1 00:00:15,316 --> 00:00:22,516 Speaker 1: Pushkin. Here's the thing I did not know about weather 2 00:00:22,596 --> 00:00:28,316 Speaker 1: forecasts until very recently. They basically all come from the government. Sure, 3 00:00:28,356 --> 00:00:30,836 Speaker 1: you may have your favorite weather app, your favorite TV 4 00:00:30,996 --> 00:00:35,796 Speaker 1: weather person, but their forecasts are almost entirely driven by 5 00:00:35,876 --> 00:00:40,556 Speaker 1: data that's collected and analyzed by government agencies. And you know, 6 00:00:40,676 --> 00:00:43,596 Speaker 1: it makes a certain kind of sense. Gathering the data 7 00:00:43,676 --> 00:00:46,876 Speaker 1: you need to make a useful forecast has traditionally been 8 00:00:47,276 --> 00:00:51,876 Speaker 1: a huge expensive undertaking, and having a reliable forecast is 9 00:00:51,996 --> 00:00:55,596 Speaker 1: really valuable for lots of people in lots of different settings. 10 00:00:55,636 --> 00:00:58,396 Speaker 1: So it's good that the government does the work and 11 00:00:58,516 --> 00:01:02,836 Speaker 1: makes forecasts freely available to everybody. But the government is 12 00:01:03,236 --> 00:01:06,476 Speaker 1: the government, and we shouldn't expect it to tailor forecasts 13 00:01:06,476 --> 00:01:09,556 Speaker 1: for different businesses, or even to build forecasts that are 14 00:01:09,596 --> 00:01:12,436 Speaker 1: really useful for people who live in other countries, in 15 00:01:12,516 --> 00:01:17,676 Speaker 1: countries where the government can't afford to produce its own forecasts. Now, 16 00:01:17,836 --> 00:01:21,316 Speaker 1: imagine what a private weather company could do. A company 17 00:01:21,316 --> 00:01:23,756 Speaker 1: that relied not only on government data, but that went 18 00:01:23,796 --> 00:01:26,836 Speaker 1: out and collected data on its own, A company that 19 00:01:26,876 --> 00:01:30,076 Speaker 1: came up with forecasts that would not have been possible before. 20 00:01:34,796 --> 00:01:37,476 Speaker 1: I'm Jacob Goldstein, and this is What's Your Problem, the 21 00:01:37,516 --> 00:01:40,756 Speaker 1: show where entrepreneurs and engineers talk about how they're going 22 00:01:40,796 --> 00:01:43,356 Speaker 1: to change the world once they solve a few problems. 23 00:01:43,876 --> 00:01:48,116 Speaker 1: My guest today is Shimon Alphabets, co founder and CEO 24 00:01:48,236 --> 00:01:51,676 Speaker 1: of tomorrow dot Io, a private company that plans to 25 00:01:51,716 --> 00:01:54,876 Speaker 1: put a constellation of weather satellites into orbit in the 26 00:01:54,876 --> 00:01:58,516 Speaker 1: next couple of years. Shimoon's problem, how do you build 27 00:01:58,556 --> 00:02:02,556 Speaker 1: a private weather company from scratch? We realized it's from 28 00:02:02,556 --> 00:02:05,956 Speaker 1: a jew political and cost effective way, and all kind 29 00:02:05,956 --> 00:02:08,276 Speaker 1: of the only way to solveet is to go to space. 30 00:02:09,236 --> 00:02:12,076 Speaker 1: Shimon and his co founders launched tomorrow dot Io in 31 00:02:12,156 --> 00:02:16,276 Speaker 1: twenty sixteen. The company hasn't launched its satellites yet, but 32 00:02:16,356 --> 00:02:20,116 Speaker 1: it already provides weather related advice for companies like Jet Blue, 33 00:02:20,356 --> 00:02:24,036 Speaker 1: Uber and the NFL. Even before he thought of founding 34 00:02:24,036 --> 00:02:27,116 Speaker 1: the company, weather was a big deal for Schimon When 35 00:02:27,116 --> 00:02:29,156 Speaker 1: he was in his twenties. He was an officer in 36 00:02:29,196 --> 00:02:32,196 Speaker 1: the Israeli Air Force. Whether it is obviously a huge 37 00:02:32,236 --> 00:02:35,996 Speaker 1: deal for pilots for planes, and Shimon was constantly on 38 00:02:36,036 --> 00:02:41,636 Speaker 1: the phone with meteorologists. The solution was, Hey, let's talk 39 00:02:41,636 --> 00:02:44,996 Speaker 1: to a meteorologist three, four or five times, a day, Hey, 40 00:02:45,036 --> 00:02:46,796 Speaker 1: what's going to happen here, what's going to happen there? 41 00:02:47,436 --> 00:02:49,956 Speaker 1: And then I take the data, I analyze what it 42 00:02:49,956 --> 00:02:52,836 Speaker 1: means for me, what it means for the organization. And 43 00:02:52,876 --> 00:02:54,836 Speaker 1: I have to do it several times because the weather 44 00:02:54,876 --> 00:02:58,036 Speaker 1: forecast is constantly changing, and I care about multiple locations 45 00:02:58,036 --> 00:03:01,236 Speaker 1: and I care about multiple parameters. So that was a 46 00:03:01,356 --> 00:03:05,316 Speaker 1: very archaic way of addressing challenges at scale. Calling the 47 00:03:05,356 --> 00:03:08,476 Speaker 1: meteorologist and deciding what each plane should do does not scale. 48 00:03:08,516 --> 00:03:11,196 Speaker 1: So in that universe you're not at scale yet, not scale, 49 00:03:11,236 --> 00:03:14,116 Speaker 1: not efficient, not automatic. When there's a human in the loop, 50 00:03:14,196 --> 00:03:18,516 Speaker 1: there will always be an error. Huh did you make mistakes? 51 00:03:19,356 --> 00:03:24,276 Speaker 1: Of course everyone makes mistakes. I made a few of them. 52 00:03:24,876 --> 00:03:28,396 Speaker 1: You know. I have colleagues that unfortunately lost their lives 53 00:03:28,436 --> 00:03:32,596 Speaker 1: due to whether they did accidents. It was very unfortunate. 