1 00:00:15,316 --> 00:00:22,676 Speaker 1: Pushkin. One of the strange things about making a podcast 2 00:00:22,836 --> 00:00:25,396 Speaker 1: is you make the show, it goes out in the world. 3 00:00:25,516 --> 00:00:28,356 Speaker 1: Your mom tells you she likes it, but you don't 4 00:00:28,476 --> 00:00:31,676 Speaker 1: really know much about what people think of the show, 5 00:00:31,876 --> 00:00:34,876 Speaker 1: and in particular, you don't know how to make your 6 00:00:34,916 --> 00:00:38,556 Speaker 1: show better. And I really really want to make this 7 00:00:38,596 --> 00:00:40,636 Speaker 1: show better. I want this to be a show that 8 00:00:40,676 --> 00:00:42,876 Speaker 1: you look forward to every week, the show that you 9 00:00:42,956 --> 00:00:46,876 Speaker 1: tell your friends about. And so to that end, please 10 00:00:47,036 --> 00:00:49,276 Speaker 1: please please tell me how to do that. Tell me 11 00:00:49,316 --> 00:00:52,196 Speaker 1: how to make what's your problem a better show, a 12 00:00:52,236 --> 00:00:55,516 Speaker 1: show you like more. If there's some person you want 13 00:00:55,556 --> 00:00:57,396 Speaker 1: me to talk to, or some subject you want me 14 00:00:57,476 --> 00:00:59,916 Speaker 1: to cover, or something you want me to do differently, anything, 15 00:00:59,996 --> 00:01:04,236 Speaker 1: really let me know. You can email me at problem 16 00:01:04,356 --> 00:01:10,116 Speaker 1: at pushkin dot fm. That's problem at pushkin dot fm. 17 00:01:10,316 --> 00:01:12,556 Speaker 1: Or you can talk to me on Twitter. I'm at 18 00:01:12,676 --> 00:01:16,036 Speaker 1: Jacob Goldstein, just my name. To be honest, we don't 19 00:01:16,156 --> 00:01:18,796 Speaker 1: get that many email messages yet, not that many people 20 00:01:18,796 --> 00:01:21,156 Speaker 1: talk to me on Twitter. So I can guarantee you 21 00:01:21,396 --> 00:01:24,996 Speaker 1: I will personally read every email and Twitter message you 22 00:01:25,036 --> 00:01:31,636 Speaker 1: send with gratitude. Thank you. Now here's the show. Here's 23 00:01:31,676 --> 00:01:36,356 Speaker 1: a big, wild idea what if scientists and engineers could 24 00:01:36,356 --> 00:01:40,316 Speaker 1: turn yeast in bacteria into tiny factories that would manufacture 25 00:01:40,356 --> 00:01:44,996 Speaker 1: everything from perfume to food to fuel. This idea has 26 00:01:45,036 --> 00:01:48,716 Speaker 1: a name synthetic biology. People have been working on it 27 00:01:48,796 --> 00:01:51,676 Speaker 1: for more than a decade now, and it's really hard. 28 00:01:52,276 --> 00:01:55,196 Speaker 1: First you have to engineer DNA and then you stick 29 00:01:55,236 --> 00:01:57,956 Speaker 1: it into yeast or bacteria in order to make the 30 00:01:57,956 --> 00:02:00,836 Speaker 1: thing you want, the fragrance or the fuel or the food. 31 00:02:01,436 --> 00:02:02,876 Speaker 1: And then you have to figure out how to do 32 00:02:02,916 --> 00:02:04,396 Speaker 1: it at scale, how to make a lot of it, 33 00:02:04,756 --> 00:02:07,276 Speaker 1: and also you have to make it cheaper or better 34 00:02:07,356 --> 00:02:11,036 Speaker 1: than what's already out there. Really, the dream of synthetic 35 00:02:11,036 --> 00:02:14,876 Speaker 1: biology is like a whole new industrial revolution, but using 36 00:02:14,916 --> 00:02:21,516 Speaker 1: cells instead of machines. I'm Jacob Goldstein and this is 37 00:02:21,516 --> 00:02:24,796 Speaker 1: What's Your Problem, the show where entrepreneurs and engineers talk 38 00:02:24,796 --> 00:02:27,236 Speaker 1: about the world they're going to build once they solve 39 00:02:27,276 --> 00:02:31,716 Speaker 1: a few problems. My guest today is Reshma Shetty. She's 40 00:02:31,756 --> 00:02:35,596 Speaker 1: the co founder and chief operations officer of Ginko Bioworks. 