where we've actually gotten discounts for some of the wineries based on the work that they've done. So they're not getting these massive premium increases that have been hitting everybody.

Hemant:

And this is not, just to clarify, this is not just a modeling exercise that manifests in an 80-page report. Your clients take active measures to implement actions to reduce the risk.

John:

And that's a huge part of what we do. So the model's at the beginning, not the end. We always start with a model. Typically we'll do an aerial model. We typically, after that, we'll then go do a ground model, actually put boots on the ground and go look at it. And then what we do after that is we come up with a set of mitigations and those mitigations almost always include either some fuel reduction or some hardening on the structure or a combination of both. Those are either done by the wineries themselves or we work with them to get all that done. At the end of the day though, once those are done, we then validate all that and that becomes something we can provide back to brokers and insurers when they go to a carrier to say, look, here's what the property looked like before, here's what it looks like now and here's the reduced ignition risk for these structures. And again, we're not trying to say if a fire is going to show up, we say when a fire shows up here, this building is significantly reduced for ignition risk. It's because of fuel reductions, it's because of material changes on the structure. And then the other thing we do as part of our services offer monitoring and protection, just because fuels continue to grow, they just never stop. And when you have a lot of fuel growth, you have to manage it.

Hemant:

It's the modeling, it's the actual implementation of the mitigation measures, boots on the ground, and then it's the monitoring to ensure, so it's a full solution. Mike, I'm going to come to you in a minute. As a recovering catastrophe modeler, I can't help but be curious about how you've put these models together. But John, maybe a clarifying question. So we talked about wineries, which I think is important because many, many of us reflexively think that wildfire risk is synonymous with personal lines risk, but there's a lot of commercial lines exposure in the wildland urban interface and with the expanding sort of zones in which wildfires can occur. So I'm glad we got a chance to talk about a winery as an example of commercial lines exposure, but what about residential? Most of the exposure to wildfire risk is residential and certainly that's been, we've seen that firsthand in Los Angeles. Does your approach to modeling and then implementing downscale to single family homes or is it just applicable to larger higher value commercial structures?

John:

Great question. When we started this, we actually started with single-family homes. So we started there, wineries became a very quick add-on and to be, it was epic. Epic invested in our company and they've got a huge wine business and wineries had a big problem, so we got very focused on that. But everything we do starts at a single structure. So we started a rooftop on a building and then we work out. So our modeling tool has been very heavily tested, not just on commercial but even more so on residential structures. So everything we do is built really to focus on, I would say we cover the gamut now, anything from a small house up to a building that's 800 feet long. And the cool thing that we've learned over the last couple years doing a significant amount of bag testing is physics tends to behave the same way, whether it's a small building or a big building.

And we've been able to back test and validate that. Now the biggest trick is when you get into smaller structures is structure-to-structure ignition risk. I know we all saw that in Palisades and Eaton on spades, and maybe we can dive into that a little bit deeper. And on a lot of the larger structures, you do have space gaps between buildings, which give you some huge advantages from things like structure to structure, radiative ignition risks. So you have to look at 'em a little differently, but the foundational physics don't change whether it's a small building or a big building. So we've been pretty lucky in that aspect.

Hemant:

Good. Thanks for clarifying. I would like to dig into some of the work you've done on the Palisades, but Mike modeling. So I think it's fair to say that much of the insurance industry is pretty skeptical about our collective ability to model wildfire risk. The standard approaches. I think it's fair to say having worked that well, it's a very complex phenomena from ignition to spread to vulnerability to fire response dynamics. It's a lot of complexity, it's very multifaceted, and the models seem to be surprised by a lot of these recent events where there's 2017 Tubbs, 2018 Camp, 2023 Maui Lahaina, now 2025 LA. What is your approach to this problem and how have you tackled such a complex modeling challenge?

