Founder Stories: Emily Leproust, Co-Founder and CEO of Twist Bioscience & Pioneering DNA Synthesis

Science and Technology Center. Oh, thanks, Jenny. It's an NSF Science and Technology Center focused on synthetic biology, focused on turning cells and tissues into an engineering medium, into a medium for engineering. And the mindset is the engineering build-test I wrote this, I apologize, I just wrote this down. The design, build, test mantra of classical engineering. The CCC is comprised of UCSF, UC Berkeley, Stanford University, San Francisco State, IBM Almaden, and the Exploratorium, and has an academic focus on creating the technologies underlying artificial cells, artificial tissues for the production of products for the betterment of mankind. Today's session is also hosted by the UCSF Blackstone Launchpad. Nate and Jenny lead that for UCSF and it's focused on promoting student entrepreneurship at UCSF., and as well the Catalyst Program that the three of us, in addition to Rupa Ramamurthy, leads. And we're focused on helping UCSF professors advance their discoveries along the translational path to product development and patient benefit. So again, we're very lucky that Emily Leproust joined us today. Twist Bioscience is the leading the leading provider of synthetic DNA to the synthetic biology industry, biotechnology, ag, pharmaceuticals, and the like. They also have some really intriguing programs in using DNA as a form of data storage, analogous to computer chips. They've just announced, and I hope Emily mentions this, discovering drugs on their— for themselves. It's a different company called Twist Biopharma, focused on antibody therapeutics. And I checked this morning, Twist has a market cap of just shy of $6 billion, billion with a B. And I know they have almost 700 employees at Twist. So without further ado, I'm going to turn it over to Emily and wait, one more thing, sorry. There is some further ado. So Emily's going to start off giving like a 20, whatever, 15, 20, 25-minute talk around Twist, synthetic DNA, synthetic biology. And then we're going to— I have a couple of questions prepared in case we get to them. But we also welcome everyone on the call to type questions into the chat or the Q&A. And one of us will check those, and we'll pass those on to Emily as well. So now without further ado, Emblée Laproust.

