Twist Bioscience Corporation ($TWST)

[Audio Gap] We'll focus more on NGS and finance and our culture. So to tell us about our [ DNA synthesizer production ], I'm very glad to welcome our CTO, Siyuan Chen. He is going to tell us some of our strategy. Thank you, Siyuan.

Hey, good morning, everyone. My name is Siyuan Chen, I am the CTO on Twist Bio. I'm an oligo chemist by training. Actually, I was the first employee on the R&D side in Twist. So like I've been working in this company for 13 years, like in a long time. I'm really excited to be here because I don't think I've ever talked to like large group investors in this setting. So I'm a little bit nervous, but I'm very excited to show you what we're able to do. So I think you guys all just went through the tour, right, the production tour. Like I'm sure you have seen a lot of really interesting, a lot of amazing stuff there. I want to say a lot of magic we do like really started with the silicon chip. So I think you have all seen a silicon chip, which is like [ 96 world play ] size. Instead of having 96 wells, we actually have 10,000 wells on it. We call them clusters. And like within each cluster, we can make 121 audios. So that's enough oligo to go to make 1 gene, and on a chip, we're able to make 10,000 genes. And like on the silicon chip, we're also able to make about 1 million oligos right? And we have multiple riders. The riders can run actually really, really fast. I'll talk a little bit more about it later. That gives us a capacity of making 32 million oligos per day capacity. It enables a lot of different applications. And like we also like do the DNA is its base by base like using [indiscernible]. We have done so much optimization like to really improve the efficiency and every single layer. So like right now, we're actually able to do as soon as it's all the way to [ 500 base bar ], so we call them [ 500 mer ], which is actually quite amazing if you really think about it. Because as I said, I was an oligo chemist. I remember when I was been work in graduate school trying to like making [ 30 mer ] on a chip, that's enough lens for hybridization like for micro array. That's quite amazing already, like you're able to do [ 30 mer ] and quality good enough to do habilitation, that's good. If you're able to do like [ 80 mer, 100 mer ], everybody look up to you saying, we can do [ 80 mer ] like, in a consistent basis. So that's quite amazing. But right now, we actually do [ 500 mer ] in a consistent basis on a production setting. So like -- and also in everyway that's like industry-leading, I think we're able to get to 1 in 2,500 base bar, 1 in 3,000 base bar, every rate, that's like really unheard of, like compared to the traditional synthesizer you get 500 base, right, that's pretty much ask you can go. So that's our foundational platform. And on the platform, we really built up a lot of DNA product, right, oligos, oligo pool, gene fragments, clonal genes and also like in the last couple of years instead of printing ATCG for different basis, we're actually able to add in modified basis to like make some siRNA antisense oligonucleotide. You're actually going to hear a talk from Dillon from GSK later today talking about how they use our platform to enable high-throughput siRNA screening. And on top of it, actually, on top of the DNA layer, we also built up a protein layer where we can do different type of antibody discovery work and like antibody production and antibody characterization. So like -- so we have this amazing platform where we can make DNA and a scale and a very fast to speed. We really built up a very impressive NPI machine on top of it. So I want to show you where we were back in 2021, like this is essentially the product we had back in 5 years ago. So we have SynBio product. We have NGS product. On the SynBio side, we have oligo pool, we have variance library, we can make gene fragments, we can make clonal genes. And on the NGL side, we have exome panels. We have some custom panels. We had a couple of library patients. They're all good product, like they're actually -- like we are still selling those products, very strong these days. But at the same time, I want to say the footprint is -- the product line is somewhat limited. And I want to show you in the last 5 years, what we have done in terms of expanding our product road map here, our product -- like a product line. So this is where we are right now in 2026. So I'm not going to go over all the product here, but I want to highlight a few things we can do, right? So on the gene side, we can do -- like we had clonal genes. 2.5 years ago, we launched Express Gene, which offers industry-leading like turnaround time for clonal gene product. And a few weeks ago, we actually announced Ultra Complex Gene and SynBio beta. That's really leveraging the 500 base-pay synthesis capability I mentioned like a couple of slides ago, like so that we don't have to really worry too much about the [ Iseco ] structure. We're just really going to make the DNA chemically and stitch only a few pieces together to make ultra-complex gene. That serves as a backbone for like a very complex mRNA and help us get in the world of nucleic acid therapeutics. You're going to hear more from Paddy later today talking about that part. At the same time, on the SynBio side, actually, we built up like a protein layer on top of it, like where we can do antibody discovery in vitro, in vivo. And we also use AI tools to do in silico discovery work. And at the same time, we built up a very impressive antibody expression system and the antibody characterization generate lots of data to help our customers to do AI model training like refinement and help their AI enabled drug discovery work. And [ Toby ] is going to talk more about it, share more insights on the AI side. And the NGS side, like we expanded our panel product like the loss of panel product. We put a lot of emphasis on MRD, molecular residue disease. So we should we really think that area kind of ramp up very, very quickly. So like Jimmy is going to cover that part later today. And on the library preparation side, like we had a couple of library per patient. We added quite a few more new products found some generic library patients, some standard fragmentation and also some more specific ones focusing on like in our cfDNA, folks on like FlexPrep like to really enable microwave conversion from -- like to NGS. That's actually really like enabled by the enzyme engineering capability we developed in the last few years, leverage our synthetic like DNA sensitive capability. So it's a really nice product. What makes it even more exciting is I think that's actually just really the beginning of what we can do. Because if you really look into all the applications like can be enabled by oligos, by DNA, like in terms of oligo number, mass lens, like in different areas, like we can see there's a lot of things we can do like which is -- we just need to choose which one we want to go as we continue to move forward. So yes, this is the table just to reference, like the way how we perceive like what DNA can do for different applications. So that's enough about NPI machine. We're going to talk a lot more about it throughout the day. What actually I want to spend like the rest of my talk here to share a little bit more about how we do operational excellence. Because that's actually a really important part. We spend a lot of time on it. We're really excited about it. And that's also a part might be a little bit underappreciated by people like from the outside world. So like I want to share a little bit more of what we do. So we always come back to the silicon chip, right? So that's really our foundational platform. We do pretty much everything on top of it. And I want to say the chip itself is actually not static. It's actually like it's -- we continue to iterate and improve this platform. So like a few things I want to share, like number one, we're able to maintain really, really good every rate in the last couple of years, like 1 in 3,000 base fare. Sometimes the best run we've ever seen was like is 1 in 4,000 base fare, which is like super, super amazing. And we also like improve the consistency of the DNA synthesis quite a bit like in the last couple of years. So believe it or not, actually the DNA synthesis is quite -- can be pretty like variable, can fluctuate quite a lot. There's even a lot of seasonality in DNA synthesis. So I really think about it like just even like if you are in a rainy day, the moisture can be high, like them to kind of factor you're DNA recoupling. Like even the rush hour, the ozone from the rush hour can help -- can generate like cash effect quality of the oligos. And sometimes like for the vendor, we buy all the ball chemical from the vendor. They might sort of chemical outside in the hot summer. Like the quality is still going to be good, but it's actually going to have an impact on the oligo quality. So we have done a lot of work actually to just really like tease out all the details, like trying to improve the performance. So like I want to say, right now, if you're looking at longitudinal data, like for the oligos has been very flat like in month by month and across all the machines that we have here. So which is something probably cannot be said by other competitors when they have like hundreds of machines running place over places. Like 1 common comment we hear from customers like they struggle with the current provider because like they get lots of batch-to-batch reproducibility issues, things like that. That's not something we have to worry about because of all the work we have done on the synthesis improvements. And at the same time, like we continue to reduce the cost of the synthesis. As you see like in the last 3 years from 2023 to 2026, we're able to reduce the synthesis cost by 60%. And a lot of cost reduction come from the use of less solvents, right? So as you see, like in the middle, when back in 2023, took us about 51 liters of chemical to make [ 1,000,100 mer ]. That's quite amazing already because that's 1 million different sequences, 51 liter chemical. And we did a calculation back in the days, it's actually -- it's like 99.8% reduction compared to column-based synthesizer. So which is like we're talking about 3 orders magnitude lower than what people normally use. Yet like in 2026, we're able to reduce that number to 14 liters for 1 million oligos. Like what does that mean? 14 liters, if you really think about it. So like looking at this bottle, this is a 500 ml bottle. So we're able -- the bottle of solvent like this can be easier to make 35,000 oligos, right? That's -- I think that's the scale we're talking about here. I think for the people who are on DNA synthesizers, a bottle of chemical probably normally used to make a few oligos. But here, we're able to pack in 35,000 oligos into a small bottle of solvents here. And more impressively, I think we have done a lot of work to reduce the turnaround time. So like in the first half of 2023 took us 26 hours to make [ 1,000,100 ] nucleotide oligos, which was like really fast in the time. And when we launched Express Gene, we try to really look into everywhere, every place we can like shrink the time. So on the right are side, we're able to reduce it from 26 hours to 13 hours. And at that time, like I were joking, that's super fast already. I don't think there's much we can do to make it even faster. Yet in 2026, we're able to shrink it down to 7 hours to make [ 100 mer ]. And that's actually a 73% reduction in turnaround time. And not only we get like kind of 37% reduction turnaround time, we actually means like we have 4x increase in capacity. So we have the same number of riders compared to 3 years ago. But like our oligo synthesizer capacity actually increased by 4x like with the same number of riders. That actually enabled a lot of new applications like new product. For example, like I think I talked about the Ultra-Complex Gene, which need 500 nucleotide oligos. If we took the 2023 chemistry, it would take us 5.5 days to make [ 500 mer ] on the rider. And that's going to take up a lot of capacity and also like the product is not going to be competitive because like from get-go, you spend almost a week just to making the oligos. But nowadays, we can actually do it in less than 30 hours for the 500-mil, which like really enabled ultra-complex gene product. And at the same time, like we built up enough, like enormous amount of capacity, 32 million oligos per day capacity, really support the needs for MRD customers because they all come in with personalized panels. Another thing we do, it's automation. We actually like take automation very seriously. We do -- try to do automation like in our front start to finish. So when you go through the production tour, like I'm sure you see a lot of automation, like we actually use a lot of [ Hamilton ], right? That's the liquid handler to move liquid or pipe liquid, move place around -- so in a lot of applications like seems like Hamilton works well enough, right? Just to put the -- lost a deck, load the taps, load a sample. And like in an hour or so, they come back with what do you need to do. So that works well for like a lot of applications, but not good enough for the gene production because the gene production is actually very -- like has many steps has 20-plus steps in the process there. If we were to use like stand-alone workstations like Hamilton, the issue we're running to is like take an operator to time to set up the deck, load the tips, low the plates,and run it. And once the run is done, have to move the plate from 1 machine to the other machine, do it again. So it's actually quite labor-intensive. That's created a lot of bottlenecks in our manufacturing process. So what we did here is we actually -- we identified all the key bottlenecks in our production process, we build up integrated automation, integrated system to like to enable all the bottleneck steps here. So those are the 4 major systems we built from oligo fragmentation system to take the rider oligos to gene fragments. And then the second 1 to -- like then we clone the fragments like then we play them, we pick all the colon -- we have to pick millions of colonies to support our demand. And then once we pick your colonies, we all the colonies goes through next-generation sequencing sample prep system. We actually process about 10 million samples per year on gene facility, which I believe like we're probably more than like the highest volume like in terms of the sample volume for the NGS. Like I don't think anyone else in the world can match the scale, the number of samples we process sequencing every year. And once we identified the like perfect clone, we can go through like plasma preparation to get purified plasma and ready to send to customers. And that's essentially how we enable the scaling with more and more integrated system, right? As you see, right now, we have a total of 20 integrated system on the production floor. And some of them you already see on the production like in IT-enabled genes and proteins, and we have quite a few systems in South San Francisco as well to support our NGS product. And that number was actually much, much lower than just a couple of years ago, like we only have 7 systems. So we continue to develop the tool, implement the tool like continue to improve our production by having more and more integrated solutions. One thing I really want to highlight, it's like when we build tools like this, we always think through like scalability and trying to make it a future proof as much as we can. So like when we always look into when we brought a new product, new process, we really want to make sure multiple processes, multiple product lines can run the same machines. So like back to the ultra-complex gene, which is -- like the workflow is somewhat different from what we normally do for like standard genes. But we're able to leverage all the systems you see on the left side, and there was a minimal addition for equipment. That's how we want to make it like future-proof and all like in our future compatible. At the same time, like with auto automation, we're able to improve our capacity, improve our throughput and also with lower footprint, right? So like when you walk into the gene lab, the gene lab 1, that's actually the footprint of the gene lab 1. So we used to have 2 assembly lines, like as you see on the right side, those 2 green blocks. So that's all the like individual automation back in the days. So that's operators basically do work on automation one, finish the work, move to the station 2. As they work down the aisle, they go from oligos to gene fragments. That's what we used to do. So it's automated, but we still need like 3 to 4 operators running through the process. It takes about 9 to 10 hours to go through their process because it's automated, but like do you need like pretty much all hands on time to make it happen. And over 10 hours, you can make 12 plates. Then in the last year, we actually put in 2 integrated systems, the 2 solid like in the green box on the right side. The footprint is 1/5 of what we have for the gene lab for the original workstations. But we're able to, like in these cases, operators only have to come in like a couple of people loading the plate, loading the tips. They come back in 6 to 8 hours, like with 16 plates ready to go. They don't have to worry too much about it. They can actually do other work like the end of the day. So now like with the integrated system, we're able to manufacture 2x the fragment with 1/5 of the space. Now like actually, we opened up the gene lab 1, we're doing more specialty work. We can use it for cell-free work. We can use it for mRNA work in the future. That's how we ensure like scalability and sustainability as we continue to grow. So Twist, like we always like go crazy about speed. I feel like we're always in pursuit of speed here. Like I think I already talked about the story about how we increase -- reduce the time for oligo synthesis like right from 26 hours to like 7 hours in the last 3 years. And we did something pretty similar for gene product. Like I remember like many, many years ago, when we launched the gene product, took us like 30-plus days to make a gene. It's a very slow process. And like we refined the process, we're able to make standard genes up to 5 kb in 10 to 15 business days. And when we launch like Express Gene, we're able to really shrink it from 10 to 15 days to 4 to 7 business days, which is faster than anyone else in the world. And if 1 seems to be even faster, we can go with gene fragments like the 2 to 4 business days, super high quality. I think James just gave you an example, like the 1-day fragrance we made for [ Mike Wiley ], actually someone we worked with like a few years ago, during the Ebola outbreak like on the NGS side. So we're able to deliver genes in less than 1 day. Actually, that gene spend more time on the FedEx truck than like in our facility. That's just how fast we are, how crazy we are when it comes to keep continuing to improve the turnaround time. And we're doing something pretty similar for the -- on the IgG side as well, right? When we launched the product, 20 to 25 business days. Good, but not fast enough. And are able to reduce it like 10 to 15 business as of today. And if 1 seems to be even faster, we can do them in cell-free manner and then you can get your antibodies in 5 business days. So -- so like I think on the operational excellence side, like the -- we look at a speed like because we always want to go faster. We're also trying to reduce the turnaround time so that with the same number of people, we can do more work. We always look for opportunity where we can like save money, like reduce the cost and also ways for us to improve the capacity, like improve the throughput. So like now this basically shows you like what we have done in the last like 3 years. Actually, even in the last 1.5 years, we have 46 tractor projects to improve like attractive CPI, continuous process improvements to improve our operational excellence, resulting in tens of million dollars of savings like in the last like 2 to 3 years. I'm not going to be able to go over all the details about this like 46 projects. That's just like too many. But I do want to highlight 1 project we did on the panel side. So this is something like -- so we always make -- like on the NGS, we make panels. We're always the best when it comes to making panels, the highest quality, faster turnaround time. Like we can make panels in 2 to 3 weeks, which is like much faster than other people, which could be easily double our time. And starting a couple of years ago, actually, we saw like MRD. We definitely see that personalized MRD is going to be a big trend. People are going to do more panels like it's going to be all personalized, there's going to be lots of them, right? So just to front our understanding, like we're like, hey, customers really need the panels to be incredibly fast because MRD patients cannot wait. The window for the test is very short, like we want to make the panel as fast as possible, ideally less than a week. And we also need to build a very highly efficient process because we need to be able to make hundreds thousands of millions of panels because that's how many cancer patients we're talking about. And we cannot compromise the quality. We still have to maintain the highest possible quality because other patients deserve like the best panel like we can make for the cancer detection. So we know that's what we need to do. Then like we planned a couple of years ahead of time, saying, "Hey, we see the surge coming. It's not there yet, but we need to be ready." So like we did a lot of planning, like a lot of preparation. We know we need to put in like fully walkaway automation system to make the panels. We need to build in like the software to really make this happen, like the integration of the software MES and hardware. And like then it's all up to execution. I think like 1 thing we do really, really well and this company is execution. At the end of the day, like with the fixed headcount for the panel production, we're able to increase our capacity by 10x. And like actually, reduce the turnaround time very significantly from like 2 to 3 weeks to make a panel to like less than 5 days to make a panel. At the same time, like we're actually able to like take the learning we have to make MRD panels like can expand it for all the panels, actually resulting in more than $2 million saving. Like in the consumables when it comes to panels. That's actually the year 1 saving. The number is only getting bigger and bigger as we continue to scale our operation. So I think this is my last slide here. I think just want to give you a taste of like how we see the world like when it comes to NPI, it comes to like operational excellence. And we're incredibly excited about what we're able to do, like I'm happy to talk to all of you like of you like off-line. I'm going to pass the mic back to you, Emily, here.

