1 00:00:01,040 --> 00:00:06,400 Speaker 1: Welcome to Zero. I'm Aukshatrati. This week moonshots, magnets and 2 00:00:06,600 --> 00:00:22,840 Speaker 1: monstrously smart people. Sometimes the climate beat is fun. Last 3 00:00:22,840 --> 00:00:25,680 Speaker 1: summer I got a chance to visit a nuclear fusion company, 4 00:00:26,079 --> 00:00:29,800 Speaker 1: multiple carbon removal firms, and at least two startups using 5 00:00:29,800 --> 00:00:33,720 Speaker 1: electricity to make iron. And you'll hear some of these 6 00:00:33,760 --> 00:00:37,199 Speaker 1: stories in future episodes on Zero. The science behind these 7 00:00:37,240 --> 00:00:40,800 Speaker 1: companies could not be more different, but their business problems 8 00:00:40,840 --> 00:00:45,559 Speaker 1: are the same hiring, financing, supply chain. The more I 9 00:00:45,640 --> 00:00:48,920 Speaker 1: observed the pressures on each company, I started to notice 10 00:00:49,040 --> 00:00:52,080 Speaker 1: that there was a split. Some companies were led by 11 00:00:52,159 --> 00:00:55,520 Speaker 1: serial entrepreneurs, someone who has experienced in dealing with the 12 00:00:55,520 --> 00:00:58,760 Speaker 1: problems of a business. Other companies were led by a 13 00:00:58,760 --> 00:01:02,760 Speaker 1: scientist or an engineer, the person whose deepest experience was 14 00:01:02,800 --> 00:01:05,920 Speaker 1: studying the science that the company was based on. The 15 00:01:06,080 --> 00:01:09,600 Speaker 1: choice of CEO is something that investors pay careful attention to. 16 00:01:10,000 --> 00:01:12,720 Speaker 1: They often do not want a scientist as the CEO. 17 00:01:12,959 --> 00:01:16,280 Speaker 1: They want an experienced entrepreneur because the problems of running 18 00:01:16,319 --> 00:01:19,240 Speaker 1: a business are hard to learn and very high stakes. 19 00:01:20,319 --> 00:01:23,600 Speaker 1: My guest today is Katie Ray, who heads a large fund, 20 00:01:23,840 --> 00:01:27,400 Speaker 1: and she has a different philosophy about meeting people where 21 00:01:27,440 --> 00:01:31,880 Speaker 1: they are. We've certainly proven over the last five years 22 00:01:31,920 --> 00:01:34,240 Speaker 1: that we've made some great bets on human beings that 23 00:01:34,319 --> 00:01:38,920 Speaker 1: are more currently engineers or post docs and now are 24 00:01:39,000 --> 00:01:44,720 Speaker 1: really fantastic CEOs. Having that fundamental belief in people's abilities 25 00:01:45,160 --> 00:01:48,680 Speaker 1: to learn and grow and run these things mean that 26 00:01:49,040 --> 00:01:52,280 Speaker 1: more of these inventions will get out. Katie is the 27 00:01:52,320 --> 00:01:55,720 Speaker 1: CEO of the Engine. It's a venture capital fund and 28 00:01:55,920 --> 00:01:59,080 Speaker 1: a public benefit corporation that was spun out of MT 29 00:01:59,280 --> 00:02:02,960 Speaker 1: in twenty six. It invests in so called tough tech 30 00:02:03,320 --> 00:02:06,120 Speaker 1: that takes a scientific idea from proof of concept to 31 00:02:06,160 --> 00:02:09,919 Speaker 1: commercial scale. The tough part is because usually it takes 32 00:02:10,000 --> 00:02:12,040 Speaker 1: years of effort and lots of money to do it. 33 00:02:12,639 --> 00:02:15,320 Speaker 1: Others call it heart tech as a way of signaling 34 00:02:15,360 --> 00:02:18,959 Speaker 1: that the technology is not based on software. Katie's job 35 00:02:19,040 --> 00:02:21,720 Speaker 1: is to deploy over six hundred million dollars of venture 36 00:02:21,760 --> 00:02:25,160 Speaker 1: funding for Boston area startups that are ready to scale. 37 00:02:25,720 --> 00:02:28,600 Speaker 1: The people who seek funding from her are often scientists 38 00:02:28,639 --> 00:02:31,640 Speaker 1: and engineers, and some of the ideas are far out 39 00:02:31,680 --> 00:02:35,520 Speaker 1: at first plush. Katie has led investments into companies as 40 00:02:35,600 --> 00:02:39,560 Speaker 1: varied as Commonwealth Fusion Systems, a company building a compact 41 00:02:39,680 --> 00:02:43,800 Speaker 1: nuclear fusion plant, Form Energy, which makes batteries that store 42 00:02:43,840 --> 00:02:48,080 Speaker 1: renewable power by rusting an unrusting iron, and Boston Metal, 43 00:02:48,200 --> 00:02:52,200 Speaker 1: a company that uses electricity to make steal. So when 44 00:02:52,280 --> 00:02:54,640 Speaker 1: Katie's choosing who to invest in, she has a lot 45 00:02:54,720 --> 00:02:57,480 Speaker 1: to think about. She has to figure out how she 46 00:02:57,520 --> 00:03:00,320 Speaker 1: can make sure that the companies have a promising idea 47 00:03:00,520 --> 00:03:03,079 Speaker 1: that works and is going to be led by someone 48 00:03:03,160 --> 00:03:06,240 Speaker 1: who can make it a functioning business. And because of 49 00:03:06,280 --> 00:03:09,400 Speaker 1: where she sits, she's invested in making sure that the 50 00:03:09,480 --> 00:03:13,840 Speaker 1: journey from scientists to CEO can happen. I sat down 51 00:03:13,840 --> 00:03:17,160 Speaker 1: with Katie at the Engine's headquarters near MT to talk 52 00:03:17,200 --> 00:03:20,360 Speaker 1: about how it all got started, how she does due 53 00:03:20,400 --> 00:03:24,120 Speaker 1: diligence on these companies with far out ideas, and why 54 00:03:24,160 --> 00:03:27,359 Speaker 1: there should be no excuses for