Thank you very much for the invitation and the introduction, it's much appreciated. I'll give you a quick overview of Twist. This is not a marketing pitch, I thought I would give you the investor pitch and that way you can get a sense of who we are as a company. So first I'll say that I would be making some forward-looking statements. But I'm very excited because DNA is changing the world for the better. And at Twist, we write DNA from scratch, and our customers are using DNA to do great things. They're using DNA to modify yeast, algae, and E. coli for the production of chemicals. It's more sustainable. Frankly, nobody cares. It's cheaper. That is, that is the economic driver to move. Our customers are modifying plants. They are replacing fertilizer made from oil with bacteria that deliver nitrogen at the root of plants, they're developing therapeutic drugs, they're developing diagnostic assays, and they even use our DNA to store data for the long term. So lots of, lots of great stuff. To do this, we have built a platform. On the left, I'm showing a 96-well plate, which a lot of you on the call should be familiar with, and everybody does DNA synthesis in 96-well plates to make 96 oligos at the same time, and you can use one plate to make one gene. At Twist, we've miniaturized the same chemistry. So we are not a chemical company, we're an engineering company, and we can make a million oligos on a silicon chip the same size as a 96-well plate, and we can turn that into up to 10,000 genes. And because we make all the products for all the customers at the same time on the same chip, We have massive software infrastructure to send the right DNA to the right person. And so I thought I was starting a biotech company. Actually, I am running a software company. We are a real company. The joke is that you're not real until you're making $100 million a year in revenue. This is our growth over the last few years. And in the first half of this year, we've almost achieved $60 million revenue. And as our revenue grows, our gross margin grows at the same time, which is very well appreciated by our investors. So from the platform that I showed you, we are building actually 4 businesses, and I'll very briefly give you a quick snapshot of each business. Let's start with synthetic biology. In bio, we have a number of products. If you're cloning, you can go on our website, order some genes. We make oligo pools for a lot of guide for for CRISPR screening. We have variant libraries for enzyme engineering, and we, in addition to making DNA, we even sell IgG. So we sell the full antibody for pharma. Everything is online. All you have to do is you upload your file with your sequences, we'll analyze it on the fly, there is an editor, so if there are problems, we'll show it to you. You can code and optimize, you can change the restriction site, do whatever you want, you choose your vector, and literally in 2 minutes you can get a quote, and you don't have to do anything. You get a clonal perfect gene delivered in the mail. And so the idea is to make it so easy and much cheaper than if you were to do it yourself. When we launched, we were a price leader. We added scale, we added user experience, we added services, we added speed, and now we are adding other products beyond DNA, including protein. That's all I'll say on synthetic biology. We also do sequencing. In sequencing, what we do is targeted sequencing. We don't do the whole genome sequencing, but in many applications such as liquid biopsy or rare disease detection and oncology population genetics, you want to focus on some part of the genome. So we'll make DNA that will be basically probes or baits that will go bind to the DNA you want in a sample, and that way you can just sequence what you want, which massively lowers the sequencing cost. We have all the reagents, we have content like exomes or custom panels, and then we have all the reagents in laboratory prep, so we have all the beads, all the buffers, all the enzymes, the blockers, the adapters, everything. We have everything from A to Z. And the reason why people switch to Twist is because of quality. And it's not me saying it now, it's been proven by hundreds of customers. But because we have a higher uniformity of oligos, the cost of sequencing when you switch to Twist is cut by half. And we know that people have a fixed budget, and so if the cost of sequencing goes down by half, you can just do research on more samples and you get better overall results from your study. And so this NGS application is taking off for Twist. Those two businesses, Symbiont and NGS, that we just reviewed, that's our revenue-making businesses. In addition, we have two other businesses that are relatively recent that should provide us with a bigger upside. First, in drug discovery, what we do is we are a partner to Pharma companies, they come to us, they give us a target or multiple targets, and we use our tools and we provide them preclinical-ready, fully human, human-derived antibodies that are ready to go into the clinic. And so it's high-value antibodies. We're not really a CRO, we are specializing in very hard-to-drug. So in some ways, we are the drug discoverer of last resort. Once you failed everything, once you've tried humanizing a mouse, a rabbit, whatever, a llama, a shark, it doesn't work, you come to us, we will do it. And we can do things that others can't for 3 reasons. First, we have more DNA than anybody else because we make our own, and so we have more mutants, our libraries are bigger. Second, we never do random synthesis, and you know when you get random synthesis, you could get an antibody that binds, may have function, but may also have immunogenicity if you don't follow the rules of the human repertoire. And so our synthesis, our DNA is always fully human and human-derived because we make it from scratch. And then finally, we have automated all of the process of drug discovery, from library production, screening, reformatting of the IgG, affinity testing, and functional testing. And so as of End of March, we had 21 partners, we had 25 active programs, 9 completed programs, and you can see that