Speaker 1: Welcome to Zero. I'm Aukshatrati. This week moonshots, magnets and
Speaker 1: monstrously smart people. Sometimes the climate beat is fun. Last
Speaker 1: summer I got a chance to visit a nuclear fusion company,
Speaker 1: multiple carbon removal firms, and at least two startups using
Speaker 1: electricity to make iron. And you'll hear some of these
Speaker 1: stories in future episodes on Zero. The science behind these
Speaker 1: companies could not be more different, but their business problems
Speaker 1: are the same hiring, financing, supply chain. The more I
Speaker 1: observed the pressures on each company, I started to notice
Speaker 1: that there was a split. Some companies were led by
Speaker 1: serial entrepreneurs, someone who has experienced in dealing with the
Speaker 1: problems of a business. Other companies were led by a
Speaker 1: scientist or an engineer, the person whose deepest experience was
Speaker 1: studying the science that the company was based on. The
Speaker 1: choice of CEO is something that investors pay careful attention to.
Speaker 1: They often do not want a scientist as the CEO.
Speaker 1: They want an experienced entrepreneur because the problems of running
Speaker 1: a business are hard to learn and very high stakes.
Speaker 1: My guest today is Katie Ray, who heads a large fund,
Speaker 1: and she has a different philosophy about meeting people where
Speaker 1: they are. We've certainly proven over the last five years
Speaker 1: that we've made some great bets on human beings that
Speaker 1: are more currently engineers or post docs and now are
Speaker 1: really fantastic CEOs. Having that fundamental belief in people's abilities
Speaker 1: to learn and grow and run these things mean that
Speaker 1: more of these inventions will get out. Katie is the
Speaker 1: CEO of the Engine. It's a venture capital fund and
Speaker 1: a public benefit corporation that was spun out of MT
Speaker 1: in twenty six. It invests in so called tough tech
Speaker 1: that takes a scientific idea from proof of concept to
Speaker 1: commercial scale. The tough part is because usually it takes
Speaker 1: years of effort and lots of money to do it.
Speaker 1: Others call it heart tech as a way of signaling
Speaker 1: that the technology is not based on software. Katie's job
Speaker 1: is to deploy over six hundred million dollars of venture
Speaker 1: funding for Boston area startups that are ready to scale.
Speaker 1: The people who seek funding from her are often scientists
Speaker 1: and engineers, and some of the ideas are far out
Speaker 1: at first plush. Katie has led investments into companies as
Speaker 1: varied as Commonwealth Fusion Systems, a company building a compact
Speaker 1: nuclear fusion plant, Form Energy, which makes batteries that store
Speaker 1: renewable power by rusting an unrusting iron, and Boston Metal,
Speaker 1: a company that uses electricity to make steal. So when
Speaker 1: Katie's choosing who to invest in, she has a lot
Speaker 1: to think about. She has to figure out how she
Speaker 1: can make sure that the companies have a promising idea
Speaker 1: that works and is going to be led by someone
Speaker 1: who can make it a functioning business. And because of
Speaker 1: where she sits, she's invested in making sure that the
Speaker 1: journey from scientists to CEO can happen. I sat down
Speaker 1: with Katie at the Engine's headquarters near MT to talk
Speaker 1: about how it all got started, how she does due
Speaker 1: diligence on these companies with far out ideas, and why
Speaker 1: there should be no excuses for not having more women
Speaker 1: in this industry. Katie, welcome to the show. Thank you
Speaker 1: so much for having me. What got you the idea
Speaker 1: of starting this in the first place? How were you
Speaker 1: able to raise money for these wacky ideas? Basically the
Speaker 1: way it started, I mean this was born out of
Speaker 1: MT right and very MT was born out of a
Speaker 1: bunch of research that showed that in the area we
Speaker 1: call tough tech, so things often with a physical instantiation,
Speaker 1: things that are not produced overnight, that take time to produce,
Speaker 1: that are often systems. Things like that we're not being
Speaker 1: invested into at the same rate, Like the rate of
Speaker 1: investment had gone down over thirty years. But then if
Speaker 1: you overlaid that with the problems in the world that
Speaker 1: needed to be solved, you would see like, we're not
Speaker 1: investing into the things that will matter to our future.