not having more women 55 00:03:27,480 --> 00:03:41,120 Speaker 1: in this industry. Katie, welcome to the show. Thank you 56 00:03:41,160 --> 00:03:44,400 Speaker 1: so much for having me. What got you the idea 57 00:03:44,400 --> 00:03:46,320 Speaker 1: of starting this in the first place? How were you 58 00:03:46,400 --> 00:03:50,400 Speaker 1: able to raise money for these wacky ideas? Basically the 59 00:03:50,440 --> 00:03:52,440 Speaker 1: way it started, I mean this was born out of 60 00:03:52,560 --> 00:03:56,160 Speaker 1: MT right and very MT was born out of a 61 00:03:56,200 --> 00:04:00,960 Speaker 1: bunch of research that showed that in the area we 62 00:04:01,040 --> 00:04:04,960 Speaker 1: call tough tech, so things often with a physical instantiation, 63 00:04:05,680 --> 00:04:11,320 Speaker 1: things that are not produced overnight, that take time to produce, 64 00:04:11,480 --> 00:04:15,160 Speaker 1: that are often systems. Things like that we're not being 65 00:04:15,200 --> 00:04:18,240 Speaker 1: invested into at the same rate, Like the rate of 66 00:04:18,279 --> 00:04:21,520 Speaker 1: investment had gone down over thirty years. But then if 67 00:04:21,520 --> 00:04:24,880 Speaker 1: you overlaid that with the problems in the world that 68 00:04:24,960 --> 00:04:27,640 Speaker 1: needed to be solved, you would see like, we're not 69 00:04:27,760 --> 00:04:31,080 Speaker 1: investing into the things that will matter to our future. 70 00:04:31,760 --> 00:04:34,840 Speaker 1: And so M. I. T very much was like, we've 71 00:04:34,839 --> 00:04:37,680 Speaker 1: got to solve some of these problems. And so they 72 00:04:37,760 --> 00:04:42,560 Speaker 1: put the shoulder of the university into saying, can we 73 00:04:42,920 --> 00:04:46,480 Speaker 1: fundamentally shift who gets funded, why do they get funded. 74 00:04:46,720 --> 00:04:49,920 Speaker 1: That's the birth story of the Engine. M I. T 75 00:04:50,520 --> 00:04:54,800 Speaker 1: was an anchor in our first fund and an amazing partner, 76 00:04:54,880 --> 00:04:58,160 Speaker 1: Like you're sitting in our new building right now, which 77 00:04:58,400 --> 00:05:01,360 Speaker 1: is only through a partnership with them. No venture fund 78 00:05:01,400 --> 00:05:04,360 Speaker 1: could build a space like this. But if you think 79 00:05:04,400 --> 00:05:08,200 Speaker 1: about the impact the Engine could have over time and 80 00:05:08,279 --> 00:05:11,960 Speaker 1: the density of tough tech founders that will be in 81 00:05:12,000 --> 00:05:14,760 Speaker 1: this building, so it will be over a thousand people here, 82 00:05:15,080 --> 00:05:18,680 Speaker 1: we have another space with another five hundred people. You 83 00:05:18,720 --> 00:05:23,200 Speaker 1: start to get a density of companies and knowledge that 84 00:05:23,320 --> 00:05:27,520 Speaker 1: can be rapidly transferred because everyone is in very close proximity. 85 00:05:27,800 --> 00:05:30,160 Speaker 1: You've seen it in biotech, you saw it in software. 86 00:05:30,480 --> 00:05:35,640 Speaker 1: You've seen these clusters develop and fund one was really 87 00:05:35,800 --> 00:05:40,200 Speaker 1: an experiment to show that we could find incredible things 88 00:05:40,240 --> 00:05:45,240 Speaker 1: to translate out and that they could be venture scale investments, 89 00:05:45,600 --> 00:05:49,040 Speaker 1: and that we could find other investors to work with 90 00:05:49,160 --> 00:05:52,560 Speaker 1: us over time to scale these companies. I think we've 91 00:05:52,600 --> 00:05:56,320 Speaker 1: done a pretty good job in that regard and us 92 00:05:56,560 --> 00:05:59,440 Speaker 1: is there a metric by that you go to have 93 00:05:59,720 --> 00:06:04,440 Speaker 1: met that target? Well, if you just think first Portfolio 94 00:06:04,600 --> 00:06:09,479 Speaker 1: had twenty seven companies, we invested less than two hundred 95 00:06:09,520 --> 00:06:13,919 Speaker 1: million so far, and they've raised three point seven billion dollars. Like, 96 00:06:14,040 --> 00:06:17,200 Speaker 1: that's a pretty good metric by anybody's standard, and that 97 00:06:17,240 --> 00:06:20,640 Speaker 1: means that many people have bet that these are venture 98 00:06:20,640 --> 00:06:24,440 Speaker 1: scale returns. You have built at the engine something that's 99 00:06:24,480 --> 00:06:29,560 Speaker 1: quite unique, which is you invest in science ideas that 100 00:06:29,640 --> 00:06:33,440 Speaker 1: have the potential to become big companies, but they start 101 00:06:33,520 --> 00:06:37,600 Speaker 1: with science ideas that I would probably reframe it slightly. 102 00:06:38,360 --> 00:06:41,720 Speaker 1: I would say I invest in two companies that are 103 00:06:41,760 --> 00:06:46,000 Speaker 1: translating out of science and engineering labs that are ready 104 00:06:46,440 --> 00:06:51,280 Speaker 1: to be built and proven outside of a lab and 105 00:06:51,320 --> 00:06:55,279 Speaker 1: then scaled. So I think that's a little bit subtly 106 00:06:55,360 --> 00:06:58,520 Speaker 1: different than what you said, But they are all based 107 00:06:58,640 --> 00:07:04,839 Speaker 1: on scientific evidence the scientific method of producing new things. 