the disease indication is extremely broad in terms of modality. We do VHS, antibodies, bispecific ADCs, we even do TCRs. In addition, we've developed our own antibodies against our own targets, and so we now have antibodies for PD-1, TGT-3, and A2A, which which, if you do immunology, those are really hard to drug target. They are GPCR protein. You don't get an immune response if you try to immunize an animal. And so there are a few small molecules against those targets, but some of them have side effects. And so by having an antibody that is more specific, you may be able to get a better better outcome. And so those antibodies, we're working to find partners to push them into the clinic. And just to give you an example of 8a, 8a is a master checkpoint inhibitor. As you know, checkpoint inhibitors have really had a huge impact. It's literally life-saving for people. Unfortunately, only 20 to 30% of people respond. So if you respond, it's great, life-saving, but for many people they don't respond, and so there is a need for more. And ACE2 is potentially the next great target, and we have antibodies against ACE2. And also we have antibodies against COVID. When COVID happened, we used our platform, which is not only going after a hard target, but we can do things very fast. And so we have antibodies against, you know, the S1 protein, of course, but we antibodies against the ACE2 human receptor. And again, those are available for licensing. So to kind of close the loop on what we've discussed so far, we have a number of products, and our idea is to enable researchers around the entire value chain, from target identification to target validation, drug discovery, drug development, and disease monitoring. Again, our goal is we don't need to be the heroes. We want our customers to be the heroes, but we want to give our customers the best tools such that they can advance maximally their research at the lowest cost possible. I'll very briefly talk about our fourth business, which is our data storage business. This is a picture from the lunar orbiter, I think back in the '60s. And so in the back is the Earth, in the front is the Moon, so it's called the moonrise, even though technically the Moon— sorry, it's called the Earthrise, even though technically the Earth doesn't rise on the Moon. That being said, NASA said let's go back to the tape and let's get the data back 40 years later, and they lost the tape They lost the machine that reads the tape, they lost the software, they had to redo all that. And when they went back, excuse my French, but it's just garbage. The data is degraded. That is because if you store something on a magnetic medium like hard drive or tape, over time it goes away. And so there's a better way to archive data, and that's in DNA, because DNA is basically permanent. You can find mammoth DNA that's a million years old stored in the ground with bacteria and fungi, and you can still get it. And so the way it works is if you have a file, a bunch of zeros and ones, you can convert that into ACDT. You can synthesize it, store it as long as you want, and after 1 day, 1 week, 1 month, 100 years, 1 millennia, 1 million years, you can PCR your file out, sequence it, and decode it. We've done demonstrations with Microsoft, Netflix, the United Nations, it works all the time. What we will do at TWIST is go after the archiving market. We're not going after the hot storage, we're not going after your file, your email in your computer getting in and out all the time, that's not what we do. We're going after the, you know, maybe unsexy market of cold storage, but it's a $35 billion $1 billion market opportunity, so a big market for us. And it works, but today it's expensive. With our initial technology, it was a cost of $1,000 per megabyte, and I think this file that I'm using here is about 50 megabytes, so it's a $50,000 file. A little bit expensive for massive adoption, and so what we are doing is we are going down the silicon size features to lower the cost. And our goal is to get to $100 per terabyte. And when we get there, we'll be able to compete effectively in the marketplace. And so we have shown last year that we were able to go from our 50-micron silicon chip down to our 10-micron silicon chip. And we're currently working on our 1-micron silicon chip. And next year we'll get our 150-nanometer chip. And when we get to 150 nanometers, that's when we'll have the $100 per terabyte. So the flip side of data storage is we are working on the technology, but we're also working with companies to create an ecosystem. And so TWIST, of course, Microsoft, Illumina, Western Digital, Seagate is not there, but Seagate is a part of it. There's more than 30 companies that are part of the DNA Data Storage Alliance to just spread the word and convince everybody that in a few years you will have one new type of storage available that will be in DNA, and there's some great disruption that will be available. In conclusion, at Qwist, we're going after a big market: $3 billion for our core business, many billions of dollars for drug discovery, $35 billion for data storage. So there's a lot of room to grow.. And in conclusion, we are a platform to write DNA. We go after a large and growing market. We never go with the me too. I don't have time for me too, so we always try to have a very highly differentiated value proposition. We are building a portfolio of businesses. Frankly, we are after total world domination. When we go into a market, we don't go in to participate, we go in to take over. And at this point, our business models for each of our businesses are validated. We are generating high revenue growth. Our investors are very happy and our customers are very happy. And at the end of the day, as a company, we have a track record of execution and innovation, but frankly, that is all thanks to the Twisters. The employees that we have are absolutely just amazing. And we have great hardware, great software, great patents, but in the end, it's always the people. And we've been able to assemble a great group of people. So with that, I think I'll stop here and thank you very much for the attention.

