#5 - Net Zero Heroes: CarbonCapture Insights

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Welcome to the 5th episode of the Tech Optimist Podcast. In starting this podcast, I very much took a let's ship it and improve approach. And, you know, episode 5, I think we keep getting better and better. So we have some very exciting guests today. Welcome to the show. We have 3 blocks for you today. In block 1, I have Adam Olliner, co-founder and CEO of Draft. This is an AV portfolio company that's really got an incredible background in AI and a focus on solving real practical business problems. for companies large and small. So I think you'll enjoy his perspective. In block 2, I chat with Adrian Corliss. He's the CEO of Carbon Capture, a company that's doing amazing work in pulling carbon from the atmosphere. Obviously, it's a huge issue for us. There's a great business opportunity there. You know, Tech Optimists, we believe that, you know, problems are solved by entrepreneurs and technology. Carbon in the atmosphere is an issue, and I think the most practical way that that's going to get solved is innovators like Carbon Capture. So I hope you tune in, learn a little bit more about their work. Finally, in Block 3, I talked to one of our principals, Sophia, and we talk about women in AI and in VC as a whole. So She's done some incredible writing and speaking on this topic, and I think you'll enjoy our conversation. As a reminder, the Tech Optimist podcast is for informational purposes only. It is not personalized financial advice, and it is not an offer to buy or sell securities. For additional important details, please see the text description accompanying this episode. Okay, let's get into Block 1 with Adam Olliner, co-founder and CEO of Graft. How are you doing today and where does this find you?

I am doing fine. I'm in San Francisco proper. How about you?

I'm in southern New Hampshire, about 45 minutes north of Boston. And I, you know, it's why did I start a venture capital firm in Southern New Hampshire? That's because that's where I lived and I was the founder. So it's—

There's some proximity to, you know, proximity to Boston at least gives you some proximity to some great universities, which maybe influenced the type of venture capital that you got into.

Yeah. I grew up in the Midwest and I went to Dartmouth out here and just kind of fell in love with the area. And my wife's from the Boston area and Yes, I can definitely rationalize. We're close to Boston. We have offices now in, you know, Menlo and New York and all around. So it works to have our back office here. But yeah, the truth of the matter is this is where I lived when I started the business. So, you know, that's how it goes.

I grew up in Connecticut, so not too far from you, but we used to rent— just a group of friends and I would rent a place up in Killington, Vermont. every summer just for a week and enjoy, you know, the Green Mountains and everything up there. And it's really, it's lovely. I could do without the insects, but it's a really beautiful area.

You know, it's from about now through, you know, Thanksgiving, it's fantastic up here in New England. Great. Well, tell our audience a little bit about kind of your fantastic, super interesting company and the genesis story behind it.

Yeah, absolutely. So, uh, so CEO and founder of this company called Graft, and the, the origin story of the company, uh, came during my time as head of machine learning at Slack. And we, we had a small team, very smart people, but not very many of us. And the company had huge amounts of data and lots of ambitions for what they wanted to do with it. So things that would make the product better, like driving engagement and offering sort of new capabilities to people, help them find things in old conversations, that sort of thing. Um, but also to run better business. So are there ways that we can, you know, improve the efficiency of our operations or identify opportunities for, you know, better customer experience and things like that? So they wanted to do a lot of things, but we didn't have very many people. So it was like, how do we accomplish these various AI ambitions given the resources that we have? And we started to look around at what the top AI companies at the time were doing. So, you know, Google, Meta, Amazon. And I was sort of surprised to discover that they were all doing the same thing and that what they were doing was different from what everyone else was doing at the time. So this was maybe about 4 or 4 and a half years ago.

Yeah.

And they were all training these really massive models.

Mm-hmm.

And then using it across the breadth of use cases. So they weren't just doing it for like predictive analytics or search or generative, they were doing it across the board. And moreover, they had all built the same infrastructure around those models to actually make them useful in production. Because by themselves, they're generic, they don't have any of the production properties that you need and so on. So you have to build a bunch of stuff around it. And, you know, Amazon spent $300 million building out that infrastructure. Meta had a team of like 250 or so engineers. engineers who were building it out. But what was surprising to me, in addition to the fact that they had all converged around this approach to doing AI across all their use cases, was that they built the same infrastructure to do it. So they had sort of discovered collectively this recipe for a new approach to AI. And this is again before we had a term for foundation models and everything like that. So the world had not woken up to this yet, but we noticed that they had. So we said, well, can we do this at Slack? So we built a version of this, shipped a bunch of features that were based on it.

Yeah.

We can, you know, I can talk details a bit there, but basically the short version is they were really impactful. Like they worked really well, much better than the sort of older approaches to doing it. And we were able to ship a lot more features more quickly. So it was like better quality, higher velocity, like what's not to love? And so this, Made me wonder, you know, could we, instead of having like 5 companies that were doing AI that way at the time in production, what about the other, you know, million? And so I started to think about what the barriers to entry for those companies were. And the 3 big ones historically had been data, infrastructure, and expertise.

Right.

So it used to be that you needed just a huge amount of really nicely curated data that's like labeled properly. And, you know, entire PhD theses were written about like, how do you pick the right examples to label? And how do you know if it's labeled right? And all of this other difficult stuff. And especially if you worked with unstructured data, you just needed piles of it, which is why only those big companies were sort of doing it historically. But these foundation models, these big pre-trained models, they mitigated that data barrier to entry in a pretty important way. And like now you can teach them new concepts with a dozen examples instead of 10,000 or whatever. And for various other reasons too, like the fact that they can work with unstructured data and other things. So that data barrier to entry was, was reduced substantially, and that left infrastructure and expertise. So if there's a way that we can bring this sort of foundation model-based modern AI approach to companies and remove the infrastructure and expertise barriers, then we've done it, right? We have sort of made AI accessible to what we call the 99%. Yeah,

that really wants to just get something done. Much more efficiently.