Thank you very much, Siyuan. Much appreciated. So next, we are going to have a fireside chat with Dr. Frances Arnold. It's my great pleasure to bring Frances here. I have my note counts, I feel like [ Alex Trevica ] on a game show. And we have a runner. So the with a microphone, thank you in the room. So I have my own questions, but if you have a burning question, please do not hesitate to get in. So to start, thank you, Frances. I remember when we're talking about a Twist, we're a baby startup company, we are thinking about who can you bring in the SAB, we need the best. And I told the Board, you all know that, but I'll tell you now. We need Frances before she wins a Nobel prize because we'll never get her after. And so you graciously accepted it. And maybe my first question will start there is we had a number of SMB meetings. We're just at the beginning. A lot of start-up sales, you see. Did you think we were going to make it? Or do you think we are crazy was never going to work.

I thought it was crazy but that you were going to make it. Because the need was so great and the ideas were clearly the right ones, right, to miniaturize and go with the silicon. I'm an engineer by training. So this is deep engineering and not appreciated by many biologists. So I thought you were exactly the right person to make that happen. And you did it.

We did it. Well, if it's good, it's the team, if it's bad to me. So is I want into the bad stuff. We just did the two. What do you think? What surprised you, good or bad?

Well, I have to say I'm blown away because I moved away from the SAB in the early days. I had to deal with a whole bunch of other things at that time. So I didn't follow in detail, and this is the first time I've actually visited the manufacturing. So I love it. I have to say I love it. The attention to execution, although you were always attentive from the very beginning. You put in quality control is #1, careful engineering is #1. So I'm not surprised. But it's fantastic because I use these products all the time in my research. And we love it. We absolutely love it, but I'm not surprised to see how well you've been able to scale this.

That's great. So you are an expert in enzyme. I think a lot of your career has been focused on enzyme. I know you have to catch a plane after this, so you'll miss our part on enzyme engineering. But just tell us why enzymes are so important from your perspective?

I've been working on enzymes forever. And these are those remarkable molecular machines that convert really cheap materials like carbon dioxide and sunlight into complex chemicals like trees or you and me. All the chemistry of the biological world is done using these machines. And now we're coming into a period of synthetic biology. It's not just all about health care folks, it's about everything. And biology can make virtually anything. It's just bringing the cost down just. It's bringing the cost down and opening up, expanding the possibilities for biology. Because biology makes you and does a pretty limited set of chemistries. But evolution is an algorithm that can go out well beyond. So I see now enabled by what you're doing in AI, a combination is enabling biology to do any chemistry you want, to make any pharmaceutical, to make any fuel, to make textiles to make all sorts of things we need in our daily lives that are done using dirty chemistry today or not even done at all. So I think we're at an inflection point really that's more exciting even than it was 12 years ago.

I totally agree. Yes. And so enzymes are proteins, right? So there are nontherapeutic floating in the 2 big categories. There's also the therapeutic protein side of it. You're an academic, you're in the SAB or fund of a number of companies on the Board of Alphabet. So you have, I think, a unique view into protein engineering and also as it relates to therapeutics. AI is challenging that field. And 1 of the questions, and I'm sure a lot of investors in the room have the question is around AI-driven drug discovery, is it a flash in the pan? Is it here to stay?

Well, what do you see? What are you seeing? Because you're seeing the same thing I'm seeing, and I want to hear it from your math.

In is we're going to need a bigger boat. In terms of capacity, I think that yes, what we see is there is a first pass of model building, where people need a lot of sequences, expressing protein, tested against a number of targets to build in the model than we find the model. And then you have to turn the crank and the turn the crank needs a lot more DNA. And I think what we're seeing is AI is going to be the first path for drug discovery. In vivo and in vitro are second needed. But AI has a huge advantage is that it's fast. It's much faster than having to either inoculate a mouse. You have to wait for the mouse to be your drug company. And all you have to do page display or use display and that takes days. So that's what we were seeing is AI as the first pass. Does that jive with what you're seeing?

Well, of course, because enzymes are more complicated than the binding proteins used in therapeutics. It's the next generation, right? So it's still -- AI is still not good enough unless you combine it with these optimization methods for which I won the Nobel Prize. So imagine that you can combine AI to get to a good starting point for chemistry, but then you combine that with iterative optimization, all of which uses synthetic DNA. In an active learning cycle, you just press the button. And I think in the next 5 years, we'll be able to genetically encode any chemistry. So I'm super excited about this. And I know it's not a flash in the pan. I write the checks for the alphabet. And they're really big checks. It's not a flash in the pan because whatever you can't do today that won't be true tomorrow. And we're just going to get better at it.

So then do you think it's going to make DNA obsolete? Or these things are going to create more demand for DNA because they're going to have to try more things?

So that's a really good question. If we get perfect at AI, right? You just make 1 gene and you're done. That is completely unrealistic, right? Because there are so many specifications that we don't even know to make on the DNA, how it cures the disease, how it catalyzes a reaction as in a particular laundry detergent formulation. I mean there's all sorts of things that we can't write down in the specifications that only become real when you translate the DNA into the sequence that you compose using AI into the real world. And what -- I think it was said -- you said it this morning. DNA is the point at which you translate your computation into the real world. That is the physical manifestation of your computation. And I think it always will be because it's not efficient just to go and make proteins synthetically. Nature does it by DNA and she does it for sugar.

I do it for sugar too. Lots of sugar. So if there are any questions, raise your hand and we have a run that will bring your questions. So the last 9 years that you were to write down, we had a number of projects together. We -- early on, we did a membrane protein cameras and then with the machine learning design charger optogenetics tools. And then very recently, we did a carbon transferases project. So as a user of DNA, how do you choose a provider? What -- you're using Twist a lot, but there are other providers. What's the criteria for you to make a decision of where to go? Or do you leave that to your team?

Well, I come from academics. We never have enough money. So price is very, very important. That's why I yank on your chain and say, "Hey, I want a better price, but I'll give something in return." So yes, price matters a lot for the academics, but I also sit on the industrial side. And their time and quality are the key things. So there are different metrics. And I have to say I'm pretty impressed that you can meet all of those metrics.

Yes. A lot of companies, between the speed, quality or price, usually, you have to choose to. Now any 2, but we wish we pride ourselves in you get all 3.

And that will be important because graduate students are relatively cheap. They used to be almost free. And so time was not manipulations, that wasn't important. Now they've become a little bit more expensive. And so we actually do a little bit of calculation over the trade-off. Do we want the whole gene or do we do all that ourselves? But in industry, time is -- especially in this new AI or a time is -- turnaround time is really critical. And as we move into active learning test build design cycles, the ability to turn that around and generate the key data will mark who wins in this race. And believe me, it is a big race. There's a huge amount of interest in biology as a manifestation of AI. And not just therapeutics, as I say, it's all of chemistry. It's going to be a race who can do this.

That's why we love speed. So there's a question. Great. Puneet?

Thanks for the question, and great to see you here. So the question is really about there are a lot of assumptions being put forth in AI in terms of speed, speed to therapeutics, the speed to drug discovery. Can you talk a little bit about in terms of scaling? Where do you see as you talked about briefly about chemists' role is going to change a bit, too. But can you talk about the scaling we're going to see? Or there is an assumption that we are hearing in the market that in 10 years, we can see a number of therapeutics coming to market that can actually resolve a number of diseases? Now that's a big assumption being put out there. Disease and biology is complex. So can you talk about this scale that needs to happen, the hiccups that can potentially happen? And then how long is this versus 10 years?

So I sit on both sides. Demis Hassabis says we're going to cure all diseases. But he doesn't talk to the FDA. And you can't just go and test these things willy-nilly. So yes, AI might cure some number of diseases, but it's all going to be at the rate at which drugs can be developed. And the pharma people are much more realistic, but maybe not quite as visionary. So what's going to happen, a lot of it depends on what happens on the regulatory side and on clinical trials. How do we understand the efficacy of these AI designed drugs.

Thank you. Any other questions in the room? I'll keep going. And if you have questions, please do raise your hand. So as a scientist, I guess, I talk to a lot of scientists. And I ask people about protein engineering or any topical loss. You have 3 people out to do it and you get 5 answers, right? I'm always shocked that there's not more standardization, and we're trying to turn into a competitive advantage, but I think, we'll do whatever you want. But what do you think that is that there's not more standardization, like everybody wants to do something different?

Hell if I know. Well, scientists are funny ducks. They like to put their own stamp on it. That's 1 thing. So there might be 1 great method out there. That doesn't mean everybody piles in to do it, especially with a complex problem like protein engineering. Every protein is different. And every landscape, we call them landscape, how you go and explore different sequences is different. How you measure everything is different. There's a great deal of bespoke engineering that goes into it, which really tends to push people into their own methodologies. That said, I do think, Emily, that there will be a push the button that maybe not be optimal for each problem, but will be optimal across most print protein engineering tasks. And that will involve machine learning, active learning cycles with machine learning. So that project we did with you way back then with the [ optogenetics ]. That was the first time machine learning was applied. And in fact, we developed those methods in 2012, 2013 and did that collaboration with you to demonstrate how you could engineer new properties. And that was well before the ChatGPT here. Yes.

Well, speaking of ChatGPT, maybe I have a personal question. So when you use an LLM, ChatGPT, it's all about the prompt, asking the right question. So obviously, you're massively accomplished. How were you able to ask the right question? How do you think you've got here? Like what made you get here?

I think it was desperation, right? If you're pushed hard enough. I came out of an engineering background, and I jumped into protein engineering when it was a brand-new field. And the people who were doing it, and I'm sure you have had exactly the same experience. The people who were doing a little bit of protein engineering were from structural biology. And their whole mindset was you had to get a crystal structure of this very complex molecule first, which -- often, people couldn't even get that in order to go in and then rationally design everything. And I came in and said, well, I won't get tenure all die first before that happens. And I had to come up with some very different engineering mindset, which is look to the best engineer on the planet and that's called evolution. So to me, it was totally obvious, but to the field, it was completely not obvious. And I think in DNA synthesis, you experienced the same thing. Everybody was doing at the same terrible way, terrible way and there's no way you could scale that. And you came in as a chemist engineer, and said, no, we have to completely rethink that. Isn't that the case?

Yes. Being a contrarian goes a long way, right? And I would say 1 to the team is that because it's incredibly hard and sometimes it's like, so I'm like good because if it was easy, every idiot will be doing it, right? And then number two, always when there's a plan, okay, let's do this, I always ask why -- why are we -- why this way? And the worst answer for me is like because everybody else is doing it. We're not doing that because everybody else, they have more resource than us. They have more experience, they have better channels, better capital access. And so how can we beat them if we do the same? So total.

But then you also have to have a vision of when you enable this capability what becomes available? And how do you capture that and capture at least some of the value of that.

Yes, totally agree. And that's why I think it's very helpful to be technical. I mean, when I started Twist, I kind of was down playing my PhD because they're like, "Oh, you're on to business." But I think you can learn business easier than learning the technology. And when you talk to a customer, if you don't understand what they're doing, when you don't even know your product, how can you be effective? And so I think that has been very useful, being able to -- you drop in any account, and I can sell any products that we have.

But what I love is what the technology has enabled that we could not do before. And in my work, some of the same things happened, and that's how I chose problems was what can I demonstrate that you just couldn't do with rational design. A good example is how do you make an enzyme work in the laundry machine. That was a big market for enzymes. How do you make it be happy? What self-respecting natural enzyme wants to work in your laundry machine with bleach and surfactants. There's no way you could design that. It had to become from directed evolution. And you can't go out to nature and find that. So you had to have some methodology. And you've done much of that, right? You've enabled people -- my methods enable people to do a whole bunch of much more important things than make laundry enzymes. Your technology has also enabled the whole synthetic biology industry, I would say, to do things well beyond what even you could imagine.

Yes. That's true. Any questions in the room? Okay. All right.

Thank you for your time, Dr. Arnold. Curious to know, there is a variety of models that exist today, [ Alphafold, Bolts ] and others that I think are adopted by pharma. I would be curious for your perspective on the utility of those models and drug design today versus what might be required from a new data generation standpoint to ultimately get to the point of -- maybe not quite a good point, but sort of push button, get drug or get got closer to that point rather than the models have been largely built on third-party existing data?

So I'm very excited about the models. I love that Demis and David Baker won the Nobel Prize for understanding protein folding and then design of proteins. But the bottom line is that structure is not function, right? Being able to predict the structure is a game, it's like winning at chess, and you have a good metric for that. But what we want in the synthetic biology community is something that does something. And the models just can't capture that right now. But I think they will. They'll start beginning to capture that as we get the right kind of data, which we don't have. So even though that problem has not been solved, we are getting close to getting things that can solve it through further experimentation. So this is why it's a beautiful time, right, for making DNA because we're close enough that we just need a whole lot more experiments in order to learn what it really takes to make something that's useful.

Question over there?

To that point, I guess, if a lot of the open source tools are protein structure today and a lot of the drug developers are incentivized to have more siloed data sets in regards to function, how do you see this playing out over the next decade? Is that going to be the path forward that each developer will kind of keep some of that data in-house? Is that going to be sort of a rate limiting factor on the field in general? Or do you think these open source tools will move towards function and more downstream practicality?

I think that's a really good question because we -- and I don't know how to answer that because I don't know how much data it will take. There are those who argue that enough data on your particular system is all you need, right? You don't need a world model that works across all modalities. And to me, as an experimentalist, that makes more sense because I know how bespoke every protein is. On the other hand, if Demis is right, you'll learn across all proteins and you just have a model that does anything. He doesn't know anything about proteins. So I mean to a first approximation. There's no quotes given here, right? This is house rules. No, I love Demis. But -- so I don't know who's going to be right. But I do know, so I'm on the Board of Generate Biomedicine. So we went public about 6 weeks ago, AI-produced antibodies, we integrate it with a lot of experiments, right, to get to the right developable drugs.

I think there was a question in the front? So -- no? Questions? All right. Maybe the last question for me, going the -- what if you have to start again today? So suspend disbelief, you're back, going to [ new cities ]. If you had to do it all again now with the AI tools with the ability to have the wet lab outsourced, even maybe at Twist, either at [ Caltech ] or not, what -- if you are to start again today, way, what would you work and how work on? And how would you do it?

Two answers. And I want to ask you that question. I love proteins. Protein engineering is not solved. So you could jump in at the place it is and still do really important work, enzymes that are not solved. But if I have a student of evolution -- evolution works at all scales. And so why not apply some of the same ideas to tens of molecules to whole cells, to organisms, to ecosystems. It's the same design process to design anything in biology. And so that means whole new scales of DNA, right. Not just 1 gene, but a whole ecosystem of organisms. And I do know that some of the most visionary people in the community are really thinking about that. How do we use AI, for example, to design whole genomes. That's happening. How do we use AI to design whole ecosystems. That's going to require a hell of a lot of DNA.

I love it.

What would you do?

So I love DNA chemistry. I know to write DNA, read DNA, sell DNA. So I think I was built to build Twist. If we had to start today, frankly, I think it will be very hard because when we started Twist 13 years ago, we knew that we had to raise $1 billion to get to exit velocity. And back then in 2013, you could do it. You could raise the first $600,000 seed round and then a $9 million A round, all the way telling everybody at the end, it's going to take $1 billion. And we could do it because capital was available. And I don't know if we could do it today. I don't know if that...

It's all being sucked up by SpaceX.

And AI. Yes. So I'm glad we did it then, because I think now it will be tough.

That's sad to hear because the Anthropics are raising lots of money, and they're going to be buying all your DNA.

Yes. And we were a work to do it. So it's good. And necessity is the model of invention.

So do you think 1 of those companies will buy Twist?

Well. Now I'm being triggered. So hopefully we'll buy a lot of DNA. But that's not our goal. Our goal is to ramp our revenue. And eventually, we'll get to buy Illumina and Thermo Fisher. That's not long-term guidance, by the way. But this is America, we have forsa every day, if there is enough zeros on the check, I will fly and wash your car. So on that, thank you again so much. I know you have to catch a plane. I very much appreciate the effort, appreciate the partnership along the years. We can't wait to continue being your DNA protein RNA to enable the great work you do.

And thank you, and thanks to the whole team here. It's just marvelous, what you've created.

Thank you. All right. So next -- sorry, back. Next, we're going to hear from Colby Souders, our Chief Scientific Officer. Colby is our own drug developer. And I believe that at least for drugs that he had his hands in the discovery are in the clinical trial stage and a few of them at Stage 3. So Colby, take it away.