So Emily, thank you very, very much for that great presentation and congratulations on the progress that you've made. I know before you were at Twist, you were at Agilent for 13 years or so working on DNA synthesis, and since you founded Twist 8 years ago, during your entire career in DNA, it must just be amazing to think about from when you started to where it is today. And the idea of $100 for a terabyte storage sometime in the future. And it's funny, I, I was just thinking about how unlikely it would be that you could store you know, photograph data in a piece of DNA, but now I completely see how it could be, and it's just an amazing business. And obviously that consortium you've got. And so I guess, you know, the thing is that we can open this up for questions right now. One of the key things from the Center for Cellular Construction being this academic consortium focused on synthetic biology, you know, using synthetic DNA to enable synthetic biology, or what are the types of things that academia can do in their basic research, their fundamental research, you know, curiosity-driven research that you think would— could have the greatest impact in industrial, uh, synthetic biology?

What can—

because many of the people on the phone here, on the Zoom here, are academics in your space. What types of things do you think that academia could be working on in the next few years to complement what industrial synthetic biology is working on?

Yeah, I would say there's probably two, in my humble opinion. So, and I come from the industrial world, I've done a PhD, I didn't even do a postdoc. I ran away from academia as fast as I could. I don't think I was good enough for academia, so I went to hide in industry. So take it as a pure industry point of view. But in my view, there's two things. One is on the very fundamental research, what kind of new CRISPRs that do new things, new tools that will push the field further. So that's one. And then the second is very applied science. There are tools existing now. How can we actually solve problems? So I'll just take a couple of examples of companies. That are to his customers. One is in the Bay Area called ZBiotics. I don't know if you've heard of it, but it's a probiotic that is engineered to digest the acetaldehyde in your gut that is formed when you drink alcohol. And so you take ZBiotics before you drink, and the next day you don't have a hangover. So it's very applied. You can try it, the next day you know that it works, and there is a huge market for it. Another example I'll take is Neoplant. It's a French company where they've engineered a plant to absorb and digest VOCs, so volatile organic compounds that are in the air. And they have little microbiome droplets that you feed the plant to basically increase the metabolism, and you can clean up a room using synthetic biology. So again, two very super applied examples, but if you use your imagination, the number of applications that are possible of synbio to real-world customers, right, to consumers, is huge. And one of my dreams is that synthetic biology becomes like quantum physics. Every phone we have, it's all based on quantum physics. No quantum physics, no phone. Nobody cares it's quantum physics, it's all about I love this hat, I get the taxi, I order some food, and I think hopefully at some point in the not so distant future synthetic biology will be like that. Nobody will care that it's synthetic biology. We have all those amazing things that change our life, we have a world where there's no more plastic, all the— every piece of plastic will be replaced by a protein that has been engineered to be whatever features we want. We love a plastic-free world. Nobody will know that it's synthetic biology, but I think that's what academia can do, is kind of translate and apply synthetic biology to real problems that will improve the health and the sustainability of the planet.

Okay, hey, thanks for that. That was a great answer. And notice the first question in the chat is from Zev Gartner around your DNA storage, data storage. So I'm just going to turn it over to Zev to ask the question.

Thanks, Charles. Emily, it's just so impressive to see how far the company's come since I think Wallace and I spoke with you maybe 6 or 7 years ago. And my question was really the DNA storage thing. It was, was new to me and really exciting. And so I was wondering, as your team is scaling the ability to make DNA in-house, how do you see that scaling along with the cost of reading out that DNA after it's being made?

I assume you're following that. Yeah, absolutely. So if you look at the Carlson curve that tracks the cost of sequencing and the cost of reading and writing DNA. DNA writing is way behind. The cost of DNA reading has come down way faster than Moore's Law, you know, many multiple millions of times over the last 10 years. And DNA synthesis has come down, and Twist says in its parts we are the cheapest supplier, 9 cents a base, and it's still linear compared to the exponential drop of reading. And so what we are doing now with our DNA writing platform for data storage is kind of catching up with sequencing. We are moving from making a million oligos on the chip to a trillion oligos. We are skipping the billion. So it's a true million-fold cost decrease. And so basically we are catching up to make it viable. And so if the cost of sequencing stays the same, you know, we'll be fine. However, if the cost of sequencing continues to drop, which I think of course it will, you know, the question is, is it going to be done 5x or 50x? You know, or 500x in 10 years, we don't know, but it's going to be done. But the faster the cost of sequencing goes down, the bigger the market we'll be able to go after. So when we are there with our price of writing, the cost of sequencing today is such that it will be economical to store data that you need to read maybe every 5 years. If you need to read the data more than once every 5 years, maybe sequencing will be too expensive. But as the price of sequencing goes down, then we'll be able to go after the market of reading once every 2 years, and then reading once every year, and then reading once every 6 months, and maybe once a quarter. So we are fine if sequencing stays the same, but as it drops, it will just make the market we can go after even bigger. Thanks.