108 00:07:05,279 --> 00:07:08,160 Speaker 1: But there is a science risk attached to these companies 109 00:07:08,640 --> 00:07:12,720 Speaker 1: that you know, what is in the lab usually works 110 00:07:12,840 --> 00:07:17,600 Speaker 1: after many many tries, and sometimes it's replicable. Having done 111 00:07:17,640 --> 00:07:19,680 Speaker 1: science for a while, I know how hard it is 112 00:07:19,720 --> 00:07:24,720 Speaker 1: to even replicate papers with detailed instructions, but taking that 113 00:07:24,840 --> 00:07:28,720 Speaker 1: and doing it at a commercial scale, where you are 114 00:07:28,760 --> 00:07:32,520 Speaker 1: doing it millions of times or millions of tons, is 115 00:07:32,560 --> 00:07:34,640 Speaker 1: a completely different challenge. So there's a ton of science 116 00:07:34,720 --> 00:07:37,000 Speaker 1: risk in all the companies that you take, or science 117 00:07:37,000 --> 00:07:40,400 Speaker 1: translation risk that you've taken in the engine, right, Yeah. 118 00:07:40,440 --> 00:07:43,680 Speaker 1: I think of it is you should have proven out 119 00:07:43,840 --> 00:07:46,960 Speaker 1: the basic fundamentals of the science risk, and then there 120 00:07:47,040 --> 00:07:51,200 Speaker 1: is engineering risk of scaling these things, which could take 121 00:07:51,240 --> 00:07:54,480 Speaker 1: you back to scientific risk where you don't understand something again. 122 00:07:55,120 --> 00:07:58,520 Speaker 1: But typically we try to make sure that you understand 123 00:07:58,600 --> 00:08:02,520 Speaker 1: the mechanisms of acts and what would happen as you scale, 124 00:08:02,760 --> 00:08:06,200 Speaker 1: and that you could simulate with software that you could 125 00:08:06,280 --> 00:08:09,000 Speaker 1: understand what they're going to look like. You know, for instance, 126 00:08:09,080 --> 00:08:11,960 Speaker 1: if you invest into a fusion company, you better be 127 00:08:12,000 --> 00:08:15,480 Speaker 1: pretty confident that when you build your magnets it's going 128 00:08:15,520 --> 00:08:19,320 Speaker 1: to work. You understand the physics of whatever your new 129 00:08:19,360 --> 00:08:23,560 Speaker 1: material is, etc. Or else, it's not really an investable asset. 130 00:08:23,960 --> 00:08:27,600 Speaker 1: So it's different than a moonshot, right, A moonshot is 131 00:08:28,200 --> 00:08:32,720 Speaker 1: in my mind, you are really heading into scientific core 132 00:08:32,800 --> 00:08:37,240 Speaker 1: scientific risk still, whereas many of these you've proven out 133 00:08:37,240 --> 00:08:41,199 Speaker 1: the core science. You're heading into engineering risk, which could 134 00:08:41,880 --> 00:08:45,280 Speaker 1: bring you back to scientific risk, but at a lesser level. 135 00:08:45,559 --> 00:08:48,480 Speaker 1: And so engineering risks can take you back to science. 136 00:08:48,800 --> 00:08:51,880 Speaker 1: You've now got more than forty companies in your portfolio. 137 00:08:52,559 --> 00:08:56,960 Speaker 1: You've raised six hundred million dollars and above you've put 138 00:08:57,000 --> 00:09:00,160 Speaker 1: in almost forty percent of that capital to work. What 139 00:09:00,240 --> 00:09:02,240 Speaker 1: are the lessons you've learned in doing this for the 140 00:09:02,320 --> 00:09:05,680 Speaker 1: last five years? You know, when we started this, we 141 00:09:05,880 --> 00:09:09,360 Speaker 1: laid out a set of assumptions about how much capital 142 00:09:09,440 --> 00:09:12,480 Speaker 1: these companies would need, how much time it would take 143 00:09:12,520 --> 00:09:15,600 Speaker 1: to translate, how you would have to build these teams, Like, 144 00:09:15,640 --> 00:09:19,880 Speaker 1: we really laid out what our hypotheses were, and I 145 00:09:19,920 --> 00:09:25,600 Speaker 1: think for the most part we were pretty close in 146 00:09:25,640 --> 00:09:30,920 Speaker 1: that the teams would need multiple sets of labs, chemistry, biology, 147 00:09:31,000 --> 00:09:35,760 Speaker 1: machines shops. They would need many kinds of experts around 148 00:09:35,800 --> 00:09:38,839 Speaker 1: them to get through their engineering risk. They would need 149 00:09:38,920 --> 00:09:43,800 Speaker 1: to be surrounded by serial entrepreneurs and business people that 150 00:09:43,840 --> 00:09:46,880 Speaker 1: could give them advice about how to scale the business 151 00:09:46,880 --> 00:09:49,120 Speaker 1: side of their company or really how to take out 152 00:09:49,200 --> 00:09:54,280 Speaker 1: business risk. That we would need to develop a robust 153 00:09:54,400 --> 00:09:59,880 Speaker 1: capital stack that understood engineering based companies and the risk 154 00:10:00,160 --> 00:10:04,720 Speaker 1: there are there, And depending on what that time frame was, 155 00:10:05,320 --> 00:10:08,400 Speaker 1: that would dictate the kind of capital they needed to 156 00:10:08,400 --> 00:10:11,960 Speaker 1: prove out those risks. It would dictate what the potential 157 00:10:12,040 --> 00:10:15,160 Speaker 1: upside would need to be to make those bets. So 158 00:10:15,200 --> 00:10:19,160 Speaker 1: I think all of those things have played out. And 159 00:10:19,240 --> 00:10:22,600 Speaker 1: that's because think about where I sit in the middle 160 00:10:22,600 --> 00:10:26,720 Speaker 1: of MIT and the Boston region. So I had a 161 00:10:26,720 --> 00:10:30,240 Speaker 1: lot of experts helping me understand that before we started. 