41 00:02:35,796 --> 00:02:38,916 Speaker 1: It's one of the biggest synthetic biology companies in the world. 42 00:02:40,436 --> 00:02:43,676 Speaker 1: One big obvious problem that Ginko faces the company has 43 00:02:43,716 --> 00:02:47,636 Speaker 1: lost billions of dollars trying to turn synthetic biology into 44 00:02:47,676 --> 00:02:51,116 Speaker 1: a viable business. So the problem for today's show is this, 45 00:02:51,756 --> 00:02:54,476 Speaker 1: how do you try and do this big, hard thing 46 00:02:54,556 --> 00:02:58,156 Speaker 1: way out there on the scientific frontier and also eventually 47 00:02:58,276 --> 00:03:03,956 Speaker 1: make enough money to create a financially sustainable business. We 48 00:03:04,036 --> 00:03:07,076 Speaker 1: started our conversation talking about the original idea for Ginko, 49 00:03:07,396 --> 00:03:09,476 Speaker 1: which came to Reshma and her co founder when they 50 00:03:09,476 --> 00:03:12,716 Speaker 1: were grad students at MIT dreaming about the big things 51 00:03:12,756 --> 00:03:16,236 Speaker 1: they wanted to do with synthetic biology. So you start 52 00:03:16,236 --> 00:03:19,356 Speaker 1: a company. We did. We did. We started a company 53 00:03:19,796 --> 00:03:25,436 Speaker 1: because we worked at a place MT, like most academic institutions, 54 00:03:25,596 --> 00:03:28,916 Speaker 1: really focuses on doing new things. Right. Can you invent 55 00:03:28,996 --> 00:03:31,876 Speaker 1: something new? Can you discover something new so that you 56 00:03:31,876 --> 00:03:34,476 Speaker 1: can publish on it, Right, And that's what academia is 57 00:03:34,516 --> 00:03:36,756 Speaker 1: built to do. And that's a really great thing because 58 00:03:36,756 --> 00:03:38,596 Speaker 1: we should be discovering new things and we should be 59 00:03:38,596 --> 00:03:41,916 Speaker 1: inventing new things. Yeah, but with biology, what we really 60 00:03:41,956 --> 00:03:45,916 Speaker 1: felt was holding us back was not just doing something 61 00:03:45,956 --> 00:03:48,516 Speaker 1: new or inventing something new. It was could you make 62 00:03:48,556 --> 00:03:52,716 Speaker 1: the process of engineering biology faster, cheaper, easier. Yeah. And 63 00:03:52,756 --> 00:03:55,476 Speaker 1: what was interesting is that we started working on that 64 00:03:55,556 --> 00:03:58,796 Speaker 1: problem during grad school, but it just wasn't very celebrated. 65 00:03:58,836 --> 00:04:00,396 Speaker 1: People were like, why do you want to make it 66 00:04:00,476 --> 00:04:02,636 Speaker 1: easier for other people to do this? That seems like 67 00:04:02,676 --> 00:04:05,596 Speaker 1: a dumb thing. Work on it, right, You're just building tools. 68 00:04:05,636 --> 00:04:08,716 Speaker 1: You're not actually doing anything with the tools exactly right. 69 00:04:08,716 --> 00:04:11,796 Speaker 1: And so there's a sort of skepticism even at a 70 00:04:11,876 --> 00:04:16,636 Speaker 1: place like MT around spending your time tool building. Okay, 71 00:04:16,796 --> 00:04:19,276 Speaker 1: And so we decided to start a company, candidly, not 72 00:04:19,396 --> 00:04:21,516 Speaker 1: because I wanted to be a founder. I actually didn't 73 00:04:21,556 --> 00:04:23,636 Speaker 1: want to be a founder. You know, I had never 74 00:04:23,876 --> 00:04:26,836 Speaker 1: dreamt about starting a company before. But we felt that 75 00:04:27,396 --> 00:04:29,396 Speaker 1: starting a company was the best way to work on 76 00:04:29,476 --> 00:04:32,436 Speaker 1: this problem of how do you make biology easier to engineer? 