If I can ask a quick follow-up question, you know, I feel like the cost of sequencing has undergone a couple inflection points as new technologies have come online, like sequencing by synthesis. And so, you know, as you're scaling down the size of your features on these chips, eventually you're going to come up against some of the same challenges faced by people in the semiconductor industry, or at least I assume. So you must have your eyes set on that new technology. Can you comment on things that you're looking at?

Yeah, actually the dirty secret of the entrepreneurs at Twist is that we try to take no risk. You always think entrepreneurs are super risky. Yeah, it's risky to lose your job, to quit your job and start a company, raise money. But we try on the technology side, we try to take no risk. And so that's why our first chip was 50 micron. Silicon speak is this is the Grand Canyon. Where we are going is 150 nanometers. 150 nanometers is technology from 2008, and so, you know, even my phone is 5 nanometers, right? So we are not going nowhere near 5 nanometers. We are sticking at 150 nanometers where it's kind of well understood from a semiconductor point of view. That means that we can focus on the real problem, which is, frankly, the real, real problem is, okay, I make a trillion oligo, I have to check that my chemistry works. Again, what Charles was saying, the design-build-test. How do you QC a trillion oligo? You can't do sequencing, so actually that's the rate-limiting step, it's not the silicon. It's how do I know that what I've made in my last experiment is good or not, to kind of direct the development of the process. That is the technical hard part, but not the silicon. We try to limit as much as we can the silicon risk that we take.

Great, very, very interesting. Thanks for those questions. The, the next question from Wallace Marshall, and this is one where you got to put on your CEO hat, Emily, because while I'm going to ask Wallace to ask the question, but it has to do with when you start a new program, is that, is that at the expense of another program, or how do you balance different opportunities? So Wallace, why don't you ask it yourself?

Thanks.

Well, I mean, Charles, stated it perfectly. You know, if you see a new opportunity, a new market where you can go in and totally dominate, and maybe you're working in a market where you thought you could dominate but that's not quite panning out as fast as you would, at what point do you decide to shut down something in order to focus fully on the next one? But if you always do that, then you never finish anything. It's like, how do you— I have no idea, like, how do you make that kind of decision strategically?

Yeah, I mean, the, the, the joke internally that we have at Twists is that we have so much cake to eat that we have to be organized in cake eating. There's just so, so much. And you have to be very careful with investors because you want to show that there's lots of opportunities. So you don't want to be a one-trick pony. You want to show there's lots of business to be gained, but you still want to show discipline and that you're focused. And so frankly, I told you about our 4 businesses. I have ideas for 10 more. If I put one more right now, I'm going to be accused of not being focused, and so I'm waiting. Those opportunities are going to wait until one of those 4 is so successful on its own that we get credit from investors to start a new one. And that's what happened. We started with only synthetic biology. In 2013, and then that was going well, ramping. Okay, then we got some leash from investors. In 2017, 2018, we launched our NGS, and that worked, started ramping up, so we got some more leash from our investors to do drug discovery. And so at the end of the day, it's management's discretion about how you allocate your resources. So you raise some money, those are your resources, and then you allocate them. And if the board doesn't like it, they fire you. They get another CEO that will be better at allocating resources. But that is the hard part. And so you never want to be too far ahead of your skis. And so that means that you have to make hard choices. So we typically do not shut things down, you know, because once a product is launched, it keeps paying revenue. But we may stop investing in that business and direct the investment on other businesses. Routinely, about twice a year at Twist, we ask the team to make a grand list of everything we could do. And it's a list that's probably 100 lines and we could be successful in all of those businesses, but we have to take a cut and the top 5 we do and the other 95 we don't. And it's hard because you could be successful in all of them, but discipline and especially discipline in capital allocation is highly valued and rewarded by investors.

Cool. Thanks for that question, Wallace, and thought-provoking answer for sure in terms of if all 100 could be successful, difficult to choose those 5. The next question is from— actually, 2 questions from Rajiv Shubert around the early days of Twist. And so I'm just going to turn it over to Rajiv to have him ask it directly.

