Shaping Research by Changing Context with Ilan Gur [Idea Machines #36]

In this conversation I talk to Ilan Gur about what it really means for technology to “escape the lab”, the power of context to shape the usefulness of research, the inadequacies of current institutional structures, how activate helps technology escape the lab *by* changing people’s context, and more.

Ilan is the CEO and founder of Activate, which is a nonprofit that runs a fellowship enabling scientists to spend two years embedded in research institutions to mature technology from a concept to a first product. In the past, he has also served as a program director at ARPA-E and was a cofounder of Seeo, where he commercial new high-density battery technology.

Links

Activate

Ilan on Twitter

Ilan on My Climate Journey Podcast 

Transcript

In the past, we've talked about the, how the whole process of really turning hardcore scientific research into products that have an impact on people's lives is fairly abstract to people outside of the system. Since you've both walked the path and now help other people do the same, let's round the conversation.

would you go into detail on what the actual actions you need to take to go from say, being a graduate student who just published a paper on a promising battery technology to an improved battery in a car.

That's that's a great place to start. let me try and answer that from a few different dimensions. I'll, I'll start by answering it, just from an anecdote about my personal experience, which I've shared in other places, but, you know, I basically. Went into my PhD program because I felt like the field I was studying material scientists, material science could, be the biggest way to make a big impact on climate change by basically taking new science and turning it into the next generation of all the technologies.

We need to have a sustainable economy. And, I was working in nanotechnology, joined. Kind of the world, the best research group in the world that that was working on how nano materials could improve solar cells. and this is before the, the enormous solar market that exists today exists. There was a sense at the time that, you know, we needed a completely new generation of technology to make solar ubiquitous and cost effective.

And so, you know, we had this great mantra around how we were going to print solar cells like newspapers, using these small colloidal nano, semiconductors. and the research was phenomenal. we were driven by the fact that what I like to say is, you know, we wrote a science paper where the first paragraph, like any, talked about how the research was going to change the world.

And it wasn't until I randomly got connected with some business school folks at Berkeley, where I was doing my PhD. and they actually. It didn't take long. they put me through just a few cycles of digging one level deeper into, how solar cells were actually made, how they were sold, what determined their, their costs and the cost of energy they produce.

and I ended up, you know, over the course of a few weeks with a spreadsheet that I still have somewhere, which told me that. If we hit all of our targets and our research in terms of what we thought could change the world. we would end up with a solar cell where even if you gave it away for free, it couldn't compete with the existing state of the art Silicon solar cells at the time.

and it was a really. Simple idea, which was, we were making dirt cheap solar cells, but they probably wouldn't last very long. And we didn't think that was such a big deal. You just print some more. and yet, certainly at the time, and it's still true. It's such a, such a predominant amount of the cost of solar energy came from the balance of systems and installations.

And I bring up the story because, for me, it was a tipping point. We had so much excitement about our research. It was even published in Forbes, you know, so a business magazine, and. It just showed how it showed, how easy it was to think you were doing something productive and successful. I it's not that I, I, I was in academia, but the reason I was there was to try and get something productive that could turn into a product.

Right. And I had missed the boat so much, even with that intention. and so that was a shock to me. And so. That was kind of the first lesson around how, you know, institutions matter and incentives matter. but what I ended up doing was then leaving academia and jumping into an early stage startup, which was an amazing vehicle to think about how this transition happens and, you know, basically the learning there, and, This is what we now, you know, this is a lot of what we now indoctrinate and try and help people understand in the fellowship we run, was that, you know, the depth and multitude of elements that determine whether a technology can actually make it from the research stage to a product in the market.

You know, first of all, you know, the idea is like, you know, the easy part in some regard. but yeah. You know, the number of levels deeper, you have to go to understand, okay, how is it going to be, how is it actually going to be valuable? Who's going to buy it. Why are they going to buy it? You know, how does, how does the whole system get built to make it, it's it's a month multi-dimensional problem where everything needs to line up between finance and the team you have in the market yet.

And it's technology. and. You know, for me, I think, you know, this we've talked before, one of the biggest things that I've come to realize is we've got, you know, we've got hundreds of billions of dollars that government spends to do the idea and ideation. We've got hundreds of billions of dollars that the private sector spends to basically take the early prototypes and the idea of a product and scale it.

and we've got really very little, that goes into how you do all the really hard stuff of translating one to the other. Yeah. So, so let's like what I'm going to actually continue to poke at. Like, what is that actual stuff? So the, the start that you joined did w what, what sort of was the origin of the technology that you were working on?

I assume it came out of a lab somewhere. I, yeah, I was involved in two startups. One was after that epiphany moment in my PhD work, I basically threw out the work we were doing, and shifted gears and ended up developing the technology. That was the basis for, for actually a solar startup thinking about sort of thin-film, nanocrystal based, solar cells, Basically realizing that the, that the lifetime was so important, we just threw out all of the organics that we were working on and focused on.

Like, you basically just need a new manufacturing approach to make something that looks like a traditional solar. So, that was a company that I kind of helped establish, but then ultimately didn't go. I was, I was meant to be sort of the founding, you know, grad student turn CTO. and then, for a number of reasons, didn't end up jumping into that as a startup and instead, through.

Just some of the serendipity of being in the Bay area and Silicon Valley ended up, on the founding team of a battery startup that came out of another research lab at Berkeley. and this was funded by, Samira and Vanessa who, when, when coastal ventures was just going to start it. yeah, so, so like let's so.