77 00:04:32,756 --> 00:04:35,276 Speaker 1: I read that some of your earliest customers were in 78 00:04:35,356 --> 00:04:38,756 Speaker 1: the in the fragrance industry, and I thought going through 79 00:04:39,236 --> 00:04:42,036 Speaker 1: that work you did might be a case study to understand, 80 00:04:42,076 --> 00:04:43,636 Speaker 1: you know, what the company does and what kind of 81 00:04:43,756 --> 00:04:46,596 Speaker 1: problems come up and how you solve those problems. Can 82 00:04:46,636 --> 00:04:48,796 Speaker 1: you talk me through that work? Yeah? So when we 83 00:04:48,876 --> 00:04:51,596 Speaker 1: first started the company, you know, we didn't have a 84 00:04:51,636 --> 00:04:54,516 Speaker 1: particular technology, we didn't have a business model, we didn't 85 00:04:54,516 --> 00:04:57,356 Speaker 1: have a way of making money, right, it was a 86 00:04:57,396 --> 00:05:01,236 Speaker 1: great way to start a company. I highly recommend. But 87 00:05:01,676 --> 00:05:04,956 Speaker 1: what we did have was that we were kind of 88 00:05:05,036 --> 00:05:08,876 Speaker 1: voracious learners. We kept coming up with wrong hypotheses. Where 89 00:05:08,956 --> 00:05:12,196 Speaker 1: we finally got some traction was actually looking at the 90 00:05:12,236 --> 00:05:14,916 Speaker 1: flavor and fragrance industry. So it turned out that this 91 00:05:15,156 --> 00:05:17,436 Speaker 1: is a huge industry. It's a multibillion dollar industry that 92 00:05:17,476 --> 00:05:19,476 Speaker 1: almost no one's heard of. I certainly hadn't heard of 93 00:05:19,516 --> 00:05:21,996 Speaker 1: it when we started the company. But they make all 94 00:05:22,036 --> 00:05:25,956 Speaker 1: of the flavors and fragrances that go into household goods. Right, 95 00:05:25,996 --> 00:05:28,756 Speaker 1: So if you go browse the DETERSI denial, you know, 96 00:05:28,916 --> 00:05:32,156 Speaker 1: and you can probably smell some of the detergents and 97 00:05:32,316 --> 00:05:36,236 Speaker 1: soaps and shampoos and whatnot that are in your local supermarket, 98 00:05:36,556 --> 00:05:41,476 Speaker 1: you know, those fragrances are all concocted by the flavor 99 00:05:41,556 --> 00:05:43,596 Speaker 1: and fragrance industry. So it turned out that there was 100 00:05:43,636 --> 00:05:45,756 Speaker 1: this pain point in the flavor and fragrance industry where 101 00:05:45,756 --> 00:05:48,556 Speaker 1: they wanted to use biology to manufacture their ingredients, but 102 00:05:48,676 --> 00:05:51,436 Speaker 1: it just you know, it wasn't feasible at scale, and 103 00:05:51,556 --> 00:05:54,396 Speaker 1: so they were really interested in using fermentation. This idea 104 00:05:54,476 --> 00:05:57,716 Speaker 1: that you could grow yeast in a vat to produce 105 00:05:57,756 --> 00:06:00,276 Speaker 1: flavors and fragrances, much like you might brew beer. And 106 00:06:00,396 --> 00:06:03,956 Speaker 1: so cracking that nut was our first sort of realization, Hey, 107 00:06:04,036 --> 00:06:07,396 Speaker 1: there's actually really a market or engineeredsels that could produce 108 00:06:07,476 --> 00:06:10,636 Speaker 1: flavor and fragrance ingredients. Are you allowed to say specifically 109 00:06:10,676 --> 00:06:12,516 Speaker 1: what you did or is it like that? So some 110 00:06:12,636 --> 00:06:15,196 Speaker 1: of our earliest contracts were around things like peach flavor, 111 00:06:15,636 --> 00:06:18,996 Speaker 1: coconut flavor, and so, so what specifically do you do? 112 00:06:19,156 --> 00:06:22,396 Speaker 