Speaker 1: And so M. I. T very much was like, we've
Speaker 1: got to solve some of these problems. And so they
Speaker 1: put the shoulder of the university into saying, can we
Speaker 1: fundamentally shift who gets funded, why do they get funded.
Speaker 1: That's the birth story of the Engine. M I. T
Speaker 1: was an anchor in our first fund and an amazing partner,
Speaker 1: Like you're sitting in our new building right now, which
Speaker 1: is only through a partnership with them. No venture fund
Speaker 1: could build a space like this. But if you think
Speaker 1: about the impact the Engine could have over time and
Speaker 1: the density of tough tech founders that will be in
Speaker 1: this building, so it will be over a thousand people here,
Speaker 1: we have another space with another five hundred people. You
Speaker 1: start to get a density of companies and knowledge that
Speaker 1: can be rapidly transferred because everyone is in very close proximity.
Speaker 1: You've seen it in biotech, you saw it in software.
Speaker 1: You've seen these clusters develop and fund one was really
Speaker 1: an experiment to show that we could find incredible things
Speaker 1: to translate out and that they could be venture scale investments,
Speaker 1: and that we could find other investors to work with
Speaker 1: us over time to scale these companies. I think we've
Speaker 1: done a pretty good job in that regard and us
Speaker 1: is there a metric by that you go to have
Speaker 1: met that target? Well, if you just think first Portfolio
Speaker 1: had twenty seven companies, we invested less than two hundred
Speaker 1: million so far, and they've raised three point seven billion dollars. Like,
Speaker 1: that's a pretty good metric by anybody's standard, and that
Speaker 1: means that many people have bet that these are venture
Speaker 1: scale returns. You have built at the engine something that's
Speaker 1: quite unique, which is you invest in science ideas that
Speaker 1: have the potential to become big companies, but they start
Speaker 1: with science ideas that I would probably reframe it slightly.
Speaker 1: I would say I invest in two companies that are
Speaker 1: translating out of science and engineering labs that are ready
Speaker 1: to be built and proven outside of a lab and
Speaker 1: then scaled. So I think that's a little bit subtly
Speaker 1: different than what you said, But they are all based
Speaker 1: on scientific evidence the scientific method of producing new things.
Speaker 1: But there is a science risk attached to these companies
Speaker 1: that you know, what is in the lab usually works
Speaker 1: after many many tries, and sometimes it's replicable. Having done
Speaker 1: science for a while, I know how hard it is
Speaker 1: to even replicate papers with detailed instructions, but taking that
Speaker 1: and doing it at a commercial scale, where you are
Speaker 1: doing it millions of times or millions of tons, is
Speaker 1: a completely different challenge. So there's a ton of science
Speaker 1: risk in all the companies that you take, or science
Speaker 1: translation risk that you've taken in the engine, right, Yeah.
Speaker 1: I think of it is you should have proven out
Speaker 1: the basic fundamentals of the science risk, and then there
Speaker 1: is engineering risk of scaling these things, which could take
Speaker 1: you back to scientific risk where you don't understand something again.
Speaker 1: But typically we try to make sure that you understand
Speaker 1: the mechanisms of acts and what would happen as you scale,
Speaker 1: and that you could simulate with software that you could
Speaker 1: understand what they're going to look like. You know, for instance,
Speaker 1: if you invest into a fusion company, you better be
Speaker 1: pretty confident that when you build your magnets it's going
Speaker 1: to work. You understand the physics of whatever your new
Speaker 1: material is, etc. Or else, it's not really an investable asset.
Speaker 1: So it's different than a moonshot, right, A moonshot is
Speaker 1: in my mind, you are really heading into scientific core
Speaker 1: scientific risk still, whereas many of these you've proven out
Speaker 1: the core science. You're heading into engineering risk, which could
Speaker 1: bring you back to scientific risk, but at a lesser level.
Speaker 1: And so engineering risks can take you back to science.
Speaker 1: You've now got more than forty companies in your portfolio.