W when we say coming out of a lab, I think it's actually worth almost disecting what that means. Cause I suspect that it means different things to different people. and so, so someone in the lab. Did some research, figure it out. Okay. We think we can extend it was, it was a lifetime, et cetera, extended battery lifetimes, or, this was about making or energy batteries, higher energy density, batteries that were still safe and stable.

using basically solid electrolytes. so, so they like publish her paper, like, like I assume that there's like, like they do some experiments. They come up with like the core. sort of process improvement. It's like, okay, we, we make batteries this, this old way, and now we need to make batteries at different way that will eventually make the battery into something useful.

then what did, like, what did they need to do? What do you, what did you all do? Yeah, the origin story of CEO is I think a great one. So ingredients in this case and, and some, and there are some universal, I think things that you can pull out of this, you had a couple of graduate students and a professor at Berkeley, Natasha Bulsara, doing research, basically a polymer expert who starts doing research in terms of how polymers can be applied to batteries.

the, the business as usual or the incentive structures within universities generally, you know, would say for Natasha to be successful in his career, he needs to make some new discoveries. He needs to write some great papers. he needs to advance, you know, as an academic, right. And he was doing that.

and. In this case, it took this moment where, you know, Natasha was a dreamer and had, you know, just had a sense of, well, wait a second, I want this to be useful. I think this can be useful. He kind of had a zero with order idea that there's this problem in batteries, where, you know, you can, if you try and use high energy density, electrodes, like lithium metal, they can short across and lithium metals, flammable and combustible.

And so, you know, There's this idea that you could make a high energy density battery. Unfortunately, it starts to look more like a bomb than a battery. and he, you know, to zero with order, the polymers that he's making could solve that problem, right. It could be robust and strong mechanically and still be highly conductive, for ions and.

Tasha to his credit is audacious enough to say, Oh, and this is a time to, we have to recognize when venture capitalists are interested in funding these things at the early stages. Right? So it takes Natasha being audacious enough to say, I think we can, we can start something. And then it takes someone in this case, like the node who is as audacious as it comes in saying, well, I think batteries are going to be a big deal.

I think this is a really smart team and they'll figure it out. And so like, let's start a company here. it turns out and, you know, I don't really, I don't know if anyone will be upset at this point to say this. Right. Like I joined the company, not being a battery expert. I kind of was the entrepreneurial scientist who jumped in to kind of help start it.

You know, I had a meeting with severe, he said like, all right, buy a cell phone. And like, you'll be employee number one, like just, just go and let's start. So there's a whole nother story about that, but, It wasn't until I was in the, you know, and coastal ventures decided to fund it. and then I actually saw the diligence, the early diligence that coastal ventures had done on the idea.

And it was like battery expert. I won't say who world-renowned battery expert, who I now highly respect. Basically said, this is total BS. You know, like there is no way this, this idea and this technology could solve this problem for these 10 reasons. and what I love about the node, and what allows him to really catalyze new things, things is, he just said he just ignored it.

He said like, all right, the experts don't think it's possible. Fine. you know, and invested in any way, a couple of million dollars to go, you know, to go start this company. And so.

You know, you have me as a scientist, who's motivated to be entrepreneurial, but has no experience. And you have a, like really incredible, you know, genius academic professor out of Berkeley and two of his students that are really entirely scientific in their thinking at the time. and now all of a sudden, like we're in a startup and we're meant to go develop a product.

And so this question of like, well, what does that actually take? Like, you know, we just got thrown into the deep end, about that. but you know, the first thing is some people to just be audacious and say, there could be value created here. Let's take these individuals. And this was one of the reasons why I found it cyclotron road and now created activate like the origin story was let's take these individuals and get them into a different mode of how they're thinking about their R and D.

And there was just an, there was an entire phase transformation that happened where all of a sudden, you know, Natasha and Mohit and Hani, and I, are now in a startup and. Our only reason for existence is figuring out how you make a product that could be impactful and get out to the market. And jeez, like, you know, I mentioned zero with order before, because like at the first order, all of the assumptions around why that technology could have been valuable in batteries were not all of them, but most of them were wrong.

and, and yet now there was no choice. We were all, I mean, Antosz was still a professor, but the rest of the team is basically now in a mode. We're like, okay, we got to figure out how to make something valuable for batteries. You know, ideally starting with the technology that we have. and you know, it's funny, you're I think your audience, a lot of your audience is sort of scientific and technical.

So what I like to say now is like, if you want to move science to products, you need to live for some time and a superposition of those States. before you can kind of collapse the wave function and understand like, where, like, what is it that you have. And for me, like what was so lucky was because of the node was there to be able to put that speculative money in those first 18 months of co, like we weren't a research project anymore, but we certainly weren't a company.

and we had to figure out like, Okay. Which parts of this are just still interesting research that Natasha can keep doing this lab, which he did. And he benefited from, and which of these might actually turn into something that could be valuable to the market and a product. I'm not actually sure. I'm answering your question.

I think you are. I think we're, we're getting to it. I'm going to like, sort of tease it out. Cause I think it's actually really like, I, I love this because it, I think that it is probably different for every situation, but then there are these similarities where it, so actually, so like during that, that 18 months, what did you spend your time on?

So I assumed there was some amount of like going and talking to battery companies and like trying to figure out their end and then some amount of like, It's like, like you were still doing experiments, like,

Well, first of all, it's worth noting. You know, there's so much value in taking some smart folks and putting them in a different mode of working. But the idea that the way to do that aggressive applied research was to be in a startup. There's a, there's a bunch of activation barriers there that we have to cross.

So luckily even node and the financing, wasn't one of them, he made that easy. but then it's like, Oh shit, like where are we going to do this work? and it's like, we can't meet up at a Starbucks and open our laptops and start prototyping. So like, my job as employee, number one, as unsexy as it was, was like buy a cell phone and like figure out like, Where are you guys going to work?

You know, like, Oh, we got to find space. We gotta, you know, I gotta go call em Bron and negotiate, you know, glove glovebox order. and see whether I can find some way, because frankly for them to build us a custom blood box is going to take three to five months. And like, I want it in six weeks, you know, like, how are we going to do that?

so that's, you know, like that's number one that was kind of just table stakes, you know, then. And I think this is the tough part, in this transition, especially when you start with venture capital, which is, you know, the team has certain assumptions, those zeros or assumptions around. Here's how we think it was valuable.