Thank you. Thank you, Emily. Pleasure to be here. Thank you, everybody, for coming and those of you making the trip, hopefully, it's been a great day so far. Fitting to follow-up that fireside chat with the AI topic, which I know is of particular interest for many. But we heard a lot of great comments on that. And so now what we're going to do here is dovetail that conversation into how we approach AI at Twist and how we solve this for other companies and how we enable AI companies to scale and to fulfill their promise to the market that they are making. So I'll dive into this in more detail. And hopefully, by the end of this, you'll understand kind of our position on the market as well as what our solutions provide to solve that. So this is a slide you saw from Siyuan in the earlier slides. So you can see, first of all, a lot of products that we make touch that AI-enabled drug discovery. Now I'll focus mostly on our IgG and antibody characterization, but also mention in the next few slides, how all those other aspects that are touching that area provide those tools and what differentiates our platform in order to enable the AI drug discovery in IgG and antibody characterization. So first point to that being, of course, we've heard a lot about DNA. But for AI-enabled companies, it's not just a DNA product that some of them need. Many of them need protein. Most of them need data. They don't necessarily need a physical product from us. They need that characterization data. But to enable that, we need to start from DNA. All biological material starts at that point. And so we've built out that speed, scale and quality to enable that. Now of course, we've talked about speed. We know with higher speed, we can evaluate more candidates in less time. That really accelerates that design-build test cycle for our AI customers and enables them to develop more therapeutics within the same amount of time. Those are all huge advantages. And of course, with scale, you need these very large data sets. I think what we've seen over the last 2 or 3 years is that folks would start with smaller data sets or they'd start with unstructured public data, and that wasn't good enough. They realized we need to do this at 10x, 100x, 1,000x what we were thinking 2 or 3 years ago. So we've made that scale. We've enabled not just our DNA but also our protein solutions side, our protein production, our data characterization delivery to match that scale because that's what those AI companies really care about. Now of course, not only does this support more candidates, but it supports other modalities and different target classes as well. So I'll mention this more toward the end. But we think about things in terms of binders and the field has gone from many binders to VHHs to IgGs, but there's a lot of other modalities that these AI companies are thinking about. And so we're just at the very early stages of scratching the surface of all those other capabilities. And now, of course, you can have speed and scale, but honestly, it means nothing if you don't have quality. Poor data, no matter how much you can make and how fast you can make it, will not be informative for a model, and it certainly will not develop a therapeutic. So you can think of quality in a number of different ways and a lot of the traditional methods that you would measure quality. But honestly, we think about it in a couple of other ways, too. So by providing flexibility in different formats or multiple production systems, now we're enabling our customers to develop their therapeutics in the context that they want. We heard a little bit on the fireside chat, but 1 of the important things is there's so many different types of biologics. Each of them needs a different system, a different format. If you don't provide customers with that flexibility to order it, the DNA, the protein, the data in the format that you need to fill your model or fill your therapeutic pipeline, then it means nothing. Again, we've got several different product lines. I'll talk a little bit about how our unique multiplex gene fragment and gene pool systems really enable new library development. And that isn't just for scale, but again, for quality because now you can start making combinations of different products in really unique ways that no other company can enable. And so that's a huge piece, not just of a product offering, but a quality because the design and the flexibility that a company has to make that design and then actually fulfill that is unparalleled with those products. And then finally, that fit-for-purpose downstream use. Again, I mentioned this, but the endpoint and the starting point is very important for these AI companies. They don't all want to follow this linear gene to protein to data. Maybe they want to come in at the protein, maybe they want to just get data from us. And so we provide that flexibility. So if somebody can start and end on that train, if you want to say, at any point. And so that's very important. So we've built this foundational system for DNA synthesis and protein production and data collection. Now the important thing, though, is you can't attack it from 1 side. So I'm saying, okay, I can build scale. I can build speed, I can make product really fast, but you also need that expertise. So you need that expertise in the characterization to fulfill the AI/ML dream of data sets. And so we've attacked it from both angles. So by this, I mean, we have been developing for over a decade now, different in vivo, in vitro antibody drug discovery platforms to fulfill these and to deliver the hundreds, maybe 1,000 therapeutic programs that we've done for partners. We need to establish all of those advanced characterization methods that all these AI/ML companies want on the back end of their data production systems. And we've already built these out by having all the deep expertise in that full end-to-end antibody discovery platform. And so this has required a number of tools where we have things that support all of these in vivo systems, all the in vitro library discovery that we've done. And so we've developed these tools, some being shown here. And all we have to do is now apply that to the scale and the speed that we've developed on the front end. And so that's been a very seamless process. So now we can deliver with binding evaluation, the affinity measurements, the developability characterization, those functional assays, the things that are fundamental and that these AI/ML companies want, but they want it in high quality, but scalable data. And so that's really what differentiates us is that many companies try to approach this from 1 side or the other. Maybe a company only has expertise in scaling. Maybe a company only has expertise and deep characterization in antibody drug discovery, but we do both, and we merge those 2 things. So on the 1 side, we've got the deep expertise in antibody characterization, protein binding, things of that nature, but we're able to apply our scalable automation and operational excellence that Siyuan was talking about earlier. So that's how we apply it. Now I'm going to get into a bit more detail on exactly the different types of workflows that these AI/ML companies are essentially ordering from us and crave in order to fill the pipeline of data that they need. So 2 main modalities here. So 1 being the library-based workflow that is very wide, the other being the clonal sequence workflow that goes very deep. So what do I mean by that? The library-wide workflow means a customer might come to us with tens of thousands or hundreds of thousands or maybe even more individual designs, and they say, I want to test all of these designs. Not sure how well my algorithm does. So I want to test all of those from at least a very basic standpoint. So now I can narrow it down. Now at the end of that, you can see a lot of those actually feed into that clonal sequence workflow. But what the clonal sequence workflow does is goes very deep. So we apply all of those characterization assays that I was talking about to the proteins that we produce on the scale of not just hundreds, not just thousands but maybe tens of thousands. So now between 1 side or the other, you can tackle a problem from 1 antibody if somebody orders it, all the way up to hundreds of thousands or millions. So being able to utilize each of these workflows, apply them at the right time and in the right sequence is very critical to the success of these AI/ML companies. Now I'll provide a couple of deeper scientific examples of where we've applied this, first being on the library side, I'll have 2 examples here. One is where we had a customer who actually just wanted the DNA delivered. They want to do the screening in their lab, but they required our library technologies to enable them to even screen that in the first place. And then in the second example, it will be a full end-to-end solution where we not only did the library production, but as I'm showing here, all of that panning and NGS output, the screening, the lead candidate selection. So here in the first example, going from 1 Nobel laureate to the other. This one is with David Baker and where we collaborated and did some work for his lab. And in this particular example, the challenge was using the algorithms that his lab has developed the protein MPNN tools in the RF diffusion algorithms to design binders, VHHs, nanobodies, however you might know them to 4 different unique binding sites on proteins and wanted to generate 9,000 unique sequences that were targeting those different sites. So we used our multiplex gene fragment technology to make all of those libraries, put them together and then send them to the Baker lab, where he was able to then validate and pick out the leads using cryo-EM and different SPR techniques to measure and validate that those models were working. And so it was very critical for us to be able to use our precise printing library technology and print those libraries, the exact sequence that he wants to be able to deliver that to multiple targets with over 9,000 designs per target. Now in the second example, this was a collaboration with Amazon Bio Discovery and Memorial Sloan Kettering Cancer Center. And so in this particular challenge, they -- Memorial Sloan Kettering designed over 300,000 unique sequences. And now this was very large because this is actually to an undrugged target, very complex protein, something very complex that had never been targeted before in biology. So we needed to use a very large library to interrogate this and figure out if we had any valuable binders. So we did all the screening. We looked at 12 different populations, millions, tens of millions of NGS reads. We found hundreds of individual clones that we then selected. But we did all of this work with the library screening, not just the library production, but put them in yeast, did the protein selection, the screening, and we found a number of different very interesting and very valuable targets. Now the great thing here is that they're coming back. So just like many AI/ML companies, they realize we're probably not going to solve it on the first round. So they're using the data from this first round of output to come back, optimize it and do a second round. Now when we look on the clonal sequence side, there's a number of different applications here. But again, I think people want a lot of data now. We need to go very deep to characterize this to train the models very precisely. The first example here, being a study where we partnered with Charlotte Deane's lab. Now she is a world-renowned expert as well who designs open source bioinformatic tools that are basically universally across the antibody industry. So very impressive. Now the aim here was to develop a tool to predict nanobody structure and properties. Now her lab had done this for antibodies before and have published those tools and methods, but nobody had ever done this for nanobodies or VHHs before. So we worked with her lab in order to develop the wet lab data that validated and fed back into these algorithms to generate. And we found a number of different really unique properties that were very interesting that hadn't really been realized before for nanobodies. And those have been now incorporated into that model. Now in the second example here for the clonal workflow, this is, again, with Amazon Bio Discovery, this time with the GrayLab. And so here, what the idea was, is we wanted to characterize all of the different models that were being published in the Amazon Bio Discovery website. What we were able to do was take 5,000 different designs across 50 different targets. It's a lot. It's a big study. We generated 70,000 data points across 7 assays for this particular one. So again, went very, very deep. This enabled them to learn which of these algorithms were valuable to predict different properties. Not every algorithm was perfect for every property, but now we know which tools to apply to which problem within that platform and we can learn and fine-tune those models now in multiple iterations of that cycle. So very valuable data set, 1 that's being used for benchmarking most of the Amazon Bio Discovery tools and that other folks is open access and people able to use. And so suffice to say, we're fully aligned with Amazon's mission there to build this ecosystem of AI agents and be able to empower these scientific capabilities and make them accessible to many researchers, not just the largest companies that are well funded AI/ML companies that can design their own algorithms, but making them available to all. Now those were some very detailed specific scientific examples, but I think what might be most valuable here in this setting is talking about the customer journey. In particular, what do most of these companies do when they come to us with a problem when they come to us with an AI/ML workflow that they need to execute on. Now most of these companies start at what we would consider the model building stage. So in this particular case, they might have a model that they've already developed, similar to some of the other examples that I just gave. And they'll say, well, first, we want to do a pilot study with you. We want to know that the data we're getting is going to be valuable and it's going to fit into our models and it's structured the way that we need. So that will usually be on the order of tens to maybe hundreds of sequences, a fairly small study. We'll do all the production and all the characterization and data delivery for those known sequences from the partner where they already maybe have data on that and they're benchmarking us to say, okay, how accurate is your data compared to what we expect. As you usually complete it in just a few weeks and for a matter of $10,000 to $100,000. Now once we pass that pilot stage, now we go into the first round of training. And so this is where we've got thousands of sequences, maybe tens of thousands of sequences for a single round for a single target. And again, we will go through the full make and test cycle to deliver data. And then this is completed, again, in about the same time frame. Slightly higher costs, but usually less than $1 million to be able to feed into the model. So now this is real world data that they're using in their design algorithm. But again, now they need to learn. So this will feed back in. Almost never are these models perfect the first time. So usually, you'll see 2 to maybe 5 additional rounds that will provide fine-tuned training for these models. So by that, we mean that it's predicting particular properties of the proteins that the company is interested in. Maybe they're assessing how well it fits also into other parameters. Once we go through this, typically, we've established a really good close long-term relationship with the partner. And more importantly, we become embedded essentially into their make test cycle. Now the interesting thing is that then most of these companies realize, okay, I need to build more foundational models. So they'll say, "Well, I had this original model I came to you with. I learned a lot from it. But now I want to predict a different property." Or maybe I say, well, that went really well. I need more data. I learned from that process that I need more data to solve additional problems. And so here is when we get into that library build process. So we have tens of thousands or hundreds of thousands of sequences. And again, those are designed across a number of different proteins now. So we're looking to build generalizable models. So we want to say, okay, I'm not solving a problem for just this 1 property or this 1 protein. Now I'm solving a problem that I can apply to a wide variety. These are much larger studies, but usually completed still in weeks and for hundreds of thousands to millions of dollars sometimes. Sorry. Then on the validation of this, that's very similar to what we saw. Again, we're going into that clonal sequence process where we're making, testing all of those, and that's so very similar to what we just saw. And again, we go through multiple cycles of that to learn and feed that back in to build these new foundational models. Now the company has a great platform. Now they have foundational models. They fine-tune them. They've tested them. They've learned from them. And now they say, okay, we're ready for therapeutic discovery. So now we'll apply this to a set of targets, usually not just 1 at a time, usually multiple. And that will be hundreds to thousands of candidates that they want to make and test. So it's not just making tens or dozens like has been published in some -- when you're making a therapeutic, you don't want to take that much risk. So it's better to make hundreds of thousands and overshoot it rather than undershoot it. And so we'll do the production. We have a second layer of characterization we'll do here. We'll get into functional characterization, so we can really tell if this was an effective therapeutic for that particular application, again, completed within a matter of less than a month for under $1 million, very effective drug discovery campaign. We definitely will take top hits into optimization models. Again, zero-shot discovery of these therapeutic candidates is not where it is today, maybe in the future, but we're still a ways away from that. And personally, I think we'll always want to do some optimization tinkering models whenever you're developing a final therapeutic. And so again, we'll do hundreds to thousands of these in a similar cycle. And then finally, once we're done with this optimization process, then the partner will nominate that lead candidate. They'll move those into efficacy testing in animal models and tox models at different CDMOs. And then once those tests, then they will enter the partner's therapeutic pipeline. So the great thing is that these aren't just theoretical. We've been doing this, and we've been doing it for a little while. Just recently, though, we've completed over 200,000 proteins expressed. Over 130,000 of those were assayed. This has generated 7 million data points for dozens of customers. So very impressive scale. Now what you see here is 1 example of that in the data output. So this is millions of dollars on a slide basically in data. So very impressive throughput and is critical for that training and process. And so the -- thank you. The amount of biophysical data and information that is available for this particular data set has enabled not only model building, but also therapeutic development. And so that's the key here is this is what we're talking about when we say we generate these large data sets not just to help enable them to build more models, but to enable therapeutic development in the future. And here's another great example of a multimillion dollar project where, again, we're looking at 50 different targets here. So again, binding candidates, 50 different proteins. We're also benchmarking against control antibodies. So those are in red that you see here. And so we're measuring the candidates that we are testing the AI design candidates against those benchmark candidates to find out which ones would be the best properties. Now we compare all of this data together. So again, it's not just binders, it's not just biophysical properties, but it's all of this combined so that now you can select lead candidates. So here, we see in this green box. You put all of this data together for somebody to again, train a model because of the model will depend on good data and bad data, the data in the yellow and the red. But then when you're selecting the therapeutic candidate, now you can select from that green box. And so that really allows people to use the most out of all of this data that we're delivering. And the last example that I'll end on here is a really interesting one because this kind of illustrates the way the market goes. So every -- in the biology, every 20 years or so, a new model, a new method will emerge. So here, what we're looking at is actually a campaign where we ran in vivo, in vitro and AI/ML altogether. So in this particular example, we have hits from each method. We took those all the way through functional characterization to find the best hits. And we found that hits from every method were valuable. So really, the message here is that it's not that 1 method supplants and replaces the last. The traditional drug discovery methods are still very viable and very useful. AI is now just a third additional tool that we can add to the mix. It's a very unique one and the newest in this series of in vivo [ hybridoma ] discovery originally, in vitro phase display discovery, and now AI/ML. The interesting thing from this one is actually, the partner has told us that they've nominated the lead, they're coming back for more optimization on that, but the lead is actually from the AI/ML library, interestingly. So that was their lead candidate, it came from that design. It passed very well through the animal efficacy studies, very useful. So we know this is working. We know this is a tool that people are going to continue to use, and we're very pleased to see that, that all 3 of these methods can work together in concert to find lead candidates. And then again, kind of teased it a little bit at the beginning, but it doesn't end here. This is -- so far, we talked a lot about monoclonal discovery, single candidates. But we see a wide future of different modalities, one of those being bispecifics. So we put a lot of effort into this recently as well. So you can see here, we don't just end at the multiplex gene fragments like I showed for the Baker lab study, but we also had the gene pools. So now being able to make a single gene of that length means now you can really uniquely pair different bispecifics together. It's a very unique thing to be able to do so that you can start, again, AI designing how they should come together. And now we can do that in that library method. But then the question is, okay, now when you want to go deep, when you want to have the characterization workflow, how do you do that and high throughput? Because bispecifics are traditionally very hard to work with. And that's where the B-Body platform comes in. So we had the licensing of the B-Body platform from [ Invenra ]. And this works extremely well in that high throughput method to make hundreds or thousands of candidates very quickly and then characterize them very deeply and keep them in that format for your downstream manufacturing in CMC. So this is where we think the AI/ML market is going next or at least 1 of the ways that it's going, 1 of many that we will continue to support. And so we've been very proud to support it and provide the data, the genes, the protein wherever somebody needs to start and stop along the way. And we're very excited about the future of the industry as well and the different modalities that it's going to enable. And with that, I'll turn it back over to Emily.

Thank you, Colby. So what does that mean in terms of business? Thank you. So in terms of business, we mentioned that we were able to deliver a number of -- a large number of antibodies that assay, less number of data points. And to help illustrate, we want to share the revenues that we got from 5 different customers. So it's not an exhaustive list. There is a mix of a frontier AI lab, a large pharma, a native biotech, a dry lab biotech. And what you can see is that we have to meet customers where they are. They all have different colors because we have a full [ money ] of product. And at different times, even the same company needs different product. And that has been our strategy to become a leader in drug discovery is we had to, one, have the full menu. If you want to hybrid, we'll do a hybrid. If you want a single-cell workflow. We have that. And so on and so on. So not only we have the full menu, but we have the best implementation of that full menu, and that is being seen here. And AI is turbocharging this. So you can see, generally, it's up into the right, and customers are doing things differently. And that is back to the key that we -- of what we provide is industrialization of what you want, very high throughput, but we're going to be flexible, and we're going to customize our solution to exactly what you want. In terms of the market, what does that mean? So we are updating what our belief in terms of where the market size are going to be. And please note that this is for 2030. And so we think that the DNA market is going to be flat at the -- remain at $2 billion. We're seeing that the antibody drug discovery is going to grow. And on top of it, in antibody discovery service we have an additional $0.5 billion that is driven by AI-driven drug discovery. And then we think that the protein expression slide is [ earth low ] putting expression, market is also going to grow. And so we think there is a big opportunity. Of note, the assumption that we have here for AI is that there's not 1 dominant model. This is an assumption that many models are going to win. And then the second assumption is that AI drug discovery is going to work. And we heard from Colby, we think it's going to work. We have example of it's going to work. But if it didn't work then, then the market will not be as big. And last, we are very happy to share that the growth between FY '25 and FY '26 so far is in the triple-digit order growth. So we've talked about AI in the context of drug discovery. But as a reminder, AI is broader than just drug discovery. And here are just 3 examples of work that has been published by customers on the left. We have using AI and [ citic DNA ] to discover, engineer develop CRISPR tools in the middle, the same leveraging AI for mRNA expression in cell and gene therapy. In say with mRNA as a modality, the promoter, the enhancers, the terminal regions are very, very important. And you have to engineer those regions. And nothing better than the combination of AI plus with DNA to be able to engineer that. And then last on the right. We can leverage AI for protein engineering. We heard from Frances that she's doing it. Others are doing it. And you'll hear from Paddy this afternoon, even our own experience in ensuring our own enzymes and protein leveraging AI. So we absolutely love AI-driven drug discovery. We think it's going to grow our markets. It's going to be a great catalyst for Twist. But -- or and we should not forget that actually, the AI, the tool is useful in a much broader fashion. So with that now, we're going to hear from our customers, and I will let Angela introduce those customers. We have a number of customers, I think you're going to like it.