54 00:03:33,116 --> 00:03:35,596 Speaker 1: It's just been there, you know. But I didn't think 55 00:03:35,596 --> 00:03:38,156 Speaker 1: I'm going to start a company around it. Shimon moved 56 00:03:38,156 --> 00:03:40,436 Speaker 1: to the US to go to business school, and one 57 00:03:40,516 --> 00:03:43,436 Speaker 1: day a few of his friends, military veterans who like 58 00:03:43,516 --> 00:03:46,796 Speaker 1: shimone wanted to start a company, started talking about the weather, 59 00:03:47,476 --> 00:03:52,276 Speaker 1: and we started talking about past experiences, and everybody We're like, 60 00:03:52,316 --> 00:03:54,876 Speaker 1: oh you also feel this way, Oh you also experienced that. 61 00:03:54,916 --> 00:03:56,796 Speaker 1: And when we started looking at it, we said, okay, 62 00:03:57,036 --> 00:03:59,436 Speaker 1: there is something here. We need to start looking into this. 63 00:03:59,716 --> 00:04:03,796 Speaker 1: And let's try and understand whether you know how the 64 00:04:03,876 --> 00:04:07,596 Speaker 1: technology how forecast is being generated, why is it limited 65 00:04:07,596 --> 00:04:11,556 Speaker 1: in accuracy? And now let's look at how businesses make decisions. 66 00:04:12,596 --> 00:04:14,236 Speaker 1: Do they do it in the same way we did 67 00:04:14,276 --> 00:04:16,356 Speaker 1: it in the past, or is there a better way 68 00:04:16,356 --> 00:04:19,636 Speaker 1: to do it? And what we found out led us 69 00:04:19,676 --> 00:04:22,276 Speaker 1: to start a company. Well, what did you find out 70 00:04:22,316 --> 00:04:24,636 Speaker 1: that led you to start a company? All right, that's 71 00:04:24,676 --> 00:04:27,316 Speaker 1: where it's becoming interesting. So the first thing we found 72 00:04:27,356 --> 00:04:31,596 Speaker 1: out is that climate change is here. That was in 73 00:04:31,676 --> 00:04:33,996 Speaker 1: twenty sixteen. It wasn't cool to speak to speak about 74 00:04:33,996 --> 00:04:36,636 Speaker 1: climate change back then. I mean I think it was cool. 75 00:04:36,956 --> 00:04:38,996 Speaker 1: I think it was cool to me in twenty sixteen. 76 00:04:39,196 --> 00:04:41,716 Speaker 1: Trust me, when I spoke to investors back at the 77 00:04:41,796 --> 00:04:44,076 Speaker 1: time and you spoke about climate change, they were like, 78 00:04:44,156 --> 00:04:46,196 Speaker 1: give me some sass solution. Don't talk to me about 79 00:04:46,236 --> 00:04:49,596 Speaker 1: climate change. Okay, fair sass software as a service keep going. 80 00:04:49,716 --> 00:04:54,036 Speaker 1: So we understood that the problem of managing whatever the 81 00:04:54,116 --> 00:04:57,876 Speaker 1: challenges are is going to get bigger climate change equals 82 00:04:58,036 --> 00:05:01,196 Speaker 1: weather events become more frequent and more volatile in any 83 00:05:01,196 --> 00:05:04,276 Speaker 1: given ear in every part of the world. More hurricanes, 84 00:05:04,356 --> 00:05:07,876 Speaker 1: more wildfires, more heat waves. Doesn't really matter where you are, 85 00:05:07,956 --> 00:05:12,436 Speaker 1: there's some extreme phenomen it is going to happen more frequently. Okay, 86 00:05:13,236 --> 00:05:15,036 Speaker 1: So that's one thing we'll learn. The second thing we'll 87 00:05:15,076 --> 00:05:19,796 Speaker 1: learn is that the technology of forecasting weather, meaning what's 88 00:05:19,836 --> 00:05:26,716 Speaker 1: responsible for the accuracy, is generated and dominated by government agencies. 89 00:05:27,436 --> 00:05:31,596 Speaker 1: And as folks who served in the government for many years, 90 00:05:32,156 --> 00:05:36,116 Speaker 1: we understood that there must be a way to privatize 91 00:05:36,716 --> 00:05:41,036 Speaker 1: and innovate faster. And just to give an example, you know, 92 00:05:41,156 --> 00:05:44,996 Speaker 1: NASA for decades innovated and paved the way to space right, 93 00:05:45,916 --> 00:05:49,916 Speaker 1: But today you have SpaceX, who's augmenting the capability of 94 00:05:50,236 --> 00:05:54,156 Speaker 1: a private company doing what the government has done for decades. Yeah, 95 00:05:54,356 --> 00:05:57,436 Speaker 1: maybe not inventing the rocket from from scratch, but definitely 96 00:05:57,476 --> 00:06:00,556 Speaker 1: taking all the decades of research and adjusting it to 97 00:06:00,756 --> 00:06:03,436 Speaker 1: a commercial use case. What we found out is that 98 00:06:03,476 --> 00:06:06,996 Speaker 1: there is an opportunity to create a SpaceX of weather. 99 00:06:07,556 --> 00:06:11,636 Speaker 1: So SpaceX built rockets. You want to build weather forecasts. 100 00:06:11,636 --> 00:06:12,916 Speaker 1: How do you do that? What do you need to 101 00:06:13,316 --> 00:06:17,916 Speaker 1: make a weather forecast, you need three ingredients. On a 102 00:06:17,996 --> 00:06:23,276 Speaker 1: very high level. You need observations that describe the atmospheric 103 00:06:23,276 --> 00:06:26,676 Speaker 1: conditions in real time, the temperature, the wind pressure. Yeah, 104 00:06:26,756 --> 00:06:28,836 Speaker 1: then you have a good real time description, right. The 105 00:06:28,916 --> 00:06:31,796 Speaker 1: next thing you need is a model, an equation, a 106 00:06:31,876 --> 00:06:35,116 Speaker 1: set of equations. Physical models doesn't really matter. The point 107 00:06:35,196 --> 00:06:37,756 Speaker 1: is that you take the observations and you assimilate them 108 00:06:37,956 --> 00:06:40,636 Speaker 1: into a model, and then the last thing you need 109 00:06:40,796 --> 00:06:43,636 Speaker 1: is a computing power on which you process the model. 