Speaker 1: You've raised six hundred million dollars and above you've put
Speaker 1: in almost forty percent of that capital to work. What
Speaker 1: are the lessons you've learned in doing this for the
Speaker 1: last five years? You know, when we started this, we
Speaker 1: laid out a set of assumptions about how much capital
Speaker 1: these companies would need, how much time it would take
Speaker 1: to translate, how you would have to build these teams, Like,
Speaker 1: we really laid out what our hypotheses were, and I
Speaker 1: think for the most part we were pretty close in
Speaker 1: that the teams would need multiple sets of labs, chemistry, biology,
Speaker 1: machines shops. They would need many kinds of experts around
Speaker 1: them to get through their engineering risk. They would need
Speaker 1: to be surrounded by serial entrepreneurs and business people that
Speaker 1: could give them advice about how to scale the business
Speaker 1: side of their company or really how to take out
Speaker 1: business risk. That we would need to develop a robust
Speaker 1: capital stack that understood engineering based companies and the risk
Speaker 1: there are there, And depending on what that time frame was,
Speaker 1: that would dictate the kind of capital they needed to
Speaker 1: prove out those risks. It would dictate what the potential
Speaker 1: upside would need to be to make those bets. So
Speaker 1: I think all of those things have played out. And
Speaker 1: that's because think about where I sit in the middle
Speaker 1: of MIT and the Boston region. So I had a
Speaker 1: lot of experts helping me understand that before we started.
Speaker 1: Are there climate tech companies that you would take as
Speaker 1: salient examples of having gone through that period and proven
Speaker 1: out their lists. Commonwealth Fusion had to prove that they
Speaker 1: could build the first magnet and get the first magnet
Speaker 1: to twenty tesla right like. That was their fundamental proof point,
Speaker 1: and they did that in under three years. So what
Speaker 1: does it mean to get the first magnet to twenty
Speaker 1: Tesla's well, Tesla is the unit of strength of a magnet,
Speaker 1: named after the famous Serbian American engineer Nicola Tesla. Twenty
Speaker 1: teslas is a really powerful magnet. An MRI machine like
Speaker 1: the one that doctors used to peer inside bodies, has
Speaker 1: a magnet that's about three teslas. Other examples form Energy.
Speaker 1: They're building kind of a long term battery for wind
Speaker 1: and solar to allow up to one hundred hours of storage.
Speaker 1: We also visited Farm Energy. You'll hear about how they
Speaker 1: got their iron air battery in a future episode. They
Speaker 1: started with an idea that wasn't working, and they pivoted
Speaker 1: to an idea that is now working, and that all
Speaker 1: happened in a four year period. They raced two technologies
Speaker 1: against each other, and they knew from the beginning they
Speaker 1: weren't sure which would work, and one did work. Awesome, Like,
Speaker 1: what a wonderful way to run a company. So that
Speaker 1: fundamentally changes the economics of wind and solar for grid all.
Speaker 1: Give me another example. Let's see Boston Metal. So this
Speaker 1: is you know, electrification of the steel making process or
Speaker 1: really metals production process, you know. So we started out
Speaker 1: in twenty eighteen and said it'll take about four years
Speaker 1: to prove this out pretty close, you know, right, even
Speaker 1: though the company had been founded in twenty twelve. So
Speaker 1: by the time you got to the investment, you're like, okay,
Speaker 1: now we are at that point there are investment makes
Speaker 1: sense because there's a four year child exactly right, and
Speaker 1: you don't know what the time period will be before
Speaker 1: we invest. That's fine. So that's why we look at
Speaker 1: a big range of companies because some of them kind
Speaker 1: of take eight or nine or ten years in a
Speaker 1: lab or getting government funding before they're really ready. Right,
Speaker 1: that's the cost of doing business, right, And but we
Speaker 1: want to look at hey, what's the one to five
Speaker 1: year period where you take out big, big technical risks
Speaker 1: so then your deal with the normal technical risks and
Speaker 1: the business risks. Right. One other complication in climate tech
Speaker 1: companies is that it's not just about the entrepreneurs. Obviously
Speaker 1: that is still a core part of building a company,
Speaker 1: but it's also about the science, and so you have
Speaker 1: to do a ton of due diligence. Is it real,
Speaker 1: is it within the limits and bounds of known science?
Speaker 1: And you do it across so many subjects. Of course,
Speaker 1: climate tech is what we're talking about, but that's not
Speaker 1: the only thing you invest in. So what's your process
Speaker 1: for making sure that when a company comes and gives
Speaker 1: a pitch that you can then go back and do
Speaker 1: your homework and say, yes, we would like to invest. Yeah.