Yeah. And because it was, it was the path that everyone has to walk to get to modern AI is to build exactly this infrastructure, the infrastructure that we ended up building at Graft and packaging as a managed service. And then the question is, well, who can that be used or what, who can use this?

Mm-hmm.

And so then we also built interfaces on top, like a UI and a chat interface, APIs and so on that would make it accessible to folks. not necessarily who don't know anything about data or technology. Like, that's not really what we're thinking about. We're more thinking you don't need to be an AI expert.

Yeah.

And that's especially important because even if you were a machine learning expert 5 years ago, the world is very different now. And a lot of those skills don't really translate. Like, you don't hear people having conversations about like bias-variance trade-off and these other concepts that were like absolutely central to conversations around machine learning solutions. Because the way we do it is just different now.

Yeah.

So, you know, we built Graft and removed these 3 barriers to entry, and that's sort of, you know, where we are in the journey is now we're starting to bring it to not just like companies that you would maybe think about when you think about the ones that would be using AI, but all of the ones that maybe you don't think about too that are part of that 99%. Yeah. So, yeah.

Where are you in the just the funding cycle. This is the business that's, you know, not that old, and you've made really a lot of progress. So give us just a little sense of kind of where you are as a company.

Yeah, so, you know, we've been very fortunate to have some fantastic investors around the table. We started off by raising a pre-seed a couple years ago that was led by GV. With NEA and then a bunch of angels, including Anthony Goldbloom, co-founder of Kaggle, and some other really fantastic folks.

It's an amazing group. Yeah. Amazing.

Yeah. So we were very lucky to have an initial check sort of written by them. And then last March, we raised $10 million in a pre-seed that was led by Radical with GV buying in the rest, and then followed that up with a sort of smaller kind of mini round immediately afterward that was that Alumni Ventures and a handful of others participated in. So, we've raised about $15.5 million so far. We're seed stage and are sort of marching our way, building the case toward our Series A. And that's kind of where we are now.

Yeah. Excellent. Talk to me about context of like this world is moving very fast, even since when you founded the business. What do you think— what are the winds that you're really trying to kind of catch in your sails right now?

Yeah, it's certainly a noisy time. And I think for, for some people, uh, that is challenging if they don't have strong theses around the direction that it's headed.

Yep.

So there's the, you know, the classic Gretzky quote about skate where the puck is going. And we have been historically very good at predicting where the puck is going to go. So, you know, before foundation models had a name, I quit my job and started a company that was based around trying to operationalize those in real-world use cases.

Yeah.

And, and that's been really beneficial to us because, you know, among those theses are there are going to be hundreds or thousands of these foundation models and there's not going to be winner-take-all.

Yeah.

So if you are starting your AI journey by trying to decide which one foundation model should I be hitching my boat to— hitching my boat, hitching my trailer to— I don't know what the metaphor is actually, but hitching my, my like future in my company to, I think you've already asked the wrong question. Yeah. Really, the question should be, how do I advance my business without having to make that choice?

Yeah.

Sort of retaining the flexibility. So that's one example of this. And similarly, this notion that actually foundation models by themselves are not enough, that you need surrounding infrastructure, that you need accessible interfaces. These are things that we said from the outset were absolutely critical to getting real value. And people gradually have been waking up to this. We're still ahead of them though.

Yeah.

So for example, when ChatGPT first came out, people started to say, hey, it looks like this thing, it's making stuff up. Like somebody should think of some way to like ground it in the data that we have. And so people started to talk about like RAG solutions and things like that, which we had already built by the time that people started talking about it. But there are other things that they're going to start to realize and that some folks are starting to wake up to, which is that actually data is not a great grounding. Most data is not complete or correct or current or clean. Like it's, it's a mess, like in the real world, even for really simple things. Like if you go to a big company and you search for like, what's the guest Wi-Fi password? You're going to get like 12 different documents because it changes over time and it's nobody's job to like delete the old ones or update them or whatever. And that's a silly example, but this extends to all of the data. People write down lots of stuff.

Now we were doing a similar project. We were doing a similar project with, um, just our employee handbook.

Yeah.

And it's like, as you point out, the data is not clean at all kind of thing.

And so something really critical that we built at Graft is also ways of incorporating, you know, knowledge, like information that's in people's heads. Like, how do you get it from human beings and include that, bringing us closer to grounding in reality as opposed to grounding in data? Excuse me. Mm-hmm. And so examples of things like that where we've just been very good at knowing, like, you know, the foundation model's not enough, you're gonna have to at least ground it in data, but the data's not gonna be enough. So you're gonna also have to bring in expertise and then thinking about the interfaces to deliver that value. So like, what are the systems of engagement that we build on top of all of this stuff? So that's been good for us in the sense that we've been right historically about the direction things were going. And that's been useful because we had a big head start. It's been difficult for buyers though. So I think we've talked with a lot of companies, you know, hundreds over the years, and a lot of them are in a position of trying to make bets and hitching their careers to it, right? Mm-hmm. Like they're, they need to be right. And it's a daunting proposition.

Yeah.

If you're not someone like me who thinks about this all the time and really believes strongly about this is the direction and making this big bet. But for a lot of folks, it's just very hard. And this manifests in interesting ways. We'll talk to companies and ask, who else are you considering? As you approach this problem. And it'll be such a hodgepodge of companies and solutions. Yeah. Like they don't have very, very much in common. It's not like these 5 companies all in a particular category because they just really don't even know how to buy for these solutions. Like, do they buy picks and shovels and hire a bunch of engineers to build it themselves? Do they buy a more generic data science platform and figure out how to accommodate it to their needs and things. And so I think for buyers, it's been a challenging period and a lot of them are either experimenting or in a sort of holding pattern while they wait to see what other people do. So that's not been a great situation when you're trying to sell something. You have to convince them that like, this will solve your problem today and you could save millions of dollars and years of time.