110 00:06:44,676 --> 00:06:48,796 Speaker 1: The output of the model is a weather forecast. And 111 00:06:49,196 --> 00:06:52,596 Speaker 1: what we found out is that there is an industry 112 00:06:52,596 --> 00:06:55,876 Speaker 1: of weather forecasting companies, you know, big brands. You know, 113 00:06:56,036 --> 00:07:00,116 Speaker 1: a blue logo, an orange logo, Acy Weather. Right. The 114 00:07:00,156 --> 00:07:04,156 Speaker 1: point is that these guys are here since the sixties, seventies, 115 00:07:04,276 --> 00:07:08,916 Speaker 1: maybe eighties. They just repackaged the forecast that the government 116 00:07:08,956 --> 00:07:15,436 Speaker 1: agency or the government agencies publish every day, every hour whatever. 117 00:07:16,196 --> 00:07:18,756 Speaker 1: So I'll say, I know, the weather forecast is like 118 00:07:18,796 --> 00:07:20,956 Speaker 1: a classic thing to complain about. Oh the weather man 119 00:07:20,996 --> 00:07:22,636 Speaker 1: said it would be Sunday and we had a picnic 120 00:07:22,636 --> 00:07:25,556 Speaker 1: and it rain. I do feel like weather forecasts are 121 00:07:25,596 --> 00:07:29,516 Speaker 1: pretty good, and clearly they've gotten better. Was there a 122 00:07:29,636 --> 00:07:35,516 Speaker 1: particular weakness or failure or set of weaknesses or failures 123 00:07:35,516 --> 00:07:39,236 Speaker 1: that you really thought you could improve. First of all, 124 00:07:39,276 --> 00:07:41,516 Speaker 1: you set out to do this. If I may ask, 125 00:07:41,556 --> 00:07:44,396 Speaker 1: where do you live, I live in New York. I 126 00:07:44,436 --> 00:07:47,676 Speaker 1: live in New York City. Okay, so you're privileged because 127 00:07:47,716 --> 00:07:49,876 Speaker 1: I am privileged. I'll be the first to say I'm 128 00:07:49,916 --> 00:07:52,916 Speaker 1: in Boston. I'm as privileged as you are. Most of 129 00:07:52,956 --> 00:07:56,236 Speaker 1: the world doesn't have Noah, the US government agency, the 130 00:07:56,276 --> 00:07:58,676 Speaker 1: big rich country government agencies that do a pretty good 131 00:07:58,756 --> 00:08:01,316 Speaker 1: job of forecasting exactly. And if you're a private company 132 00:08:01,316 --> 00:08:06,236 Speaker 1: and you try to provide equally accurate forecast for the 133 00:08:06,276 --> 00:08:09,676 Speaker 1: rest of the world, you're limited. You cannot provide it. 134 00:08:09,996 --> 00:08:12,156 Speaker 1: So there is a global problem. So one thing you 135 00:08:12,196 --> 00:08:15,956 Speaker 1: want to do better is provide better forecasts for people 136 00:08:16,116 --> 00:08:18,516 Speaker 1: businesses who don't live in rich countries that have big, 137 00:08:18,556 --> 00:08:22,156 Speaker 1: fancy weather agencies like NOAH in the US. That's one thing. 138 00:08:22,516 --> 00:08:25,556 Speaker 1: The second thing is that even within the US, you know, 139 00:08:25,916 --> 00:08:30,116 Speaker 1: the agency's main job is to save people's lives, Okay, 140 00:08:30,156 --> 00:08:34,236 Speaker 1: it is not to optimize businesses. Right. That also seems reasonable, 141 00:08:34,276 --> 00:08:37,996 Speaker 1: like absolutely, that's what I want them to be optimized for. Absolutely, 142 00:08:38,036 --> 00:08:41,876 Speaker 1: we're on the same page here. But with some scientific 143 00:08:41,916 --> 00:08:46,156 Speaker 1: improvement you can help businesses have better outcome, improve their 144 00:08:46,196 --> 00:08:50,876 Speaker 1: top line, their bottom line, their safety, their efficiency. So 145 00:08:51,156 --> 00:08:53,236 Speaker 1: there is a lot of room for improvement. The other 146 00:08:53,316 --> 00:08:55,996 Speaker 1: element of it is that remember that I said, you 147 00:08:56,036 --> 00:08:59,916 Speaker 1: know it's it's one thing to handle the forecast. The 148 00:08:59,956 --> 00:09:04,036 Speaker 1: second thing is that once you improve either on the observation, 149 00:09:04,156 --> 00:09:06,276 Speaker 1: on the modeling, on the computing power, and you get 150 00:09:06,276 --> 00:09:10,116 Speaker 1: to more accurate forecasts, there is the are part of it, 151 00:09:10,276 --> 00:09:12,156 Speaker 1: which is how you make decisions and how do you 152 00:09:12,156 --> 00:09:15,996 Speaker 1: do it at scale? Right, So this is something that 153 00:09:16,036 --> 00:09:20,236 Speaker 1: the government almost doesn't address at all, except maybe like 154 00:09:20,276 --> 00:09:22,836 Speaker 1: when you need to evacuate a city for a hurricane 155 00:09:22,916 --> 00:09:28,716 Speaker 1: or something, right, very rare circumstances, exactly exactly. Now, I'll 156 00:09:28,716 --> 00:09:32,116 Speaker 1: give an example. A lot of company like utilities or airlines. 157 00:09:32,716 --> 00:09:35,316 Speaker 1: They work in a very similar way to the way 158 00:09:35,356 --> 00:09:38,316 Speaker 1: that I described in my military service. Some guy calling 159 00:09:38,396 --> 00:09:40,196 Speaker 1: some other guy on the phone and being like, what 160 00:09:40,236 --> 00:09:42,916 Speaker 1: should we do. You go to a metrologist, You speak 161 00:09:42,956 --> 00:09:45,316 Speaker 1: to that meteorologist or get a report of road data, 162 00:09:45,356 --> 00:09:47,636 Speaker 1: and then you have to do a full analysis of 163 00:09:47,636 --> 00:09:49,956 Speaker 1: what it means and make a decision it's not scalable. 164 00:09:50,476 --> 00:09:53,076 Speaker 1: Or if I have thousands of trucks driving in the country, 165 00:09:53,196 --> 00:09:56,236 Speaker 1: or if I have thousands of miles of railways track, 166 00:09:56,796 --> 00:09:59,276 Speaker 1: or if I have many airplanes in the air and 167 00:09:59,316 --> 00:10:02,476 Speaker 1: I care about hundreds of airports globally, it's very hard 168 00:10:02,516 --> 00:10:07,476 Speaker 1: to rely on five or ten even meteorologists on staff. Right. 169 00:10:09,676 --> 00:10:13,036 Speaker 1: I've heard you say that a mistake you made early 170 00:10:13,116 --> 00:10:18,196 Speaker 1: on was optimizing for accuracy, and that's really interesting to me, 171 00:10:18,276 --> 00:10:19,916 Speaker 1: and I want you to tell me what that means. 