162 00:10:30,920 --> 00:10:33,600 Speaker 1: Are there climate tech companies that you would take as 163 00:10:33,640 --> 00:10:36,640 Speaker 1: salient examples of having gone through that period and proven 164 00:10:36,679 --> 00:10:40,760 Speaker 1: out their lists. Commonwealth Fusion had to prove that they 165 00:10:40,800 --> 00:10:44,760 Speaker 1: could build the first magnet and get the first magnet 166 00:10:44,880 --> 00:10:48,679 Speaker 1: to twenty tesla right like. That was their fundamental proof point, 167 00:10:49,760 --> 00:10:53,880 Speaker 1: and they did that in under three years. So what 168 00:10:54,000 --> 00:10:56,280 Speaker 1: does it mean to get the first magnet to twenty 169 00:10:56,440 --> 00:10:59,880 Speaker 1: Tesla's well, Tesla is the unit of strength of a magnet, 170 00:11:00,240 --> 00:11:04,640 Speaker 1: named after the famous Serbian American engineer Nicola Tesla. Twenty 171 00:11:04,679 --> 00:11:08,440 Speaker 1: teslas is a really powerful magnet. An MRI machine like 172 00:11:08,520 --> 00:11:11,400 Speaker 1: the one that doctors used to peer inside bodies, has 173 00:11:11,400 --> 00:11:16,320 Speaker 1: a magnet that's about three teslas. Other examples form Energy. 174 00:11:16,840 --> 00:11:20,600 Speaker 1: They're building kind of a long term battery for wind 175 00:11:20,640 --> 00:11:23,720 Speaker 1: and solar to allow up to one hundred hours of storage. 176 00:11:24,000 --> 00:11:27,200 Speaker 1: We also visited Farm Energy. You'll hear about how they 177 00:11:27,280 --> 00:11:30,520 Speaker 1: got their iron air battery in a future episode. They 178 00:11:30,559 --> 00:11:33,360 Speaker 1: started with an idea that wasn't working, and they pivoted 179 00:11:33,360 --> 00:11:35,320 Speaker 1: to an idea that is now working, and that all 180 00:11:35,360 --> 00:11:39,439 Speaker 1: happened in a four year period. They raced two technologies 181 00:11:39,679 --> 00:11:43,400 Speaker 1: against each other, and they knew from the beginning they 182 00:11:43,480 --> 00:11:48,000 Speaker 1: weren't sure which would work, and one did work. Awesome, Like, 183 00:11:48,120 --> 00:11:51,840 Speaker 1: what a wonderful way to run a company. So that 184 00:11:52,040 --> 00:11:56,000 Speaker 1: fundamentally changes the economics of wind and solar for grid all. 185 00:11:57,200 --> 00:12:01,240 Speaker 1: Give me another example. Let's see Boston Metal. So this 186 00:12:01,440 --> 00:12:05,240 Speaker 1: is you know, electrification of the steel making process or 187 00:12:05,320 --> 00:12:09,680 Speaker 1: really metals production process, you know. So we started out 188 00:12:09,840 --> 00:12:15,080 Speaker 1: in twenty eighteen and said it'll take about four years 189 00:12:15,200 --> 00:12:19,680 Speaker 1: to prove this out pretty close, you know, right, even 190 00:12:19,679 --> 00:12:21,640 Speaker 1: though the company had been founded in twenty twelve. So 191 00:12:21,679 --> 00:12:23,920 Speaker 1: by the time you got to the investment, you're like, okay, 192 00:12:23,960 --> 00:12:26,480 Speaker 1: now we are at that point there are investment makes 193 00:12:26,480 --> 00:12:29,160 Speaker 1: sense because there's a four year child exactly right, and 194 00:12:29,440 --> 00:12:31,680 Speaker 1: you don't know what the time period will be before 195 00:12:31,760 --> 00:12:35,000 Speaker 1: we invest. That's fine. So that's why we look at 196 00:12:35,040 --> 00:12:38,400 Speaker 1: a big range of companies because some of them kind 197 00:12:38,440 --> 00:12:42,760 Speaker 1: of take eight or nine or ten years in a 198 00:12:42,880 --> 00:12:47,679 Speaker 1: lab or getting government funding before they're really ready. Right, 199 00:12:48,160 --> 00:12:51,679 Speaker 1: that's the cost of doing business, right, And but we 200 00:12:51,760 --> 00:12:54,880 Speaker 1: want to look at hey, what's the one to five 201 00:12:55,000 --> 00:12:58,720 Speaker 1: year period where you take out big, big technical risks 202 00:12:59,120 --> 00:13:02,720 Speaker 1: so then your deal with the normal technical risks and 203 00:13:02,760 --> 00:13:06,559 Speaker 1: the business risks. Right. One other complication in climate tech 204 00:13:06,600 --> 00:13:11,000 Speaker 1: companies is that it's not just about the entrepreneurs. Obviously 205 00:13:11,080 --> 00:13:13,800 Speaker 1: that is still a core part of building a company, 206 00:13:13,960 --> 00:13:17,760 Speaker 1: but it's also about the science, and so you have 207 00:13:17,800 --> 00:13:20,200 Speaker 1: to do a ton of due diligence. Is it real, 208 00:13:20,640 --> 00:13:24,079 Speaker 1: is it within the limits and bounds of known science? 209 00:13:24,720 --> 00:13:26,880 Speaker 1: And you do it across so many subjects. Of course, 210 00:13:26,880 --> 00:13:29,160 Speaker 1: climate tech is what we're talking about, but that's not 211 00:13:29,200 --> 00:13:32,080 Speaker 1: the only thing you invest in. So what's your process 212 00:13:32,160 --> 00:13:35,560 Speaker 1: for making sure that when a company comes and gives 213 00:13:35,600 --> 00:13:38,800 Speaker 1: a pitch that you can then go back and do 214 00:13:38,880 --> 00:13:42,200 Speaker 1: your homework and say, yes, we would like to invest. Yeah. 215 00:13:42,280 --> 00:13:45,400 Speaker 1: So we run multiple processes all at the same time. 216 00:13:45,520 --> 00:13:51,520 Speaker 1: So we do fundamental landscaping of basically any area we 217 00:13:51,559 --> 00:13:55,640 Speaker 1: would be interested investing in, and we look at which 218 00:13:55,720 --> 00:13:59,080 Speaker 1: labs are doing research in there, what is the research 219 00:13:59,120 --> 00:14:02,319 Speaker 1: they're doing, what the findings they're having, So you're looking 220 00:14:02,320 --> 00:14:05,920 Speaker 1: at all the things that maybe created the breakthrough in 221 00:14:05,960 --> 00:14:11,319 Speaker 1: the first place, right because science is this wonderfully collaborative space. 