Yeah. And I think the idea of, you know, AI as a service Kind of, you know, is kind of how we try to think about it even within our own firm, which is like, you know, what's the use case? What's a problem? Where can we pick off something that is where we can really elevate our people by taking some information-intensive drudgery out of their lives? Yeah. And really finding a partner I think that can help you on that journey, right? Kind of thing. So it's, instead of buying a product, you're really, you know, what we try to do is buy a relationship with people that we trust and want to work with and help us figure stuff out together kind of thing. So.

Yeah, I think that that's smart for a few reasons. One of which is, as you mentioned, the industry is changing so quickly. Not in unpredictable ways, but it is changing quickly. So what that means is any product that you buy, if the product is static, it will be stale very quickly.

Mm-hmm.

So you are building your— even if you really are just buying a product and using just the product, the team that's behind it needs to really have their heads screwed on straight. Otherwise, that product is going to become stagnant very, very quickly. And there'll be some new thing that you have to migrate to. So instead, you're trying to figure out like who really has their finger on the pulse of these things, who's going to make sure that this is always using the latest, greatest technologies that— and using them because they will have the best impact on our business. And it's just important to have those kind of relationships.

Just to bring it to life for our customers, our audience, if you will, maybe just an example of a generic customer of yours and how you start working with them and what's the relationship like? How are you helping them become more successful?

Yeah, I mean, in a very generic way, right, the, the journey is connect Graft to your data sources, build some useful things in the platform, and then deliver that value through APIs or chat or whatever appropriate interfaces there And we lean in with our customers because we want to see them successful with the product. And so we help them onboard their data, build the initial things, and make sure that they see the value from the first set of things that they want to do with it. And beyond that, they can go in and customize things and optimize them, extend them, build new solutions. They have access to the platform. They can do all that stuff themselves. But we really like to make sure that they see the outcomes that they wanted to see when they set off on this journey. Because I think a lot of folks are seeing in the market products that amount to toys, where, you know, it'll perform some, you know, sleight of hand, a nice card trick or something, and it looks pretty cool. Then you go to try to use it to solve real problems, and it's kind of disappointing. You realize that actually you need a lot more than that. Yeah. And I think that has left a bad taste in a lot of people's mouths. And so, we try to make sure that when we bring someone into the platform that we ensure that they get the outcomes that they want and they realize like, okay, no, this is the whole thing. I don't need to also buy 5 other products or hire a bunch of other people to try to get this value. This delivers. And also, I can then use this as a jumping-off point for all these other initiatives.

Adam, if our community could help you, if you have an ask, what would it be?

We love to talk to folks who have real important urgent business needs.

Okay.

And I, I'm not even talking about folks who already think that they want to solve the problem with AI because that's not always the right thing. Yeah. It's increasingly, it's a good idea because it proves to be incredibly versatile and powerful. But really, I just want to talk to people who are like, I have these teams and they're doing a huge amount, like they're inundated with data. They're doing a huge amount of work trying to solve problems X, Y, and Z with that data. They could really use some help. Like I would love for them to be more efficient. Or we want to, we're building this product. We want to add these capabilities to it, but we don't have the backing infrastructure or expertise to add them. Whether or not they know for sure it's an AI related problem, as long as there's some data and there's an urgent, important business need that they're trying to solve, we would just love to talk to them. Because if nothing else, like I will say, like if nothing else, I will try to point them in the right direction to like help solve their problem, whether that's graphed or not. And so like I'm inviting the conversations because I would like to be helpful to people. We also think our product will probably help a lot of them.

Yep.

And like to give a couple of concrete examples of this, You know, we talked with a manufacturing company, for instance, whose technicians are— they have piles of manuals and part databases and historical ticket systems of all the problems they've experienced before and how they were resolved, conversations in Slack and so on. So you have this data scattered everywhere. When something breaks on the factory floor, they spend like 24 hours trying to figure out what it is and how to fix it. And they told us that if we could reduce that by 1%, it would save them $1 million a year. And we were like, we can absolutely help with this problem. So like, let's, let's have that conversation. And it's a good example of something where it's driven by a business need. Like, I want to reduce the time to resolution on these issues because it is costing me a huge amount of money for being so slow. Let's start there. And then we can figure out whether AI, let alone Graft, is the right solution to that problem. And on the—

yeah, come to you with their business problems.

Yeah, exactly. Come to me with a real problem that you want to solve that you feel like you don't necessarily know how. Yeah.

Well, we can help. I'm sure among our audience out there, there are people that can relate to these kinds of issues. And yeah, it's worth at least a 20-minute conversation. So, encourage people to reach out to you. Final question, Adam. What's your favorite kind of personal productivity hack? What have you learned over the years that just helps you get more done in a given day?

This will sound a little perverse given the prompt, but try to do less. So, you know, one of the things that I try to do ever since I became CEO, I've been less successful at this, but I used to very regularly take a walk every day.

Yeah.

So there are podcasts that I listen to because I'm a big podcast person. And not all of them are work-related because sometimes the most important thing for me to do is like go on a hike or just go take a walk for an hour. And, you know, the human brain is an amazing instrument and it benefits from rest, but it also is working all the time.

Mm-hmm.