172 00:10:22,036 --> 00:10:26,236 Speaker 1: So at the beginning, we thought that if we just 173 00:10:26,396 --> 00:10:31,956 Speaker 1: create a more accurate forecast, that's it. It's done. Deal 174 00:10:32,156 --> 00:10:34,596 Speaker 1: with serve like that's a hugely valuable thing, right, even 175 00:10:34,636 --> 00:10:36,796 Speaker 1: if you're a little bit more accurate, that's worth a 176 00:10:36,836 --> 00:10:39,476 Speaker 1: ton of money to an airline or the NFL or 177 00:10:39,556 --> 00:10:43,036 Speaker 1: any any number of really big companies. But what we 178 00:10:43,156 --> 00:10:47,076 Speaker 1: found out, but we learned that most of the businesses 179 00:10:47,116 --> 00:10:51,756 Speaker 1: that are impacted by weather do not know what to 180 00:10:51,796 --> 00:10:55,196 Speaker 1: do with a weather forecast or with a weather data 181 00:10:55,276 --> 00:11:00,516 Speaker 1: and they need the full loop, the translation to insights 182 00:11:00,596 --> 00:11:05,596 Speaker 1: and decisions. And that's what helped us design our platform 183 00:11:05,956 --> 00:11:09,436 Speaker 1: and the way we're operating today. So nobody understands hands 184 00:11:09,476 --> 00:11:12,796 Speaker 1: how to read a weather forecast. Basically, how do you 185 00:11:12,796 --> 00:11:15,916 Speaker 1: think from a business perspective? Yeah, so you realize from 186 00:11:15,916 --> 00:11:19,036 Speaker 1: that that like, providing these people with a better weather 187 00:11:19,036 --> 00:11:21,316 Speaker 1: forecast isn't actually going to help them solve their problem 188 00:11:21,356 --> 00:11:25,276 Speaker 1: because they don't because they're not experts in analyzing the 189 00:11:25,356 --> 00:11:28,636 Speaker 1: meaning of a weather forecast. Yeah. I mean you've named 190 00:11:28,636 --> 00:11:31,276 Speaker 1: a lot of your clients publicly, right, I mean whatever, 191 00:11:31,356 --> 00:11:34,796 Speaker 1: Delta and jet Blue and what Uber and the NFL. 192 00:11:34,836 --> 00:11:36,796 Speaker 1: I guess the NFL isn't playing now, Like what do 193 00:11:36,836 --> 00:11:39,076 Speaker 1: you telling JetBlue today? Like what do they want to 194 00:11:39,116 --> 00:11:43,476 Speaker 1: know today? So you know, it's almost summertime. In the 195 00:11:43,516 --> 00:11:46,796 Speaker 1: summer time, as you know, in the biggest hubs like 196 00:11:46,996 --> 00:11:52,236 Speaker 1: JFK or Boston Logan, you have disruptions related to lightning 197 00:11:52,316 --> 00:11:56,196 Speaker 1: strikes thunderstorms. Well, well exactly, they'll shut the airport down 198 00:11:56,276 --> 00:11:59,036 Speaker 1: for hours and everything will be a total mess exactly. 199 00:11:59,196 --> 00:12:02,156 Speaker 1: So instead of someone looking at a model and whatever, 200 00:12:02,476 --> 00:12:06,716 Speaker 1: we're just basically providing a weekly calendar that says, expect 201 00:12:06,756 --> 00:12:09,556 Speaker 1: the disruptions between dead time to dead time. Here are 202 00:12:09,596 --> 00:12:13,436 Speaker 1: the recommendations to do. ABC staff more people here, staffless 203 00:12:13,476 --> 00:12:17,996 Speaker 1: people there. So we actually go into the operational recommendations 204 00:12:18,156 --> 00:12:21,036 Speaker 1: as a result of the expected disruption as a result 205 00:12:21,076 --> 00:12:23,876 Speaker 1: of the weather forecast. Now, listen carefully to what I'm saying. 206 00:12:24,156 --> 00:12:26,676 Speaker 1: It's as a result to the weather forecast. So if 207 00:12:26,756 --> 00:12:31,076 Speaker 1: you're not relying on an accurate forecast, the business insight 208 00:12:31,236 --> 00:12:35,076 Speaker 1: is useless. It's actually damaging. So there's no way to 209 00:12:35,196 --> 00:12:41,676 Speaker 1: get around the need to improve the accuracy. Specifically, what 210 00:12:41,876 --> 00:12:46,556 Speaker 1: are you better at forecasting than anybody else right now? 211 00:12:47,476 --> 00:12:54,036 Speaker 1: Precipitation data. We provide global real time and now casting 212 00:12:54,356 --> 00:12:58,276 Speaker 1: data that is providing a kind of like minute by 213 00:12:58,356 --> 00:13:01,356 Speaker 1: minute forecast for a range of about six hours on 214 00:13:01,476 --> 00:13:05,796 Speaker 1: every point on Earth, which is quite useful. For example, 215 00:13:06,436 --> 00:13:09,636 Speaker 1: you have some sixty minutes minute by minute works that 216 00:13:09,756 --> 00:13:12,076 Speaker 1: you have on some phones, but it's only in the 217 00:13:12,236 --> 00:13:14,996 Speaker 1: US and in the UK. Yes, I do find I 218 00:13:15,116 --> 00:13:17,356 Speaker 1: have that on my phone and I find it's pretty good. 219 00:13:17,916 --> 00:13:19,996 Speaker 1: Dark Skies. I have Dark Skies on my phone and 220 00:13:20,076 --> 00:13:22,036 Speaker 1: it's good. But you're saying, if I if I left 221 00:13:22,116 --> 00:13:25,356 Speaker 1: the US, if I went on vacation to Mexico or something, 222 00:13:25,436 --> 00:13:29,476 Speaker 1: it just wouldn't work. It's not available. It's just not available. 