Hi Emily, and thank you Charles. Yeah, I have a question about, well essentially I'm curious about how the journey started. You highlight a lot about where Twist is now and where it's going in the future, but I'd love to hear that transition that you made from Agile into Twist. I'm sure there were a lot of challenges, and I think that's part of my second question is how did you overcome, which I often see from hearing from other entrepreneurs, is the early adoption of your MVP product. So I figured, yeah, I basically bundled my two questions into one question there.

Thank you. Happy to do that. So I'll start with a disclaimer because I don't know if you guys know, but I got sued by Agilent. So my story of how to exit may not be the best one. So buyer beware. But so I was in R&D at Agilent. I was doing really well, starting new businesses and It was ranked number 1 for 10 years in a row, doing really well, but I wanted to be on the commercial side. I wanted to— I was great at making DNA, I was great at reading DNA, but I wanted to sell DNA. I was wondering how to do that, and my two co-founders, Bill Benny and Bill Peck, they actually invented the idea of Swoosh, invented the technology. They are the engineers. I'm a chemist. I said earlier we're not a chemical company, we're an engineering company. And they invented the idea on a piece of paper, almost napkin, how to do it. And they were looking for a CEO to do all the commercial side, go raise some money, so sell stock and then sell BNA. And so I am the victim. It was very exciting to me because They basically gave me my shot of getting out of R&D into a commercial position. There was a misunderstanding with Agilent, and they thought that I had taken the technology from them. In the end, our defense was very straightforward, which was, if you think we stole it, why don't you have it? And they didn't have it because my co-founders, I had invented it. And so in the end, we settled and basically we won and things went forward. But the way it started was I absolutely wanted to have a different day job and definitely being a CEO, not always for the faint of heart, but it's been extremely exciting and rewarding. I was very lucky that Bill Banye, one of my co-founders, had been a CEO before and so he knew how crappy of a job it is and so he didn't want to do it himself and so that's why he was very happy to pawn it off to me to do it. But I've learned a lot, I've raised $1.2 billion now of funding, we still have $500 million in the bank. And I've learned how to build a team and and launch product and scale a business. And so it's been extremely rewarding, but at the base, for me, there was a need for a career change and get out of that R&D track into more of a commercial track. But I'll say that if you're thinking of doing that, you need to ask yourself, you know, do I like to raise money? If you do not like to go pitch and raise money from investors, do not become a CEO because that is the one job you can't delegate. You have to do it yourself. You send someone else, you send a banker, you send a chief business officer, it looks really bad. So if you don't feel like you love selling stock, find someone else to be the CEO because that's one thing that you absolutely can't delegate. Then your second question about early adoption, So it's very important to get early feedback from customers. And it's important for two reasons. One, it tells you, the company, that you're on the right track. Because if you make something early and— or if you make something and nobody's going to buy it, the sooner you find out, the better off you are. And so it's called getting out of the building. Getting out of the building, putting in the end of the customer early and often is important. And then it's also very important for fundraising, because at each round of fundraising, the next investors always say, "Can I speak to a few customers?" And if you can't produce a few happy customers, it's going to be a red flag. And so there is that balance, right, of getting in front of customers as early as possible. And at the same time, I can guarantee you that at the beginning of Twists, we could make jeans, but we were so slow. It would take 30 days to make a jean. And so we had to— the key is to find customers that are happy with— that are going to be happy with what you have. So it's very important to do SWAT, which is sell what's available today. And so when we started Twists, we're like, Who wants slow jeans but super cheap? And so that's what we did, and people were like, "Oh yeah, this is slow, but it's perfect jeans, it's super cheap, I want more of that." And so that was our early adoption, and then over time we were able to shrink down our turnaround time, and we went from 30 days, I think now it's 12 days, but it was still the same price, low price, and so we were able to appeal to a wider and wider range of people. And in the near future, we're going to lower our turnaround time even more, and then it will just be a few days. And so if you— it will be faster to just get it from us than to go in the lab to clone it yourself. And so over time, as the product gets better and better, the slice of who is interested in it gets bigger. Great.