222 00:14:11,679 --> 00:14:16,640 Speaker 1: Nothing just emerges from the ether. It is a collaborative. 223 00:14:16,679 --> 00:14:19,520 Speaker 1: So we look at those collaborations. We look at who 224 00:14:19,600 --> 00:14:23,520 Speaker 1: got grants, what were they about, and why things are developing. 225 00:14:24,080 --> 00:14:29,160 Speaker 1: That gives us this place to start from any investment. 226 00:14:29,520 --> 00:14:32,720 Speaker 1: Now that has taken us years to build knowledge in 227 00:14:32,760 --> 00:14:36,560 Speaker 1: different areas, and we're continually building knowledge and there we 228 00:14:36,640 --> 00:14:39,800 Speaker 1: have a director of research who has many PhDs who 229 00:14:39,880 --> 00:14:42,520 Speaker 1: work with us to kind of build these maps of 230 00:14:42,560 --> 00:14:46,880 Speaker 1: what's happening. So that's one process. The second process is 231 00:14:47,080 --> 00:14:50,760 Speaker 1: specifically to a company. So our team is kind of 232 00:14:50,840 --> 00:14:53,160 Speaker 1: built to be able to look at things like this. 233 00:14:53,760 --> 00:14:58,400 Speaker 1: So most of our investment professionals have PhDs in a 234 00:14:58,440 --> 00:15:02,080 Speaker 1: certain area and are d networked into others. So we 235 00:15:02,120 --> 00:15:04,880 Speaker 1: will talk to people on the industry side and we 236 00:15:04,920 --> 00:15:07,400 Speaker 1: will talk to people on the academic side to really 237 00:15:07,440 --> 00:15:11,760 Speaker 1: get competing views of will this technology work. And that's 238 00:15:11,880 --> 00:15:14,800 Speaker 1: of course after looking at the basic science, reading all 239 00:15:14,840 --> 00:15:17,600 Speaker 1: the papers, you know, trying to really understand that. So 240 00:15:17,640 --> 00:15:22,960 Speaker 1: when we look at companies and ideas, we're first starting 241 00:15:23,000 --> 00:15:26,160 Speaker 1: with what is their ambition and do they have the 242 00:15:26,280 --> 00:15:36,360 Speaker 1: team to back that up. After the break, I asked 243 00:15:36,440 --> 00:15:39,480 Speaker 1: Katie why the engine has so many signed to CEOs 244 00:15:39,520 --> 00:15:43,160 Speaker 1: compared to Silicon Valley startups and why there are not 245 00:15:43,400 --> 00:16:00,880 Speaker 1: enough women CEOs in this industry. One of the things 246 00:16:00,920 --> 00:16:03,800 Speaker 1: that we noticed in Silicon Valley during our tour of 247 00:16:03,880 --> 00:16:07,920 Speaker 1: startups there and meeting venture capitalists was there was a bias, 248 00:16:07,960 --> 00:16:13,200 Speaker 1: i would say, towards finding entrepreneurs behind climate tech companies 249 00:16:13,640 --> 00:16:17,320 Speaker 1: that have had some business experience. They may be engineers, 250 00:16:17,320 --> 00:16:20,560 Speaker 1: but that they have built companies or they are not engineers, 251 00:16:20,560 --> 00:16:23,800 Speaker 1: but they are commercial minds who can capture the difficulties 252 00:16:23,800 --> 00:16:27,000 Speaker 1: of what science they're trying to convert into a technology. 253 00:16:27,840 --> 00:16:32,440 Speaker 1: If you look at your portfolio, majority of the companies 254 00:16:32,600 --> 00:16:35,600 Speaker 1: still have their CEOs to be the scientists who started 255 00:16:35,640 --> 00:16:39,560 Speaker 1: with the idea. Why is that? Yeah, I think, well, 256 00:16:39,720 --> 00:16:42,280 Speaker 1: don't forget we work at the earliest stage as a 257 00:16:42,360 --> 00:16:46,000 Speaker 1: venture right, so we're a true translation out of labs, 258 00:16:47,360 --> 00:16:54,080 Speaker 1: and so our fundamental hypothesis is that there are some 259 00:16:54,480 --> 00:16:59,400 Speaker 1: very tricky engineering and business decisions that have to be 260 00:16:59,480 --> 00:17:04,720 Speaker 1: made in these deeply technical companies, and that if those 261 00:17:04,760 --> 00:17:08,200 Speaker 1: trade offs aren't really understood, you could waste a lot 262 00:17:08,240 --> 00:17:12,120 Speaker 1: of money. So we think, certainly in the earliest phases 263 00:17:12,119 --> 00:17:17,200 Speaker 1: of these things, you cannot just divorce the science from 264 00:17:17,240 --> 00:17:21,399 Speaker 1: the scientist or divorce the engineering from the engineer, and 265 00:17:21,880 --> 00:17:28,000 Speaker 1: that the better model is to surround that engineer with 266 00:17:28,119 --> 00:17:34,280 Speaker 1: business talent and thinking to influence how they make those tradeoffs. 