223 00:13:30,036 --> 00:13:32,996 Speaker 1: And we created this thing on a global scale with 224 00:13:33,276 --> 00:13:36,316 Speaker 1: longer time horizon. That's one example. And the other example 225 00:13:36,476 --> 00:13:39,916 Speaker 1: is like quind, we forecast twind in higher accuracy for 226 00:13:40,036 --> 00:13:41,876 Speaker 1: the next day, two days, three days, which is very 227 00:13:41,956 --> 00:13:44,596 Speaker 1: useful for farms. I think, so okay, and are you 228 00:13:44,716 --> 00:13:47,956 Speaker 1: just better at that because you're more focused on it 229 00:13:48,036 --> 00:13:50,396 Speaker 1: and you've trained the models more than and it's more 230 00:13:50,436 --> 00:13:52,516 Speaker 1: important to your clients than it is to say, a 231 00:13:52,596 --> 00:13:55,556 Speaker 1: government agency, so you have an incentive to figure it 232 00:13:55,596 --> 00:13:59,236 Speaker 1: out exactly that. I'm sure that if Noah wanted to 233 00:13:59,556 --> 00:14:03,196 Speaker 1: double down on that, specifically Noah the government agency, they 234 00:14:03,236 --> 00:14:05,076 Speaker 1: would have been able to do that. But they have 235 00:14:05,196 --> 00:14:08,396 Speaker 1: no incentive, and you know, the pace of making a 236 00:14:08,476 --> 00:14:13,636 Speaker 1: decision in a large organization, it's just not enabling them 237 00:14:13,716 --> 00:14:17,836 Speaker 1: to move fast enough. The next problem Shimona's colleagues are 238 00:14:17,836 --> 00:14:20,556 Speaker 1: trying to solve, how do you predict the weather for 239 00:14:20,716 --> 00:14:23,236 Speaker 1: people who live in countries that can't afford a big 240 00:14:23,396 --> 00:14:27,196 Speaker 1: national weather service like Noah to do that. Tomorrow, dot 241 00:14:27,276 --> 00:14:36,876 Speaker 1: Io is going to go to space. That's the end 242 00:14:36,916 --> 00:14:39,836 Speaker 1: of the ads. Now we're going back to the show. Tomorrow. 243 00:14:39,916 --> 00:14:44,036 Speaker 1: Dot Io's next big project is putting a constellation of 244 00:14:44,116 --> 00:14:47,316 Speaker 1: weather satellites into space. And there are two big questions 245 00:14:47,396 --> 00:14:50,716 Speaker 1: I had about that. What problem will it solve and 246 00:14:51,036 --> 00:14:53,596 Speaker 1: what's it going to take to make it happen. So 247 00:14:54,436 --> 00:14:56,436 Speaker 1: the first thing I'll say, what motivated us to get 248 00:14:56,476 --> 00:15:00,476 Speaker 1: to space. The main motivation was how do we optimize 249 00:15:00,516 --> 00:15:03,316 Speaker 1: forecast and make it more accurate? And when we looked 250 00:15:03,356 --> 00:15:07,716 Speaker 1: at the blend between okay, we have observations, we have models, 251 00:15:08,356 --> 00:15:11,356 Speaker 1: we found out that the lack of observations on a 252 00:15:11,396 --> 00:15:15,196 Speaker 1: global scale are the main reason why we cannot improve 253 00:15:15,236 --> 00:15:19,356 Speaker 1: whether focussing significantly on a global scale. So the problem 254 00:15:19,476 --> 00:15:21,836 Speaker 1: wasn't the models. The problem wasn't the computing power. The 255 00:15:21,876 --> 00:15:24,676 Speaker 1: problem is just there's just not enough data when you 256 00:15:24,716 --> 00:15:27,276 Speaker 1: get outside of what outside of the US, Europe, Japan, 257 00:15:27,436 --> 00:15:31,396 Speaker 1: basically the data quality falls off. Yeah, okay. And the 258 00:15:31,556 --> 00:15:36,036 Speaker 1: most important weather sensor that we identified and I think 259 00:15:36,236 --> 00:15:40,316 Speaker 1: is agreed on all the community from NOAH to NASA 260 00:15:40,396 --> 00:15:44,356 Speaker 1: to others, is Doppler radar. A Toppler radar, just to 261 00:15:44,396 --> 00:15:45,636 Speaker 1: be clear, is it the one where you see a 262 00:15:45,676 --> 00:15:47,876 Speaker 1: color like if it's raining really hard, it's red or 263 00:15:47,916 --> 00:15:52,436 Speaker 1: something that stopple? Correct? Okay, Now, radars are looking out 264 00:15:52,476 --> 00:15:54,596 Speaker 1: in the sky and they help us know where is 265 00:15:54,636 --> 00:15:58,356 Speaker 1: the training in real time, how the cloud formation looks like. 266 00:15:58,516 --> 00:16:01,316 Speaker 1: It gives you some kind of three D description of 267 00:16:01,356 --> 00:16:06,676 Speaker 1: the atmosphere. Okay, now what we found out is that 268 00:16:06,996 --> 00:16:11,796 Speaker 1: five billion people leave outside of radar coverage. Five billion. 269 00:16:11,916 --> 00:16:14,756 Speaker 1: He goes out of the border to Mexico, all the 270 00:16:14,876 --> 00:16:19,196 Speaker 1: way to southern South America, and basically you don't know 271 00:16:19,236 --> 00:16:22,156 Speaker 1: where it's raining in real time, say for Africa, India 272 00:16:22,316 --> 00:16:24,956 Speaker 1: and many other places. That is surprising to me. Maybe 273 00:16:24,996 --> 00:16:29,396 Speaker 1: I'm naive, but like, were you surprised when you learned that, No, 274 00:16:29,596 --> 00:16:31,156 Speaker 1: because I came from a place where it was not 275 00:16:31,676 --> 00:16:34,596 Speaker 1: Oh you didn't have it either. You didn't have it either. Yeah, 276 00:16:34,636 --> 00:16:36,996 Speaker 1: it was pretty broken most of the time. And it's 277 00:16:37,316 --> 00:16:40,396 Speaker 1: not a new technology, right, it's a decades old. It's 278 00:16:40,436 --> 00:16:43,396 Speaker 1: not a new technology. But the implication of not having 279 00:16:43,476 --> 00:16:48,596 Speaker 1: it is huge. You cannot provide flood alerts. Pilots when 280 00:16:48,636 --> 00:16:51,436 Speaker 1: they fly, for example, to Cancun, they don't know the 281 00:16:51,596 --> 00:16:54,436 Speaker 1: weather in the route. It's a huge problem for the economy. 