Fantastic. Thank you, Emily. We've had some great discussion about our internal platform. And now we are going to hear from directly from customers. We have some prerecorded videos, and we have some customers here with us to present live and in person. Our first is a video coming from Josh Meier, who is the co-founder of Chai Discovery, where he leads the development of AI-driven technologies to accelerate drug discovery and molecular engineering. If we could queue that video, that would be great.

Hello, everyone. I'm Joshua Meier, Co-Founder and CEO of Chai Discovery. And today, I'm excited to tell you a little bit about what we're working on at Chai and how we're leveraging experimental capabilities at Twist in order to fuel our journey. A bit of background about myself. I started my career at OpenAI back on -- in the early team and the nonprofit days. We worked on GPT1 and GPT2 back then, showed some of the early scaling laws. And I realized that if the models were able to learn how to speak English and speak French, why wouldn't they someday be able to learn how to speak DNA and proteins? So I went to Facebook of all places where I trained the first language models, transformer language models for protein sequences. And then before starting Chai, was Chief AI Officer at a company called Absci, another antibody pipeline company, where we also did a lot of great work with Twist. Another fun fact is I was actually 1 of the Twist beta users back in my academic days. I was working at the Broad Institute working on gene editing, and we actually tested some of the first oligo pools coming out of Twist. So I've been working with the Twist family for a very long time and very fortunate that we're a continued partner of them now at Chai. At Chai Discovery, we're building a computer-aided design suite for molecules. And the big vision of what we're trying to do in this space is to generate antibodies that are ready to go as therapeutics. If you think about the process of making a drug, there is many, many steps that go into the process even all the way from the preclinical stage. You need to get a molecule that binds to a target efficiently. So for instance, what you can see here is we might take a drug target in purple, we'll have a specific portion on that drug target where we need the antibody to bind. And then actually, what our models are doing here is these are diffusion models that are placing the atoms in 3D space so that they actually bind the targets effectively. So we need to do this in a specific way. We need to do this in a high-affinity way. But even if we can get a binder to these targets, there's a big difference between making a binder and making a drug. So these molecules also need to have a whole host of drug developability properties. When we produce the antibody, we need it to be specific to the target. We need it to be expressed with high yield. And we need to have low viscosity, so we can't have the antibodies self interacting and binding to 1 another. They just need to bind the target. And then we also need them to be stable. Even if we can make antibodies that bind these targets and have these drug developability properties, we also need them to actually have therapeutic function. So 1 of the really nice things about these models is that they reason at the atomic layer. These aren't like the NLP models that I was working on at OpenAI that reads it in English or reads it in French, these models actually reads it in atoms. And that's allowed us to come up with very specific designs. So for instance, you can see here a peptide MHC complex, where we would like to get a design that is specific to a single point mutant on cancer. And it turns out that now the models are able to do things like this. And if we can bring all these challenges together, then the outcome will be generating drug-like antibodies including to think to hard targets, which are difficult to go after with traditional methods. So really opening up the surface area for the kinds of molecules that we can make and the kinds of targets where we can apply them. So how does Twist fit into this picture? Well, if we look at how the field has evolved over literally the past year, the success rates of AI methods in molecular design have really gone through the roof. So literally a year ago, back in May of 2025, the state-of-the-art for antibody design was a 0.1% success rate, meaning 1 in 1,000 of the antibodies that you would make would actually bind in the lab. Well, 1 of the really exciting things that we've seen with Chai is that if you look at the Chai [ t model ] that we published in June of last year, we were able to go from that 0.1% success rate to about a 16% success rate, meaning if I make 1,000 antibodies, now 160 of them are going to bind. So really an over 100-fold improvement in the number of designs that are binding the target. In order to make this happen, there is both a ton of compute power that we need in order to train our models, but then, of course, also the data sets in order to train the models as well. So this is where a company like Twist can really come in. I think Twist really has this potential to provide a ton of the data need it in this area. The technology is, again, a great fit where you can start from the beginning of actually synthesizing the DNA. If you think about what happens when we're building out a training data set at Chai, you might have a certain sequence that you want to design. We might use our model, for instance, to design an antibody molecule. We can take that protein, and we can think about what the DNA is. So Twist, of course, great technology and DNA synthesis. We can either make gene fragments. So each 1 of those sequences can actually be -- we can order those independently. Or we can actually even bring this together into larger oligo pools and then synthesize many, many molecules at the same time. So we can scale data sets that way. The other thing that we would like to do is if you look at the way that antibodies are conventionally discovered, you might take a target and put it into an animal to immunize it and basically have the model create antibody -- the animal create antibodies against the target, which we would then extract from the animal. Or we might run a phage or a yeast display library, where we might have a fixed library, so a bunch of random sequences that have been fixed that we try to latch on to a target. But what's exciting about AI is actually now, if I want to cut down those time lines and also go after those harder targets, I might take a target generate those molecules with the AI model and then go straight into the lab and order DNA for each of these. So whereas before, I might have used the same library each of the times or I might not have even used a library at all, you're now for every target actually having AI-driven repertoires or AI-driven sequences that were going straight to testing in the lab. So I can imagine that this is, again, another place where we have a close partnership with Twist, where we can very quickly send sequences to Twist on a Monday, for instance and then a couple of days later, actually have DNA synthesized so that we can go and confirm whether these designs actually work in the lab. So to give you an example of a case study of how we might use a method with these success rates in order to either create data sets or validate our models, let's talk about that drug developability challenge. So 1 of the exciting things we're seeing with the latest models is that we can generate an antibody that doesn't just find the target, like I showed on the last slide, but actually has drug developability properties like thermal stability, poly reactivity, self-interaction, hydrophobicity as well as a host of manufacturing properties. And now again, let's think about how we actually build this data set. We would take a bunch of targets for this benchmark. We generate antibodies against each of them. And now we have to, again, we go to Twist for instance, and we generate those sequences. And then we turn them into antibodies, and we measure developability properties. And 1 of the great things about working with a company like Twist is that there's actually expertise in all these areas. So you could actually run this entire study at Twist, right? So everything from the gene fragment synthesis, to the antibody production, to the measure in the developability properties, that can all be done over here. And 1 of the things that we've loved about the partnership with Twist is just how responsive Twist is to some of this feedback from a customer like ours. And they have been since the early days of working together. And I think that's really important if you think about where this industry is headed. The workflows that we are running today in the lab look pretty different than they were even just a year ago, right? Because if you're going from testing a system where 1 in 1,000 of your antibodies bind versus where 100,000 or 200,000 bind, it really changes the kind of experiments that you might want to run and the feedback loops that you'd like to run. So if it only takes now 24 hours to design an antibody in the computer, the next bottleneck is actually how fast can actually synthesize that molecule and then run these downstream experiments. So we're really happy that Twist is continuing to invest in this and continue to make those time lines faster because that's something that can directly benefit the feedback loops through which we can validate our models and then also continue to build their training sets. Maybe lastly, the thing I'll say is that this is a really exciting area to be building in right now. I think both the advancement and experimental methods and how fast we can turn things around. And then also just the pace at which the models are developing really creates an interesting flywheel where as the models get better, there's more DNA that we need to order. And then as we can synthesize DNA faster, and we can run these experiments faster with that models update faster. So this whole thing goes into a really nice flywheel. And as a company like Chai, we were -- we've skipped over this slide earlier, the company has raised about $0.25 billion of capital in order to go and kind of push the frontier in these models and then take those models and deploy them in some of the largest pharma companies in the world. But in order to do all of that, that quickly, we've need it to work with partners like Twist who've been able to iterate really quickly in our needs, able to push the balance on how many of these antibodies we can actually produce, the amount of sequences we can measure. And this has been extremely productive for us because it means that we haven't had to go and develop all these things in-house. We can instead rely on a partner like Twist who has the expertise to really deliver on the scale that we need in order to push forward the bounds of AI. Thank you very much for having us, and hope the rest of the Investor Day goes great.

All right. Fantastic. So Chai Discovery, they do not have a wet lab, right? You heard from them directly as to how they leverage our services, and they use a wide range of services. We've worked with Joshua for many years in different capacities. And so it was a great example of a very satisfied customer who continues to push the bounds of research. Now I'm pleased to introduce a real live person in the room, Dillon Flood, who is Co-Founder and Scientific Director of Elsie Bio, a wholly owned subsidiary of GSK, where he leads the development in next-generation oligonucleotide therapeutics and RNA engineering technologies. Our objective is to show a lot of different a lot of different customers doing a lot of different things. So Dillon, over to you. Thanks so much for being here.

Thank you. Yes. Thanks for having me today, guys, and I'm excited to show you a little bit about what we're doing at Elsie Bio. Like Angela mentioned, we were a small scrappy San Diego biotech company that started in about 2021. And in 2024, it became a part of a much larger company called GSK. It's been a wild ride. It's been super fun. But I'll tell you today about our really interesting technology that we built that allows us to really increase the efficiency of oligonucleotide screening and selection to make better drugs. We did that in collab -- with a lot of collaboration with Siyuan and his chemistry team here. It's been -- as myself as a trained chemist, it's been wonderful to be able to ask to pitch these crazy types of requests to Siyuan and their team and have them come back and say, "You know what, that's wild, but in my work, let's go for it." So with that, we've opened up some really big doors. And instead of using this technology to look at -- to train models for antibody type drug discovery, I'll cut to the chase. We're now using it to look at oligo nucleotide drug discovery. So kind of a different bend, but it all comes back to DNA writing. So what are oligonucleotide therapeutics? These are short synthetic pieces of RNA or DNA that are used to target RNA or DNA in the cell. What we typically think of when we talk about RNA therapeutics are things called antisense oligonucleotides and short interfering RNAs. I'm sure you guys have heard of them. They're fantastic drug modalities. And for a long time, folks thought these things were extremely programmable. So when we started the company about 5 years ago, there was a lot of dogma in the field and people thought that these things we're super programmable, you could use [ Watson Crick Franklin ] base pairing to program your sequence, you slap on a few patterns and you're good to go. That might have worked for some of the early targets, but now that these things are being going from rare diseases to common diseases, right, we need better therapeutics. And 1 of the things that makes us hard is therapeutic sequence prediction. There is a lot of -- you can very easily predict short-range sequence, shape and folding on kind of shorter RNA. But once you get to full target length, it gets really hard. And that's because these things are not a one-dimensional string of [ ATG and Cs ], right? They're a dynamic folded structure that has secondary structure, Tertiary structure and a really kind of under defined protein RNA [ interactome ]. So what we did is develop a technology that we call ROSALIND. It's a DNA-encoded library based technology that allows us to rapidly screen massive amounts of chemical diversity through our platform. And this allows us to perform our selection techniques in a system that natively recapitulates the RNA structure and hopefully that binding interactome as well. So why did we develop ROSALIND? It was really to get at this, eliminating the -- what we call the oligo design question for any 1 target. There's tens of thousands of sequences you can make to that target. There's tens of thousands of patterns of chemical modifications. You can pattern on that sequence, and there's tens of thousands of ways to stitch these things together. I'm not a mathematician, but I'm trusting that people who put that on the slide. There's a lot of ways to put these things together. So what we tried to do was come up with a way that we can take a much larger chunk of the pie to really look at the chemical diversity that we're seeing in this space. And that's because most folks, if you look at any -- a lot of the drugs in the market now, were discovered after focused, kind of dogmatic type drug discovery efforts where people looked at 100 to maybe 500 different constructs. And we think there's just so much more out there to explore that we can find better molecules out there even in crowded spaces. So what we did is we develop our ROSALIND platform. Again, it's a DNA coated discovery engine. That's all super fun and great. But what it actually allows us to do is increase the amount of screening and selection we can do at these various stages by orders of magnitude. So when we look for our sequence to any target, we're now instead of screening a couple of hundred, we are screening 10,000 to 100,000 constructs in a single tube all at once. And this is how we define our selectivity and our activity of our constructs. We then take 1 of those winner sequences, and we start to pattern on modifications to these things. DNA is a great format for storing information and doing all sorts of stuff, but it's not a really good drug. So we need to modify it. We take our best -- our best sequence and we start patterning in our modification patterns. We can do this on the scale of 10,000 to 50,000 constructs at a time, and this really helps us fine-tune our toxicity in our PK/PD profile. And the last bar yet is not yet DNA-encoded, but what we can do is fine-tune those properties through optimizing the linkages that link all these nucleotides together. So I'm going to show you some data. It's a little embarrassing because it's all from 5 years ago. It's the oldest data we have. It's all the lawyers let out the door. But even the first time we ran a oligo discovery campaign with our ROSALIND platform, that was only -- we were only able to do that because Siyuan picked up my phone call and said, "Sure, I think we can put degenerative basis in there." And that was the start of a great partnership. But what we did here is we took a highly studied transcript -- target transcript. This is for TTR. It was the benchmark case for all the RNA companies out there on [ Ilan, Riona ], so on and so forth. And we said we're going to go find a better sequence to target this gene. What most of these companies have done is they've all landed in a similar locus, which is kind of by the 5 prime -- or the 3 prime UTR. What we did is ran our exhaustive screen or exhaustive search of this transcript. And we found constructs that were distal to that area that were about 20x more potent. So we thought that was proof positive and increased screening would give better results. What we did then next is take our single sequence. We threw away the dogmatic [ gapmer ] type approach and sort of patterning in modified nucleotides into these things. What we knew about that was that, that would -- that would help us modulate the protein binding effects of these strains of nucleic acids. And what we can see here is that we could modulate our -- or we could keep our high activity constructs where really modulating our toxicity profiles. What we can then do is take that kind of second-generation lead, and we can pattern in with some really cool chemistry that my other co-founder at Elsie developed, we could fine-tune the properties of these constructs. I won't spend too much time on that because I want to get to what I think is even cooler, which is where we are now. Back in the day, we were producing libraries with Twist with all DNA constructs. We are then brute forcing our Gen 2 constructs with classical column-based synthesis approaches. But thanks to another phone call that Siyuan graciously picked up. We've now been able to incorporate all sorts of interesting modified sugars into this -- into our synthesis process. And what this allows us to do is really change the activity and protein binding impact -- or profiles of these types of molecules. So I'll talk kind of about these ones on the right here. It's 2 prime MOE and 2 prime LNA. These are kind of the base kind of state of play for antisense oligo nucleotides out there as well as the PS bond. But after much back and forth with Siyuan and his great team, we were able to get some really cool methods that could allow us to incorporate PS bonds into these constructs at high scale. I'm not talking just in a couple of sequences here and there. This is across the entire chip with complete control, with as good a control as you get with regular DNA. We were then able to incorporate 2 things, the [ MOE and LNA ], like I mentioned. And really interestingly, we were able to incorporate [ MOE ] at very high fidelities. This is notorious for being a hard to incorporate base in oligo chemistry. So the fact that we're able to do it on the silicon chip was amazing to us as chemists, super exciting as a scientist, but really started to enable what we're doing next. And what that is, is building fit-for-purpose data to train our own AI/ML models to power our kind of the entire foundation of our oligo discovery platform now. Right now, we are using this data like today, we're using this data to start triaging compounds that come out of our selection and screening processes for possible tox effects. This is great. This is a wonderful use of AI. It takes our screening from hundreds of compounds down to maybe 800, just a huge win for us in time and money. That said, where I see this going is as we continue to increase this data corpus that we have, we're going to be able to start using this in a generative fashion to predesign the constructs we want to move forward before we even get to the screening phase. And with that, this doesn't happen alone. Our small little team of 20 is now a part of a massive team of about 20,000 researchers. So it's been a great -- it's been great to integrate into GSK. Very different than the Elsie vibe, but it's fun. But with that, that is the Elsie story, and I'm happy to answer any questions if anybody has any. Just shout it out.

Back to mic, and we'll run the mic back to you.

Thanks very much. The predictive toxicology and PK/PD, it seems like that's a huge potential maybe for AI, but the models haven't made a ton of progress yet. And given most failures are kind of like the Phase II tox phase, I'm just curious, as someone that's working [indiscernible], what your perspective is on the progress we've made on productive toxicology? And if you think there's potential to improve approval rates over time, if that's an area that can contribute there?

Yes. I think in a therapeutic modality like this where there are strong class effects, class tox effects, and you can boil that down to interaction with a protein or a class of proteins, there was actually a lot of promise in being able to predict that, right? Because what we're functionally doing here is taking constructs that will fold into 3-dimensional shapes, expose under these proteins, seeing how they're binding those proteins and ultra-high-throughput and using that to make predictions. And if you can basically boil down what is causing the tox and it's something you can address, I think we're going to be able to build the data to actually predict on it. But some of these other larger multisystem tox effects might be harder.