Michael:

You're right. I mean the state of catastrophe modeling for wildfire is complex and I think there is concern in the industry that it doesn't quantify the risks efficiently. So we've applied Occam's razor and focused just on what's important and that's how to save the structure. The structure is what is insured, and that's what we want to save. So to do that, we model the structure and its immediate environment using thermodynamics. So structure materials will fail. That's either they break in the case of glass or melt in the case of vinyl siding and then ignite or ignite. So wood siding will ignite and there's a characteristic temperature at which that failure happens for each material. So we model how much energy the local fuels cast onto every part of the structure and look for those places where the structural material reaches or exceeds the failure temperature. So knowing those locations and then knowing those fuels that contribute to that excess energy on the material, we can then specify mitigation. So first we know, hey, it's going to fail at these locations. We know by how much it's going to fail, and we know the contributing fuels, so now we can come up with a solution. So to put it in cat model terms, we've focused on the vulnerability and the exposure to some extent from our perspective, hazard is not destiny.

Hemant:

So I like the analogy of Occam's Razor. I think all modelers looking at modelers out there, we all should do a better job simplifying our models and focusing on what really matters. But so you're not actually trying to build another stochastic, probabilistic wildfire model that spends a lot of energy trying to figure out how to model ignition rates, spread propagation models, fuel loads between the ignition and the community. You're focused on how do you model the impact and the benefits of mitigation so that people can actually mitigate the risk in the real world. This is not an analytical exercise. This is optimized to create actionable insight that you can take action and actually reduce the risk in the first place.

Michael:

That's exactly right. This is really focused on risk management. We want to reduce the risk. We want to actively reduce the risk. So first we quantify, so

Hemant:

This is a high resolution of it's not just a structure, you do have to consider the immediate environs around the structure because fuels do matter. So what's the zone and resolution that you are focused on to create an assessment for an individual property?

Michael:

Great question. So the fuels do matter, right? And it is typically the fuels that are right around the structure. So if you think in terms of within wildfire, there's a zone zero to zero to five feet moving out to 10 to 20, 30 feet, we usually model on a grid of about 300 in either direction, top bottom going out. Now, for a commercial structure that'll be larger, but within the first say 10 meters, 20 meters, you've really captured in just that space, the majority of the fuels that will have an impact on the structure. So really it's a very tight area around the structure that is relevant. There's always the issue of long distance embers, but we capture that in a different way. But really it's focusing on right around the fuels, right around the environment. And that's sufficient to do a good job of modeling. We don't have to worry, as you say, we don't have to worry about the starts, the spread for miles around. It's really assumed that fire arrives at the structure and we're going to look at that environment immediately around within 20, 30 meters, right around the structure.

John:

Hey, yes, John, I just want to throw in because I think that's a great description. The other couple of big things that always have to be considered on all this is terrain and wind because we see high wind events and low wind events. So we model temperature, terrain, humidity, and wind, and all of those add together. I think to give us a better understanding of this, because you get very different outcomes when you're spinning up 80 mile an hour winds versus 20. So I just want to throw out it gets a little more expensive than even that

Hemant:

That's course. So you are modeling hazard in the sense that the immediate environments of the property, some of the environmental conditions that create the load on the property so that you can then identify how to take action to mitigate those exposures. What are the different vectors of risks that you model? I know wildfire, we often talk about it, it's a monolithic thing, but actually wildfire is a very complex set of vectors that can drive ultimate damage. So are you modeling multiple vectors of this risk?

Michael:

By necessity? Right, so for sure. So there's 200-plus years of thermodynamics history. Sadie Carne published his treaties reflections on the motive power of fire, that was back in 1824, and we've had recently 70 years of modern fire science. So what that tells us is that there are three basic ways wildfire will ignite a structure. So there's convective energy, so think about a flame that's actually touching the structure that would be convective. So then there's radiant energy, and my favorite image mentally is your dining alfresco and it's a little bit chilly and they put a heat lamp right next to you and you could feel that heat coming off of it, blow that up to an entire tree. And that's a tremendous, or several trees. That's a tremendous amount of radiant energy on the side of the structure, which can cause. And then, finally, there's embers.