Speaker 1: So we run multiple processes all at the same time.
Speaker 1: So we do fundamental landscaping of basically any area we
Speaker 1: would be interested investing in, and we look at which
Speaker 1: labs are doing research in there, what is the research
Speaker 1: they're doing, what the findings they're having, So you're looking
Speaker 1: at all the things that maybe created the breakthrough in
Speaker 1: the first place, right because science is this wonderfully collaborative space.
Speaker 1: Nothing just emerges from the ether. It is a collaborative.
Speaker 1: So we look at those collaborations. We look at who
Speaker 1: got grants, what were they about, and why things are developing.
Speaker 1: That gives us this place to start from any investment.
Speaker 1: Now that has taken us years to build knowledge in
Speaker 1: different areas, and we're continually building knowledge and there we
Speaker 1: have a director of research who has many PhDs who
Speaker 1: work with us to kind of build these maps of
Speaker 1: what's happening. So that's one process. The second process is
Speaker 1: specifically to a company. So our team is kind of
Speaker 1: built to be able to look at things like this.
Speaker 1: So most of our investment professionals have PhDs in a
Speaker 1: certain area and are d networked into others. So we
Speaker 1: will talk to people on the industry side and we
Speaker 1: will talk to people on the academic side to really
Speaker 1: get competing views of will this technology work. And that's
Speaker 1: of course after looking at the basic science, reading all
Speaker 1: the papers, you know, trying to really understand that. So
Speaker 1: when we look at companies and ideas, we're first starting
Speaker 1: with what is their ambition and do they have the
Speaker 1: team to back that up. After the break, I asked
Speaker 1: Katie why the engine has so many signed to CEOs
Speaker 1: compared to Silicon Valley startups and why there are not
Speaker 1: enough women CEOs in this industry. One of the things
Speaker 1: that we noticed in Silicon Valley during our tour of
Speaker 1: startups there and meeting venture capitalists was there was a bias,
Speaker 1: i would say, towards finding entrepreneurs behind climate tech companies
Speaker 1: that have had some business experience. They may be engineers,
Speaker 1: but that they have built companies or they are not engineers,
Speaker 1: but they are commercial minds who can capture the difficulties
Speaker 1: of what science they're trying to convert into a technology.
Speaker 1: If you look at your portfolio, majority of the companies
Speaker 1: still have their CEOs to be the scientists who started
Speaker 1: with the idea. Why is that? Yeah, I think, well,
Speaker 1: don't forget we work at the earliest stage as a
Speaker 1: venture right, so we're a true translation out of labs,
Speaker 1: and so our fundamental hypothesis is that there are some
Speaker 1: very tricky engineering and business decisions that have to be
Speaker 1: made in these deeply technical companies, and that if those
Speaker 1: trade offs aren't really understood, you could waste a lot
Speaker 1: of money. So we think, certainly in the earliest phases
Speaker 1: of these things, you cannot just divorce the science from
Speaker 1: the scientist or divorce the engineering from the engineer, and
Speaker 1: that the better model is to surround that engineer with
Speaker 1: business talent and thinking to influence how they make those tradeoffs.
Speaker 1: But it's not the exclusive model, and it doesn't mean
Speaker 1: that the scientists should always be the CEO. I think
Speaker 1: those decisions get made over time. But I'll tell you,
Speaker 1: in the pool of people that could be CEOs, these
Speaker 1: are monstrously smart, ambitious people, and when they start to
Speaker 1: understand that it is their job to learn the world
Speaker 1: of business and understand deal making, understand margin, understand how
Speaker 1: to grow a big business. A lot of them are
Speaker 1: very capable of doing this. And that doesn't mean everyone
Speaker 1: should be the CEO, but I think having this mindset
Speaker 1: that they shouldn't be, you're really leaving a lot of
Speaker 1: great companies to the side. And we think that that's
Speaker 1: as risky is saying it has to be a business
Speaker 1: person that runs it. And I think, you know, we've
Speaker 1: certainly proven over the last five years that we've made
Speaker 1: some great bets on human beings that are we're currently
Speaker 1: engineers or post docs and now are really fantastic CEOs.