Okay. Can you hear me?

Well enough.

I'll try it again. Okay. I'm kind of loud, but you probably want people to hear. You can hear? Okay. All right. Thank you for doing this. In your presentation and in the other presentations, it's been really exciting to hear how quickly AI is leading to development of a higher number of well-profiled drug candidates in much shorter duration. I'm going to kind of actually ask a little bit of a financial modeling question, which I know is a bit unfair. But you're generating a lot of data. You're probably better than me, so don't worry about it. So you're generating a lot of data upfront. Is there a point where -- using you as an example of an important customer for Twist, where you've essentially generated enough data recognizing how much you have, your staffing constraints, your capital constraints where there's kind of like a bolus of activity with Twist and then it drops? Or is it the opposite where there's kind of a consistent build of demand for Twist? Because I think a lot of us in this room are really excited about this. But I think we're trying to figure out like our customers like you going to spend a lot on Twist upfront and then kind of pull back for a while? Or does it actually amplify over time?

Yes. I think that's a really great question, something that I have to talk to our higher ups about all the time. So what I see is that kind of far off future where kind of the screening dies off is something that's in the far unforeseeable feature just yet. That's when AI takes all of our jobs and we don't work anymore, right? But in the near term, what I see is that we have just started to produce models that are useful and exciting to the internal stakeholders that we're working with. We've been able to deploy these on programs not only to just reduce tox and triage, but also go the other way and tuning polypharmacology that are interesting and elusive and hard to get at. So what I see in the next 5 to -- 3 to 5 to 6 to 7 years is a steady ramp-up of needing to build data. And this platform relies on ultra-high throughput paralyze synthesis to do that data build. I think there could be a time in the next short to medium term where for a single modality, we have enough data that we can be predictive. But as a large pharma, you have -- we have a handful of modalities that we want to go after that we can't even start to think about modeling it because we don't have the data to do it, right? If we're putting our flag in the ground in ASOs and siRNAs. This is a rapidly expanding field. There are self-amplifying RNAs. There are upregulating RNAs. There are RNAs that interact with the reg RNAs. This is a whole field of emerging RNA biology that we're still going to go after, [ 8Rs ], all these things. So I see while I'm still around, I don't see that dying out anytime soon.

Super helpful. Thank you.

Thank you so much. I am continuously inspired by our customers and the endless creativity for the next problem, the harder problem, the deeper they go each and every time. And you just heard it directly from Dillon. It's not a problem that's going to be solved in our lifetimes. So with that, we're going to change directions a little bit. We're going to move away from drug discovery, and we're going to focus on enzyme engineering for sustainability. So we're really going to change it up. Our next presenter is also joining us via video because she is in Australia. Vanessa Vongsouthi is Research Founder and Head of Bioengineering and Discovery Research for Samsara Eco. She leads development of AI designed enzymes that enable infinite recycling of plastics and textiles through advanced circular biomanufacturing. Vanessa?

Hello, everyone. Thank you for having me today as part of the Twist Bioscience Investor Day. My name is Vanessa Vongsouthi. I'm 1 of the research founders and Head of Bioengineering and Discovery Research here at Samsara Eco. Twist is 1 of our longest collaborators and enablers in our discovery and scale-up ecosystem here at Samsara. So it's a real pleasure to be here today, and I'm really looking forward to taking you through what we're building. So at Samsara, our mission is to create circularity for the world's most valuable materials. And we started this mission in 2021 by targeting a group of materials that is arguably 1 of the hardest to ignore, and that's plastic. The world has produced over 10 billion tons of virgin plastic from fossil fuels to date, and this really isn't just a problem of waste. So every kilogram or ton of virgin plastic begins its life as oil or gas, extracted, refined, and then moved through a supply chain that is long, carbon intensive and increasingly exposed to geopolitical risks. So it's no surprise then that the production of virgin plastic currently contributes to over 3% of global greenhouse gas emissions. And this is expected to reach 15% by 2050 if we stay on this trajectory. Despite this, only 10% of all the plastics we produce globally get recycled today. And this rate is as low as 0.3% when it comes to textiles to textile recycling. The reality is that no matter how meticulously we sought the plastic waste at home, most of the things that we consume actually don't make the cut for traditional mechanical recycling. Usually, they're too contaminated, contain dyes or a mix with other plastics. And textiles have an even slimmer chance of making it through. In practice, it's really only the cleanest, clearest plastics that enter the mechanical recycling waste stream, where they're melted and extruded into recycled plastic. But with each of these cycles, the plastic also loses some of its material quality and strength until it eventually ends up in landfill or has to be blended with increasing amounts of virgin materials. And so this is really the problem that Samsara was founded to solve. And it's what brought us to leveraging enzymes to deliver material circularity. So this slide gives you an overview of our technology platform. It's an integrated system that takes end-of-life products and turns them into virgin identical circular materials. At the heart of our platform is a machine learning-driven enzyme design engine. The designs we generate are brought to life using Twist's clonal genes. They're delivered to our labs ready to experimentally screen in the 96-well plate format. And so rather than cloning and sequencing genes ourselves, we received them sequence-confirmed and ready to slot into our enzyme screening workflows. This means we can move really quickly from a computational design sequence to an experimental data point at a pace and scale that just really wouldn't be possible otherwise. From screening our engineered enzymes, we take the most promising candidates forward through characterization and process integration until they ultimately feed into our chemo endomatic recycling process that you see here on the right. As input to our process, we can take colored, mixed or degraded plastics in textiles, and then our enzymes get to work, breaking them down into their original chemical building blocks, also known as monomers. These monomers are equivalent to what we have to extract from petrochemicals today, which means we can purify and repolymerize them into virgin quality materials that can be manufactured into brand-new products. Importantly, this really enables infinite recycling. And so we see no loss in the quality or the integrity of the material no matter how many cycles it goes through. So at the core of everything we do are our enzymes. And specifically, these are new to nature enzymes engineered to break down plastics at speed and scale. These aren't just enzymes borrowed directly from nature and dropped into an industrial process. Whilst our naturally occurring enzymes can degrade plastics, they rarely meet the demands of the real production environment. Often, they're too slow, too unstable or unable to withstand the operating conditions that we require. And so we use natural enzymes only as a starting point and a foundation that we can then build on to create proteins that are optimized for speed, stability and precision at scale. What makes this challenging but also exciting is the sheer scale of the protein design problem. So to give you a sense of this challenge, a typical enzyme with just under 300 amino assets has more possible sequence combinations than there are items in the observable universe. And so the question we face in enzyme design is how do you search that vast space efficiently to find the very few sequences with the properties you actually need for an industrial process? As you can imagine, searching through by random trial and error would be near impossible. And so we need smarter, more principled ways to navigate our search. And this is what our platform is built to do. It does turn out that 1 of the most powerful approaches we can take is to learn not just from the enzymes that exist in nature today, but actually from their entire evolutionary history. Like species, enzymes have a history that stretches back billions of years. And along the way, countless enzyme variants have existed and disappeared. And so using a technique known as ancestral sequence reconstruction, we can actually infer all of those earlier sequences and bring them back into view. This matters enormously through our understanding of how different protein families work that also gives us very rich training data. So rather than only having a few hundred natural sequences that are relevant, leveraging this method gives us tens of thousands of ancestral sequences that allow our deep learning models to identify the patterns that better link things like protein sequence to protein function, activity and stability. And so a really great illustration of this is our Nylon 6,6 [ hydro lease ], which is estimated to sit at about 10 to 82 possible sequences away from the closest naturally occurring enzyme. And to our knowledge, it was the first-ever enzyme that was characterized to be capable of degrading Nylon 6,6. And so this is a breakthrough that was really only reachable because of the richness of this evolutionary data. And so if we compare mechanical, chemical and enzymatic recycling approaches side-by-side, the advantages are clear. So our process enables a true closed-loop circularity, returning plastics all the way back to their original monomers with no loss of the material quality. It handles the mixed plastics and composites that other technologies reject and it operates at a low carbon footprint relative to virgin plastic production. And critically, we've demonstrated that the monomers we produce can be repolymerized into virgin identical end products that look, feel and perform exactly the same way that fossil derived materials do. Our milestones to date reflects the real-world traction that our technology is generating. On the product side, with our partner, Lululemon, we produced the world's first Nylon 66 enzymatically recycled Nylon 66 garment and launched a full retail collection with them. We've also produced a clear recycled PET bottle. These are consumer-facing products that prove our materials are virgin identical in every sense that matters. The industry response has also been equally strong. So Lululemon has committed to sourcing 20% of their fiber portfolio from Samsara over the next 10 years. And LSKD, another athleisure brand founded here in Australia, have also followed with a long-term agreement. And we've also announced a polyurethane collaboration with the LYCRA Company. Our first facility is now open here in Australia, as you can see in the top right, and our commercial plant is on track for 2028. We've raised $107 million to get there. And so the technology works, the market is ready, and we now have the backing to build this. The plastics are really just the beginning. At Samsara, we believe biology scaled into industrial processes is 1 of the most powerful tools we have for addressing the material challenges of our time. Our platform is built on the marriage of protein design, process chemistry and engineering. And it's that combination that makes what we do differentiated. We built this platform for plastics, but it's designed to go much further. The same approach of co-designing proteins and processes that scale applies equally to other polymers, screen chemicals, critical minerals and even carbon capture. We're not building a single product company. We're building the infrastructure to scale biology into industry. So thank you so much for your time and for the opportunity to present at the Twist Bioscience Investor Day. On a personal note, I've been working in this field for nearly a decade now, and the difference that Twist's technology has made to the pace and discovery of innovation is something that we genuinely feel firsthand at Samsara every single day, and it's just fundamentally changed what's possible for companies like ours. So if you'd like to continue the conversation, please don't hesitate to reach out, and you can also follow Samsara Eco on LinkedIn. Thank you. [Break]

So for those on the webcast, we are coming back from lunch. And for the second part of the day for the afternoon, we are going to start by a focus on NGS strategy. That focus will be in 2 parts, and we will start first with our SVP of Product and Marketing and imagine they get away.

All right. I might be measured by [indiscernible] how many of you I put to sleep. So stay away from me. I'll take off this afternoon, starting with NGS portfolio, and then I will dive into MRD specifically. Coming back to the slide, I'm part of the commercial execution, and I'm also part of the NTI machine at Twist. So my focus, like I said, is NGS and plus at RD. We are a $200-plus million NGS business. And in the last 5 years, we grew on average, about 30% per year. For those of you who follow the industry, post dynamics, the industry grew low single visit, right? So how do we win and get to the 200-plus new in today is through all of these things on street, quality, workflow, the asset customization, not going to bore you reading through every light. A full portfolio of products. But I want to pick up where he want last off that I mentioned, right, on the bottom left corner, we sequence 20,000 samples per day. for our team production process using NGS. And you do the math, 6 to 7 event samples per year. So we know and we know what we're doing here. And that's all you need to know the left. Where do we sit in our portfolio? three sequencing we are compatible with all sequencers out there. We are specializing from sample to that library ready to load on the sequencer, and that's where we play. Our bread and butter starts from target capture. What is that? That is you sequence only the things you're interested in, and we enable our customers to do that. Over the years, Twist has built a very strong reputation this panel and [indiscernible] high uniformity and low sequencing costs. And more importantly often, we enable our customers to fail less of their samples because of lower coverage. And for those who lab operations, lower failure rates makes the world of different maybe can attest to that. So you may ask, why is this even relevant? How genome sequencing is taking over at target capture. So let's do that math together. So in 2021, per gig day of sequencing cost $6 the highest input. Today, the number is 1 to 2. So as a sequencing dropped to sixfold. In the last 5 years. Now before we say everything goes to whole genome sequencing, you look at a typical cancer panel on the left in the top chart -- from a typical panel to go to [indiscernible], it's 100x step-up of sequencing data quantity required from exome genome is another 100 fold of sequencing data required. And so if you take sixfold of reduction in sequencing costs and you say a moving panel from panel to genome, you are going to pay 10x more in sequencing costs than you paid in 2021 and not at that's not that. Now that is not to say genome sequencing. By the way, WGS stands for 4 genome sequencing. Sorry, I should point out that last slide. I shouldn't say [indiscernible] Hogan sequencing is not relevant. It is very relevant. And therefore, we are fully embracing it. In the past 5 years, we have built up a full library preparation portfolio that enables us to capture that whole genome sequencing market. In fact, this portfolio specifically currently grow 56% year-over-year for us. So why does that enable us to win? If all you got is a chip, why library? Well, looking to what semis buffer, it's DNA and a time in suffer this water in sales, everybody has it. But now I don't have to say DNA, and I don't even have to say enzymes and enzyme engineering because speakers in the morning has told all the stories. Now you realize we have every reason to play and win in this space, $1 billion market, a lot of room for us to continue to grow game and win. Whole genome sequencing, we offer the highest throughput. [indiscernible] put 1,500 samples in a single flow cell but a way to run whole genome sequencing on it. Going beyond 4-genome sequencing, you can put 3,000 samples again, industry highest on a single flow cell or a lane on it and to really enable super high throughput sequencing. We did something that's not done by anyone before. and that is putting 1,100 samples in a single 96 or plate. What is that? Previously, you will need 12 liquid handlers filling up the entire lab. With this technology, you can go away because we put 12 samples into a single well in the very first step of lab processing. What you have to do, 2 things you have to do in that 1 step, adding DNA barcode or tagging, so you know where the DNA is coming from when you do sequencing. Two, you normalize those 2 different samples to even so that when you're sequencing them, those soft apps get reasonably equivalent coverage. And that is very difficult to do and is enabled by what we call a normalization [indiscernible] Everything is done in 1 step, therefore, 100 samples in the single 96 well play. Patty is going to talk about our end of engineering and how that enables high performance, right? Just again, this is an exciting part of NGS portfolio for us. As if you haven't heard enough about this, I'm going to go yet again. Largest -- a lot of the largest clinical last really trust us. Why is that? Do we just do better? Maybe. But [indiscernible] or glad to say, the reason come back to the chip. So bottom left, up to 1 million oligos, many thousands of clusters or we call clusters, what we call it, you can understand as well if you may. And in each well, 121 unique different oligos in right? Our competition, what do they do? They use 96 or 384 wells to do the same thing. So I'll use a typical common scenario. You want to build a panel of 50,000 trips again, cyclical. How do you do it in the [indiscernible] or 384-well plate or at a time. It required 130 synthesizer machine or less machines but different batches, adding up to 130 different production runs. What do you hear when you hear that, they're variation, variation, variation. What it is for us, you carve a small corner out of that chip, the entire 50,000 in 1 machine on 1 ship in 1 back all that together. No variation, no variation, no variation. And that's not the end of it. If you do it, 50,000 wells and 384 plates, just how do they come together and you're going to pipe 500 times to bring those 50,000 different well together. For us, 121 unique oligos are already in one well, 120x less tieing. What do you hear? Consistency, consistency, consistency from lot and that's what our customers counting on trust to deliver over and over again, right? That is the unfair advantage is very, very difficult to overcome. So it's not just all about technologies. I want to go deeper into applications. We use liquid bonds as a field. As many of you know, 3 key applications in local -- and again, many of you know, we are a leading supplier in early cancer detection through our methylation detection system. We are a leading supplier in therapy selection for the [indiscernible] biopsy through our custom panels for concrete has genomic profile. And we are a key supplier not leaving in Amart. And so how we play in this space, in liquid biopsy, many companies, many last in this field, right? We are, first and foremost, making clear to our customers, we do not compete with them. We are not in the course race. And we don't bet on a single course. But we work very, very hard for all the horses running on our platform for them to have a better chance to win because when they win even. And that's how we operate in this field. Going into MRD from this point, clearly, there is work for us to do in MRD space, as you saw from the pre slide. I want to step back from the technology from the application even just talk through the patient's angle for MRD. Linda , 63 years old, we've hired 2 years ago after a close to 40 years of teaching. 2.5 months ago, diagnosed with colon sensor. A month ago, had surgery removed confirmed stage II colon cancer. 1 month post surgery today, she goes into the oncology office oncologist office and ask the question stock with now. And the oncologist will wait for an MRD results to sell Linda, whether you can go home, all done, or more chemotherapy is needed. And the #1 utility of MRD, which is a treatment decision at that point. My mother went through kidney cancer and breast cancer 2 years ago, surgery, all done fully recovered. But I can tell you that week 5, you go into the doctor's office, you try to find offer surgery, are used on or more things are coming every day matters. You can't sleep. And for this first utility, you are leasing against and it's not easy even though if you think there's 4 weeks of window for you to take action for Linda to know what's next because these tissue samples is time to be put on the glass, shift session doing axonal genome sequence, do the analysis through the panel design or the MRD tunnels, process in text thing now, the window closing fickly and then they'll stop. Were waiting, right? And that rate to speed is a fundamental need for MRD. So the second, which is good news, Linda's good. The oncologist that you can go home, just come back every 3 or every 6 months to monitor making sure it does not come back. So once she comes back, she does another test -- this is the second utility of MRD to really monitor disease recurrence. In this study is no longer above speed. In fact, has nothing to do with speed anymore. It is all about sensitivity because different sites metastasize require a different level of sensitivity to detect and you want that test to detect every way of recurrence. And so with that patient angle in mind, we'll look into MRD Express from Twist. So 2 dates prior to this, it takes us a week to get [indiscernible] tunnel produced and shipped to our customers. I probed that we just went through with Linda. That's not good enough. And therefore, we completely redid how we make MRD panels. Pull just about everything back to the single thing we're really, really good at -- that is the ship. The entire MRD panel production now happens on the chip. And that enables us to do it in a single day. Without any compromise on sensitivity, we're known now to enable ultrasensitivity without losing that with no compromise on single day. We solve that first problem. Now moving on to the second problem. Many of you follow the space, there are many different tests out there. There's something about fragments to analyze that using AI to analyze full genome sequencing results and they have 6 channels, methylation, multi-omic markers to the tech MRD. In the personalized space, you have digital PCR, try to measure structural variations. You have amplicon panels trying to do the same thing. And you even have technology that tried to enrich patients specific variant trucks, so many technologies. I am so confused which one do we choose. Right? This is all about sensitivity. And we want to have you see the way we see it. People often equate local biopsy I'm already included to looking for a needle in the haystack isn't exactly accurate, right? Because patients and their normal DNA in blood 99% of time, they are identical, which means you are actually looking for a needle in the sack needles. And so the first question, for get of all those technology, you ask yourself to say, 2 scenarios. I tell you, there's a 1 million needles. There's one that's a different [indiscernible] It's not going to be easy for you. The second, I tell you, same 1 million stack of needles. Look for a needle that's 5x bigger and part of that needle is black. You now have a much better chance of finding a needle because you know exactly what you're looking for. And that is the difference between tumor informed and tumor-agnostic period. And so now you know we're trying to secure that. Now how do you further enhance sensitivity. Now I'll give you a scenario #2. There are 3 stack of needles. In stack, number one, 1 million there are 16 special needles that look different. In a second step, there's 64 of them that look different. In the third stack, there are 2,000 that look different. Now all you have to do is go up, choose 1 pile and look find 2 needles a lot different, which tiles you go to Pole 3, where there are 2,000 I only have find to Congress, you just figure it out the size of the panel truly matters because that gives you the auto sensitivity and twist technology enable you to look for that 2,000 needles in that spec needles. So sensitivity and speed, as you hear heard, is taken care of by our platform. I purposely skipped the other element that's very important which is cost. But cost is a soft problem for Twist. At the same time, the cost factor itself has very little to do with Lind. Has a lot to do with clinical labs, maybe margin of operations. We care about that too, but speed and sensitivity really change Linda's life. And so we're a great hopeful you're committed, but we want to take a step back to say this is not about Twist and for MRD field in general. Right now, as we say in this period, currently about 1 million MRD test is done per year, just about and it's growing about 50% year-over-year. So in 2 years, someone does do the math I did it for you, 2.3 million MRD tests. In an ideal world, every patient gets for test potentially. But right now, it's just around 2 or slightly over 2. So that is 1.15 billion or more than 1 million MRD panels that needs to be produced. For us, every panel is how many targets, 2,000 probes. And you do the math, we have to produce 4.6 billion all oligos just for the MRD field if we were to meet all MRD needs in the field in 2 years, 4.6 billion. [indiscernible] C1 says, no problem. All I need is for writers. You've seen quite a few there. That's all that is for us. Now if you look the standard oligo production way, which also is playing in this field, same math. They are not doing 2,000. We say they can do 10x less. So only 200 for them to do, which translated into instead of 4.6 billion, they only have to do 460 million oligos. 1/10 of us, easy to do, right? But if you do the math, that translates into still 1.5 million oligos per day. What is a typical vendor for primer oligo synthesis capacity 150,000 oligos. We can't do it. They can't do it until they grow 10x. And that end picture is super important. Right? We have a future-proof capacity figured out as of today right now, and that's what we have. And for the oligo vendor, we'll lend them a hand. We'll make sure this capacity issue for them in a couple of years, they don't have to worry about it. All right. Thank you, everybody .