These are firebrands generate secondary ignition. So consider embers, small embers, penetrating vents or gaps and landing inside the house and either lighting the curtains on fire or a pile of boxes or some clothing, so then the house would burn from the inside out. So those are the three main factors. Now we divide ember into two subcategories. One kind of just penetration that we discussed or just mentioned, but then the other one is accumulation. So if you think about taking charcoal briquettes from your grill and dumping a bunch of those briquettes on your roof or stacking them right up against the side of the house, you can imagine that you could generate sufficient energy to cause an ignition. So that for us is four. And then we have a fifth we've identified using the same physics, but it helps us model by wrapping them all together so that we can do structure to structure analysis. So structures are just like a tree or any other fuel. Once a structure goes, that's a tremendous amount of energy that can be transferred to a structure that we care about. So the neighbor's house could certainly burn our house down.

Hemant:

So these are all modeled in the physics space PIM model. So I think you're also with this approach, challenging some conventional wisdom, which is often wrong that once the fire gets to the property, there's little you can do. It sounds like in your experience and your modeling, you assume the fire gets to the property and you can still take active real-world measures to mitigate the risk even if it does arrive at your doorstep. How do you know this works?

Michael:

It's a great question, and you're exactly right. So we do assume fire arrives. So our whole goal is to identify how do you mitigate the risk by assuming the fire arrives and we know that we can do this, so how do we do this? So we are always devising new ways to test our models, but first they're all based on over 200 years of sound science. We also only use data and empirically derived equations that have been validated repeatedly in the published literature. And then lastly, we back test model against structures that have experienced wildfire and for which we know the outcome. So I don't know, listeners may or may not know that California and other states, they inspect every structure inside a fire perimeter and they provide a disposition. So we use that disposition as the ground truth and then compare our model's prediction of how that structure will fare in the wildfire, in a wildfire with the disposition given by the inspectors.

And so we're pleased to say that our models are very accurate and they predict destruction and survival, and it's important to be able to do both, predict both correctly. Obviously, if our model says a structure won't burn, and in reality it does burn well, that doesn't really serve the owner or the insurance company that underwrote it. On the other hand, if a model predicts structures will burn and they don't, then folks are needlessly spending money on mitigations and insurance rates will then be higher for everybody because if we say, Hey, all of these structures are going to burn, but a large portion of them don't, then excess money spent on mitigations that weren't necessary and rates are higher for everybody. So it's important to be able to get it right on both ends.

Hemant:

You've done some testing on the Palisades, can we talk about that, John, you alluded earlier to some of the work that you've done on the recent fire.

Michael:

Yeah, John, do you want to throw up the chart that we have? So we took the structure, so we talked about kind of the five vectors that we use in our modeling and just using one of them, we quickly did an analysis on the Palisades fire. Over 12,000 structures were affected, the Palisades fire, and you can see by this chart that we very accurately predicted both the destroyed and the node damage structures. So over 4,000 structures experienced node damage, and almost 89% of those we correctly predicted would have no damage. Well over 6,000 structures were destroyed and we correctly predicted 88% of those.

Hemant:

So even in a relatively localized actual event such as LA, the Palisades, specifically with structure to structure, you guys are quite predictive of not only what was destroyed but what was not destroyed. These are remarkable, almost 90%. Are you surprised by, is this what you typically see when you backtest against events or is there something about what happened in the Palisades that makes this an anomaly?

Michael:

No, these are typical results. So we've modeled structures out of a variety of fires over the last five years in a variety of different terrains, different wind conditions from kind of moderate to very high in the Palisades, like in the mountain fire last fall. And the model performs very well under all of these conditions. So these are typical results for us.

John:

We weren't sure what, oops, sorry, hang on.

Hemant:

Go ahead, John.