And I learned this, you know, as far back as grad school where, you know, trying to solve some really intractable challenge or question. And sometimes the way that I would crack it is I would like go away for the weekend. Like, you know, go off into the mountains and go camping. And I would just be like staring at a lake or something like that and be like, oh, like I have a great idea. And I would go back and be so much more productive. And the times when I would really get in a rut was when I didn't take those breaks, when I didn't do anything except the work. And so I was just much more productive when I worked until it was time to not work and then made sure that I didn't for a while.

No, I mean, I can relate in that We have CEO dinners where a bunch of founders and CEOs get together. And thinking about someone else's problem for a while is actually so helpful to solve your own problems time and time again. It's a break. It's orthogonal. Yeah, it just reframes things a lot of times and it's just like serving others is not only good karma, the right thing to do, but I actually think it actually helps you in your own productivity and problem-solving as well as just getting your mind thinking about it in an upside-down way or just turning it off for a while. So true. So true.

Yeah, because there's definitely a difference too between busyness and productivity.

Yeah.

And I think a lot of folks are like, how much doing can I fit into a day? When it's often much more effective to take that pause and say, actually, there are only these 2 things that are really important for me to accomplish today. I'm just going to set everything else aside, focus on those 2 things, and do them better as a result. And that's Really important.

Yeah. And I think it's actually the promise to products like Graph too for an entire organization, which is, you know, if in the manufacturing example, if we can free up people from where's that manual again kind of thing and, oh, this is the old one. And we can just move us all to kind of higher level thinking and more breaks and more, you know.

Yeah, I think that's a really good point, right? Like a lot of the work that keeps people busy that feels like it's kind of piled up is exactly the kind of work that a human doesn't have to put a lot of cognitive effort into, which can therefore probably be automated to some extent with AI. And that isn't the work that we should be spending our time doing, not least of which because there's technologies that's going to do it for us pretty soon.

Yeah, and life's too short. Excellent.

Life is too short.

Absolutely. Yeah. Adam, really nice having a chat with you. Good luck, continued success. And again, a kind of a call to our community to, you know, reach out to Adam with your business problem.

Yeah, I'll solve all your problems. Thanks so much, Mike. Appreciate it.

Especially if you have a big budget.

Okay.

All right. Thanks.

Thanks a lot. Take care. Bye-bye.

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Okay, next up, Block 2, we have Adrian Corliss, CEO of Carbon Capture. So, um, nice to meet you. I like to start out, where does this find you today?

So you got me in, uh, the Arts District in downtown Los Angeles.

Awesome, awesome. And, uh, tell the audience a little bit about your business and its kind of genesis story. We always love to hear on how these things get started.

Yeah, this is a fun one. So, you know, I wasn't there on day one. I joined a little bit later, but Carbon Capture came out of an incubator called Idea Lab. So Bill Gross is a prolific serial, actually parallel entrepreneur.

Legend, yeah.

150 unique companies. But, you know, in the last part of his career, he's really shifted focus towards solving bigger problems around climate. And going back to Davos, I think it was 2018, he was coming back from Davos. and Mark Benioff were sharing, you know, places or they were in the same vicinity on the plane and they were having a debate about, you know, what is the biggest, most existential problem that we need to be solving at this time? And it really became a debate around, is it really about the oceans or is it really about the atmosphere? And I think Bill, he's a convincing guy. He won the day and they agreed that, yeah, they really needed to be focused on really dealing with the amount of carbon that's in the atmosphere. And, You know, at that time there were already—

They're somewhat related too.

There is an interface there, which is really important. But going back, you know, to that, you know, 2018, there were still at that point only a handful of direct air capture companies. I happened to have run one of them. I was the CEO of Carbon Engineering for a number of years up in Canada. So, you know, back then, you know, they're really in sort of, I would say, you know, DAC 1.0. A few companies, they were sponsored by, you know, billionaires like Bill Gates or Marie Edwards. or the Bronfmans, but there was really no clarity about what a business would look like around direct air capture. But around 2018, and I think after the IPCC report came out and said there is no way to net zero except through DAC or through engineered carbon removal, and the size of that opportunity was defined, capital started to flow. And so, you know, Bill and Mark each put some money in, like Bill does, he just puts a small team together and just starts to look for some white space, like what is missing in this industry that could be served by some new technology, a different way to think with the business. He did that for a couple of years. They raised $6 or $7 million in seed rounds. And I think in, you know, around mid-2021, it was apparent that the tailwinds for direct air capture and access to capital were there. And the technology platform that had been developed was coming together. And he reached out to me. And because I'd had this background already running Carbon Engineering, there wasn't that many of sort of ex-CEOs that had DAC experience. And so, he reached out to me. I was running a different startup at the time up in Canada. And he said, hey, would you be open to this? And I said, yes, because I had been paying attention to what was going on. And this opportunity was exactly what I wanted to get back into. And he said, well, would you move to California? And I said, well, give me an hour. I had to go to my wife. And I said, this is something that's important, right? I said, it's something I want to do. Right. I got a note back right away saying, Literally go for it. So we, you know, lifted up our roots, you know, slowly over the last year or two. But, and, you know, I've made my way down to California. And that's kind of how I ended up here, you know, again, taking over from this company. It was an early-stage startup, Bill, probably maybe 5 or 6 people located up in Pasadena. And so, you know, we just went to work. And so there, we immediately went to market, raised $35 million in an A round, just you know, at that time just gave us the, you know, the dry powder we needed to build the business. And I think, you know, since then, you know, we've now, you know, total raised about $90 million so far. We just closed an extension to our A round, which has a different flavor to it as well. You know, we've really now shifted gears away from pure, you know, I would say VCs and family offices to more of a focus on strategics. We've closed this last round to include Amazon, Aramco Ventures, Yeah. Siemens Financial as well, just as a kind of an indication of where we're heading next in terms of execution on the commercial side of the business.