They won't ask to worry about it because we'll take it on. So next, thank you, [indiscernible] It's great. Next, we're going to hear from Pat is going to continue our strategic talk in NGS Diagnostics with some details around our enzyme engineering and our nitric acid therapy

I'm going to new technology here. Right. Really excited to present for you guys. So I'm a unique chemist by training. Number of different jobs we've had over time and are somewhat grizzled veteran commercial leader educated in the school of life. I'm going to talk to you about what's quantum engineering, what's cool and sequencing, what can be coming in new click at and therapeutics. And for those in the room that are going to understand this really well. I'm going to talk to you about commercial execution. This is my favorite slide deck ever and outside of my family. This is my favorite stuff to talk about. Second take is accent in the company. And all I can advise you to do is listen super fast because there's a lot of content coming enzyme engineering for NGS. Why do we care? -- right? We're a [indiscernible] company. But I think you've heard enough coming through today about why downstream at the chip matters. And I simply can't talk with a level of intelligence that our customers have demonstrated around what's coming with the we start to look at things like what our customers also do with our product, for example, in target richen sequencing workflows or how we use enzymes internally on the tour, I'm sure you would have seen some enzymes being used to produce product. It caters important that we have a best-in-class enzyme portfolio. It matters for our customers, and I'm going to explain why for the kits that we produce. It matters for us internally, control the supply chain matters, improved economics. My guess is not everybody in our competitive field loves our emergence in the space. So being in control of supply chain is incredibly important. We're fast, but also by being in control, it allows us to have a global strategy for commercializing product, make their own staff that economics are more favorable. It allows you to sell all across the world through different distribution channels and allows us to truly enable the global community to get onto our platform. And the reason we like this space is we have an advantage -- and I think you've seen it many, many times coming through customer presentations. We can make a lot of DNA. We make it in parallel. It's incredibly high quality. The economics are enabling. And when you partner with that with our application expertise, and I'm sure you saw in the core, we do have a lot of sequencing. Tens and tens of thousands of samples. Every single day, we sequence to make sure we're in good shape to go in the right tube to go to the right customer on the right day so we've got this incredible throughput, incredible speed, the ability to make enzymes ourselves and screen into an application that we understand is incredibly enabling. And it's so important that we keep all of those pieces rolling together to drive great product out to the market and deliver good infrastructure for our own internal services. If you go one step further as we go through a cycle of design build test learn launch, we just continue to improve. We get faster, we get more effective, our knowledge continues to develop. We launched really interesting products. And if you look at the workflow, you've seen it before, we're going to start with an enzyme target. We're going to use 0 shop design. We're going to lead off into the Twist Gene Sensus platform. I'm sure you've got deja vu over again around this slide. We're going to crank out the sequences of interest. We're going to go into a high-throughput protein expression purification into a screen where we know our application expertise is incredibly enabling. We're going to pick out the enzymes of interest with features that we like. We're probably going to turn the crank on this a few times because this does have some runway. And so we're going to really optimize what we're building to create super products that when we found the features that we like in screening, we're going to hang on. We're going to actually drive into making kits. And if you think about our overall, how do we launch products, how do we drive our NPI machine? I'm sure you've seen so far of you've just had Jimmy present who's full of ideas. Dr. Chen, wherever he is, is just an idea a [indiscernible] and our tone at the top. We know what our customers are doing. We have another chemist that leads a company that has intimate knowledge of what's going on out in the field. We're constantly ideating -- we're into our enzyme engineering platform to try and build out the enzymes of interest that then go into our product development pipeline, driven hard through NPI and ultimately out into launch products. So pretty straightforward, right? You can see the molecular advantage we have and how we drive that into a new product pipeline. So I'll take just a couple of case studies. So for anyone that's a close follower of the sequencing world, there's a couple of important enzymes. We'll start with ligase. Now I'm going to be a little bit honest. I'm kind of pleased that Dr. Arnold has gone talking about enzyme engineering in front of I was kind of nervous, afraid and excited all rolled into one, so whatever that is. But ligase matters in the sequencing workflow. You have to get the template that you want to analyze, modify, you have to ligate on adapters to get it to stick on a sequencer to allow you to characterize. Ligase is a crucial component of that workflow. And so you can see the methodologies we used up above, and we challenged C1 to say, right, let's make a ligase that's best-in-class. Let's get something that works with low input templates, something that's good for cell-free, something that really drives conversion efficiency and the way I think about it is no molecule left behind. There's nothing gets left in the tube. It all goes on to the sequencer. And also an important point here, buffer sensitivity. Customers do the strangest things with your products.So building a system that's robust and could tolerate the variability in behavior is very, very important. And so again, LLM-based design or basically high-through expression screening and why we find a really genuinely interesting ligase. And I'll save this for the exam that's coming at the end, so everyone will be paying attention. But the most important point here is we just draw in the bottom left corner when you look at conversion. The twist is a big green bar. Big is good, okay? What that means is the conversion rate of template to something that can be sequenced, that is lights out versus the competition. And then also importantly, if you go to the bottom right corner, you just look at how does the enzyme perform in a broad range of salt conditions, i.e., when the customer has got some pretty sketchy samples going into a workflow, does it behave well? And again, conversion matters. No molecule gets left behind. Look at the behavior of the green line versus what's on the market today. That means you're going to yield a very, very competent, capable product. Flush with success and not wanting to let Dr. Chen rest on his laurel, we thought, okay, ligase is important. High fidelity polymerase is next. It's pretty obvious, right? Even I could build that product portfolio out. So we set off for the same challenge. GC bias is a problem. We know that. We use polymeries to assemble genes in the factory here, so on the other side of the business, but we also need to QC genes to say, okay, we can ship this and collect revenue. So similar strategy, get out there, let's get in there and use our LLMs to sort of teach us what makes a good polymeries, high-throughput synthesis, expression, characterization or assay to learn how it behaves in application, space filling design of experiments to optimize the buffer. Again, same thing, broad range of input you need something that works in the customers' hands and lo and behold, outcomes and exceptional polymeries. And I'll draw your attention to just a couple of points. If you just look up in the top left corner, you can green for twist or go or yes, purchase order, I guess, please. You can see improved performance at the edges of GC content, both sides. You can see an error rate that's incredibly tight, incredibly high fidelity. And in general, for high fidelity polymerases, it's hard to get them to behave in a product. You have to get goldilocks, you have to get it just right. And that is a beautiful performance from polymerase. And then most importantly, comparing to Polymerase, and I'm very pleased to be anonymized for that polymerase is, but any enzyme fans out there may know, you can see slight improvement. And by slight improvement, I mean when we use that in our gene factory, I take that top result there, we can't ship that product. Right? That's the wrong gene. So I've spent a bunch of money and I can't ship the product. So for those of you that haven't worked with Emily, that's a bad spot to be in. It's really bad. If you use the twist polymerase, what it does do is it allows us to read through the complex sequence. So then when we QC and say, yes, the product is there. So now I can ship. And I'll still get the phone call where revenues, but at least we ship this product. If we just pause for a second, it looks incremental, okay? I don't feel anybody getting the purchase orders out to buy the polymeries. But let's just hold off and think that one through a little bit. So we moved approximately 1 million genes roughly last year, ballpark, plus or minus a little bit. I think a 5% improvement in terms of polymerase performance and acceptable performance. And yes, this is good to ship because we've got a polymerase that works and reads through difficult stuff, allow me to QC and release product. That's quite useful. Not to mention the fact that we're also vertical now in supply. I don't need polymerase Q or polymerase K. This is our polymerase, and that is a very robust position to be in. I'm going to segue over to the other side of the screen here, and we're going to look at how our polymerase performs in the sequencing experiments for customers. It allows them to amplify and access parts of the genome where others polymerase will stutter, okay? That's a problem. You start to get constrained in terms of how good your whole genome sequencing experiment is. And that's something we're working hard to improve upon and core enzymeology, novel differentiated features are fundamental to driving that behavior into those products. So I still don't feel like a soldier anything. So let's go to the next slide, and we took these core enzymes. And built these beautiful kits around them, the TRUAP library prep kit and the PCR-free whole genome sequencing library prep kit. Really elegant products, best-in-class ligase, high fidelity polymeries. You can see the incremental improvements. Now if I think about the customer and what does it mean for them, think about what it costs to press the start button on your sequencer. It doesn't matter which sequencer, press the start button. It's a well-finished BMW, right? So utilizing that sequencing real estate matters. If I'm in a commercial setting, independent of which workflow I'm using here, I get a 5% improvement in the number of samples I can sequence or a 10% improvement in the number of samples actually on to the sequencer, the impact to my business, the impact to my research has greatly improved. And that's a bit that matters, right? At scale, these incremental improvements lined up give you absolute success in your lab. And again, you probably heard that a few times today, we'll meet the customer where they're at. We don't make kings in Queens, but we're going to enable each of the segments to do a good job. So again, PCR-free. And so by definition, no polymerase or our library prep kit that utilizes the polymerase to kind of give you the best of both worlds, lower input, but still getting incredibly good data out of that -- out of that workflow. That's C1 2 for 2. So now he's resting on these laurels. And so now what's coming is what other enzymes do we need to build out into the portfolio. And methylation is an important marker in the oncology space, there are some constraints on workflows. By sulfide treatment, it's been around for quite a long time. And it's a very, very useful workflow, but to industrialize that, put that into a facility that's running hundreds of thousands of samples. It's a little bit of an art form and it struggles with low input quantity of template. It destroys the template basically. And then you've got the emergence of enzymatic methods, which are good, but have room for improvement, and you want to really think hard about supply chain. So now we've had -- can and the team working hard on a cytosine deaminase that makes the difference or detect the difference between methyl C and C, again, important methylation marker. Same idea that I've gone through before around the utilization of AI for design. We've come up with an enzyme that's 42% of the residues are changed from wild type. Now I do like myself, a good dose of evolution in our platforms, right? And there's no better advocate for evolutionary methods than Dr. Arnold that we seen earlier. And imagine that, 32 million oligonucleotides every single day, the ability to screen for features of interest because we know our application. And think about being able to play in that space and start to deliver enzymes and reagents with features that are coming from remarkable primary amino acid sequences. That's true enablement and there's differentiation here and more differentiation to come in the future as we build this portfolio out. So just in case you haven't noticed, quite excited about the enzyme portfolio and how we can utilize that internally. So a bunch of enzymes is fine, and you've seen how good we are at making DNA. The quality system that stands behind making sure the right nucleic acid, right tube, right customer at the right time. But you also have to have an NPI machine that's sustained by a quality system that scales. And we have a unique challenge. You have the Wild West of the gene synthesis community. They need a molecularly pure product. But if you're a researcher, my guess is you're not too worried about ISO-13485. Then on the other side of our business, when you go all the way through to a clinical lab or even as you start to think about nucleic acid therapeutics, the quality system has to scale up. So you've got sample extraction, library preparation, target capture sequencing on any platform, the quality system to wrap around the products that you see and some examples up at the back there has to stand behind that customer base, something that's very understated in the company. James gave a beautiful presentation showing the fine details of how we move a piece of DNA around the building, that underpins a quality system that supports our diagnostic customers in perpetuity. We audit incredibly well, and it's actually become a weapon of offense to help support, sustain and help our customers grow. Now if I just pause for a minute and start to build, I'll segue over, Jimmy's presentation was beautiful early. It's very hard to follow Jimmy from a presentation standpoint, and also personalize the topic. But if you look at the continuum of cancer care and research, and Jimmy is far more articulate than I around that continuum. But you've obviously -- you've got your screening, early cancer detection, you have a bad outcome. Potentially into surgery, you've got your molecular residual disease test, therapeutic intervention if required and then continued monitoring. And this is what excites me about Twist Beyond belief, and we're just at the very beginning of this on the NGS application side. From a screening standpoint, the product portfolio is incredibly strong. As we go through early detection, this efficient use of your sequencer, really efficient and effective target enrichment is enabling whether it's [indiscernible] methylation markers, liquid biopsy or comprehensive profiling of a tumor. Jimmy talked about MRD and our capacity and the emerging trend of sensitivity and the impact it's having to earlier detection of recurrence of disease, massively impactful and a growing body of clinical evidence saying that's good for the patient. And then through into obviously, treatment response and monitoring. And just walking along the bottom here, that has an exquisite collection of products. And if you think about the future of precision medicine, this platform is incredibly well positioned today around the sequencing side, the diagnostic side, what we're going to enable in MRD. And then ultimately, you have how the pieces come together. On the DSPS side in drug discovery, the platform is incredible. Most new therapeutics are biological. And so at the risk of turning into a seminar, where the biological molecules start. Piece of DNA doesn't matter the moat. It starts with a piece of DNA. And if you want true precision medicine, you're going to need a lot of different DNA sequences. So we're excited about the therapeutic pathway, [indiscernible] our antibody discovery capability, NG characterization, shortening the time of drug discovery against the most complex of disease. That's a very, very powerful offering and not to mention what we can do in the mRNA space. So you can see we're well positioned through that continuum of care where the technology and the products enable the community to address some very, very difficult challenges. I'm going to dream just a little bit, just a [indiscernible] Maybe AI won't get that like a Milligan on smidge. I'm going to dream just a little bit. But having spoken to the lab of Onco earlier on, not really dreaming that much. But just imagine the situation where we'll talk about nucleic acid therapeutics. It's one of many potential new growth frontiers for Twist. So let's pause for a second. The infrastructure that's been built out in CRO, CDMOs is built up. So you can spend approximately $1 billion to build out the infrastructure to make a nucleic acid therapeutic at scale. If you need to make kilograms of nucleic acid, that's what it takes to make it happen. But we think that there's an opportunity in building out. As precision medicine matures, it's no longer about -- it's not just about the kg or the swimming pool scale manufacturing. It's about building out across different sequences at smaller quantities to help challenge a well-characterized disease. And so that's -- the need is capacity, scale and economics to enable needle-to-needle success, needle for liquid biopsy to start you into the standard of care, you go through the journey that Jimmy had described earlier, and I tried to copy my slide back to a needle in the arm with your personalized therapeutic to attack your form of cancer. That needs built out. And so just to capture a picture, what I was trying to say, we talked about the workflow. We talked about speed. We talked about economics. We talked about really the impact that, that's going to have the patients. And so the question is, what's that going to take? And I'm not about to claim that the problem is solved. But it's going to take affordable price. A therapeutic that costs $1 million for each individual. That's not going to happen. It's got to be high quality. I'd like to have the right sequence shown to my arm at the right level of purity, please? It's got to have scale. So WHO is predicting, I think I might be off by a couple of million, but it's about 22 million, 23 million cancer cases by 2029. We've got ever-improving diagnostic tests, disease monitoring that patient population is going to continue to increase. So you need scale to get hundreds of thousands, millions of doses out to the global community. It can't just be a medication for a few people. You need speed that I'm horribly underqualified to talk about neoantigen escape. And I'll leave that to some of our customers to talk about that. But the point stands, the longer you wait to get a therapeutic into an arm of a patient, the disease is changing. And ultimately, you end up treating something where you're looking in the rearview mirror rather than treating the disease they've got at the time of therapeutic injection. And then complexity. I'm sure you heard we launched a complex product just recently, expanding the sequence space, the number of sequences we can accept and deliver to support an even broader operation. And that product portfolio sits quite well. So if you can think of a company that can make millions of genes, doses per year, that has formed for putting the right nucleic acid, the right concentration in the right tube shipped to the right customer on the right day at economics that are truly enabling okay? Then I would challenge that I'm not saying that the biological challenge is fixed. But if you think about distribution, if you think about molecular quality, that puts a massive dent into the challenges facing companies in this space today. And so we're writing the future of nucleic acid therapeutics -- stuff was brilliant. I cannot tell you how much I enjoyed that from about 100 years ago, learning about antisense oligonucleotides as a postgraduate -- or sorry, as a PhD student, it's brilliant to see what you're doing. It's absolutely incredible. If you remember the key criteria to deliver on this promise. It has to. It has to go through here because there's no one else with distribution or scale to make it happen.