So obviously like the node wasn't going to fund without at least like, Some plan around like, okay, we're going to take your money. And here are the experiments or the development we were going to do. Right. So the development was like, okay, we've got a polymer that can do X, Y, and Z. And we need a polymer that could do a, B and C.

And so the first part of this effort is going to be to, you know, we're gonna. Take the synthesis. We're going to make these better polymers. We're going to show that we can get the properties you need for it to be valuable. we're going to show that we can develop a process where this can actually be a scalable polymer to produce because otherwise it's going to be way too expensive.

Yeah. And then we're actually going to figure out, like, how do you make a battery so that we can show that you can put this into a battery. And what's interesting is you look at all three of those things. If we were to go back and look at the early plans and experiments that we had on those. Like they were totally, you know, they're pointed in the wrong direction because we didn't understand what the real problems or, and so you then set out on, you set out on building the, the lab infrastructure and the experiments to go do that.

I mean, all you can do is March in the direction that you is your current assumption. Right. and so that's what we did. we found. We were, I remember like the node was blown away just in terms of the rate at which we were able to make progress. and then alongside that, you know, my job as the kind of person to start thinking about how the technology met the market within the team, you know, like I started going to industry conferences and shadowing people.

On the technical side that I knew just walking around with them and asking questions. And I started to realize, Oh, you know, we, in our, in our thinking, we said that X innovation was going to make the battery five times better. Like once you actually understand how the battery gets made and what the convention is and the different, like, it turns out that five times better as closer to like 1.2 times, it's better.

Like, you know, the differences were that big. Yeah. and so, So then this really hairy and stressful process of, okay, w we want to make progress against the dimension, but here we are learning things where that suggested like that vector, you know, our assumptions were wrong and maybe it's not actually as important as we thought.

And now you've got to figure out, okay, well, how do we change our experiments to actually work in a direction that matters with a lot of limited information and. You know, that's, it's an insane process. it's an insane and hairy process from so many elements because it's, you know, imperfect information.

you know, sometimes you don't, it's, it's a lot of unknown unknowns where like, you're not, you're not totally even missing the thing that's going to kill you. meanwhile, you have different people on the team, who all are at different levels of, of their own understanding and perception around, you know, Maybe difference in the, in the spectrum of like a dreamer of like, yeah, people are telling us it won't be as good, but like radically can be, you know, and it's like, yes, theoretically, it can be, I'll give you an example from, from the CEO days, which is.

You know, theoretically lithium metal as a, as an anode and a battery can give you enormous energy densities. cause rather than sticking the lithium ions and holding them within some other material. Right? So in the battery, normally Lithium's, intercalated into some other compounds. So you have to carry the weight and volume of that compound around in your battery.

Even though it's not playing an active role, lithium lamp metals, pure lithium. So you don't have to carry any of that baggage around. And so you can have a really lightweight small battery. You know, one of the big epiphanies for us was like, yes. if you can essentially have a two micron thick. Piece of lithium in your battery.

and the way we were thinking about making the batteries, it was like you take a piece of lithium foil that would make it so easy and you'd have lithium foil, the, one of the electrodes that you've turned into the battery, you know, so one of the aha moments as stupid as it was, was like, all right, let's go source a five micron thick foil of lithium, right?

No, like, that's not like no one makes that. because guess what? Like, you know, lithium at those, you can't even handle with them at that thickness. Cause we've just burst into flame, you know? well, no, cause it was just too, you know, Lithium's not very robust mechanically. Okay. So like you can buy a hundred microns, thick foil of lithium and handle it nicely and easily.

And you can buy that really cheap. You start talking to people about like, can I source 10 microns of lithium? And, you know, and they say, yeah, you can, there's one person, there's one group in the world that can do that for you. And it's going to cost a thousand, 10,000 times more than a hundred microns.

Now, all of a sudden, like the entire, the entire proposition goes away. and then you're stuck saying like, okay, does this kill the idea? Or actually it probably means we got to figure out a different way to get two to five micron thick lithium. Which is like an entirely new development path and expense that we just never thought about, you know?

And is it gonna work? Is it gonna be possible? So this is like, you know, this is that there's this multi Plex, you know, divergent and crazy, you know, optimization that has to happen in terms of like, okay, what do we do next? And, and at the. End of it where you actually building batteries or were you like ha ha like, what is the, like, once you figure out the, like, once you've sort of like gone through that optimization, you actually even have a process to make batteries better.

Like, how does that process end up in a device that's using a battery, Yeah. great question. So in that multi sort of, multi-dimensional. Development that we had to be doing. You know, one of the questions basically was like, okay, let's imagine we can make this phenomenal electrolyte, which could enable this phenomenal battery, like a, how are we going to actually prove that the battery is better?

You know? you could imagine partnering with battery companies to do that existing battery companies, but like they have no idea how to handle our stuff. Yeah. And they don't have the equipment. So like, no, we can't do that. Like we got to figure out how to make batteries ourselves. So now, like all of a sudden, like you've got an innovation, which is a materials innovation for a component of the battery that you can think Naval and stuff anymore.

And all of a sudden, like we have to figure out how to make batteries okay. And produce them. and then we need to think about, are we just going to produce them as at the pilot stage? And then we'll teach a partner how to produce them, or are we going to actually have to build in our little startup, like the entire battery manufacturing capability for this entirely new batteries.

And this is where that multidimensional optimization and what we like to tell our fellows now is like, You know, everything has to align in terms of the way you're going to go build this. So for instance, you can have, and I like to think about this in thermodynamics and kinetics. Like the thermodynamics can be great.

Oh, we actually do have a magic material that could build a magic battery and we think it's possible. Like we know it's possible. And then the kinetics could kill you. Meaning for us as a small company to actually build out and figure out how to manufacture these at scale, it might take $200 million of capital to do the development, to figure that out.

if at that moment in time, the venture capital community doesn't have the appetite to put $200 billion into a battery manufacturing company, then that's not going to happen. And that's going to be the reason why that entire vector doesn't make sense. so this is, you know, like the idea that to navigate how something is going to get to a product into the market.