Speaker 1: Now you set out a thesis or try and fund
Speaker 1: companies that do hard science, and you were able to
Speaker 1: prove it out. In the process, you were also able
Speaker 1: to raise more money for your own funds. Was that
Speaker 1: process easy? Is it getting easier? You know? How is
Speaker 1: capital in this area of tough tech coming through? Yeah?
Speaker 1: I mean, I think for the good or the bad
Speaker 1: of this, two or three fundamental things have happened in
Speaker 1: the last five years, right COVID sent to us deeply
Speaker 1: into really understanding we have a global supply chain issue,
Speaker 1: like how we produce things, how we get healthcare, how
Speaker 1: we actually do Almost everything we do today is going
Speaker 1: to need to shift. So you see the US Government
Speaker 1: Act and things like the Chips Act and now the IRA.
Speaker 1: Then I think while people were kind of at home,
Speaker 1: there were so many things that happened in climate that
Speaker 1: I think galvanized this understanding that this stuff is real.
Speaker 1: Like we're not thirty years away from climate change. We
Speaker 1: are drastically in the middle of this. Now we have
Speaker 1: war in Russia, Ukraine and a lot of difficulty in China,
Speaker 1: so like that's just going to exacerbate all those issues.
Speaker 1: So I think it used to be that people thought, oh,
Speaker 1: software and pharmaceuticals, those are the easy places to invest,
Speaker 1: And now I think LPSC we have fundamental risks that
Speaker 1: if we don't tackle with real deep science and engineering,
Speaker 1: that will take us a full step forward or two
Speaker 1: steps forward. We're in trouble right and LPs are limited partners.
Speaker 1: These are the people who put in the money in
Speaker 1: funds like yours, who then then you used to deploy
Speaker 1: towards companies. You mentioned m T was a founding LP.
Speaker 1: Who are the others that are named? It's not something
Speaker 1: we typically talk about, but our limited partners are endowments
Speaker 1: and family offices that are at the scale of endowments
Speaker 1: that fundamentally see the type of impact and returns that
Speaker 1: we could have and love the types of things we're
Speaker 1: investing into. Now, many of the problems that your companies
Speaker 1: are trying to solve are addressing climate change are bigger
Speaker 1: problems globally, and the opportunity that they have to become
Speaker 1: big companies is if they have a global market. Are
Speaker 1: there examples of problems being solved in like India or
Speaker 1: Nimbabwe or places where it's harder to scale a company
Speaker 1: or harder to take a new technology and deploy it,
Speaker 1: even if it might be the better, cheaper solution. Of course,
Speaker 1: this is a very complex question and not one that
Speaker 1: I can answer in one sentence. But the way we
Speaker 1: think about who we invest into and why we invest
Speaker 1: into these companies is we look at a global map
Speaker 1: and global scale. Can they efficiently and as quickly as
Speaker 1: possible get to scaling globally? I mean that's also a
Speaker 1: question of returns, right if they can get to scaling globally.
Speaker 1: Often they're a great company, but we also fundamentally look
Speaker 1: at it from a values perspective of you know, will
Speaker 1: they have an effect not just in the richest areas
Speaker 1: of the world but everywhere. So for instance, wind and solar.
Speaker 1: If you look at kind of all of Africa's current
Speaker 1: infrastructure for energy production, you would hope a lot of
Speaker 1: that is not just wind, renewables, not burning as much diesel,
Speaker 1: having a very flexible grid. So when we ask companies, hey,
Speaker 1: how could you ever get to that part of the world,
Speaker 1: We love to see companies that have thought it through,
Speaker 1: have pushed themselves to think that through, to think through
Speaker 1: partnerships that could help them get there faster because I
Speaker 1: think it has bigger impact and faster speed to market. Yeah.
Speaker 1: Is there an example or two. It doesn't have to
Speaker 1: be climate tic. Yeah. Biobot, which is a wastewater analysis company,
Speaker 1: basically scaled very rapidly in lots of parts of the
Speaker 1: world to really look at wastewater to understand what is
Speaker 1: in your wastewater, whether it was for COVID or opioids
Speaker 1: like these are huge global problems of where to put
Speaker 1: your public health dollars and if you don't know where
Speaker 1: the problems are. You're just smattering your dollars everywhere. So
Speaker 1: a company like that can get to global scale very
Speaker 1: quickly and have big impact very quickly because they have
Speaker 1: thought about the rest of the world, and so they
Speaker 1: have different partnerships. We talked a little bit about how
Speaker 1: many of your companies. A majority of your companies have
Speaker 1: scientists entrepreneurs. Many people thinking about solving problems right now
Speaker 1: are thinking about shouting a company. Who would be the
Speaker 1: right person to start a company. One of the privileges
Speaker 1: that I have is sitting amongst some of the best
Speaker 1: universities in the world who've done an amazing job of
Speaker 1: attracting people from all over the globe. They're incredibly diverse group.