267 00:17:34,760 --> 00:17:37,440 Speaker 1: But it's not the exclusive model, and it doesn't mean 268 00:17:38,040 --> 00:17:41,200 Speaker 1: that the scientists should always be the CEO. I think 269 00:17:41,240 --> 00:17:44,800 Speaker 1: those decisions get made over time. But I'll tell you, 270 00:17:45,320 --> 00:17:49,000 Speaker 1: in the pool of people that could be CEOs, these 271 00:17:49,040 --> 00:17:55,280 Speaker 1: are monstrously smart, ambitious people, and when they start to 272 00:17:55,400 --> 00:17:59,520 Speaker 1: understand that it is their job to learn the world 273 00:17:59,560 --> 00:18:05,240 Speaker 1: of business and understand deal making, understand margin, understand how 274 00:18:05,240 --> 00:18:08,560 Speaker 1: to grow a big business. A lot of them are 275 00:18:08,840 --> 00:18:12,720 Speaker 1: very capable of doing this. And that doesn't mean everyone 276 00:18:12,760 --> 00:18:16,440 Speaker 1: should be the CEO, but I think having this mindset 277 00:18:16,480 --> 00:18:19,919 Speaker 1: that they shouldn't be, you're really leaving a lot of 278 00:18:19,960 --> 00:18:23,320 Speaker 1: great companies to the side. And we think that that's 279 00:18:24,680 --> 00:18:27,119 Speaker 1: as risky is saying it has to be a business 280 00:18:27,119 --> 00:18:30,639 Speaker 1: person that runs it. And I think, you know, we've 281 00:18:30,680 --> 00:18:34,320 Speaker 1: certainly proven over the last five years that we've made 282 00:18:34,359 --> 00:18:37,640 Speaker 1: some great bets on human beings that are we're currently 283 00:18:37,840 --> 00:18:42,360 Speaker 1: engineers or post docs and now are really fantastic CEOs. 284 00:18:42,640 --> 00:18:45,800 Speaker 1: Now you set out a thesis or try and fund 285 00:18:45,880 --> 00:18:49,159 Speaker 1: companies that do hard science, and you were able to 286 00:18:49,320 --> 00:18:52,080 Speaker 1: prove it out. In the process, you were also able 287 00:18:52,080 --> 00:18:54,560 Speaker 1: to raise more money for your own funds. Was that 288 00:18:54,640 --> 00:18:59,000 Speaker 1: process easy? Is it getting easier? You know? How is 289 00:18:59,119 --> 00:19:03,440 Speaker 1: capital in this area of tough tech coming through? Yeah? 290 00:19:03,480 --> 00:19:07,160 Speaker 1: I mean, I think for the good or the bad 291 00:19:07,200 --> 00:19:11,359 Speaker 1: of this, two or three fundamental things have happened in 292 00:19:11,400 --> 00:19:16,280 Speaker 1: the last five years, right COVID sent to us deeply 293 00:19:16,480 --> 00:19:20,840 Speaker 1: into really understanding we have a global supply chain issue, 294 00:19:21,320 --> 00:19:26,920 Speaker 1: like how we produce things, how we get healthcare, how 295 00:19:27,000 --> 00:19:31,080 Speaker 1: we actually do Almost everything we do today is going 296 00:19:31,160 --> 00:19:34,000 Speaker 1: to need to shift. So you see the US Government 297 00:19:34,040 --> 00:19:36,720 Speaker 1: Act and things like the Chips Act and now the IRA. 298 00:19:37,640 --> 00:19:41,000 Speaker 1: Then I think while people were kind of at home, 299 00:19:41,600 --> 00:19:45,119 Speaker 1: there were so many things that happened in climate that 300 00:19:45,240 --> 00:19:50,800 Speaker 1: I think galvanized this understanding that this stuff is real. 301 00:19:50,960 --> 00:19:53,760 Speaker 1: Like we're not thirty years away from climate change. We 302 00:19:53,840 --> 00:19:57,480 Speaker 1: are drastically in the middle of this. Now we have 303 00:19:57,920 --> 00:20:03,480 Speaker 1: war in Russia, Ukraine and a lot of difficulty in China, 304 00:20:03,640 --> 00:20:07,000 Speaker 1: so like that's just going to exacerbate all those issues. 305 00:20:07,400 --> 00:20:10,959 Speaker 1: So I think it used to be that people thought, oh, 306 00:20:11,160 --> 00:20:14,040 Speaker 1: software and pharmaceuticals, those are the easy places to invest, 307 00:20:14,560 --> 00:20:19,679 Speaker 1: And now I think LPSC we have fundamental risks that 308 00:20:19,760 --> 00:20:24,320 Speaker 1: if we don't tackle with real deep science and engineering, 309 00:20:24,359 --> 00:20:28,440 Speaker 1: that will take us a full step forward or two 310 00:20:28,560 --> 00:20:33,680 Speaker 1: steps forward. We're in trouble right and LPs are limited partners. 311 00:20:33,960 --> 00:20:35,800 Speaker 1: These are the people who put in the money in 312 00:20:35,960 --> 00:20:39,439 Speaker 1: funds like yours, who then then you used to deploy 313 00:20:39,640 --> 00:20:43,880 Speaker 1: towards companies. You mentioned m T was a founding LP. 314 00:20:44,600 --> 00:20:47,879 Speaker 1: Who are the others that are named? It's not something 315 00:20:47,920 --> 00:20:52,960 Speaker 1: we typically talk about, but our limited partners are endowments 316 00:20:53,240 --> 00:20:58,000 Speaker 1: and family offices that are at the scale of endowments 317 00:20:58,040 --> 00:21:03,159 Speaker 1: that fundamentally see the type of impact and returns that 318 00:21:03,200 --> 00:21:06,280 Speaker 1: we could have and love the types of things we're 319 00:21:06,320 --> 00:21:09,840 Speaker 1: investing into. Now, many of the problems that your companies 320 00:21:09,880 --> 00:21:13,040 Speaker 1: are trying to solve are addressing climate change are bigger 321 00:21:13,080 --> 00:21:18,879 Speaker 1: problems globally, and the opportunity that they have to become 322 00:21:18,880 --> 00:21:22,120 Speaker 1: big companies is if they have a global market. Are 323 00:21:22,160 --> 00:21:26,159 Speaker 1: there examples of problems