There's a lot of strategy, but there's a lot of dynamic optimization that needs to happen as you learn more and you understand your context. And one of the things you and I have talked about this a little bit, but one of the things that's that for me is the biggest challenge in this, especially for hard technologies that take infrastructure and capital and manufacturing is.

There's in theory, there's a really broad, in terms of ways, you can take an idea from research and get it out to market. And what I mean by that is you can, from the university lab, Natasha could have licensed the technology to a big company, right? he could start a company which he did and that company could raise venture capital money to go try and become the biggest battery manufacturer in the world.

and raise a lot of money or it could spend some time working and developing technology and then it could license it to a big company where it can be acquired by a big company. Yeah. Interestingly w the lesson learned for me was one, the only way we were able to get that group of people into the mode where we were working aggressively in the supplied R and D way was because of a node gave us a couple million dollars.

Yeah. But now the entire organization of the startup was founded on the nodes venture capital money. And the node is notorious in the best possible way of being an amazing venture capitalist. In the sense that his view is if I'm going to invest in you as a VC, my goal is to make you the next multi-billion dollar company.

That is the industry leader in this space. And my incentive is to make my bets count. Yeah. And so I would rather do everything. I would rather do everything we can to get it, to beat that you you've learned and adapted this so that it can be the biggest battery company in the world or fail Trump. and there are no other sort of off routes.

And what I mean by that is, you know, we recognized, we recognize probably two years in a CEO that. The idea that we could line up all those stars and create a battery. You know, I, I remember learning, you know, the battery manufacturing industry, you looked at the most successful battery manufacturers like Panasonic's battery manufacturing business unit had like 5%, margins in terms of like income net income, or operating margins.

And. You basically said, like, I don't know how to justify a big, massive multi-billion dollar business. That sucks. It's a shitty business. Right? Like, so, so we started, then we started thinking like, and we even have a chance at that. Like we gotta go figure out how to we had enough money to go figure out how to manufacture an entirely new battery chemistry.

Like I w how are we going to do that? And so it started to realize like, And we had developed, we had some really amazing ideas. We had a really amazing kind of early development and validation, and we had people from big corporates coming to us with a lot of interest, including one that ultimately came through an intermediary.

We got the sentence. Well, maybe there's an acquisition here that someone would want to do. And this was early in the company. and you know, the VC board that we had basically said, like, okay, we know we put, we put $5 million in, you could get acquired for $30 million. The technology could end up in a company that actually has the ability to manufacture and the distribution channels.

and their view was no, that's not good. That's not, I mean, you know, basically if you think about it, if I'm a venture capitalist and I funded you out of a $500 billion fund, right. what I need you to do is. Build a company that's big enough that returns to me and to my investors. You know, something on the order of hundred, a billion dollars, right?

If you returned to me $30 million after three years, it's an amazing return on investment from a, from a pure kind of, we gave you X, you turned it into Y, but in absolute terms, I've spent three years with you. And all you're giving me back for my fund is, you know, 30 million bucks, which doesn't move the needle.

You basically proven to me that I just wasted the last three years from you, because you're not the thing that's going to make the fund successful. Yeah, it's a really long-winded story. But the point of the story is to suggest that, even though there are a lot in theory, there are a lot of different ways to get the technology out to market and to get it to scale. Capital sources and institutional structures and incentives. They all act as band pass filters. and they cut you down where all of a sudden, like your only option to be successful is in some narrow range.

and. That's. Yeah, it's just an interesting thing too, to think about relative to how we encourage more of this. Yeah. Oh, well, I really appreciate you going down into the nitty gritties because I think it's, one just valuable to sort of have out there. Like, I feel like people don't go into that and what it does is it then sort of frames the work that you're doing both at activate and that you did do at RPE, because I feel like both of those, like that sort of your whole.

career has now been trying like the hypothesis of my life. Yes. Yeah, exactly. It's like, so it's like, how do we play with those constraints? actually one question I have about this idea of the phase change, and sort of let's, let's look at the phase change, the, the super position. Do you feel like the idea of, of kind of.

Burning the boats in a way of like taking that VC money, that then sort of really focused you on, on the product. Do you think that that is. Essential or like, because like, I, I actually, I've sort of like mixed, like I have very mixed feelings because as you pointed out, like it both focused you, but at the same time, it, it put those constraints on you that you sort of like needed to go big and go home or go home.

and so sort of like left. Didn't leave a lot of room for playing. So, so where, where do you, where do you come down on this sort of, like going all in or like, is there, is there like a point in time when it's correct to do that? We got to think about a few they're few different pieces. So I think what you're asking, you know, One is, is it essential to get into a different institutional mindset or structure or incentive structure to do this translational work?

The other is, is it essential that you're all in, so to speak, right? That that's your entire life? I think with, with very few exceptions, the first is essential. and the second is important. and. But, but maybe, you know, but, but maybe not essential. I mean, the, you know, the, this is the, I mean, this is the reason why we created activate, and this is the reason why, you know, we started cyclotron road as, as kind of the precursor experiment, which was, The first thing we have to recognize is like, we've lost big picture, at least in the U S but I think this is true globally. Like we've lost a really critical modality of how we do research, with which is a place with amazing people who understand science and engineering at the earliest stages of technology development and who are incentivized to create a product to create something practical.

And, you know, I think you, you know this, right? Like, you know, this story, but the best research in the world used to happen in companies. and, and you know, whether it was the companies themselves funding it, you know, think about bell labs, whether it was the companies themselves funding it. Whether it was funding that was really government funding through a monopoly that allowed them to fund it, or whether the government funded it directly, which the government used to fund a lot of research within companies, because guess what that's where the best research in the world happened.

you know, we had, we had people who were thinking about who understood cutting edge science, and yet we're in an organizational structure that cared about products. and now we have, frankly, startups are one of the only places that we really have that in an intense way. Yeah. Oh, and I was going to, and that startups, you, you don't have as much of sort of that continued institutional knowledge, right?