My next favorite topic commercial, execution. And we said it very eloquently this morning. Twist, on June North, we are in the business of delighting our customers. And Paul is going to talk about culture in a little while. It's one of these things to me that really matters when we started this -- the commercial side of the business together, what every single twister to care about our customers' scientific success. That is absolutely no compromise. If you don't care, you don't belong here. I don't have to explain the economics of a business with high customer retention versus not. I think Bain Capital describes that better than I ever could. Every twist of cares. Our tone at the top, and we mean this. We're going to play in markets where there's going to be a #1 and there's going to be #2. Old-school methods have made the contribution. They've been super. But now with speed, economics throughput, they're holding the community back. It's time for this platform to be worldwide. From a sales standpoint, our philosophy, our commercial execution. There's one way to go. That's up into the right. We've had 13 sequential quarters of let of rip. And that matters not just for the business results, but from a culture standpoint, do people care day by day, hour by hour when they execute? And the answer is yes, okay? That's why you're here, you're drawn to this company because we know the importance of our technology to the global community. Philosophically, we have an OEM strategy. We sell product to people we see in the field. We'll sell to our competition. Now there's 2 things that go into that. First of all, the economics matter. And secondly, it's Twist a sales team. So I expect the to Twist sales team to win every deal. So our team has a chance to use our platform, that's great. We'll sell the product and expect the Twist sales team to outperform anybody in fact. At the risk of being complementary, I would say we put that team up against anybody else, and I would expect a very favorable result. As a salesperson, you are or your numbers say you are, you are what your numbers say you are. If you're behind, we're going to muscle win to help you. If you're ahead, guess what, we're going to muscle win to help you, and we are going over the top together. As a commercial leader, if you don't address your commercial problem, you become the problem, and I will manage that. But on the other side of that, we reward performance and reward performance well if you're not performing, it's much better that you work for the competition. And when we look at our channel strategy, it's kind of omnichannel, okay? With direct sales is key account management, as our accounts have grown in size, the needs change. It's no longer about selling a product. It's truly the product, product quality, scaling, supply chain, procurement, quality, regulatory audit, it's a much, much bigger challenge that takes a certain skill set to drive key account management. And then we've got a strong team out in academic and government sales taking the word Twist out all across the community. We have scaled growth channels for certain areas we can't afford to send a sales rep. So you've got inside sales, an exclusively well-educated team, great farm for potential future account and key account managers. And we have digital channels that continue to improve e-commerce punch out API-based ordering to make it easy to interact with Twist. June North, we are in the business of delighting customers. And then we have our OEM channel, which expands our reach into areas where we may not be strong, which is very complementary to what we do. But the multichannel model allows Twist to serve everyone global community as soon as reasonably possible. And this is where we're excited about the business. It's diversified. It's robust. If you look at some of the simple things, your customer types, that is a broad range of customers. Large pharma biotech big tech academia, government AI native. I don't need to read the slide diagnostics, and we see what's happening in the cancer space where you see the flow of cash into the area. Our demand drivers, it's an all-you-can-eat DNA buffet, the applications that continue to develop when you partner with the community, the makes great ideas that our customer base is using on the platform. It's incredible. It's fast, and it needs our scale. Then obviously, our funding pools. It's not too difficult to see the flow of cash from the AI-driven companies into our space. So we like the way the funding environment is developing. And just as a reminder, the word resilience, we've survived and thrived in the hardest biotech funding environment for quite a long time. So I hope it's clear, and you can see we're making good progress as a business across many applications, many markets. And because of that, the resilience in our business is incredibly strong. So I think with that, that's probably enough for me, and it's back over to you, Angela.

And so for the webcast, we will see you in 20 minutes. We are going to cut the webcast for 2 customer presentations. You will then join us for the last 3. [Break]

The last 3 customer talks are amazing. So and they are with us in the room today. It's my pleasure to introduce John Overton, Chief Sequencing Officer of Regeneron Genetics Center, where he oversees large-scale genomic sequencing initiatives that advance precision medicine and human genetics research. I saw John present at a sales meeting for Twist. And when we are talking about who to bring in for customer stories, I request John. He graciously accepted. Over to you, John.

Thank you very much. Yes, and thank you for having me here today. I'm going to take a slightly different angle than the first couple of talks. And I'm going to give you a story. And I'm going to talk to you about why in this world of rapidly decreasing whole genome sequencing cost, I don't think that's the best approach that we use for our large-scale drug discovery research, especially at a place like Regeneron. So the RGC has been around since 2013, and it was founded with the individual goal. We were going to use the power of the human genome to find the individual differences between 2 people that make you either susceptible to or resistant to developing disease. Now I wasn't in the talk through most of the day. So I'm going to take one step back here and just talk about DNA for a second. All right, DNA, 4 pieces of information, ATG&C. It's those patterns over and over again make each one of us unique. But the genome is big. It's 3 billion pieces of information, actually $6 billion because you have 1 copy from your mom and you have one copy from your debt. So we're looking for these individual variants that make each person different. And now at Regeneron, what we do is we sequence people. We sequence through DNA. We have access to their medical records. We compare them and drives our drug discovery process. To date, we've done that for over 3.5 million people. It's one of the world's largest database to do this research. It drives our drug discovery and clinical trials. We do this through 2 different types of technology. One is genotyping. The other one is whole exome sequencing, which twist is an expert at. So what are these approaches? Genotyping, it's been around for a very long time. A couple of decades. It's an array-based approach, you have a probe, each one of those probes detects a variant in the genome tells you what variants that position. it's kind of low throughput. They're spread throughout the genome. It's not high resolution. There are tools that we use. They have a couple of hundred thousand probes in them. You look at common variation. And what you do is you create something called an imputed genome. And the reason we can impute your genome or guess at it is because when we inherited our DNA from our ancestors, you didn't get on ATGC at the time. You got a whole bunch of them. You've got 1 million. You've got 5 million, you got 10 million, you here them in big chunks. And so I can look at your genome. And if you have a variant here, here and here and the reference has the same variance here, here and here, everything else in the middle is probably the same. We can assume that. So we do that genotyping. We combine it with something called whole exome sequencing. We do this with Twist, where we get very fine resolution of the coding portions of the genome. That 1% of the genome that makes the proteins. We can do that very quickly. It's about 20,000 genes in the genome. We take these 2 things, we combine them together, and we get a very, very good imputed genome. But the question I get asked all the time, probably at least once a week, why not just sequence the whole genome? Costs are going down, just sequence the whole genome. If I sequence the whole genome, I get a lot more variance. It's a lot more than if I do an imputed genome. It's about 3.6 million variants on average in a person, but this comes at a significant cost in a significant amount of time. Most of the variants you detect in the genome, they're incredibly rare. They won't make each one of us unique, but they're not seen very many times. They don't have a great impact on the research that we like to do. It's also a lot more expensive. And so I'm going to take you through some data that hopefully convince you of that. So if you haven't heard the U.K. [ Biobank ] before, this is an incredibly interesting cohort. It's 0.5 million people in the U.K. They've agreed to be part of a research cohort. They signed up a couple of decades ago. We sequenced their DNA and they have folded them longitudinally, so we can use them research. These 0.5 million people, incredibly unique because they've been genotyped, they've been excellent sequenced and they've been whole genome sequenced. This is unprecedented. This is never going to happen again. This is not a cost efficient thing to do. But it's been -- it's moved as the progression of the technology has moved, but it allows us to compare how each one of these technologies performs in the drug discovery and in the research world. And so first, just going to take you through the number of variants that are detected. It's kind of small here, but in the top box, whole exome sequencing that was done at Regeneron, you detect about 17 million variants when you do that. The genotyping and the imputation, it's about 110 million, 111 million variants combine those together, you get about 126 million variants and 150,000 people that we studied. Whole genome sequencing, nearly 600 million variants, 5x more than that imputed genome. It's a lot more data. But what does that really mean? If you look in here and you compare the number of variants in the coding sequences, the coding sequences, the 1% of the genome is the exome, these make your proteins. These are the things that we can make drugs against, whether you do an imputed genome or you do a real genome, you get the exact same numbers. It's about 6.7 total million variants that you get out of there and the overlap between those, it's 97%. So whether you do either one of these assays, you're getting about the same exact number of variance. If you look at the individual level in the chart that I've just highlighted, per person, it's about 20,000 variants, a coding region, the genome, regardless which assay you look at, but the really important part is right here. In the right-hand side of this panel, what I'm showing you is the number of observations in an imputed genome or in a sequenced genome of each variant that we find. So there's 475 million additional variants in a whole genome -- over 300 million of those, they're only seen 1 person. Just 1 person that is not going to contribute to research. And almost all of them are seen in less than 3 people. It's not worth it. These are not going to be strong enough to stand up to the type of statistics that we need to drive these processes. But if you don't believe me, I can show you because we have medical information on these people. We plugged 100 traits out of this data set. It's 80 quantitative traits. These are things like height and obesity, they have a distribution, 20 binary traits is like, do you have the disease or don't you? Do I have endometriosis, don't I have it? And we did analysis with the data and we looked for new discoveries. When you run this analysis, you get about the exact same number of discoveries, regardless of which data set you use. If you look on the right-hand side of that panel, there's less than a 1% difference in the discoveries that you make if you run an imputed genome or you run a whole exome. It's about 3,500 of those when you do the intersection, again, 97% of the discoveries they're exactly the same. When you look at the 3% that are different, most of these are not reaching strong statistical significance. And if we run another 150,000 samples, they don't replicate. These are artifacts that probably aren't going to show up in a larger data set. And so this is an incredibly efficient way to do this. But this actually -- this is a ridiculous experiment because it's matched on the same number of people sequence, 150,000. But we know targeted sequencing, it's a lot cheaper than doing a whole genome. So normally, when you're doing these projects, they're on a fixed budget, not on a fixed number of samples. So if we do this again and we fix the budgets, we made a guess when we did this type of analysis that the average lab can probably produce exome and genotyping data about 3x cheaper than a whole genome. It's a fair assumption. We have the data here. If you look at 48,000 whole genomes or you look at 150-or-so thousand imputed genomes, data is right here, it's 3x more samples, you get about 5x more results. So on the same budget, you can stretch that and get a lot more information. It's also replicated here. So a whole genomes, if we have 150,000 of them or nearly 500,000 imputed genomes, again, 3x more samples, almost 5x more results again. Now if you're really efficient, and we've worked with Twist over the years to really drive that cost down, if you can be 10x cheaper than a whole genome, data is still here, 50,000 whole genomes, 0.5 million imputed genomes, it's 20x more results. So on the same budget going to an imputed genome, you can generate many, many more results here. And as the cost of whole genomes continue to go down, targeted sequencing costs are going to go down 2. It's just going to happen. And so we should be easy for us to keep pushing these numbers higher and higher, especially the company like Regeneron, we have ambitions to do millions of samples. It's going to be driven by that cost we can get. Now one more point here though, I keep talking about imputed genomes, the arrays that we use that it's an antiquated technology. I talked with Emily years ago, I wanted to make arrays obsolete just as much as she did, and we've worked on these over the years because the problem was I would do whole exome sequencing and I would do a raise, I couldn't keep up. That array technology, it's decades old, 2 decades old. It hasn't changed over time. And I wanted something new because I couldn't get these data sets done at the same time. And as we scale the millions I wanted to be able to do exome sequencing, Twist is already an expert at and genotyping at the exact same time. And so I brought this to the Twist team, and this was an incredibly ambitious project. There was no one else that could do this. I needed 600,000 probes, and I wanted a couple of little pools. The other technology providers at the time, they didn't even want to talk to me. Trust said, let's do it. And so we wanted to replace those arrays. I needed something that didn't exist. They were on board with that. We created something called the Twist Snip Diversity Panel. We did this in about 2019, 2020. We went through, we selected regions of common variation in the genome, ended up being about 600,000 probes. Now when we capture a sample, there's over 1 million probes in that tube when we are capturing the DNA for these samples. It's about 600,000 probes, but it actually gets a lot more veins than an array. And array, 1 probe gets 1 variant. When we capture things, we get pieces of DNA at a time on average each one of those pieces of DNA has about 2.5 variants in it. So we get about 1.5 million variance. When we combine these together, we have an incredibly powerful tool where I can get my exome sequencing in my arrays at the same time, incredibly low cost and take advantage of the power of sequencing, so I can fly through samples. One more very important part, many of the research to date, it's highly, highly focused on people of European ancestry -- when we designed this, we made sure that it would work in all ancestries equally, and we've been able to show that over and over again, that imports incredibly well across all continental ancestries. So I hope that -- be able to show you that even though the whole -- the cost of whole genome sequencing is plummeting, targeted sequencing is going to go away. It is the tool that we're going to have to use to create databases of millions and millions of samples and the data shows that that's the case. So I want to thank you again. I love to talk about this more and just keep working with the Twist on this. Thank you.

Thank you so much, John. I think you can see why -- some of you in this room have asked me that question. With whole genome sequencing is an exome sequencing going away. So I can say no all day but it's so much better for John. He's so much more eloquent than me in how he delivers the message and how he proves all the things that they're doing at the RGC and the amazing discovery that they're doing. All right. So our next customer presentation is from Josh Clevenger, who is an expert in plant breeding and genomics and a faculty investigator HudsonAlpha Institute for biotechnology. We're going to change directions. And we're going to go to the plant side of the business. Josh?