being solved in like India or 324 00:21:26,280 --> 00:21:30,960 Speaker 1: Nimbabwe or places where it's harder to scale a company 325 00:21:31,119 --> 00:21:33,439 Speaker 1: or harder to take a new technology and deploy it, 326 00:21:33,480 --> 00:21:37,560 Speaker 1: even if it might be the better, cheaper solution. Of course, 327 00:21:37,600 --> 00:21:41,040 Speaker 1: this is a very complex question and not one that 328 00:21:41,119 --> 00:21:44,879 Speaker 1: I can answer in one sentence. But the way we 329 00:21:45,040 --> 00:21:49,640 Speaker 1: think about who we invest into and why we invest 330 00:21:49,680 --> 00:21:53,440 Speaker 1: into these companies is we look at a global map 331 00:21:53,520 --> 00:21:58,440 Speaker 1: and global scale. Can they efficiently and as quickly as 332 00:21:58,480 --> 00:22:03,600 Speaker 1: possible get to scaling globally? I mean that's also a 333 00:22:03,720 --> 00:22:06,400 Speaker 1: question of returns, right if they can get to scaling globally. 334 00:22:06,440 --> 00:22:10,560 Speaker 1: Often they're a great company, but we also fundamentally look 335 00:22:10,600 --> 00:22:14,600 Speaker 1: at it from a values perspective of you know, will 336 00:22:14,680 --> 00:22:18,639 Speaker 1: they have an effect not just in the richest areas 337 00:22:18,680 --> 00:22:22,880 Speaker 1: of the world but everywhere. So for instance, wind and solar. 338 00:22:23,560 --> 00:22:28,280 Speaker 1: If you look at kind of all of Africa's current 339 00:22:28,320 --> 00:22:32,760 Speaker 1: infrastructure for energy production, you would hope a lot of 340 00:22:32,800 --> 00:22:36,920 Speaker 1: that is not just wind, renewables, not burning as much diesel, 341 00:22:37,400 --> 00:22:44,320 Speaker 1: having a very flexible grid. So when we ask companies, hey, 342 00:22:44,480 --> 00:22:47,560 Speaker 1: how could you ever get to that part of the world, 343 00:22:48,480 --> 00:22:51,560 Speaker 1: We love to see companies that have thought it through, 344 00:22:51,720 --> 00:22:54,840 Speaker 1: have pushed themselves to think that through, to think through 345 00:22:54,920 --> 00:22:59,000 Speaker 1: partnerships that could help them get there faster because I 346 00:22:59,040 --> 00:23:02,760 Speaker 1: think it has bigger impact and faster speed to market. Yeah. 347 00:23:02,920 --> 00:23:04,680 Speaker 1: Is there an example or two. It doesn't have to 348 00:23:04,720 --> 00:23:10,520 Speaker 1: be climate tic. Yeah. Biobot, which is a wastewater analysis company, 349 00:23:11,080 --> 00:23:15,359 Speaker 1: basically scaled very rapidly in lots of parts of the 350 00:23:15,359 --> 00:23:19,119 Speaker 1: world to really look at wastewater to understand what is 351 00:23:19,119 --> 00:23:22,560 Speaker 1: in your wastewater, whether it was for COVID or opioids 352 00:23:22,560 --> 00:23:25,600 Speaker 1: like these are huge global problems of where to put 353 00:23:25,600 --> 00:23:28,360 Speaker 1: your public health dollars and if you don't know where 354 00:23:28,400 --> 00:23:32,200 Speaker 1: the problems are. You're just smattering your dollars everywhere. So 355 00:23:32,240 --> 00:23:35,520 Speaker 1: a company like that can get to global scale very 356 00:23:35,640 --> 00:23:41,159 Speaker 1: quickly and have big impact very quickly because they have 357 00:23:41,320 --> 00:23:43,720 Speaker 1: thought about the rest of the world, and so they 358 00:23:43,760 --> 00:23:47,719 Speaker 1: have different partnerships. We talked a little bit about how 359 00:23:48,000 --> 00:23:50,560 Speaker 1: many of your companies. A majority of your companies have 360 00:23:50,680 --> 00:23:56,000 Speaker 1: scientists entrepreneurs. Many people thinking about solving problems right now 361 00:23:56,280 --> 00:23:59,199 Speaker 1: are thinking about shouting a company. Who would be the 362 00:23:59,320 --> 00:24:02,920 Speaker 1: right person to start a company. One of the privileges 363 00:24:02,920 --> 00:24:07,400 Speaker 1: that I have is sitting amongst some of the best 364 00:24:07,520 --> 00:24:11,840 Speaker 1: universities in the world who've done an amazing job of 365 00:24:11,880 --> 00:24:17,560 Speaker 1: attracting people from all over the globe. They're incredibly diverse group. 366 00:24:18,320 --> 00:24:22,360 Speaker 1: I think the universities have done a great job of 367 00:24:22,400 --> 00:24:25,800 Speaker 1: saying to people like, hey, you have agency in solving 368 00:24:25,800 --> 00:24:29,800 Speaker 1: these problems. You can learn how to run companies. So 369 00:24:29,920 --> 00:24:33,640 Speaker 1: I think it's part of our value system as well 370 00:24:34,160 --> 00:24:38,800 Speaker 1: that we want to believe and help lots of people 371 00:24:38,880 --> 00:24:42,560 Speaker 1: learn how to be entrepreneurs. So we run programs like Blueprint, 372 00:24:42,880 --> 00:24:46,040 Speaker 1: which are for PhDs in post docs to learn what 373 00:24:46,160 --> 00:24:49,200 Speaker 1: it would really take to start and run a tough 374 00:24:49,280 --> 00:24:54,080 Speaker 1: tech company, and I think that that open stance is 375 00:24:54,240 --> 00:24:58,359 Speaker 1: I think the right one for creating more entrepreneurs. That 376 00:24:58,400 --> 00:25:00,280 Speaker 1: doesn't mean they all are going to get back act 377 00:25:00,320 --> 00:25:03,239 Speaker 