John:

We weren't sure exactly what this was primarily there was a WUI interface on this, but a lot of these were structured structure. I think everybody knew that coming in. So we wanted these numbers to come out this way and we do this blind. So when we run PIM, it doesn't know the outcome. So when we ran our ignition structure structure model on this, we didn't know the outcome. The one thing that did surprise me about this fire is the distribution. It matches the historical distribution almost exactly, meaning you almost always either see a building completely destroyed or no damage, and this was such a large fire. I think we all expected to see a little more in the middle and the damage distribution, but fires generally hold to be very binary in nature. And we were really pleased to see this level of correlation to what we've historically seen.

Hemant:

And I know some who are listening are concerned about the binary nature, but the flip side of that same coin is that unlike other perils, mitigation has a huge ROI because you're not just partially mitigating damage, okay, it was 20% prior, now it's only 10%, it's zero or one. And so the impact of mitigation is essentially the whole value of the insured values of the structure.

Michael:

Yeah, this is a very important point.

Hemant:

So one of the clear insights from the Palisades and what we all observed after the fact was that as you noted, the vector was a lot of house-to-house, almost network-like, behavior. You mentioned that you do model those vectors of the risk. How does that look like in the Palisades? Because there's a tightly relatively tight community and a lot of structure-to-structure burn. Are you able to, oh, okay. So what are we looking at here guys?

Michael:

So you see here, sorry Mike. This is a map of just zoomed in portion of the affected areas in the Palisades, and these are various neighborhoods. And where you see a purple line is from our model's perspective structures that are connected in terms of fire hazards. So in other words, if one of the structures on a chain ignites, then anything connected to it by purple line will also ignite. And there's a number of things to notice on here. You can see some that are just kind of single chains, so aligned between each, it's a one-to-one. Other sections are highly interconnected. You can see kind of in the bottom center there that those structures are highly interconnected. So if one of those structures ignite, not just one on either side, but several structures are close enough that they will ignite. So you can imagine that in a densely connected cluster like that, once one structure ignites, it will rapidly spread to almost all if not all of the structures in there. Whereas on a single connected line or chain, you would need one and it would be easier to defend or easier to mitigate because you could break a chain by just cutting one line.

Hemant:

So you can actually, by modeling these interconnections between the structures and these vectors of transmission, you can actually kind of assess not only mitigation, but you can micro-zone the risk. Because even though you might say to somebody casually, they say everything on this image is similar or high risk, but you can identify that there's clusters that are more amenable to mitigation than others, and conversely higher risk than others just by understanding the physics of structure to structured transmission.

Michael:

Absolutely. So any of those structures where there is no purple connecting line, those are kind of standalone structures. Those already are lower risk, right? Because you don't have to mitigate it against other structures nearby. If there is a two structures that are connected and it's only those two structures, well there's an opportunity maybe to either as neighbors work on getting insurance together or you could easily mitigate by putting in a fireproof privacy fence between the two of them. This is, again, we're always focused on how do we mitigate and how do we reduce the risk? And part of that is our initial assessment of what is the risk, right? So we do want to know, hey, this is a highly interconnected risk environment structure to structure, or no, it may be in a high-risk hazard zone, but it's relatively isolated and so much more easily mitigated. So these are all things that we take into considerate.

John:

Emma, if you look right here, I dunno if you can see the cursor.

Michael:

Yeah,

Hemant:

I can. Yeah.

John:

So right here, this is a connected cluster, but there were no complete losses. So they're all green in here, so nothing jumps the road. We had a couple with a little bit of damage, but you can look at these clusters over here. Everything's destroyed, everything's destroyed. So once you start getting the losses, you can see the clusters connecting. Occasionally you'll get breaks, but here's another cluster here, one damage, nothing actually caught on fire and they all stayed in the same state, but where you typically start seeing a lot of black dots, you're going to see the entire cluster lost. And that's what we, because PIM was predicting structure to structure ignition, and this is down here where we're densely interconnected on the bottom again, everything's lost. So there's opportunities when you're doing things like mitigations to break clusters like over here where you're really close to potentially a break point. So you can minimize your grouping of risk, but you also can very much see in this fire that once you get something going and it lights on fire, it tends to follow the whole path all the way down.