So for our audience, tell them a little bit about the state of technology and where are you in the kind of figuring it out? How do we make this work? How do we make this work at scale? What's, what's the state of your company in the industry today?

Okay. So I mean, I would say that, you know, there's lots of different ways you can think about carbon removal. You know, you can think about sinking carbon into the atmosphere, you can think about soil amendments and biochar, and you can think about biological approaches for removal. Specifically, when you get into engineered solutions, that subset gets a lot smaller, and direct air capture kind of dominates the engineered carbon removal side of this. And the reason it's considered engineering is that you're using machines. It's really something that's You know, it's something that's easy to measure and verify. And depending on where the CO2 is going, and typically you're talking about, you know, putting it into geological formations, it's as well. So there's a lot of reasons why people really like engineered solutions. Direct air capture particularly is really talking about machines that are stripping CO2 out of the atmosphere. So tackling, you know, that 430 ppm. Mm-hmm. trading it and getting it out of the atmosphere and into somewhere permanent. And so I think that, you know, that's sort of the basic premise of what DAC or direct air capture is. Yeah. Within that, there are a couple different approaches that have been deployed, and Carbon Engineering took a very process-heavy approach. So using liquid contactors and basically an acid-base reaction, you know, reacting the CO2 in the atmosphere with potassium hydroxide, forming carbonates, and effectively you know, thinking about then the rest of the system is how do you get carbonates, you know, to break down? They're very stable molecules and get pure CO2 for sequestration. That was sort of that approach. And it's a series of really kind of clever process steps, but doesn't really work at anything but very large scale. Yeah. We're taking a different approach where we're focused on material science. So we're looking at solid sorbents, amines, MOFs, things like that, or hybrids of the two that have a really high affinity for CO2 that we can structure basically put them into filters inside our machines, pass air over them, load them up, and then in a second step, heat them up, remove the CO2, and then repeat. And so that idea of a cyclic process has the benefits of really a much, much simpler process. And there's really only one piece of equipment. The challenges obviously are that, you know, it has a different approach in terms of scale. But the big advantages around this is that we can think very in very much a modular way about thinking about, you know, a relatively modest building block that we can build lots of and deploy at scale.

Yeah, it's like a data center. Yeah, right. That's a really good way to look at it. You can rack them up, right? Kind of thing.

That's right. And as you're building out your capacity and better stuff is available, you put the better stuff in. And I think the nice thing about thinking about sorbents as being the engine and the amount of investment that's going into material science right now. There are probably now over 100 unique programs in universities and corporate labs and our labs as well to develop next-generation sorbents that are really tackling things like increased capacity, better kinetics, better durabilities, those sorts of things that are ultimately, you know, pathways to drive the cost of direct air capture and carbon removal down over time. So the nice thing again is the approach we're taking is one where we're defining the interfaces of those materials as we're putting into machines that, you know, as better materials are available, we incorporate them. And as those better materials are available, we can also upgrade what we've already deployed. So, you know, the sorbents themselves don't last as long as the machines do. So there's always this opportunity to think about, you know, technology improving over time. And I kind of think about it, kind of the best analogy would be software, right? Where—

Yeah.

You've got a machine and, you know, improvements and upgrades over time. you know, in the core software or the sorbents allows that machine just to do more work over time. So there's a couple of reasons why that modularity we think is going to ultimately win the day. And also, we know for a fact in terms of what we've seen over the last 2 years from where we started to the materials we're testing in our labs right now, the rate of advancement is so fast. Yeah. And I'll guarantee it's not stopped. I mean, we're still in the infancy of the efforts that are going to go towards materials developed specifically for direct air capture.

How are you thinking about the business model? Are you gonna, are you gonna be a consumables company? Are you gonna be carbon capture as a service? Have you thought about, you know, different ways to play it, right?

Yeah, there's, I think it's a great question. So there's, there's 2, 2 ways we look at our business. The first is, you know, the carbon capture itself is a technology company that's developing these machines. We are also developing and putting in place, the capacity to manufacture them and sell them to developers of projects. So, you know, from that perspective, we're a tech OEM, and that is, you know, the long-term growth plan for the organization. The challenge is, you know, now there isn't, you know, there isn't a network or a group of developers that are ready or that have the experience to do direct air capture themselves. So, and, you know, on the other hand, we also have this really unique— point in time where we've got things like the Inflation Reduction Act and the 45G that offer up this $180 a ton for direct air capture plus sequestration at the same time that a growing number of voluntary credit buyers, you know, the large tech companies are willing to pay $600 to $800 a ton for the early stages of direct air capture. So we've got this other opportunity right now, which isn't going to last forever, but there is a really lucrative opportunity. Opportunity for developers over the next, say, decade or so that will help, you know, help get the business going. But also it's a great opportunity for us to have, you know, a position and play in that other part of the business, which is actually to go and generate removal credits and be customer-facing, be at the top of the revenue stack, which again, you couldn't do as an OEM. So, you know, right now we are playing both sides of this business, but in the long run, I think when we start to think about how do you get from millions of tons to billions of tons. You know, I think there's going to be, you know, enough developers out there that I think if we stuck to being a tech OEM, we're going to be probably the best. That's the best value that we can add to this industry.