1: by the engine, or should all be entrepreneurs, but I 378 00:25:03,280 --> 00:25:08,400 Speaker 1: think having that fundamental belief in people's abilities to learn 379 00:25:08,520 --> 00:25:12,119 Speaker 1: and grow and run these things mean that more of 380 00:25:12,160 --> 00:25:14,840 Speaker 1: these inventions will get out. So that's how we think 381 00:25:14,880 --> 00:25:18,600 Speaker 1: of it. Right. This is something that probably you've been 382 00:25:18,600 --> 00:25:21,160 Speaker 1: asked many times, is there are not very many women 383 00:25:21,200 --> 00:25:24,080 Speaker 1: in this industry, not on the venture capitalist side, not 384 00:25:24,200 --> 00:25:28,080 Speaker 1: in the founding of hart tech companies. There's also a 385 00:25:28,080 --> 00:25:31,040 Speaker 1: supply chain problem here, which is that women don't make 386 00:25:31,080 --> 00:25:36,960 Speaker 1: it to the science Technology engineering MATHS degree graduate PhD 387 00:25:37,119 --> 00:25:40,960 Speaker 1: level as often as men do. So there's a whole 388 00:25:41,320 --> 00:25:44,919 Speaker 1: set of reasons why there is lack of gender diversity, 389 00:25:45,240 --> 00:25:48,840 Speaker 1: But what has been your approach to try and tackle 390 00:25:48,840 --> 00:25:51,119 Speaker 1: it as much as you can? If you look at 391 00:25:51,920 --> 00:25:54,879 Speaker 1: just take MT in Harvard, there's an enormous number of 392 00:25:54,880 --> 00:25:58,399 Speaker 1: women here in PhD and postdoctoral programs, So there's really 393 00:25:58,440 --> 00:26:03,120 Speaker 1: no excuse that can't be backed as entrepreneurs out of there. 394 00:26:03,640 --> 00:26:07,439 Speaker 1: So anybody who thinks it's a pipeline problem I just 395 00:26:07,440 --> 00:26:13,000 Speaker 1: don't buy it. There's tons of women, and I think 396 00:26:14,240 --> 00:26:17,720 Speaker 1: they are often ignored, and so taking look at the 397 00:26:17,720 --> 00:26:20,000 Speaker 1: mirror of how are you making decisions? Are you truly 398 00:26:20,040 --> 00:26:22,919 Speaker 1: making them equitably? Because I don't think it's as much 399 00:26:22,960 --> 00:26:25,800 Speaker 1: of a pipeline problem as people believe. Interesting and so 400 00:26:25,960 --> 00:26:28,439 Speaker 1: if it's not, because that's the excuse many make. You know, 401 00:26:29,080 --> 00:26:31,480 Speaker 1: most of the venture capitalists we spoke to in Silicon 402 00:26:31,520 --> 00:26:34,480 Speaker 1: Valley are all men, and when we ask that question them, 403 00:26:34,520 --> 00:26:36,239 Speaker 1: that's what they say, you know, we would love to. 404 00:26:37,040 --> 00:26:38,840 Speaker 1: But then they go, oh, but there are not that 405 00:26:38,960 --> 00:26:44,560 Speaker 1: many of them. Come on, yes, there are fewer women. Yes, 406 00:26:45,000 --> 00:26:47,600 Speaker 1: but what you see is when there are fewer women, 407 00:26:47,880 --> 00:26:52,640 Speaker 1: often they're better than their male counterpart. Okay, last question 408 00:26:52,680 --> 00:26:55,040 Speaker 1: for you. If you had a billboard outside a house 409 00:26:55,240 --> 00:26:57,280 Speaker 1: by the message of the world, what would you put 410 00:26:57,320 --> 00:27:00,640 Speaker 1: on it? Better on young people's ambition? That's what you do, 411 00:27:00,920 --> 00:27:06,160 Speaker 1: That's what I do. Thank you so much for the time. 412 00:27:06,320 --> 00:27:09,240 Speaker 1: This is a great conversation. Yes, so fun. Thank you 413 00:27:09,280 --> 00:27:19,400 Speaker 1: for the provocative questions and fun. The thing that I'm 414 00:27:19,400 --> 00:27:21,640 Speaker 1: really taking away from this is how much I want 415 00:27:21,640 --> 00:27:25,040 Speaker 1: to do that blueprint program, the one that Katie described 416 00:27:25,160 --> 00:27:28,200 Speaker 1: where PhDs and postdocs learned what it's like to start 417 00:27:28,240 --> 00:27:31,760 Speaker 1: and run a tough tech company. Is there a blueprint 418 00:27:32,200 --> 00:27:37,200 Speaker 1: coming up? Yeah, there's a blueprint right now for seven weeks. 419 00:27:37,359 --> 00:27:39,560 Speaker 1: Can I attend one of these? Sure? I do have 420 00:27:39,560 --> 00:27:41,280 Speaker 1: a PhD. I'm not starting a company, but I just 421 00:27:41,320 --> 00:27:51,720 Speaker 1: want to go through the process. You're absolutely welcome. Thanks 422 00:27:51,720 --> 00:27:54,440 Speaker 1: for listening to Zero. If you liked this episode, please 423 00:27:54,480 --> 00:27:56,840 Speaker 1: take a moment to rate, review, and subscribe to the 424 00:27:56,840 --> 00:28:00,840 Speaker 1: show on Apple Podcasts or Spotify, Send it to a friend, 425 00:28:01,040 --> 00:28:05,000 Speaker 1: or share it with someone who likes Shark Tank. If 426 00:28:05,040 --> 00:28:08,119 Speaker 1: you've got a suggestion for guests, or topics or something 427 00:28:08,119 --> 00:28:10,359 Speaker 1: you just want us to look into, get in touch 428 00:28:10,440 --> 00:28:14,159 Speaker 1: at Zero pod at Bloomberg dot Net. Zero's producer is 429 00:28:14,200 --> 00:28:18,280 Speaker 1: Oscar Boyd and senior producer is Christine driscoll. Our theme 430 00:28:18,359 --> 00:28:21,680 Speaker 1: music is composed by Wonderlely Special thanks to Kira bin 431 00:28:21,760 --> 00:28:24,400 Speaker 1: ram I'm Kshatrati back next week.