1: I mean, do you take some jeans from a peach 113 00:06:22,476 --> 00:06:24,596 Speaker 1: and stick them in a yeast and the yeast makes 114 00:06:24,716 --> 00:06:27,836 Speaker 1: peach smell? Is it like that? Yeah, basically, except you're 115 00:06:27,836 --> 00:06:30,276 Speaker 1: not just limited to beach, because it turns out that, 116 00:06:30,876 --> 00:06:32,756 Speaker 1: you know, biology has a lot of reuse and a 117 00:06:32,836 --> 00:06:35,356 Speaker 1: lot of commonality, and so it might be the actually 118 00:06:35,396 --> 00:06:38,036 Speaker 1: the best genes for making a peach flavor might not 119 00:06:38,156 --> 00:06:40,676 Speaker 1: come from a peach. They might come from another species, 120 00:06:40,756 --> 00:06:42,876 Speaker 1: which is kind of fascinating, right, And so you keep 121 00:06:42,916 --> 00:06:46,276 Speaker 1: tweaking it and iterating on it until you're making a 122 00:06:46,396 --> 00:06:50,036 Speaker 1: lot of your ingredient of interest. And so, right now, 123 00:06:50,116 --> 00:06:52,356 Speaker 1: somewhere in the world, is there like a vat full 124 00:06:52,436 --> 00:06:55,716 Speaker 1: of yeast, cranking out peach smell that's going to go 125 00:06:55,796 --> 00:06:59,436 Speaker 1: into like soap or shampoo or something. There absolutely is, yes, 126 00:06:59,996 --> 00:07:03,196 Speaker 1: So where is the company now? What's sort of what 127 00:07:03,436 --> 00:07:06,076 Speaker 1: is the state of Ginko now? The span of what 128 00:07:06,196 --> 00:07:10,036 Speaker 1: we work on is huge, right, everything from food to health, 129 00:07:10,596 --> 00:07:14,356 Speaker 1: to act culture, to flavors and fragrances. So we started 130 00:07:14,396 --> 00:07:17,436 Speaker 1: a joint venture with Buyer, the largest ad company in 131 00:07:17,516 --> 00:07:22,596 Speaker 1: the world, working on essentially a biofertilizer, So being able 132 00:07:22,756 --> 00:07:25,836 Speaker 1: to reduce the amount of fertilizer that we need to 133 00:07:25,996 --> 00:07:30,956 Speaker 1: grow crops like corn by essentially engineering microbes to provide 134 00:07:31,036 --> 00:07:34,436 Speaker 1: the fertilizer to provide the fixed nitrogen rather than fertilizer. 135 00:07:34,716 --> 00:07:37,716 Speaker 1: And if you actually look at it, fertilizer is incredibly 136 00:07:37,796 --> 00:07:40,236 Speaker 1: bad for the environment. The process by which we make 137 00:07:40,276 --> 00:07:42,676 Speaker 1: fertilizer in this world, I don't know, It's like something 138 00:07:42,796 --> 00:07:46,236 Speaker 1: like fertilizer is like six or seven percent greenhouse gas emissions. 139 00:07:46,276 --> 00:07:49,356 Speaker 1: I mean, it's something completely interesting. So it's the production 140 00:07:49,476 --> 00:07:53,196 Speaker 1: of fertilizer emits a lot of carbon dioxide into the atmosphere. 141 00:07:54,116 --> 00:07:57,236 Speaker 1: So if you can if you can reduce the need 142 00:07:57,316 --> 00:08:03,036 Speaker 1: to make fertilizer then that helps exactly solve the problem. Yeah, 143 00:08:03,076 --> 00:08:05,716 Speaker 1: so can you sort of give me a comparison of 144 00:08:05,796 --> 00:08:07,396 Speaker 1: the state of the world as it was when you 145 00:08:07,476 --> 00:08:09,396 Speaker 1: were in grad school versus the state of the world 146 00:08:09,476 --> 00:08:12,316 Speaker 1: that you have created now? In grad school, it probably 147 00:08:12,356 --> 00:08:18,876 Speaker 1: take me nine months or so to basically design, build, 148 00:08:18,996 --> 00:08:24,316 Speaker 1: and test twenty designs at a time, like twenty different Yeah, 149 00:08:24,396 --> 00:08:27,836 Speaker 1: twenty different genes or to many different pathways. Maybe at 150 00:08:27,876 --> 00:08:29,876 Speaker 1: a time that was like state of the art. How 151 00:08:29,956 --> 00:08:34,036 Speaker 1: long would it take Ginko now to try twenty designs. Well, 152 00:08:34,156 --> 00:08:37,236 Speaker 1: we wouldn't ever bother to try twenty designs. We only 153 00:08:37,316 --> 00:08:41,436 Speaker 1: try thousands of designs at a time. Okay, It still 154 00:08:41,476 --> 00:08:43,516 Speaker 1: takes a lot longer than i'd like. It still probably 155 00:08:43,636 --> 00:08:47,116 Speaker 1: takes three months to kind of go through the whole cycle, 156 00:08:47,196 --> 00:08:50,116 Speaker 1: maybe two months if we're lucky. So, if before it 157 00:08:50,316 --> 00:08:54,076 Speaker 1: was you could try twenty designs in nine months, how 158 00:08:54,156 --> 00:08:56,516 Speaker 1: many designs could you try now in two or three months. 159 00:08:56,796 --> 00:08:59,436 Speaker 1: Now I'm trying thousands of designs in two or three months. 160 00:08:59,676 --> 00:09:04,236 Speaker 1: Thousands Okay, So it's thousands of times faster than it 161 00:09:04,436 --> 00:09:06,716 Speaker 1: was when you started. Yeah, and then I can learn 162 00:09:07,036 --> 00:09:09,596 Speaker 1: from which ones worked and which ones didn't and even 163 00:09:09,676 --> 00:09:12,876 Speaker 1: more intelligently do my next round of thousands of designs. 164 00:09:13,116 --> 00:09:15,076 Speaker 1: And so is what you have built sort of like 165 00:09:15,196 --> 00:09:20,196 Speaker 1: a factory for testing? Have you built like a factory lab? Yeah, 166 00:09:20,196 --> 00:09:23,276 Speaker 1: it's it's essentially a factory for cell programming. What's it 167 00:09:23,396 --> 00:09:27,836 Speaker 1: look like. It's a whole bunch of robots sort of 168 00:09:27,956 --> 00:09:32,676 Speaker 1: placed in rows across the lab with these essentially train 169 00:09:32,796 --> 00:09:36,156 Speaker 1: tracks that can move samples between the different robots, and 170 00:09:36,316 --> 00:09:40,196 Speaker 1: so it's pretty cool looking. So like the robots are 171 00:09:40,236 --> 00:09:43,476 Speaker 1: like robot arms, and what's going on the train tracks? Yeah, 172 00:09:43,596 --> 00:09:46,636 Speaker 1: these these big arms that can basically move plates from 173 00:09:47,076 --> 00:09:50,476 Speaker 1: one robot to another and train tracks to be able 174 00:09:50,516 --> 00:09:53,436 Speaker 1: to move samples between the robots. To do the processing 175 00:09:53,476 --> 00:09:56,436 Speaker 1: steps you need to either read the DNA or write 176 00:09:56,476 --> 00:10:00,636 Speaker 1: the DNA, or put the DNA into cells or test 177 00:10:00,716 --> 00:10:03,116 Speaker 1: how those cells are performing. So the robots are doing 178 00:10:03,156 --> 00:10:08,996 Speaker 1: all of those steps. Yes, that's a dream after the break, 179 00:10:09,196 --> 00:10:12,076 Speaker 1: a problem Ginko has not solved yet. How do you 180 00:10:12,156 --> 00:10:15,436 Speaker 1: build a company based on this wild, radically new technology 181 00:10:15,876 --> 00:10:26,076 Speaker 1: and also make a profit. That's the end of the ads. 182 00:10:26,516 --> 00:10:29,156 Speaker 1: Now we're going back to the show. Kinko went public 183 00:10:29,276 --> 00:10:31,836 Speaker 1: last year and the company is now worth billions of dollars, 184 00:10:32,156 --> 00:10:34,836 Speaker 1: but Reshma and our co founders still haven't made the 185 00:10:34,916 --> 00:10:38,436 Speaker 1: company work as a profitable business. They have revenue, but 186 00:10:38,596 --> 00:10:41,236 Speaker 1: it doesn't come close to covering their costs, and so 187 00:10:41,476 --> 00:10:44,436 Speaker 1: the problem of becoming a profitable company is what we 188 00:10:44,516 --> 00:10:47,356 Speaker 1: focused on in the second part of the interview. It's 189 00:10:47,516 --> 00:10:50,156 Speaker 1: impressive in a way that you know, it's continued to 190 00:10:50,196 --> 00:10:52,716 Speaker 1: get funded and you've grown so big, and you have 191 00:10:52,996 --> 00:10:55,596 Speaker 1: lost and continued to lose a lot of money. I 192 00:10:55,636 --> 00:10:58,796 Speaker 1: feel like in the long run, that's a problem that 193 00:10:59,276 --> 00:11:01,876 Speaker 1: you have to solve eventually, Like how do you solve 194 00:11:01,956 --> 00:11:04,596 Speaker 1: that problem? So in our business, the way our business 195 00:11:04,636 --> 00:11:08,556 Speaker 1: model works is when we collaborate with a customer on 196 00:11:08,636 --> 00:11:12,556 Speaker 1: a self program, we get two different kinds of value 197 00:11:12,596 --> 00:11:16,436 Speaker 1: out of the relationship. We get fees and milestone payments 198 00:11:16,716 --> 00:11:20,636 Speaker 1: from our customers to help offset our dcosts, and we 199 00:11:20,716 --> 00:11:24,556 Speaker 1: get what's called downstream value share. Downstream value share typically 200 00:11:24,596 --> 00:11:29,796 Speaker 1: comes in the form of either royalties on products made 201 00:11:29,916 --> 00:11:34,436 Speaker 1: using Ginko organisms or equity stakes in our customers. And 202 00:11:34,636 --> 00:11:36,476 Speaker 1: we've been expanding a lot in the number of cell 203 00:11:36,556 --> 00:11:39,596 Speaker 1: programs that we've taken on, but many of them are 204 00:11:39,636 --> 00:11:43,596 Speaker 1: still in the development stage, and so essentially the way 205 00:11:43,676 --> 00:11:46,316 Speaker 1: we ultimately become a sustainable business is as more and 206 00:11:46,436 --> 00:11:50,316 Speaker 1: more of our cell programs successfully complete and recognize that 207 00:11:50,436 --> 00:11:53,996 Speaker 1: downstream value share. That's where the rail like ultimate long 208 00:11:54,076 --> 00:11:57,476 Speaker 1: term value potential of Ginko is. So you're basically saying 209 00:11:57,836 --> 00:12:00,956 Speaker 1: when the investments you've already made payoff, that will do it. 210 00:12:01,036 --> 00:12:02,876 Speaker 1: I mean, I get the math of what you're saying 211 00:12:03,156 --> 00:12:04,676 Speaker 1: is that you've put a lot of money into things, 212 00:12:04,716 --> 00:12:06,356 Speaker 1: and if they make a lot of money in the future, 213 00:12:06,396 --> 00:12:08,676 Speaker 1: then you will be profitable. Is that right? Yeah? I 214 00:12:08,716 --> 00:12:11,676 Speaker 1: mean it's like, um, you know, think about it. If 215 00:12:11,836 --> 00:12:15,596 Speaker 1: your typical like pharma biotech company, they lose the hundreds 216 00:12:15,636 --> 00:12:18,636 Speaker 1: of millions of dollars, right, but if the drug works, 217 00:12:19,396 --> 00:12:22,076 Speaker 1: there's like a big, big enough path at the end 218 00:12:22,116 --> 00:12:25,636 Speaker 1: to justify that upfront risk. Basically, it's sort of all 219 00:12:25,756 --> 00:12:28,676 Speaker 1: long shots, right. The drug companies essentially make lots and 220 00:12:28,756 --> 00:12:31,996 Speaker 1: lots of long shot bets and most of them don't work, 221 00:12:32,556 --> 00:12:34,356 Speaker 1: and then once in a while they have a huge 222 00:12:34,476 --> 00:12:37,356 Speaker 1: winner that makes up for all the ones that don't. Exactly, 