Yeah. You know, again, it seems to me, you know, with technology, so often there's You know, the fork in the road, which is we invent a new technology and there's unintended negative consequences or externalities. And there is always this pullback. There's always this point where society says, you know, we've created this problem, we need to go backwards. And the truth of the matter is, is the problems we've created in our environment, we're not going to go back to 1810. And we really— the problems that get created, including carbon, you know, we need technology solutions. And so we are not going to get there by reusing, saving, those kinds of things. We are going to have to— there's not enough time. The problem is too big. And, you know, we need to be pulling, like you said, billions of tons out of the atmosphere. And it's, you know, companies like yours that give us some semblance of hope that that's in our lifetime. So,

yeah. Well, and what I'd say, Mike, too, is that, you know, you're right, but this is a math problem, right? Yeah. It's— this is an issue of, you know, we need to somehow, one way or another, and probably, you know, in dozens of different ways, deal with 40 billion tons of atmosphere that's going up into the atmosphere every year. And, and, you know, probably 3/4 of that we can just quit putting up there, but probably 10 you can't. And so it's that last 10 which actually— and, you know, if, if you cut— you can think about it as this horrific climate problem, or you could think about it as this, you know, really incredible new industry that, that I kind of— you have no choice, it has to emerge. And it's going to be an industry that's going to be of the order of magnitude of today's energy industry, the oil and gas industry. And so because of that, you know, this is a soft landing for, I think, the people that are gonna be most affected by a transition in the energy economy. And so I think from that perspective, and we're seeing it already, I think even in the nature of how investments are starting to happen in this space, that, you know, this is, I think, being now recognized as just, you know, an sort of a once-in-a-generation opportunity to be at the forefront of a nascent industry that, you know, it's hard to argue that it will not grow to be the size it needs to be to solve the problem.

Yeah, couldn't agree more. How can our community help?

You know, when I think about the different roles that people can play in helping us get that first project to life, it's going to be certainly on the offtake side. We have a lot of of our existing partners that are interested in thinking very strategically about how do they support the offtake requirements to have a project that can be funded. Second, there are, again, a lot of partners that are interested in various parts of the business. And, you know, part of it could be, you know, partners like Aramco Ventures who are very thoughtful around how do they start to participate in a carbon management industry. Yeah. Siemens, who's thinking again about, you know, what role can they play in the industrialization of a new industry. You know, we've got Amazon who's thinking again about, you know, they have this supply chain and they have this platform that is a really unique opportunity for them to think about deploying and really democratizing the distribution of credits through their retail platforms. These are all things that, you know, we've kind of are at play, but there's 1,000 others I'm sure that are represented within your community that could also help us in both a technology front, on an offtake front, on an energy front. I mean, I think that, you know, this idea of, you know, unintended consequences of, you know, bringing forward a technology like this is that it is going to have to also be done in addition to all the other sort of— and meeting all the other demands for renewables. So, you know, I think strategic partnerships around energy are going to be a huge part of our success as well. So I think those are all, you know, any of the communities—

Strategic, big strategic partnerships. to help you really with this pilot and subsequent rollout.

Yeah. And I think people that have specifically kind of gone through that journey of doing sort of first of a kind in a new technology field that is, you know, it's not a cookie cutter doing another solar project. So we know we need, and we're all always looking for help and partnerships for people that can think very creatively around ways to put those projects together, the right partnerships and the ways to motivate people in a way which is a little bit different, but it's a little bit future-looking compared to just a simple transactional relationship.

That's fantastic. So, what's your favorite piece of content, just to end on a lighter note, that you've consumed recently that you've really enjoyed?

Oh, I have to say, like, I'm really hooked on Audible. So, you know—

Me too.

I think right now, I was able to listen. Anyway, it's The Art of the Impossible is something I'm listening to right now. And it's just really about changing the way you're approaching problems. And surprising, there's no simple thing. It's like literally, it's a laundry list of things you need to do to kind of hack your brain and get it thinking in certain ways. And I'm not there yet, but I think it's fascinating reading.

Yeah. I get teased by my wife that she doesn't, you know, because I sometimes brag, you know, I read 50 books a year. I read 5 books a year and I listen to 45 while I'm exercising or commuting. But I think it counts.

I

think, yeah, I mean, Audible is fantastic.

Excellent. Yeah. So, but, and let me also say, Mike, you know, I also, if it's probably, you know, maybe apparent because we're doing this podcast, really had also appreciated the process of working with alumni in getting this last round done as well too. So, your team is incredible and was really engaging and engaged. But also, you guys made your decisions quickly and we really appreciate being able to partner with you guys.

Well, we think there's, you can do well and do good, that those are And your company is a great example of that. And again, I encourage our huge community to, you know, check you guys out. And if you can and if it's a fit, reach out. We do better together than any of us can do on our own. So, Adrian, keep up the good work. We'll talk again soon. Appreciate it.

Mike, appreciate the time.

All right. Have a good one.

Bye now.

Bye. Just taking a quick break so I can tell you about the Deep Tech Fund from Alumni Ventures. AV is one of the only VC firms focused on making venture capital accessible to individual investors like you. In fact, AV is one of the most active and best-performing VCs in the US, and we co-invest alongside renowned lead investors. With our Deep Tech Fund, you'll have the opportunity to invest in innovative solutions to major technical and scientific challenges, which can have a hugely positive effect on society. Companies that have the potential to redefine industries and create a more sustainable future and deliver significant financial returns. So if you're interested, visit us at av.vc/funds/deep-tech. Now back to the show.

Okay, last up in section 3, here's my conversation with Sophia Zhao, where we discuss women in AI and more broadly, women kicking ass in a variety of sectors. Enjoy. Sophia, hi, how are you doing today? Let's jump, jump right into this. So introduce yourself to our audience.

Sure. Hi everyone, my name is Sophia. I am a senior principal on the AI and Women's Fund. My background is in startups and banking, where I've always worked with CXOs and founders on capital raising, capital advisory, go-to-market strategies and operation. I'm an alumna of EBC and Yale School of Management. And in the past 5 years, I've been more so focused on the exciting world of Web3, with the current year focused on AI. I love being plugged into different accelerators, hackathons, and demo days, and I volunteer as a judge and mentor. Very, very happy to be here.