282 00:16:54,836 --> 00:16:57,716 Speaker 1: The next point is that the oceans and the seas 283 00:16:58,636 --> 00:17:02,356 Speaker 1: are not covered with radars, and every time, for example, 284 00:17:02,396 --> 00:17:05,556 Speaker 1: a hurricane is formed over the Atlantic. The US government 285 00:17:05,756 --> 00:17:08,196 Speaker 1: is flying airplanes over the eye of the storm to 286 00:17:08,396 --> 00:17:10,716 Speaker 1: scan it with a radar so we can send it 287 00:17:10,796 --> 00:17:12,276 Speaker 1: back to the model that as an understand if it's 288 00:17:12,276 --> 00:17:14,436 Speaker 1: going to be category one, two or three, when and 289 00:17:14,516 --> 00:17:16,516 Speaker 1: where it's going to eat, and whether we should evacuate 290 00:17:16,596 --> 00:17:21,436 Speaker 1: Miami or New Orleans. So rest assured, nobody's flying any 291 00:17:21,516 --> 00:17:24,276 Speaker 1: airplane over a typhoon or a cyclone in the East. 292 00:17:24,436 --> 00:17:26,796 Speaker 1: So this is a huge in Asia. In Asia, they're 293 00:17:26,876 --> 00:17:28,876 Speaker 1: not going out in Asia to get a really accurate 294 00:17:28,956 --> 00:17:31,116 Speaker 1: forecast of where it's going to go. They can't afford 295 00:17:31,276 --> 00:17:33,516 Speaker 1: to do that. But you can do it from space. 296 00:17:33,676 --> 00:17:36,476 Speaker 1: Is that where this is going so exactly, So we 297 00:17:36,636 --> 00:17:40,476 Speaker 1: realize that from a geopolitical and cost effective way and 298 00:17:40,556 --> 00:17:42,476 Speaker 1: all kind of the only way to solve it is 299 00:17:42,516 --> 00:17:45,356 Speaker 1: to go to space. The problem is that radars are 300 00:17:45,396 --> 00:17:48,916 Speaker 1: pretty big. We actually the world has one radar in 301 00:17:49,036 --> 00:17:53,356 Speaker 1: space today. It is called the GPM. It's a program 302 00:17:53,516 --> 00:17:57,436 Speaker 1: by NASA with the collaboration of the Japanese agency. It's 303 00:17:57,556 --> 00:18:00,596 Speaker 1: more than a billion dollar program that created one radar 304 00:18:00,676 --> 00:18:04,676 Speaker 1: in space, a very sophisticated one. We have one radar 305 00:18:04,756 --> 00:18:08,036 Speaker 1: in space today. That radar cost about a billion dollar 306 00:18:08,156 --> 00:18:11,916 Speaker 1: if not more, and it samples every point on Earth 307 00:18:12,036 --> 00:18:14,956 Speaker 1: every three days, So it's not very useful for hurricane 308 00:18:15,036 --> 00:18:18,996 Speaker 1: forecasting because imagine you just sample the hurricanes moving too fast, 309 00:18:19,356 --> 00:18:22,516 Speaker 1: or general weather forecasting. So what we were trying to 310 00:18:22,636 --> 00:18:26,476 Speaker 1: do was to say, how can we take this huge 311 00:18:26,596 --> 00:18:29,596 Speaker 1: radar and minimize it so we can put many of them. 312 00:18:30,716 --> 00:18:32,876 Speaker 1: But we are a small company, we don't have a 313 00:18:32,956 --> 00:18:35,836 Speaker 1: billion dollar How can we actually do it in a 314 00:18:35,916 --> 00:18:39,436 Speaker 1: way that will be cost effective? And the goal is, 315 00:18:39,636 --> 00:18:42,596 Speaker 1: of course, to monitor every point on Earth with a 316 00:18:42,716 --> 00:18:46,156 Speaker 1: radar in almost real time, because when you do that, 317 00:18:47,036 --> 00:18:51,596 Speaker 1: you are going to improve weather forecasting dramatically. You're going 318 00:18:51,676 --> 00:18:54,956 Speaker 1: to improve hurricane cyclone typhoons, you are going to be 319 00:18:55,236 --> 00:18:58,676 Speaker 1: able to provide flood alerts for every point on Earth. 320 00:18:59,436 --> 00:19:02,476 Speaker 1: And it will improve also climate science because now climate 321 00:19:02,516 --> 00:19:05,516 Speaker 1: scientists will have better understanding of what actually happen. No, 322 00:19:05,676 --> 00:19:07,596 Speaker 1: I'm sold on why it would be useful. It seems 323 00:19:07,636 --> 00:19:09,756 Speaker 1: like the hard thing is how do you do it exactly? 324 00:19:09,876 --> 00:19:12,956 Speaker 1: So how do we do that? The first thing we 325 00:19:13,076 --> 00:19:15,236 Speaker 1: did was to focus on the sensor. How can we 326 00:19:15,316 --> 00:19:19,116 Speaker 1: build a sensor that we'll keep most, if not all, 327 00:19:19,156 --> 00:19:23,916 Speaker 1: the characteristics of the radar. We looked at and how 328 00:19:23,996 --> 00:19:26,996 Speaker 1: can we make it small enough so we can launch 329 00:19:27,036 --> 00:19:31,596 Speaker 1: it a not a nano or micro satellite, but something 330 00:19:32,156 --> 00:19:35,676 Speaker 1: smaller than you know, the stationary satellites, a low orbit. 331 00:19:36,236 --> 00:19:40,236 Speaker 1: And bottom line, we've finished the development of the radar 332 00:19:40,836 --> 00:19:42,636 Speaker 1: and in a few months we're going to launch the 333 00:19:42,676 --> 00:19:46,196 Speaker 1: first satellite out of a constellation of about thirty And 334 00:19:46,316 --> 00:19:49,756 Speaker 1: our constellation is going to have two types of sensors. 335 00:19:49,916 --> 00:19:52,196 Speaker 1: One is the radar, the second is a microwave sounder. 336 00:19:52,716 --> 00:19:55,676 Speaker 1: The combination of the two is going to provide a 337 00:19:55,876 --> 00:19:59,156 Speaker 1: very good scientific result for every point on Earth. You 338 00:19:59,276 --> 00:20:02,036 Speaker 1: sound very confident, like are you at a point where 339 00:20:02,076 --> 00:20:03,476 Speaker 1: you know it's going to work or is it the 340 00:20:03,556 --> 00:20:05,636 Speaker 1: kind of thing that you hope is going to work. No, 341 00:20:05,796 --> 00:20:07,836 Speaker 1: we know it's going to work. The question is, okay, 342 00:20:07,916 --> 00:20:10,076 Speaker 1: will it take us more time? Will we fail in 343 00:20:10,116 --> 00:20:12,356 Speaker 1: the first lunch? Will we need to reiterate between one 344 00:20:12,436 --> 00:20:15,516 Speaker 1: lunch to a number? But it is feasible, it is working. 