Like you have, a bunch of people, like a bunch of people who, as you said, like, are sort of new to that phase and that, that way of thinking, And I guess so, so we could almost sort of think of, of activate as allowing people to be in that super position longer. Would that be an accurate way of describing it?

I mean, that's exactly it, which is why we take right. Our fellowship takes people who have the motivation to go figure out how the research gets out of the lab. It basically simulates for them what the first couple of years. Would look like in a, the node funded early stage startup. but without the constraints of that funding, meaning they have all of a sudden, it's like, Oh shit, they've got two years.

Their entire life and success in their life at that moment is can I figure out how my research turns into something valuable and yet there's not the supposition that they already have something valuable, right. That would be putting the cart before the horse. Right. They're really allowed to explore that and figure out, Oh, you know what.

You know, we've got a fellow who spent time in the program and then said, actually, no, like this isn't for me. And he's now a professor at Oxford, you know, we've got other fellows in the program who have said, actually this isn't for me and now they're running groups at Apple. and, and then we have a lot that spend that time and they say, Oh, you know what?

This is a start. This is the majority of them. Like, and the startup is the right vehicle to move this forward. But then we get this additional classification that comes in, where some of them are saying, you know what? And this is, and I want to go build a VC, go big, go home startup. And I'm going to go raise money from Linode or someone like him.

But we have others who have taken the time and that superposition to say, there's something valuable there. We built here, but the traditional venture capital funding model, you know, the time counts, it's not going to work well for me at this stage. So now I'm going to try and build something in a different way, you know, partnering with corporations and selling early things to them.

If you think about, you know, we focus at activate on, you know, hard. Physical natural science technologies and with really focused on industrial markets. And what I like to say is, if you think about the biggest industrial companies in the world, it's hard to find many that have their origin stories in a financial VC funding landscape, right?

Yeah. most of them have had to build, you know, what I like to say is in industrial markets, it would harder technology. The incubation period between proof of concept or sorry between proof of interest in the marketplace and proof of value is very high, meaning, it can take a long time for something that could be valuable.

To actually be accepted by the industry as we actually believe this is valuable because we've, de-risked the technology enough, it's been in the field for, you know, a hundred thousand hours or whatever else. And so those, what you find is that to get technologies that can go into those markets and companies that can have the reputation of being able to deliver with the reliability, et cetera, that you need.

Oftentimes it's the slower growth companies that just took a lot more time to incubate. We don't have right now capital. We don't have capital structures that allow people to build those kinds of companies. I'll stop. No, send me like riled up in different dimensions and this, this is so good. so the extreme version of that argument would be that just the, the timescale and the return expectation of.

The venture cap, venture capital as a, as a institutional structure, just doesn't align with the, sort of the necessities of a lot of hard technology like that. That's, that's the extreme version of, and you're, you're welcome to push back on that, but. That's that that's the hypothesis. In other words, often align, it only aligns in these magic convergent moments where, you know, the market is, you know, way out of equilibrium and wants to move on, you know, a hundred times faster.

And, and there, there are great counterpoints to all of this. Right. you know, the counter points are, you know, like if, if the market is not, Let me think about what the counterpoint yeah. Look at. Look at the automotive market, right? it wasn't until the automotive market basically. Realized like, Oh, like internal combustion vehicles may be dead. Like our entire business and capabilities may be dead. That all of a sudden they're willing to make big investments in acquisitions and take things on.

and you know, I think from the financial lens, the argument would be, okay, well, don't just push the technology. Don't, don't push on a rope, meaning like to innovate in the electric vehicles before anyone actually has any appetite, but. As you know, it's not like then the world will never change, you know, like I never push it.

the story I love on this is, You know, Dick's, Watson's at our board at SunPower. Dick was the founder of a company called SunPower. that was one of the first kind of biggest solar companies in the world when the solar market took off, it still is, Dick left his job as a tenured professor at Stanford in the mid to late seventies, mid seventies to go start a solar energy company, the world expert in the field.

and he basically. It felt like university wasn't the right mode for him. and at the time, the idea of like building a solar company that put solar cells on your roof, like, you know, th the cost of solar energy with the Silicon solar cell would probably, you know, it was like, I don't know, somewhere between 50 and a hundred X, what would make economic sense?

And this is in the mid seventies, you know, Dick founded like. Against all odds built a small team with government grants with early investors was able to spend 15 to 20 years building a, like a suite of technology around solar, getting the data points around validation and everything else. And I think a lot of people, I imagine a lot of VCs and others who interacted with them at the time, just thought, you know, This guy's nuts.

Like he's going to be spending his whole life just kind of building and tinkering and it's, this is not a real business, you know? And then in the mid nineties, all of a sudden, you know, Japan and Germany decided let's make solar real and they put some real incentives and subsidies in place. And all of a sudden there was this market and it just so happened that Dick and his company and the way he says it is like he was at the time he got to the point where he understood how to.

You understood how to make the cells work, how to make them cheap, how to manufacture. what he didn't understand was how to make them, like, you know, how to make a million cells a day, a million wafers a day. and it just so happened that at, at that moment, you know, the market was starting to turn on.

He bought TJ Rogers is running Cypress semiconductor who says like, Oh, well we know how to manufacture things at scale. we don't do a million wafers a day, but, but that's an interesting challenge. and now all of a sudden, there's an opportunity to take everything Dick's done and create a massive business and a massive new industry.

Yeah. and so, you know, like, Is there a way to encourage people to be as like ridiculously, you know, naively, you know, whatever, like whatever it was that you would call J Dick in the, in the first part of that journey. Tenacious. I don't know. But what I know is there are a lot of people and we've now seen this in the fellowship program.

Like, and you've probably seen this, like, there are a lot of scientists, engineers who are willing to commit to decades of their life to go develop a field, to develop something that can make that impact. And. what I know is if Dick would have decided to commit those two decades at Stanford, he would have learned 0.0, zero 1% of the things he needed to learn, to figure out how that technology was going to be productive and valuable.