What's your favorite thing about venture capital?

Well, that would relate back to my parents. My parents are immigrants, so they immigrated to Canada, and I watched them create everything from very little or nothing. And that really made me have a lot of respect and awe for founders and entrepreneurs. And in my early careers, I've always worked with CSOs and founders. So I want to help more people, and I'm very interested in venture capital because I will be in a very privileged position to help a portfolio of founders To help them change the world in a way.

Yeah. Sophia, some of your content, some of your webinars, some of your blogs have gotten huge uptake, you know, being quoted by kind of major publications. You know, tell us a little bit more. I mean, you hit a chord on some stuff. What's that all about? What's been the cause of that? What's— tell us more.

Yeah, for sure. It's really exciting times because I am looking to back amazing AI founders, looking to back amazing women leaders. So in our recent publication, the angle that we're focused on is women in AI. And that seemed to be a very interesting topic resonating with our community. as well as the greater community because it hits 2 important things. One is AI, which is all over the news right now. Yes. It's got a lot of breathless coverage. It exploded since November 2022 when ChatGPT burst into the world. And also women founders. I mean, there's a lot of advocacy to feature, to acknowledge, to encourage more women professionals in the field of AI, in deep tech, to be recognized, to encourage more people to come into the industry. So, in terms of content, we produced webinars, we produced blogs, and I'm super thrilled about our blog, which we're turning into a mini-series of Women in AI. We invite women researchers, founders, VCs from everywhere to share their unique perspective about AI, their personal experiences, and what's going to happen in 2024. I'm very happy about the response and looking forward to network and invite more women to contribute to this important topic.

Yeah, I mean, my observation As well was that there's been a bit of— there's been a little bit of coverage in the AI world that has been, you know, identifying, you know, here's the difference makers in the AI space, and it lists all men. And just factually, That is in 2024, just blatantly inaccurate. You know, that if you're looking at, you know, the senior positions, you know, we back these companies, we write checks, we, you know, we're a very active VC firm, as you know. And, you know, the prevalence of women in Positions of senior management, CTOs, they're all over the place. And, you know, to write what I would call a mainstream article about the space and not be talking to women, to me, is just like not doing your job. I mean, that's, that, that's crazy land to me. I don't I don't know what you feel.

Absolutely. I mean, I think it's great that we have an AI fund set up now at Alumni Ventures. I mean, I think as a VC, we're not starting from zero. You know, we've invested over $200 million in 357 startups that were founded, co-founded, or led by women.

So

from an industry stat perspective, we are investing at a higher rate than the VC market. And, you know, we are especially strong with female-only founded companies as well. So I'm super proud about these stats, but we can certainly do more with our community support, which is also why we're launching the Women's Fund. But I think one misconception about backing women leaders is that, oh, you're backing femtech, you're backing the lifestyle company, which I would strongly argue that's not true. I think it makes sense that, okay, lifestyle and femtech starting by women is sensible because we understand the pain point. But there are lots of other women founders punching ceilings in gaming, in science. So we invest in CarryFirst, which is like a gaming and payment deal in Africa. Lucy Hoffman, she's the founder, and they're tackling an enormous opportunity as a leading mobile company publisher. And we co-invested with a16z into that round. Or, you know, we're thinking about how is AI impacting or what sectors is AI impacting? We're thinking about healthcare. So we invested in Centene, which is a tech bio platform powered by AI. It helps decoding the signal of immune repertoire so we can discover disease a lot faster. So, the co-founder and CEO, Lily Woolman, she has a PhD in computational biology. She's a superstar. And, we fought our way into this oversubscribed round. So, these are just 2 examples of amazing women founders not in femtech, not in lifestyle.

Yeah. No. And, I just interviewed 2 days ago, You know, one of the co-founders of Nanopath, who met her co-founder in the PhD program at Dartmouth, right? And tackling hard problems, deep science. Yeah. So there's still some education to be done on the kinds of companies that these amazing founders are starting. But, you know, hopefully, you know, we can make a dent in that. So put on your prediction hat for 2024. What do you see out there right now? Again, you're kind of at the cutting edge of entrepreneurs and startups. And what are predictions? What are areas that you're super excited about, Sophia, right now?

Yeah. Well, I've been consuming a lot of like AI trend reports from different sources. And for our community, if you're listening, I highly recommend looking at the video published by IBM. There's also an SAP report. So there's a ton of resources out there educating everyone about the potential of AI and what's going to happen next. For me, I feel that This is a year where the AI buzz gets a little bit more demystified. It's not going to be, oh, AI is taking over our jobs. AI is going to completely change the way or revolutionize certain things. I think now we're getting a grip on what AI really can do. So for instance, AI could be an add-on. could be an enhancer in a current business model. If you look at Grammarly, when ChatGPT came out, our team was joking, oh, what's going to happen to Grammarly? So now if you go to the Grammarly website, they have a gen AI tool that enables you to write better. So organizations are integrating AI to make their products better. And I think another trend I am excited for is we're seeing the LLMs, the large language models, are going to— we're going to see more smaller language models that are more cost and resource efficient. It takes a ton of money and resource to run a big one. I was just speaking with a University of Cambridge team. They're building this incremental text-to-speech system that actually can run on your device. On your phone and they're speaking with gaming publishers. So I think we're going to see a little bit more manageable-sized systems that are more portable and more cost and resource efficient. And another trend I'm thinking about is more of a multimodal type of model. So it's basically a model that's capable of processing information from different modalities. So think images, videos, text. So if you look at Google's multimodal model, Gemini, now when they receive a photo of a cookie, it could generate a written recipe and vice versa. So that's pretty cool. I'm excited about that. And then lastly is more on the regulation front. I think there's a lot of uncertainty of, okay, who owns the IP? How are people going to be sued? Data security, data privacy. We had a webinar on, you know, Treasure Trove of AI. If you haven't seen it, I encourage you to tune into that webinar where we invited some of our law firm friends to share their perspectives. So I'm not going to spill any beans from there. I think there'll be a little bit more clarity on the regulation front as well.