345 00:20:15,876 --> 00:20:17,796 Speaker 1: It is And how much is it going to cost 346 00:20:17,836 --> 00:20:21,316 Speaker 1: you to get roughly thirty satellites up and monitoring the weather. 347 00:20:21,916 --> 00:20:29,476 Speaker 1: Our early estimations, which so far given the inflation, are 348 00:20:29,556 --> 00:20:32,636 Speaker 1: still are still in the same ballpark. We're looking at 349 00:20:33,156 --> 00:20:36,676 Speaker 1: around one hundred million dollars for the entire constellation. So 350 00:20:36,876 --> 00:20:39,196 Speaker 1: that's a big cost reduction. That's compared to what is 351 00:20:39,276 --> 00:20:41,916 Speaker 1: it saying a billion for an existing one that only 352 00:20:41,996 --> 00:20:45,276 Speaker 1: does once every three days? Yeah, what are the things 353 00:20:45,316 --> 00:20:47,156 Speaker 1: that might go wrong? I mean, it seems like a 354 00:20:47,276 --> 00:20:50,116 Speaker 1: quite hard thing that you're trying to do. I feel like, 355 00:20:50,276 --> 00:20:51,916 Speaker 1: as you're describing it, it's like, oh, yeah, now, all 356 00:20:51,956 --> 00:20:54,156 Speaker 1: we got to do is get these thirty satellites up 357 00:20:54,156 --> 00:20:56,676 Speaker 1: into space and we're going to go But so imagine 358 00:20:56,716 --> 00:20:58,596 Speaker 1: it's still going to be quite hard and lots of 359 00:20:58,636 --> 00:21:00,916 Speaker 1: things can go wrong, of course, So you want me 360 00:21:00,956 --> 00:21:03,316 Speaker 1: to give you examples of things that can go wrong? Yeah, 361 00:21:03,396 --> 00:21:06,676 Speaker 1: what are you worried about? Okay, the rocket can explode 362 00:21:06,716 --> 00:21:10,476 Speaker 1: in lunch. Sure. Classic second thing is that you know, 363 00:21:10,916 --> 00:21:14,636 Speaker 1: we may have some communication malfunction. We may have some 364 00:21:16,436 --> 00:21:19,276 Speaker 1: when we build our satellites and the radars. We may 365 00:21:19,756 --> 00:21:23,516 Speaker 1: have to wait for longer than expected for chips to 366 00:21:23,636 --> 00:21:26,756 Speaker 1: arrive or all kinds of chips. Radiance like supply chain, 367 00:21:26,836 --> 00:21:29,916 Speaker 1: supply chain, supply chain issues is something pretty big right now. 368 00:21:30,636 --> 00:21:34,636 Speaker 1: There are so many things that can happen. But are 369 00:21:34,716 --> 00:21:36,436 Speaker 1: you sure the thing you built is going to work? 370 00:21:36,516 --> 00:21:38,356 Speaker 1: All the things you've described as like, oh yeah, the 371 00:21:38,436 --> 00:21:40,516 Speaker 1: rocket could blow up, that's not really our faulter, the 372 00:21:40,596 --> 00:21:43,276 Speaker 1: chip oncome, that's not really our fault. Like, is it 373 00:21:43,476 --> 00:21:44,716 Speaker 1: is it at the point where it's like, oh, yes, 374 00:21:44,796 --> 00:21:46,716 Speaker 1: this will definitely work. Is it like that or is 375 00:21:46,716 --> 00:21:50,116 Speaker 1: it possible that Okay, it's gonna work. It's gonna work. 376 00:21:50,316 --> 00:21:52,996 Speaker 1: The question is is it going to be more expensive 377 00:21:53,076 --> 00:21:55,796 Speaker 1: than we thought, It's going to take longer, and there 378 00:21:55,876 --> 00:22:00,876 Speaker 1: might be you know, business implications on tomorrow. But it 379 00:22:01,116 --> 00:22:04,476 Speaker 1: is going to work. It's not a question of science 380 00:22:04,756 --> 00:22:07,396 Speaker 1: business like like might you run out of money before 381 00:22:07,436 --> 00:22:09,676 Speaker 1: you can get it going? When you say business, everything 382 00:22:09,756 --> 00:22:12,876 Speaker 1: can happen in that context. But this thing is working. 383 00:22:13,756 --> 00:22:16,356 Speaker 1: Pending one hundred million to put a fleet of satellites 384 00:22:16,356 --> 00:22:19,876 Speaker 1: into space, is it's still a lot for your business? 385 00:22:20,196 --> 00:22:23,476 Speaker 1: It is a lot. And the market is very bad. 386 00:22:23,516 --> 00:22:25,996 Speaker 1: It's probably the toughest market in the last twenty years 387 00:22:26,556 --> 00:22:30,676 Speaker 1: for tech companies. The market for raising funding you mean, yeah, yeah. 388 00:22:31,116 --> 00:22:36,276 Speaker 1: The investors are not very happy to see businesses that 389 00:22:36,916 --> 00:22:39,836 Speaker 1: waste money or spend money or invest money, depending on 390 00:22:39,916 --> 00:22:44,036 Speaker 1: how you look at it to build a solution, and 391 00:22:44,156 --> 00:22:49,116 Speaker 1: it's definitely a challenge, and I just hope that, you know, 392 00:22:49,276 --> 00:22:53,876 Speaker 1: the investment community will keep supporting us. We'll get to 393 00:22:53,916 --> 00:22:55,836 Speaker 1: the lightning round in a minute, but before we do, 394 00:22:56,356 --> 00:22:58,756 Speaker 1: I just want to say that what Shimone talked about 395 00:22:58,796 --> 00:23:01,916 Speaker 1: in this episode is actually a really good example of 396 00:23:01,996 --> 00:23:04,796 Speaker 1: a big idea that came up in an earlier episode 397 00:23:04,916 --> 00:23:07,836 Speaker 1: of the show. It was the episode where I interviewed 398 00:23:07,876 --> 00:23:10,476 Speaker 1: the founder of the company Rocket Left, and I was 399 00:23:10,556 --> 00:23:13,596 Speaker 1: going on about how making rockets and satellites cheaper was 400 00:23:13,676 --> 00:23:15,716 Speaker 1: a big deal, and he made the point that the 401 00:23:15,796 --> 00:23:19,036 Speaker 1: big breakthrough is not just that they're cheaper. It's that 402 00:23:19,276 --> 00:23:23,276 Speaker 1: cheaper rockets and satellites enable people to do big new things, 403 00:23:23,556 --> 00:23:27,396 Speaker 1: things that just did not get done before. And Shimone's 404 00:23:27,396 --> 00:23:31,076 Speaker 1: plan tomorrow dot Io's plan is a perfect example of 405 00:23:31,196 --> 00:23:34,196 Speaker 1: that idea. Even a decade ago, it would have been 406 00:23:34,276 --> 00:23:38,676 Speaker 1: prohibitively expensive, but today it's possible to put a constellation 407 00:23:38,756 --> 00:23:42,356 Speaker 1: of satellites into orbit to improve forecasts everywhere on the 408 00:23:42,476 --> 00:23:45,396 Speaker 1: globe for a price that is affordable for a startup. 