The, the next question is from Ofer Yisrael Bernia, and it's sort of related to your list of 100 things that you could do and how do you choose those 5. So I'm going to turn it over to Ofer to, uh, tell— to say his question— I mean, to, to read— to ask you the question.

Sorry. Hi, uh, Thank you, Emily, for a great company and product. I'm a happy customer at UCSF here, recurrent one as well. My question is from the academic side. I'm still a postdoc, but my passion is translating, is commercializing, as you mentioned. But it's very hard lacking the the knowledge of prioritizing those problems that are worth solving, not academically necessarily, but commercially. How do I— how do you go about this process of this is important, but this doesn't have a market?

Yeah, so there's in general, you know, two commercial approaches. One approach is the Apple approach with the iPhone, which is you create a brand new market that never existed before. The first time we all got our iPhones, like, oh my God, best thing since sliced bread. So that's called new market creation. And if you can create a new market that didn't exist before, it's amazing because you get the first mover advantage, and it's very hard for others to compete because you're always ahead.. So that's one way. The other way is to take an existing market and then you come out with a better mousetrap. So it's the same product, right, but it's faster, better, cheaper, higher quality. And in some way it's less risky because the market is there, but because there's a lot of entrenched players, You have to have a very high differentiation because if you're 5% better, it's almost impossible to take any rooms. So first of all, you have to decide which way you're gonna do. And for instance, at Twist, we never, never go into a new market. We always take an existing market and then we apply technology to be better. Like the data storage market, that market already exists for the last 40 years. We're just gonna come in with DNA. And wreck it, wreck the market, move everyone to DNA and take over. So that's— we have to first of all think through which direction you want to be, and then you'll never figure it out just sitting in your lab. It's all about getting out of the building, it's all about talking to people, and validating your ideas. And also sometimes just being wrong. Like when we started Twist, we thought that the academic market would switch to Twist really quickly because we are so cheap. Academia is very price sensitive. And what we find out, frankly, is that we have a lot of academic customers, but a lot of postdocs are thinking, you know what, '11 days? I can do it in 5 or 3 days. I'm gonna spend the weekends and nights, I'm gonna do it myself.' And so, you know, we were wrong. The good news is that we were right on the big companies. Big companies, they want to outsource more, and so that part of the market discovery, we were right. But basically, the only way is to go go to talk to people and validate your ideas. And it's a little bit— and you have to be careful because it's kind of like if you had a time machine and you went back 100 years ago and you ask a farmer, what do you want? And a farmer 100 years ago will say, I want a stronger horse, right? And a good entrepreneur will say, I'm gonna build a truck. 'Cause the farmer will never say, "I want a truck." They've never seen a truck. And so that's where you have to talk to people, but then use your knowledge and interpretation to make something better. But ultimately, you have to get out of the building and then, and again, put yourself in the shoes of the customers. So I don't know if that's helpful, but it's probably the most critical part of the company after fundraising. Startups that hire a marketing team really quickly is a red flag for investors because the CEO should know in their gut what the product is going to be, should have a very strong conviction. If you rely on someone else, for that product conviction is a little bit of a red flag. So unfortunately, you have to figure it out yourself and then have conviction.

Yeah, thanks for that question and that great answer. I have a question. When you were talking about your antibody libraries, you really laid out very, very clearly about, you know, why yours are better and are going to be even better with respect to using, you know, human immune repertoire genes, et cetera, et cetera, et cetera. And given just the huge effort today on antibody discovery, antibody libraries, and you listed all the different formats, et cetera, et cetera, What if you go up a level of, uh, an order of magnitude, and you want to engineer not just single proteins, discover single proteins, but you want to engineer metabolic pathways, you know, an entire system of linked enzymes that catalyze, you know, the products of one reaction become the substrates the next, etc., etc. Or, or this whole notion of artificial cells, you know, sort of from first principles coming up with a new cell with its own genome. And if you think about what the Center for Cellular Construction is trying to do with respect to the cell, the cell as a unit of engineering analogous to what you've done with DNA, as a unit of engineering. What do you see in the future with respect to how twist thinking or twist technology could be applied not just to single proteins like antibodies, but to entire metabolic pathways or entire genomes?