No, no. Dig on Stanford. Just again, it's like that's, that's how academia doesn't incentivize like actually going out and like building the same thing over and over again. So I've spent a lot of time thought thinking about this, and frankly, where I end up is you can't, you can't blame VC or wall street, right?

Like, you know, frankly, the earliest investors in Dick's company, a company that might take, you know, Edison talked about like you can't beat compound interest. Like those investors are not going to make their money, no matter how successful it happens. If the success starts 20 years later. and so. The only way to encourage that type of work at that stage is to start to think about Dick's company in the early stages, as a research lab, as a really interesting applied research lab and what I'm hoping and what I've been really working toward is how do we get to government to realize that like startups, a network of startups, you know, a constellation of starting, however you want to think about it, like.

Like that should be the most powerful way to do applied research, I think in today's world. Yeah. there are a lot of problems with how you do that. There are a lot of challenges in how you think about government funding, startups as research labs, but, but I think that's, I think that's a really compelling, you know, direction, from a policy person.

w what do you think some of the biggest challenges are. I'm trying to think of who, who pointed this out for me? I think it was Nathan Coons, who, I don't know if you've met Nathan, but he, He started a company called Kymeta, out of intellectual ventures, which interestingly enough, if you argue, there are not enough modalities around how to do applied research in the world or in this country, you know, intellectual ventures is one of those strange modalities and experiments.

but I think he's the one who pointed out to me initially. what has all this stuck in my head as the biggest challenge, which is. When you fund research, you know, as you know, science technology can be easily used irresponsibly, in research funding, let's put it this way. R and D funding could be used irresponsibly either because you're going to go develop an evil technology, or because you're going to go squander the money by like, you know, buying yourself a Ferrari on the side.

Right. Yeah. When the governor buying expensive equipment, when it's not even when it's not necessary. Right. Like, I think that's the most insidious one where it's like, not even clearly fraud, but just like, do you really need like a million dollar fem, right. yes. and I think Nate, when Nathan pointed out, you know, when government funds things, you know, if the government sends a check to Virginia tech to do research.

there are a lot of guardrails and bounds, that would make it very hard for that money to be spent in a really irresponsible way. or to, for that project to be ethically misled. It happens. Yeah. you know, one of the benefits of startups is there they're much less tightly bound and they have a lot more dynamicism in terms of how they get led.

The incentives are different, but it also means you now have less control mechanisms. And that's one, that's kind of stuck with me as, as a challenge, right? not that it's not, yeah, I think about it as like, it's one that's worth thinking about how to manage or you think about it as like, well, you know, there's a risk reward to everything you do.

one of the interesting things, I don't know if we, you and I have talked about it. One of the interesting things I've found is that when we think about science, whether it's. Frankly, whether it's government, any of the actors who fund science and engineering and research, you're willing to take insane amounts of technical risk and scientific risk.

there's very little willingness to take kind of institutional risk, or, or modality risk. you know, I, I, I had an interaction with, you know, a large private foundation that funds a lot of research. and I, you know, basically noticed most, if not all of their programs, you need to be, you need to be a large university to apply.

and when I sort of asked them about like, well, why is that? You've got now a lot of interesting research that could even the applied stuff, even in their applied programs. And part of it is like, well, you know, no one ever lost their job funding. You know, stamp a Stanford professor or Stanford to do research.

Whereas like, I don't know, like God forbid I accidentally fund Theranose to do research, that that's, you know, that's death. So that's another one that we got to figure out how to get around. I call that asymmetric career risk where nobody nobody ever gets fired for, Funding the safe thing, right?

Like the, the sort of like mean results. But when we're talking, like the things that we're talking about are we're, we're counting on the outlier results. And the problem with outlier results is that they can be outliers on the good side, or they can be outliers on the bad side. And so if you. Fund a outlier result on the good side, then, then you're the hero.

Yes. But then if you fund an outlier on the bad side, then you get fired. And so the sort of like expected career value for, for a funder, if the government or large, large. Yeah, exactly. And it's like ed, unlike a VC who gets to participate in the outside's upside of a positive outlier, Someone in the government or a, another large funding organization, like sure.

They'll, they'll like, people be like, yeah, you funded it. But then they won't get that much, participation in the upside if it, if it really pays off. And so, so that's. What? Yeah, but I think the sweats really well thing, but let's think about like, you know, and again, all day long, that makes sense. If you look at it through the downside risks, but let's think about all the upside opportunity that we're giving up.

And I'll give you the example here, which is Saul and other lab, you know, like I was a good friend. I like, I think he's one of the most brilliant people on the planet in science and technology and engineering. You could do good. other lab. You know, the amount of upstream swimming, Saul has had to do for other labs to exist.

And the idea that like, you know, you have to be a one in 1,000,000,001 in a hundred million type of person to be able to end up doing something like other lab, which is essentially a different modality for doing R D you know, in a very different way than activate. It sits somewhere between a startup and a research lab.

and you know, I think the question we need to be asking ourselves is like, you know, how do we allow? Not just like, how, how do we make it possible so that not just Saul could go run another lab. Right? But that the hundred or thousand top scientists and engineers who have the same motivations assault, Could think about that as a career path for themselves and something successful to do, you know, salt takes a lot of risks.

Other, you know, like other lab is in some ways an insane proposition, and yet it allows them to do the work that he does. And it's like, frankly, it's, it's gotta be, you know, one of the most productive research labs in terms of applied research in the world on a per dollar basis. So, so, so, so big that I would propose.

Which is like, this is going to be like a deeply unsatisfying thing, but I'd love to get your take on it is that the missing ingredient is trust. And I realize that sounds very sort of like Woohoo, but I mean, it, in a very, like if you look at, if you just sort of like, look at history and you look at the people who do these crazy things, like what ends up happening is like, it comes down to like one person trust another person they're like, look.