409 00:23:45,836 --> 00:23:47,636 Speaker 1: As long as they can get a few more years 410 00:23:47,676 --> 00:23:50,556 Speaker 1: of funding, we'll have the lightning round with Shimone in 411 00:23:50,676 --> 00:24:01,116 Speaker 1: just a minute. Now, let's get back to the show. Okay, 412 00:24:01,436 --> 00:24:03,036 Speaker 1: I know you have to go soon, but let's do 413 00:24:03,196 --> 00:24:06,396 Speaker 1: a quick lightning round. What is one piece of advice 414 00:24:06,476 --> 00:24:08,836 Speaker 1: you'd give to someone trying to solve a hard problem. 415 00:24:09,556 --> 00:24:12,956 Speaker 1: Focus on the problem and not on a solution. The 416 00:24:13,036 --> 00:24:16,156 Speaker 1: solution will be obsolete. There are many kinds of solutions, 417 00:24:16,196 --> 00:24:18,836 Speaker 1: but if you're focused on the problem, you're going to 418 00:24:18,916 --> 00:24:22,436 Speaker 1: objectively look at what's right, what's wrong, and you'll be 419 00:24:22,476 --> 00:24:25,396 Speaker 1: able to ditch something that doesn't work and find out 420 00:24:25,476 --> 00:24:27,956 Speaker 1: something that is better. Focus on the problem. What do 421 00:24:28,036 --> 00:24:31,476 Speaker 1: you prefer really hot weather or really cold weather? Hot? 422 00:24:31,956 --> 00:24:36,156 Speaker 1: Okay hot? Could there really be a shark nado like 423 00:24:36,396 --> 00:24:40,956 Speaker 1: in the movie Shark Nado? I don't know. What's the 424 00:24:41,036 --> 00:24:48,276 Speaker 1: most underrated weather hazard? Most underrated heatwave? Lots of people 425 00:24:48,636 --> 00:24:52,436 Speaker 1: die from heatwave annually, strokes, health issues, heart attacks. I 426 00:24:52,516 --> 00:24:55,076 Speaker 1: actually testified in front of the Congress in the summer 427 00:24:55,436 --> 00:24:59,716 Speaker 1: of twenty one on this topic, specifically, other than weather, 428 00:25:00,116 --> 00:25:05,836 Speaker 1: what's the domain where people should use probabilistic thinking more finance, 429 00:25:05,956 --> 00:25:08,636 Speaker 1: for sure? I mean, how do you manage your investments? 430 00:25:09,396 --> 00:25:13,476 Speaker 1: Although I feel like bad. Use of probabilistic thinking was 431 00:25:13,516 --> 00:25:16,436 Speaker 1: a major problem in the run up to the financial 432 00:25:16,476 --> 00:25:18,116 Speaker 1: crisis of two thousand and eight. I don't know if 433 00:25:18,156 --> 00:25:21,796 Speaker 1: you remember, but people kept saying like this is a 434 00:25:21,916 --> 00:25:26,996 Speaker 1: one in ten thousand year move in whatever you know race, Like, 435 00:25:27,316 --> 00:25:30,636 Speaker 1: clearly it's not your model is wrong? Right, yeah? Yeah, 436 00:25:30,836 --> 00:25:34,556 Speaker 1: I'm thinking more in a personal level. On a personal level, 437 00:25:34,636 --> 00:25:37,356 Speaker 1: like a household, how can a household manage their risk 438 00:25:37,436 --> 00:25:40,396 Speaker 1: and everything? I think they should think about all the scenarios, 439 00:25:40,436 --> 00:25:43,196 Speaker 1: all the probabilities, and I think people don't do that enough. 440 00:25:43,876 --> 00:25:47,276 Speaker 1: So whether it's like this classic way to make small talk, 441 00:25:47,436 --> 00:25:49,316 Speaker 1: you know, when you don't want to talk about work, right, 442 00:25:49,756 --> 00:25:52,076 Speaker 1: So what do you talk about when you want to 443 00:25:52,076 --> 00:25:54,836 Speaker 1: make small talk and don't want to talk about work? Oh? Football, 444 00:25:54,916 --> 00:25:59,316 Speaker 1: I mean soccer? I guess that's the other classic, right sports? Yeah? Yeah, 445 00:25:59,876 --> 00:26:02,236 Speaker 1: but I'm very passionate about it for real. I mean 446 00:26:02,236 --> 00:26:03,876 Speaker 1: I can we could talk about it for an hour. 447 00:26:04,036 --> 00:26:05,556 Speaker 1: Who's your team? What do you say? Who's your club? 448 00:26:05,676 --> 00:26:09,556 Speaker 1: Who's your club? My club is in Israelity team called 449 00:26:09,996 --> 00:26:16,436 Speaker 1: mac It's uh, how's doing and learning? Will if everything 450 00:26:16,516 --> 00:26:18,596 Speaker 1: goes well, what's a problem You'll be trying to solve 451 00:26:18,636 --> 00:26:24,196 Speaker 1: in five years how to reduce carbon emission with our solution. 452 00:26:24,516 --> 00:26:27,156 Speaker 1: That will be probably the next step and will be 453 00:26:27,316 --> 00:26:31,276 Speaker 1: the most impactful thing we can do. But we'll try, okay. 454 00:26:32,756 --> 00:26:35,916 Speaker 1: Simon Alphabets is the co founder and CEO of tomorrow 455 00:26:35,996 --> 00:26:39,876 Speaker 1: dot Ido. Today's show was produced by Edith Russelo, edited 456 00:26:39,916 --> 00:26:43,236 Speaker 1: by Robert Smith, and engineered by Amanda ka Wong. I'm 457 00:26:43,356 --> 00:26:45,716 Speaker 1: Jacob Goldstein, and I'll be back next week with another 458 00:26:45,756 --> 00:26:47,036 Speaker 1: episode of What's Your Problem.