That's a great question. Right now, we— the maximum length we build is 5 kb. So it's not a single gene. Maybe you can pack a few— you can create an operon there, but we will not make a long piece of DNA yet. And so our goal is to make those 5-kb units as inexpensive as possible, and over time either partner with others to stitch them together or do it on our own. But our goal is to lower the price, increase the length, and such that people will see DNA more like code, order lines of code, and then boot them to see if they work. So you can, you know, already we are the cheapest 5-kb. It's only 15 cents per base. Again, for a perfect 5-kb, it's really inexpensive. But we want to go longer, we want to go cheaper, such that the budget that researchers have can be leveraged to try more lines of codes of life.

Got it. Emily, Rajiv has another question that I'm going to paraphrase. You mentioned how you started this with yourself and two other people, you know, the three people founded it. And then I mentioned that Twist has got 700 employees right now. So Rajiv's question is, how did you build your team? You know, both, you know, the original, you know, the early— you already described the three of you, how you complemented each other. And but how, what, how does Twist go about building such a great team of, um, employees.

You point it on the— you nail it on the head. The people are the most important, and that's why I end where I ended in my talk. And actually, the initial recruiting is actually really hard. There's a lot of people that don't want to join a startup. Some, you know, they're afraid that that the company is going to run out of money. And so they're afraid that the pay is going to be lower, that the benefits are going to be lower, that the work is going to be harder. And so it's hard to attract the initial team. And frankly, my advice is be a nice person. And, you know, I've always tried to be a gracious, nice person and give credit to others. When I left Agilent, I got 100 emails saying, I don't know where you're going, I don't know what you're doing, I want to come too. And my other two co-founders, same thing. They were nice people, and when they quit to start Twist, they got lots of emails saying, I want to, I don't know what you're doing, but I want to be there. And actually, our engineering team My co-founder, the engineer, has started 4 companies and it's the same team that has gone with him every time. Just because they love working with him, he's a great guy, he's pushing the envelope. We're not pushovers, we demand really hard things, but it's fun. And I think that's the best thing is Have people want to be around you. The worst thing as managers and leaders is people get an email and they have to be in a meeting with you and they're like, "Oh man, I don't want to do this." Someone gets an invite to be with you, they're like, "I can't believe I get to spend—" Frankly, that's number one. So create your own network of people that just can't wait to be around you. And then the second advice is have a very exciting mission. You can make more money selling used cars than making DNA, but nobody wants to sell used cars. No offense to those people, you have to do it, but it's not exciting, right? But wow, writing DNA to increase health and sustainability, sign me up, right? It's so exciting. I'm going to change the world for the better. The power of that is quite amazing. And then the third piece is I always try to pay people as much as you can. It's like what we try to do at Twists is that the only way for you to get paid more at a different company than Twists is to get a raise. If you find the same job at Twists as somewhere else, We always pay more. And that means that it's hard to leave. Once you get people in, try to make it hard to leave. And then ask hard things. People want to do hard things. They may not admit it, but it's exciting to do hard things. And even today, people come to me and say, the next project, one of the top 5 projects that makes it. They're like, I mean, how are we going to— it's impossible. I'm like, you told me the last project was impossible, and the last one before was impossible. And then they're like, no, this one is really impossible. In you I trust. And for sure they make it work. And so, set hard things, be a nice person, pay people as much as you can, and have a great vision. And It will work itself out.

Yeah, that's, uh, that's a great, that's a great set of advice, I want to say. And so, you know, I noticed we're at the top of the hour, and so I want to start off by, uh, thanking Emily specifically and her counterpart, um, Angela Bidding, that's on the phone, that helped arrange this. And it was just really inspirational talk and you know, just true congratulations on the success you've, you've had and the impact you're having. And best of luck on the two new projects around antibody discovery for therapeutics and data storage. And so on behalf of the CCC and the Blackstone Launchpad at UCSF and Catalyst, we just want to all thank you for participating. And I want to thank everybody that Zoomed in for this, this town hall today. And best wishes to you all.

Thank you very much. Thank you, Emily.