I trust you to go spend this money responsibly. And then they sort of like take the guard rails off. And, I was, I was talking to Donald Raven the other week, and he ran, this, this program called venture research for BP in the eighties where they funded. Crazy scientific research. So it's like less on the applied side, but more on the, like, like, like literally scientific research that couldn't get funded.

And the thing that struck me was that he spent sometimes up to a year getting to know the scientist who was applying for funding. And so, and I think that what happened there is eventually it got to the point where I just trusted them as a person. And so I was wondering like, well, think about, think about Bob Taylor at DARPA ARPA.

Right. Like, you know, his whole mode was, let me find the smartest people, let me spend enough time with them where I can understand which of them, you know, does the best job of calling bullshit on the rest of them, you know? And then let me go give them money. Yeah. And yeah. Right. I think that's, I think, I think you're, You know, I think one of the, one of the challenges with the, with the trust and with some of those modes is, you know, I think right now, one of the really important questions in science and research is, you know, how do you think more inclusively?

and how do you make sure that you're not. Just super, you know, reinforcing biases in terms of what it means to be good and excellent, that, you know, like that's, that's one of the things that you, you really need to then struggle with, which is, you know, trust is a very efficient mode. but, you know, people build trust quickly based on their biases.

So. I just point that out because it's worth, you know, absolutely being part of, part of it, thinking it's like something that's sort of like really grappling with hard. Yeah. now that said, yeah, I mean, I don't, I don't think that prevents you from. You know, th the two things that I think for us is special about activate one is, we're, we're, we're supporting people outside of the normal incentive structures. Right. and two is we, we fund people, who have great ideas and who have the right motivations, you know, but the whole reason back to the superposition, like the whole reason we exist.

Is to give someone a chance to go through that first 18 months where they realize all their assumptions are wrong. So we don't pick them based on our, their assumptions. Right. We pick them based on other attributes, and. You know, I'd say in the last five years, we're learning a lot about how improve processes to do that with less bias.

and you know, one thing we can't control is unfortunately, one of the limitations of an entrepreneurial though, is you're dependent on. You know, you're not that you're to be successful at gaining resources to support your project. You're not dependent on a really clear pipeline, you know, within your university or whatever else.

You're dependent on pulling resources from a bunch of parts of the world and the ecosystem, and those places all have biases. So yeah. You know, if I think of our women fellows, our fellows of color, you know, I would say if. You know, blind tests, their strength is individuals and ideas at the beginning of the fellowship.

And how much progress do they make in two years? No question. There's a deficit there. and you know, I think the work we need to do is we need to do a better job at, you know, Not being biased and picking those people and instead, right. Because you've worried they're going to fail or whatever it is.

Right. But instead, like we need to be working really aggressively to make sure that we counter all the other biases. I feel like it's starting to happen. Yeah. and, and sort of, so I got on the, on the note of the sort of, the 18 months of the fellowship, do you feel like. And this is, this would be, it's very hard to do counterfactual.

So it's just based on your feeling, like, do you feel like on the margin, like that is sort of the correct length and amount and the amount of money that you give them? Or like what, what, what is, what is the marginal value of say keeping people in that superposition longer or, giving them more resources while they're in it?

Yeah, that's a great question. it's super tricky. when we sort of penciled out the model, that's now, you know, activate and synchrotron road, on a napkin, There was a question of like, all right, well, as you know, my initial model for this was very different. and the model we have was really bound by some of the constraints in terms of how much funding we have.

And so we looked at the amount of funding we had and we said, Oh, we could probably support people for two years. And I walked around and talked to folks and. Well, it was interesting is anyone I talked to who was on the entrepreneurial kind of VC side of the spectrum was like two years way too cushy.

Like that's ridiculous. You're just going to get people into a really relaxed state. The anyone I talked to on the more science, academic research side that realized like the only way you're going to get funding for this is through a grant or something, you know, like. Funding cycles happen on the order of years.

Yeah, they were all like, so, so the entrepreneurs said like, Oh, you should make it one year. The academics all said, like, it has to be at least three years. and so we landed on two. the I've actually found it to be pretty appropriate. we do is really unique in the sense that we believe that there's value in.

The science and engineering expert inventor actually understanding the connection point to how what they're doing could be valuable. Yeah. Right. Absolutely. And so what that means, because they can then be a mobile, the dimensional player, and they can start to connect the dots and innovative ways that they wouldn't otherwise in the applied research perspective.

You know, the other mode is you take the inventor scientist and you just hybridize. You pair them with someone who, is thinking more about the practical market and you find a way to make that work. But at least for us, there's this sense of like, we can be creating these rare breed of like, You know, super, super scientists who are thinking applied and are still cutting edge experts.

There are many people who have that capacity, but if you create one and we've seen this kind of time and time, again, like those people can be really powerful drivers. Okay. So for us, a big part of the two years is we found that. There's so much nuance in debt. Like the things that we've been talking about here to really understand, like, how did the capital markets work w w VCs fund things a certain way.

how does manufacturing work? How do you think about techno economics? Like, Oh, wait a sec. Yeah, just like that, that mental transition. It takes time. You know, what we find is we tell people things in the two year fellowship, and every year we bring in a new group in the beginning of the fellowship and basically the way the fellowship works is there are a lot of ways it works, but one way is every week we get the cohort together and we're exposing them to ideas for founders and others, founder stories.

Here's how venture capital works, et cetera. And we do that for them in the first year. And then we often are repeating some of that stuff for the new cohort that comes in next year. And the second year fellows will sit in on a session and be like, Oh shit. Like you told me all this stuff before, but I didn't, I just wasn't even a place where I could even understand where to which bucket to put that into my brain.

But like, now that I've been now that I've been hit with this stuff so many times, like I'm starting to understand it. so that's one and then the other is. You know, I wish we were a program that could find the talent that we have. And then once they're in the program, if they're doing well, six months in, I give them a two or $5 million grant to keep working on it, but we don't have that luxury.