223 00:12:37,796 --> 00:12:41,116 Speaker 1: but in our case, we instead of a company that 224 00:12:41,356 --> 00:12:44,996 Speaker 1: is betting up like typically in pharma or in biopharma, 225 00:12:45,356 --> 00:12:47,836 Speaker 1: you know, the company might have one bet, right, one 226 00:12:47,996 --> 00:12:50,476 Speaker 1: long shot bet, or maybe a couple of long shot bets. 227 00:12:50,956 --> 00:12:53,996 Speaker 1: We're a platform company, so we actually have lots and 228 00:12:54,116 --> 00:12:57,116 Speaker 1: lots of long, long shot bets, and you know, and honestly, 229 00:12:57,436 --> 00:12:59,636 Speaker 1: some of them are not going to work, right. You know, 230 00:12:59,876 --> 00:13:02,716 Speaker 1: some cell programs won't work, or the cell program will work, 231 00:13:02,756 --> 00:13:04,916 Speaker 1: but it won't commercialize the way we think, or whatnot. 232 00:13:05,356 --> 00:13:08,116 Speaker 1: The question is will there be enough winners, and will 233 00:13:08,156 --> 00:13:10,636 Speaker 1: they be those winners be big enough to justify the 234 00:13:10,716 --> 00:13:14,076 Speaker 1: whole thing? And we're betting that they will. I recognize 235 00:13:14,116 --> 00:13:16,316 Speaker 1: that we're sort of at a time will tell ending 236 00:13:16,476 --> 00:13:20,476 Speaker 1: of the narrative, and it's valid. It's clearly true. Time 237 00:13:20,516 --> 00:13:25,236 Speaker 1: will indeed tell. I'm just trying to think of how 238 00:13:25,276 --> 00:13:27,036 Speaker 1: to parse it. I mean, I guess another way to 239 00:13:27,116 --> 00:13:30,556 Speaker 1: think about it is whether and to what extent these 240 00:13:30,716 --> 00:13:34,356 Speaker 1: various bets will pay off. Let me flip the question 241 00:13:34,436 --> 00:13:37,396 Speaker 1: on you, right, yeah, please please, Here's the way I 242 00:13:37,556 --> 00:13:39,756 Speaker 1: look at it. Right, the world has a lot of 243 00:13:39,836 --> 00:13:43,996 Speaker 1: serious things that it is facing, right, climate change, pandemics, 244 00:13:44,516 --> 00:13:49,556 Speaker 1: you know, supply chain issues, right, having enough food to 245 00:13:49,716 --> 00:13:52,836 Speaker 1: feed our planet having enough clean water to provide to 246 00:13:52,956 --> 00:13:56,476 Speaker 1: our planet. Right to me, the cost of not doing 247 00:13:56,596 --> 00:14:00,316 Speaker 1: something is unfathomable to me. So I would much rather 248 00:14:00,796 --> 00:14:04,676 Speaker 1: try and fail at this, which at least has gives 249 00:14:04,756 --> 00:14:08,556 Speaker 1: us a shot of solving some of these pressing world problems, 250 00:14:09,196 --> 00:14:13,796 Speaker 1: then not try at all. In a minute, the Lightning Round, 251 00:14:13,956 --> 00:14:17,396 Speaker 1: where we learned Rashma's favorite micro organism, her least favorite 252 00:14:17,396 --> 00:14:19,916 Speaker 1: trait in a coworker, and the one piece of advice 253 00:14:19,956 --> 00:14:21,956 Speaker 1: she'd give to somebody who is trying to solve a 254 00:14:22,036 --> 00:14:32,356 Speaker 1: hard problem. Now let's get back to what's your problem? Now, 255 00:14:32,396 --> 00:14:35,196 Speaker 1: it's just the lightning round. Can we do the lightning round? Absolutely? 