Speaker 1: I think the universities have done a great job of
Speaker 1: saying to people like, hey, you have agency in solving
Speaker 1: these problems. You can learn how to run companies. So
Speaker 1: I think it's part of our value system as well
Speaker 1: that we want to believe and help lots of people
Speaker 1: learn how to be entrepreneurs. So we run programs like Blueprint,
Speaker 1: which are for PhDs in post docs to learn what
Speaker 1: it would really take to start and run a tough
Speaker 1: tech company, and I think that that open stance is
Speaker 1: I think the right one for creating more entrepreneurs. That
Speaker 1: doesn't mean they all are going to get back act
Speaker 1: by the engine, or should all be entrepreneurs, but I
Speaker 1: think having that fundamental belief in people's abilities to learn
Speaker 1: and grow and run these things mean that more of
Speaker 1: these inventions will get out. So that's how we think
Speaker 1: of it. Right. This is something that probably you've been
Speaker 1: asked many times, is there are not very many women
Speaker 1: in this industry, not on the venture capitalist side, not
Speaker 1: in the founding of hart tech companies. There's also a
Speaker 1: supply chain problem here, which is that women don't make
Speaker 1: it to the science Technology engineering MATHS degree graduate PhD
Speaker 1: level as often as men do. So there's a whole
Speaker 1: set of reasons why there is lack of gender diversity,
Speaker 1: But what has been your approach to try and tackle
Speaker 1: it as much as you can? If you look at
Speaker 1: just take MT in Harvard, there's an enormous number of
Speaker 1: women here in PhD and postdoctoral programs, So there's really
Speaker 1: no excuse that can't be backed as entrepreneurs out of there.
Speaker 1: So anybody who thinks it's a pipeline problem I just
Speaker 1: don't buy it. There's tons of women, and I think
Speaker 1: they are often ignored, and so taking look at the
Speaker 1: mirror of how are you making decisions? Are you truly
Speaker 1: making them equitably? Because I don't think it's as much
Speaker 1: of a pipeline problem as people believe. Interesting and so
Speaker 1: if it's not, because that's the excuse many make. You know,
Speaker 1: most of the venture capitalists we spoke to in Silicon
Speaker 1: Valley are all men, and when we ask that question them,
Speaker 1: that's what they say, you know, we would love to.
Speaker 1: But then they go, oh, but there are not that
Speaker 1: many of them. Come on, yes, there are fewer women. Yes,
Speaker 1: but what you see is when there are fewer women,
Speaker 1: often they're better than their male counterpart. Okay, last question
Speaker 1: for you. If you had a billboard outside a house
Speaker 1: by the message of the world, what would you put
Speaker 1: on it? Better on young people's ambition? That's what you do,
Speaker 1: That's what I do. Thank you so much for the time.
Speaker 1: This is a great conversation. Yes, so fun. Thank you
Speaker 1: for the provocative questions and fun. The thing that I'm
Speaker 1: really taking away from this is how much I want
Speaker 1: to do that blueprint program, the one that Katie described
Speaker 1: where PhDs and postdocs learned what it's like to start
Speaker 1: and run a tough tech company. Is there a blueprint
Speaker 1: coming up? Yeah, there's a blueprint right now for seven weeks.
Speaker 1: Can I attend one of these? Sure? I do have
Speaker 1: a PhD. I'm not starting a company, but I just
Speaker 1: want to go through the process. You're absolutely welcome. Thanks
Speaker 1: for listening to Zero. If you liked this episode, please
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Speaker 1: Oscar Boyd and senior producer is Christine driscoll. Our theme
Speaker 1: music is composed by Wonderlely Special thanks to Kira bin
Speaker 1: ram I'm Kshatrati back next week.