Hemant:

So this is really important. This suggests that this class of modeling, this kind of vulnerability out versus more traditional CAT modeling hazard in approach is not only applicable in the canonical wildland urban interface setting of a high-value home up on a ridge or a winery. This kind of modeling is also very applicable for what we saw in the Palisades, what we saw in the tubs fire and coffee park, what we saw in Maui and Laa, more residential, denser residential neighborhoods as well. And it sounds like the practical measures you can take to mitigate are also applicable in these settings as well.

Michael:

Absolutely.

Hemant:

So as we wrap up, since we're talking about the Palisades, we take a step back, how realistic is it to implement these kinds of measures in scale? Because it's one thing to mitigate a home on a ridge or a winery or a cluster of wine or high-value wineries in Napa when you're rebuilding or looking at an existing neighborhood of hundreds or thousands of homes. In your experience, are these measures realistic, whether it's in the context of building back better or mitigating an existing development?

John:

Man, that's an awesome question I'm going to let Mike jump into, but there's two things to think about on this, and if you think about that picture, we just talked about insurance today is very much a singular item, but when you get into denser communities, I think short answer is yes, this can be dealt with, but I think there needs to be some attitude changes on how this gets done. Insurance carriers want to distribute the risk out. They don't want a lot of risk in one area, but if you look at a cluster of homes, there's significantly more value in protecting a cluster than a single home, which means you need now community-driven efforts, whether it's through HOAs or communities to go do it. Of course you can protect between structures, but now you're getting into very expensive mitigations. If you can't put a thermal barrier between structures, stopping a propagation is going to be very difficult. So I think it does require some different thinking on the way that you protect in high density neighborhoods. You're going to have to really think about building in smaller clusters and then protect all the homes in a cluster and then you protect the entire thing.

Hemant:

That's a really important point. It does require a different mindset about creating a kind of herd immunity in the community. It's not about every man for himself or herself creating an island of resiliency. You have to have a community-wide approach. And it sounds like from your experience, the flip side is that's a bit more challenging. You have to have concerted effort at the scale of a community as appropriately defined, but also is the benefit that you can actually mitigate risk to the entire community by preventing the conditions for anybody to be damaged. So it has interesting implications for insurance. I think insurance has a role to this to play as well, doesn't it guys?

John:

Absolutely.

Hemant:

Yeah. I think I recall when we met at the symposium, a couple of the underwriters were already starting to get their heads around. This requires a kind of a paradigm shift because insurers are trained for good reason to underwrite against with the prime directive of diversification, right? In a way with wildfire, yes, diversification is one way to reduce your risk accumulation, but when you're trying to encourage mitigation, you want to actually identify clusters of exposure and encourage them all to mitigate together in a zone of resiliency and then you ensure them all because none of them are likely to be lost. It is an interesting paradigm shift, and I think the role of risk capital and insurers-approach this peril needs to shift as well based upon the insights that you guys are providing. Well, thank you so much for this super engaging conversation. I love your approach.

I love the antecedents, the company starting the real world and operations, logistics and the practical measures. Then how do you actually mitigate risk to the structure as the focus for how do you build a model rather than how do you build a traditional cap model? And I think a broader adoption of this kind of technology will help practical measures to assess the mitigation and then implement them in the real world, which is what we all need to do, not just assess the risk, but reduce the risk. So John, Mike, thank you so much for this super engaging conversation and I look forward to your ongoing success and following your progress at FortressFire.

John:

Thanks for having us on today, we appreciate it.

Hemant:

Okay, thanks guys. Great discussion.

John:

Thank you.