256 00:14:35,716 --> 00:14:38,836 Speaker 1: What's your favorite micro organism? E coli? That's what I did. 257 00:14:38,916 --> 00:14:42,476 Speaker 1: My PhD on tell me why E Coli is great? So. 258 00:14:43,516 --> 00:14:46,916 Speaker 1: Equali is this little microbe. It's found in every person's gout, 259 00:14:47,116 --> 00:14:49,356 Speaker 1: smells awful, smells like pooh. Right, it's part of what 260 00:14:49,476 --> 00:14:52,676 Speaker 1: makes your poo smell like pooh. And we did a 261 00:14:52,756 --> 00:14:56,476 Speaker 1: project when we were at MIT mentoring a group of 262 00:14:56,556 --> 00:14:59,836 Speaker 1: undergraduates to change the smell of ecali to smell like 263 00:14:59,876 --> 00:15:02,516 Speaker 1: weren't a green and bananas. So while Eli gets a 264 00:15:02,556 --> 00:15:06,796 Speaker 1: bad rap for smelling bad, You can actually reprogramage to 265 00:15:06,796 --> 00:15:09,956 Speaker 1: smell pretty good. What's your favorite trade and a co worker? 266 00:15:10,196 --> 00:15:13,196 Speaker 1: Passion and curiosity? What's your least favorite trait in a 267 00:15:13,276 --> 00:15:16,996 Speaker 1: co worker? Self promotion? If you have a ten minute 268 00:15:17,036 --> 00:15:18,476 Speaker 1: break in the middle of the day, what do you 269 00:15:18,556 --> 00:15:22,476 Speaker 1: do to relax? Take a walk through the lapse? What 270 00:15:22,836 --> 00:15:25,116 Speaker 1: is one piece of advice you would give to somebody 271 00:15:25,196 --> 00:15:27,836 Speaker 1: who's trying to solve a hard problem? Better to try 272 00:15:28,236 --> 00:15:31,836 Speaker 1: and fail than not try at all. It sounds trite, right, 273 00:15:31,996 --> 00:15:36,116 Speaker 1: but you know, the way I could get myself to 274 00:15:36,196 --> 00:15:38,476 Speaker 1: a place where I felt comfortable, you know, making the 275 00:15:38,556 --> 00:15:41,876 Speaker 1: leap and starting a company is that, you know, starting 276 00:15:41,916 --> 00:15:44,196 Speaker 1: a company working on really hard problems, you may or 277 00:15:44,276 --> 00:15:47,756 Speaker 1: may not succeed, right. You just don't know. It's a crapshoot, right, 278 00:15:48,116 --> 00:15:50,316 Speaker 1: And so what you want to do is be able 279 00:15:50,316 --> 00:15:53,076 Speaker 1: to get to yourself to a place where you'll never 280 00:15:53,236 --> 00:15:56,476 Speaker 1: regret try, right. And the nice thing about hard problems 281 00:15:56,556 --> 00:15:59,716 Speaker 1: and important problems is that it's very hard to regret 282 00:15:59,836 --> 00:16:07,436 Speaker 1: working on them. Rash Machete is the co founder and 283 00:16:07,556 --> 00:16:11,356 Speaker 1: chief operations officer of ging Go Buy Awards. Today's show 284 00:16:11,556 --> 00:16:14,796 Speaker 1: was produced by Edith Russolo, edited by Robert Smith and 285 00:16:14,996 --> 00:16:18,196 Speaker 1: engineered by Amanda kay Waugh. Our theme music is by 286 00:16:18,236 --> 00:16:21,956 Speaker 1: Luis Gara. A huge team of people makes this show possible. 287 00:16:22,356 --> 00:16:27,036 Speaker 1: This team includes, but is not limited to, Jacob Weisberg, Milobell, 288 00:16:27,156 --> 00:16:30,716 Speaker 1: Leta Mulad, Justine Lang, Heather Fame, John Schnars, Kry Brody, 289 00:16:30,796 --> 00:16:35,076 Speaker 1: Carli Nigliori, Christina Sullivan, Jason Gambrel, Grant Hays, Eric Sandler, 290 00:16:35,156 --> 00:16:39,556 Speaker 1: Maggie Taylor, Morgan Rattner, Nicole Morano, Mary Beth Smith, Royston Baserve, 291 00:16:39,796 --> 00:16:43,716 Speaker 1: Maya Kanig, Daniella Lakhan, Kazeia Tan and David Clover. What's 292 00:16:43,756 --> 00:16:47,276 Speaker 1: Your Problem is a co production of Pushkin Industries and iHeartMedia. 293 00:16:47,676 --> 00:16:50,876 Speaker 1: To find more Pushkin podcasts, listen on the iHeartRadio app, 294 00:16:51,036 --> 00:16:55,156 Speaker 1: Apple Podcasts, or wherever. I'm Jacob Goldstein and I'll be 295 00:16:55,276 --> 00:16:57,916 Speaker 1: back next week with another episode of What's Your Problem