Yeah. I mean, I go to first principles on all these enabling technologies too, where I still want to see a team that is using AI to solve a problem that is like an order of magnitude better. than what you can do. So to me, it's AI, language models, software technology. At the end of the day though, you have to identify a pain point, a group of customers, and something that is now made possible because of this technology that you couldn't do before. And so I'm seeing some exciting startups really in kind of vertical slices where they're saying, like you said, Grammarly has been in the business of helping people with their writing. And I think we're gonna see existing companies using it to do what they do just better and more efficiently. And then I think you're gonna see like, you know what, until we had this technology, this This problem required thousands of people or, you know, the data was just too much to be timely in its solution. So yeah, I think we're going to see a whole class of kind of niche verticals where very small teams with this technology can do amazing work. So I think there's also a scale thing that I

Mm-hmm.

Feel that we're gonna see teams that are an order of magnitude smaller accomplish really amazing stuff. So again, and I've seen this through my career where, you know, it seems like every decade what you can accomplish is like 1/10 the team size and 1/10 the money. And I think this is the kind of technology that's going to enable many, many more of these kind of micro teams taking on actually relatively large problems. So I think all of that's exciting.

Yeah.

So tell me a little bit more, because again, you're involved in a bunch of different projects. Tell me more about this, the AV Women's Fund that you're working on with Laura and some of that team as well.

Yeah, we are just kicking off our fund. And, you know, we are building our networks, we are plugging into different communities. And I think our not-so-secret sauce in terms of building our community is going back to our roots, the alumni network. So building awareness of our fund, amplifying our brand, reaching out to more people. That's great for getting access to founders, supporters, investors. So we're prioritizing community building right now, while also researching and producing educational and interesting content to engage with our community and also educate them on the opportunities. I think I'm personally excited that it's an all-women team with diverse backgrounds and, you know, ethnicity. There's a lot of diversity in our team. And I think having a team that is so diverse contributes to creativity, and we can really build on top of each other. really support each other. In terms of our focus, I think there's really no limit. I would hope that anyone looking to join our women's fund as a supporter or investor to maybe focus more on, hey, we are backing amazing teams, amazing founders, and they just happen to be a woman. That's all. Um, yeah.

Yeah. So you're, I know you're a reader. What are you reading right

now? Thank you, Mike. Uh, I am reading this book called Good Inside by Dr. Becky Kennedy. So, um, oh

wow, you're putting a plug in. Yeah. So you got it right there. I have not read it. Tell me about it. Yeah.

Uh, well, I just started reading this book. I'm a mother of a 9-month-old and it's first time being a parent. So I've been researching good books so I could be educated on, you know, how to be a parent. Like, what are the best practices that people are advocating just to get more perspectives in? I think, you know, looking back to how I grew up, you know, I think every generation is different, right? I think our generation is so blessed with this abundance of information. And new ways of thinking and best practices. So I'm, I'm a nerd. I like to learn.

Excellent. And I think you're also, I, I, one of the things is you're into croissants of all things. Is that, is that true? I didn't know that about you.

Um, yeah, I, I'm a croissant snob in a way. Okay. Very particular about my croissant. So, as a VC, we evaluate deals, right? So, we have a scorecard. You know, we think through every criteria in a prudent way, trying to balance gut feeling with objectivity. And, you know, we have our IC. So, when it comes to croissant, I don't have a rigorous evaluation process or criteria like that. But I think if anyone's looking to put on a croissant critic hat, I would say, look for croissant that's got a beautiful honeycomb structure. So you cut it in half and you see if they have a beautiful honeycomb. The honeycomb appears that way because you have a lot of butter and a lot of layers when you're making croissant. When the Butter in the layers gets baked, they expand and fill the croissant with that structure. And when you smell it, it should be very buttery with a hint of acidity, and it should be a little flaky. So when you see a croissant that's flat, it might be made out of margarine. That that is not a a good croissant from a you know good butter, good process. So yeah, I'm—

do you make them yourself? Are you, are you just a consumer or are you a creator as well?

Ah, during pandemic I took on the hobby very seriously. So I made my own croissant at home and now I'm too busy. So— Now

you have a 9-month-old. Yes.

Yeah. Yeah.

So great. It was great chatting with you today. Sophia, keep up the great work and, um, keep producing that great content. Keep doing those amazing deals, and we'll talk soon.

Great. Thank you very much for having me. It's a pleasure. Thank you, Mike.

All right, have a good one. See you soon.

See you.

Hey everyone, taking a quick break to share more about the Women's Fund from Alumni Ventures. AV is one of the only VC firms focused on making venture capital accessible to individual accredited investors like you. In fact, AV is one of the most active and best-performing VCs in the US, and we co-invest alongside renowned lead investors. With the Alumni Ventures Women's Fund, you'll have the opportunity to help us invest in fiery female founders. PitchBook reports female-led startups are more capital efficient and exit faster, yet only receive 15% of all venture capital dollars. We see this as a great opportunity, and we're starting from a position of strength. Alumni Ventures has already invested in over 350 startups founded, co-founded, or led by women. So join us in the Alumni Ventures Women's Fund to put your investing capital to back a diversified portfolio of female-led, high-velocity startups as they change the world. Visit av.vc/women. Now back to the show.

Thanks for listening to this week's show. If you like it, please subscribe, please share it. Have a great week and we'll see you next week. Thanks.

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