I don't control the purse strings. So our fellows will, all we're giving them is sort of the institutional, you know, Umbrella and the support and the runway. So the other reason that two years ends up being important is given how speculative, what they're working on is, you know, the amount of time it takes to get a grant proposal together into a funding agency, get it funded.

So you have cash in the bank to do the work or on the venture side or the corporate side to have an engagement with a corporation, that's going to get you funded or to develop a pitch. That's strong enough that BC's like. Like those things, any of those things happen on a time constant of roughly a year.

Yeah. and so the idea that in the first year of our, of our fellowship, you know, fellows are basically shifting their mindset and. Like building the foundation for where that those funding and resources are going to come from. So that in the second year of the fellowship, they're actually able to hit the ground running, in the best cases, you know, in some cases that first year, it doesn't, you know, like what, what they're working on doesn't resonate or they miss the window on the grant, like whatever it is, it seems to work and this is sort of like, something that I.

Struggle with, and I, and you know, much more about it than me. this idea of a sort of push versus pull on the people coming to the program. And what I mean by that is, There's there's one school of thought that says that people need to be like intensely, intrinsically motivated. They need to like, be like banging down your door to join the program.

And then there's another school of thought that, there are people who like don't even know that they should be banging down your door and that you need to go out there and sort of like forcefully opened their eyes and then they will be amazing. Where do you, where do you sort of fall on that spectrum?

When you're thinking about where the best fellows come from,

this is a really, this is a really hard, it's a really good question. and there are a few different things that come to mind for me. One is what's, what's the risk of like, what's the risk of a program like ours? You know, one could argue that. Being an entrepreneur is not something you should just fall into.

Like you will not succeed as an entrepreneur unless you woke up and said, Oh, like, there is no other thing I could imagine doing then this startup, you know, and it's right. and, and argument that has been made that I think is, is reasonable, which is to say, you know, if you give people a nice path, To start thinking about themselves as an entrepreneur.

Like you're basically setting them up for failure because those people probably shouldn't be doing it. Like the Darwinian selection that occurs in terms of what there's, someone actually is willing in the middle of their piece deprogram to say, you know what? I'm just gonna put everything aside. I'm gonna figure out how to go raise venture capital money and find the person who's going to help me do that.

Like, that's an important selection because the other people just shouldn't be entrepreneurs. It's one thing that keeps me up at night, which is, you know, what we found is actually quite the opposite. you know, because it's so hard, like it's almost stupid for someone who's got a PhD, in science to take that, right?

Like, let's just think about a few pieces of this. Traditional entrepreneurial story in the software space. like what's the, what's the, what's the calculus you're doing here. First of all, like, you might be 20 years old and decided to basically go to do, do this. Right? Meaning you don't have a family, you know, you haven't already, you're not in your mid thirties, right?

Like where you have to actually figure out like what your life has looked like. So it's a different calculus already from the get-go then it's like, well, what does it take to go get learning cycles? Okay. Like, I'm going to stop going to class and I'm going to meet up with some friends and I'm going to start prototyping.

but like I could argue that I'll probably get some really, I'll probably get some really satisfying learning cycles, like on the order of months. Right. Yeah. So that's number two. and then number three, and by the way, like I'm gonna, I'm gonna find other people to do it with, they're going to be my co-founders.

Like, I don't have a lot of like vested interest in these ideas. Like they're brand new, I'm just in the, in the early creative. So if you now contrast that with someone who. Let's just say they've spent the last, you know, five to 10 years becoming a cutting edge expert in material science. they've developed an idea that they've been working on as a research program for probably five years.

That is now the basis of something that they think could be valuable. Okay. So now what's their calculus. okay. Let's see. I want to just go be an entrepreneur, first of all. I'm later in life, we already covered that. Yeah. so my, my analysis is different. And the other people I need to do this with me are probably all still later along life, they're more expensive.

They have other constraints. So that makes it harder. Second of all, if I decide to step away, I stopped going to my grad school classes and do this. well, how am I going to start doing it? Like, where am I getting my learning cycles to do it? I need to raise enough money to get a. Venture capitalists to fund me.

Then I got to go negotiate with Ember on an order of glove boxes and wait the six months for the like potentially it's like a year of my life before I'm actually in a startup. Yeah. Which is a big deal and a big opportunity cost. and then, you know, and then thirdly, like I have a watch a different amount of vested interest in what are like, I've already spent five years on this idea.

so. The thought that we should imagine that people are just going to jump into these things at the same rates or paces, as in other areas of entrepreneurship, I think is sort of ridiculous. And frankly, even if I think about, so you say like, Oh, you're giving them a fellowship. That's really cushy. Like, I look at it like the people who are cutting into our fellowship are people who could get professor jobs at any university in the country.

A lot of them. Like, so for me, it's not like cushy, like they're basically deciding to do something that on the face of it is totally stupid, which is like walk away from that path and like go into this fellowship where like, who knows what's next. and my read is like, if we have, if we have amazing individuals who w who are willing to take that step, like.

The least we could do is provide that about a cushion. I've I've totally forgotten the question here. I think, I think we we're, we're really attacking it, which is this idea of like, do the best. Do you need to. Filter people by the ones that are willing to bang down your door or do you need to go out and find the best people and open their eyes to what they should be doing?

yeah, from our experience, the ones that bang down our door are in fact, the most entrepreneurial of our fellows and they are the ones that make the most progress, and, and have sort of the highest likelihood to succeed. what's interesting for me is the other ones, the folks who come into our program, who didn't bang down our door, who basically looked at it and said like, yeah, I've always wondered.

You know, maybe I don't know, I want to be a professor. and the example that I would give is Raymond White at camp who came out of Bob Grubbs, his lab, Nobel Laureate at Caltech. You know, Raymond, it wasn't until after he got into our first cohort where he basically said, you know what, this is kind of a hedge for me.

I figured because you're connected to the national lab. I'll keep publishing. And like, I'll kind of probe this. but I can, I can still go