Thank you. Thank you. Actually, I was looking at this and wondering how I can advance slides, but it's a big green arrow. So you can't be -- you can't screw that up. So I just want to say real quickly, it was amazing listening to all the incredible sort of health care applications. And one thing I like to talk about when I'm talking about representing those of us who work in food security is that there hasn't been a single human born that can exist without eating food. And so the work that we do across the globe that helps secure that global food supply chain is incredibly important. And what I'm going to do today is talk to you about really how Twist has changed the game and giving us the ability to do things that we never could have thought possible before. So in my lab, what I'm focusing on is how do we translate genomics into applied crop improvement. And I work with companies, large and small, USDA, private and public, to help provide them with the ability to actually take the food that they're interested in and make the improvements that they need to make. And I want you to think about it as we talk about this. When you go in the grocery store and you look at just the foods in the produce department. Every single one of those foods has a different genome that's incredibly complex and different than the one next to it. And so we have to have the ability to understand and make improvements and insights into all of those genomes at once. Otherwise, the panoply of food that you enjoy, you wouldn't have access to. And so my lab has really been built on Twist's library prep. And there's very simple reasons why that is. And I'll just go very quickly through those because the other things I want to talk about are more important. The first thing we heard about is ligase and ligation and the normalization by ligation. So every week, I'm extracting DNA from a dozen different plant and animal species. I have to get DNA from the clippings of lizards of their tails, from soil, from blood, from shaving of seeds, from seeds themselves, and they're coming from all over the world. I don't want to have to worry about normalizing all of that DNA. And with Twist FlexPrep, I actually never quantify the DNA. I extract it from all these things, and I go straight into library prep. And that is incredibly impactful. That drives the speed and scale of the things that we can do. And the second thing is the complexity. So we talked about enrichment panels and enrichment panel sequencing and the ability of Twist to print these probes at speed and scale at a cost that makes sense. And so again, we're developing probes that target genes and pine trees or the genes and horses or the genes in a peanut plant or blueberries or raspberries or any of the crops that we're interested in. And so we really want for those panels to have that complexity. So these graphs really just show the dots at the top is better. And so the complexity of the library prep and the ability to do that enrichment at scale allows us to, again, go straight from DNA from lots of different sources into that prep and make those insights in a better way. Okay. So the way I wanted to basically talk about the work is we have a lot of work going on in a lot of different ways. So I'm not going to show data. Instead, I'm going to show you the young scientists who are actually using Twist library prep and Twist technology to dream the dreams that they want to dream and then to go on and make a difference in the world. And so from left to right, we have Zach Myers. Zach Myers is actually looking at how to take hundreds of thousands of these enrichment panel results or whole genome sequencing of plants in a library and actually do what we call the single experiment. So every time that we have a disease that is affecting agriculture, every day that we don't have a tool to combat that disease, there are farmers and there are real people that are suffering. And so we want to be able to get at those tools faster than ever before. So Zach is working on that. Holly Wright is extremely passionate about a wonderful crop called finger millet. It was actually -- is very tolerant to high heat and drought stress. And so being able to layer on genomic data onto that crop and which hasn't been done before is helping with breeders and farmers in areas of the world where they rely on that crop. And Ash Meida is working on how to layer on AI into selection modules. So we can actually just read the DNA of tens of thousands of plants and make that selection first without having to put out in the field and select for phenotypically. I have young scientists in my group that actually are working from Southern Alabama and supporting breeding programs all over the world. So we just get slivers of seeds that come in. We sequence that DNA and then we make selections for them in Argentina and Brazil and Africa and even China. In India, Justin Vaughan is a good friend of mine, who is working on how do we utilize all the sequencing data on pan-genome graphs. So we actually are one of the only groups right now that's able to take low coverage whole genome sequencing and call genotypes and impute them on the graph to actually have access to all the variation. So if you've heard about pan-genome graphs are incredibly powerful, but it's still really hard to access that information. Ethan Thompson on the bottom right is actually a young scientist that is changing supply chains with the discovery he makes by the ability to actually sequence populations at scale and ask these questions. And then [ Embryo Right, ] I'm very proud of. She was an intern in my lab and now is an expert at getting DNA from any kind of tissue that comes in from any parts of the world. And again, working in the place where we work, where we have to deal with all these complex tissues, that is extremely important, and there's not a lot of groups that can do that. Okay. So some more targeted sort of examples of what we work on and how genomics protects our food supply. So quite simply, if you have a global pathogen that's affecting global supply chains, this is peanut, which we worked on with the USDA and Mars. It only affected peanuts in Argentina, but they export all of their peanuts for Europe and the rest of the world. And so that pathogen actually affected complete global supply chains. So we worked with them. And again, the ability to understand the gene that conferred resistance to that trait, we had to be able to sequence at scale. And we had to be able to sequence those things from tissue that came from another country and it had to be fast and accurate. And then we use that information to map the resistance to layer on pan genomes to identify those genes. But then what we did was we worked with 5 different breeding programs in the U.S. and South America to make rapid selection. And in 3 years, I was able to show Mr. Frank Mars, I said, Mr. Mars, this is the peanut I developed for you that's resistant to [indiscernible] and it's high [indiscernible] and it's going to be great for your global M&M supply chain. And that is an incredible achievement that we could only have done if we had the ability to do that sequencing work. So this is kind of a fun example, too. Kendall Lee is actually a co-founder of a company I founded to scale long-read sequencing, and she did a post doc on blueberry. So blueberries in Southeast Georgia are having problems with late-season freezes. So actually, they would flower, it would freeze and then you wouldn't get blueberries, and that was really a disaster. And blueberry is of the top 3 -- I'm sorry, Georgia is one of the top 3 blueberry-producing states in the country. So she looked at the populations in the breeding program and identified 2 variants where she can make selections so that the blueberry would flower later. So it would get out of the window of that late season freeze and then it would produce the fruit sooner, so faster. So you're actually getting blueberry production in the same window and you're moving it in a way that it actually offsets that risk. So that's really incredible result. And then sort of a less sort of scientifically satisfying but incredibly important thing that we're able to do with Twist enrichment panels and the FlexPrep technology is we're starting to actually move into the germplasm collections. So germplasm collections of crops are one of our most treasured resources. It's way more valuable than gold and diamonds and silver and all of those things because it's the only source of genetics to solve the problems of agriculture that we have. And once it's gone, it's gone because the places we collected them from are now parking lots and gas stations and hotels and McDonald's and other things. And so we don't have access to them again. Because of working with Twist, and actually, they've been a great partner with us, we're starting to move into the germplasm collections and create what's called digital backups. So that even if that seed does not survive, we know the genome of all of those. And I'm showing you sorghum because sorghum is really interesting. There's 45,000 sessions of sorghum in the world left and there is no curator. That means there's nobody that goes in and make sure that those seeds survive. So being able to do this is a really big deal. That's our first project that we're working on. And then in the end, I have a project that is really fun in which we actually do the breeding in high schools. We're in 15 high schools in Southern Alabama. I go into those high schools. They each have a plant they sequence. They extract the DNA of that plant, and then we use Twist library prep, sequence it and then analyze the data with the students and then they tell me which plants they want to move on to the breeding program. And what's interesting is that, that area of Alabama is basically the center of peanut breeding in the United States and peanut production. And so a lot of these kids are learning about how to use sequencing to improve the crops that are really driving the economy in their region. And we could not be able to do that without the ability to actually have these incredible preps. So again, thank you. I hope that I convinced you that this ability really is driving food security globally in a really big way. So thank you very much. [p id=703517233 name=Angela Bitting type=E ] Thank you so much, Josh. You truly are saving the world one plant at a time. It is really impressive what you are doing and all the people you are training under your tutelage. Really, your impact is so much greater because you're training the next generation. So thank you for all your work. Thank you for letting us be a part of it. Our final presentation from a customer today is from OncoDNA. And we have 2 presenters, not just 1. You get a twofer. We have Koenraad Eycken, who is CPO, Sales and Business Development for OncoDNA; and Christophe Van Huffel, Co-Founder and CFO of OncoRNA. Welcome, gentlemen. Please come tell us all the cool things you're doing. [p id=1905934051 name=Koenraad Eycken type=D ] Thank you for the invitation to speak here about OncoDNA. So what we do at OncoDNA is really linked to the patients and the oncologists. So what we see is we talked about a lot of details about sequencing. But actually, the oncologist, what they need is just something very simple, what can I do with my patient. And this is what we do as OncoDNA. We make the tumor profiles actionable for the oncologist. So what it means is we take a tumor sample or a blood sample and then we create a report for the oncologist that is really very clear where they find out which variants or which biomarkers are available in the sample and what they can do with it for the patient. And as OncoDNA, this is what we've been doing for the last 13, 14 years already since it's founded in 2012, we've tested already more than 100,000-plus samples. And we are working in a global setting. So we have 45 distributors in different countries. We have customers sending in samples or we are sending in reagents to different geographical regions. And how we started was really in a centralized setting. So meaning that all the samples were sent to OncoDNA in Belgium because as you can hear from the accent, we are not a U.S.-based company. So the samples were sent to Belgium. We did all the wet lab work, the bioIT and the reporting, and we did it based on an amplicon-based solution. But we found out that there were some limitations because there were new -- more and more information available into the market. So we needed to go to a newer version of our solution. And at a certain moment, we needed -- we faced some challenges with an amplicon-based solution to go for a bigger panel to do CMP detection to do complex biomarkers. So this is when we moved to Twist when we came in contact with Twist, and we tested their solution. And we really saw a difference with the capture-based technology. So we saw a higher quality of the sequencing, and we saw that the uniformity and other performance parameters were really outperforming the amplicon-based. And therefore, it was easier for us because a good quality sample means that the interpretation is going to be easier. If you have a good uniformity, it means you can run more samples on the same sequencing flow cells. So it really means more patients that can be analyzed and also more information can be found in the tumor. That was interesting, but what was also interesting. So we opened a new business line, thanks to Twist, because in Europe, it's not common to send samples to a central lab. So now that we were with Twist, they were also -- we could also offer our solution on Illumina sequencer or Element Bioscience NGI that are now coming up. And we really changed our business model. So instead of sending samples into the lab, we now send reagents to the labs in Europe, and we do the bioIT and reporting still in our cloud solution. So this really made us an extra business line next to the centralized business. And it has been an accelerator for OncoDNA because when you see here in the graph of the number of samples we've tested over the last years, the green is the decentralized model, and you see that it appears and then keeps on increasing in the time. And it's not -- we're not going to stop here. So what we did in 2019, we started collaborating with Twist. We did our centralized solution. In '21, we launched our decentralized solution. In '24, we launched our liquid biopsy panel. In '25, we are going this year -- last year, we launched the large RNA panel. And in this year, we're going to launch a large RNA panel as well. And what we hope before 2030 that we can also launch a personalized vaccine into the market. Before we talk about the vaccine that Christophe will talk about it, maybe I just want to show 1 or 2 slides extra. So the OncoSelect Gene Panel, it's a liquid biopsy panel, which is really the newest -- the hype in the market. So we really need to go into the liquid biopsies as well. And together with Twist, we've been able to make a test that performs very well with a good performance characteristics. We're also working together with them with the personalized MRD panel where we will do the variant selection. Twist will make the panel and then our customers can run it in their own lab and do the interpretation in our software platform. And actually, with all of those different tests, we are -- we can, as OncoDNA, be present at different time points when the patient needs to be tested, and all the data can be shown in our own platform. Unfortunately, this is not enough because even with all of those testings, there are still a lot of patients who cannot receive a good treatment and who are still sick, and that's why we have OncoRNA that has created a solution. [p id=D88 name=Christophe Van Huffel type=D ] Thank you, Koenraad. Thank you, Twist, also for having us here today. It was a great day and a nice presentation. So about those patients, so about half of our patients when we apply all our panels and whole genome analysis, we don't find always actionable mutations. Actually, half of them have many mutations that are not characteristics of associated threats that we can identify. So the approach here is those mutations on some antigens at the surface of the tumor. They are specific, unique to the patient. It's a set of markers there that can trigger the immune system. We can't predict that. And of course, this was revised. The RNA-based approach was revised with the pandemic with Moderna and BioNTech being involved in that approach, in fact, before switching to the pandemic vaccines. And they used what we discussed earlier this morning or this afternoon, also, cloning. So they would clone and the limitations of cloning is that you can only fit a certain number of those markers within a plasmid. When it's too long, too large, you're limited. And they had to select up to 30, 34 neoantigens. And we believe that OncoDNA that, in fact, this limitation is a selection that's based on bioinformatic approaches, but it's only not reflecting the particular situation of the immune system of the patient. So what we discussed then and at that time also with the revival of RNA approaches was also concomitant with actually the pool technology from Twist that we're around 2021 there. The pool technology of Twist was enabling to synthesize oligonucleotide pools of 300 base pairs in length. And we needed about 500 base pair to really design individual RNAs, each containing 1 neoantigen. And those pools, we've seen that during the day, I mean, have features with 121 neoantigens. So I approached Siyuan and it took only 15 minutes over a Team's call across the pond to convince him. Two weeks later, we had already -- he had made DNA, but also RNA out of that. At the same time, I had a call with 1 of the 2 manufacturers of the lipid nanoparticles, Aquitas, which was the one used by Pfizer BioNTech. And it took also 15 minutes just on a Teams call and said, let's do it. And 1.5 months later, we had actually injected a set of mice to prove that we were able to trigger the immune system in those animals. So we have now something that's quite unique and only enabled by Twist, which is typically in a tumor, you have about 100 to 400 neoantigens in any individual situation. So that really fits well with the format of feature of 121 if you try to select the best out of it. So -- but that's already important to be able to put more than 30 as, let's say, Moderna is doing. We put 120 or 100 or eventually all the neoantigens we find to, instead of selecting bioinformatically those antigens, we put them back in the patient and let the patient's immune system do the selection because those bioinformatic tools are based on selection process of in vitro studies and not in a natural environment. So then, of course, the length of the oligos was critical. And there's really no limit to the pool diversity. We could go above 120 if needed. And in some melanoma tumors, we reached 400, 500 neoantigens, and we could use that. However, we believe it's important to have a large number because a tumor is only a sample that you take. You may have multiclonal situations. And you want to be able to lock as many degrees of freedom for those tumors to evolve and escape from their immune control and also to be able, as we've shown, to have even mutations that code for genes that not necessarily are activated yet in the tumor for which RNA is not expressed, but may be expressed later on during a relapse situation in a metastase because there's drifting of those neoantigens and the tumor composition over time. So typically, so what Twist chip technology makes possible is really to be able also to shrink that time to -- our goal is to be able from needle to needle to go in 3 weeks with the delivery to the patient. By doing so, we sequence with the Twist whole exome sequencing panel. We do a neoantigen design then. And the Twist chip synthesis' 5 days turnaround time. We then go for that process, which is processed in a single tube where the pool of 121 antigens is found is encapsulated and injectable. It's important to be short because, for instance, we collaborate with the doctor who is also involved in the BioNTech pancreatic cancer situation. So he recruited 30 patients [ for by now ] and only 5 were, in the end, treated. Although 30 were eligible, but it takes more than a month, if not 2 months to make. And by the time the drug or the therapy is available, the patient has evolved in a situation where it's no more the point to treat him. So that's really [indiscernible] and shrinking that time is really critical as we think in this situation. That's something that Twist really enables and can make a big difference. No other competition from Twist is able to have both that speed, the right format and the price point that matches the needs for this therapy. So in summary, we have more than 100 neoantigens. We believe it's important to have more. The 3 weeks turnaround time, we've shown that. And surprisingly that 90% when you -- instead of concatenate the neoantigen as a long insert, treat them individually, 1 RNA, 1 neoantigen as a pool, they trigger at 90%, each of them a CD8 cytotoxic immune response. We've been able to have a nondilutive grant from the EU also of EUR 31 million. And with that, we hope to enter into Phase II. Finance also a dedicated production facility where we can -- because it's synthetic based, so we can, in the same facility, produce for many patients in parallel. And for thousands and thousands of patients. So it's only waiting for growth here that we can order more Twist panels and also more pools from Twist, which has been really the enabler of this switch from technology to something that maybe is going to deliver something fantastic for the patients. Thank you very much for... [p id=703517233 name=Angela Bitting type=E ] Thank you so much, Christophe. Finishing very strong with OncoDNA. And we now have a break for you. We have some local Portland fair as we have all day and all evening. We have Poplandia popcorn. It's quite a thing here. So please join us for a little short break, and we will return at 4:05 or 7:05 Eastern. Thank you. [Break] [p id=242921299 name=Emily Leproust type=E ] Thank you very much. Appreciate it. Very often, when we do investor engagements, we get the question, okay, you're going to be profitable. And now we delivered, which is a great improvement compared to the past. And often, we get the question and then what And so finally, we're going to hear from Adam what we will do after we turn profitable. [p id=1867872015 name=Adam Laponis type=E ] Thank you, Emily. All right. Well, thank you all for spending the day with us. I'm Adam Laponis. I am not a DNA chemist. I'm the CFO at Twist. And I have the honor of working with this team. What you've heard today is a lot from our customers. You've heard from the team, you've heard about the strategy. But I will also tell you, it is the culture, it is the mission and it is this team that makes Twist unique. I'm excited to share with you the financial outlook for Twist. We're at an inflection point, and we have a strong opportunity for growth and growth of profitability. But before we go there, I want to spend a little time grounding us in some of the recent financial performance. Okay. In 2023, we opened the Factory of the Future we're standing in right now, and Twist has been in the platform build-out stage up until today. In 2023, we had $245 million of revenue, and 13 sequential quarters later of growth, we're now forecasting $442 million to $447 million in fiscal 2026, a compound annual growth rate of 22% over the last 3 years. Now let's keep in mind, the backdrop we did this in was not exactly easy. The life science and tools space has been struggling to have single-digit growth in many, many of the peers that we compete with. And we also did this while executing with financial discipline. The gross margin of the business has grown over 15 points in the last 3 years from 37% in 2023 to above 50% in fiscal '25, now guiding to be above 52% in fiscal 2026. And during this time, we've held operating expenses relatively flat for the last 3 years, improving adjusted EBITDA from negative 60% of revenue in fiscal '23 to forecasting to be breakeven 130 days from now in fiscal Q4 of 2026. That's financial discipline in action. This next slide is probably the most important slide in my presentation, so I'll spend some time on it. We are at the inflection point of profitability. And as Emily pointed out, the question is, where do we go from here We have a strong growth trajectory to more than double revenue from organic growth from 2027 to 2031. Now let me be clear, that's a floor, not a ceiling from which we believe we have many opportunities for upside, many of which you heard about in the conversations that we had today with our customers and our team. We're forecasting to be adjusted EBITDA positive in Q4 and make continued progress in '27 and beyond, improving profitability sequentially. As we cross the cash flow -- free cash flow point, we plan to continue to reinvest our profits into driving the NPI machine and the continuous process improvements that you heard about today. We also have a line of sight to a long-term gross margin of above 60%, and we'll spend some time talking about how we get there as the business continues to grow and mature over time. And finally, we see significant leverage for opportunity for operating leverage in the business and a long runway in our current facilities, and we're charting a course to be above $1 billion of annual revenue capacity by 2030. So let's dive into each one of these parts. Let's talk about growth. And where it starts, as we've hit on all day today, is on the chip. It feeds our NPI machine. We leverage it for our operational excellence, and we drive commercial execution across the business. I think this is best -- the growth opportunity is best described through our available market size. We talked about in 2020, Twist has an available market of about $2 billion. And through the NPIs and the new product we've launched as well as the category growth we've seen in our existing markets, we're at over $7 billion today and growing. And by 2030, we expect to be at $13 billion of SAM. And that's because of the NPI machine as well as the markets we participate in growing. Looking over the DSPS side of the business, how do we grow It's doing what we've been doing time and time again, continuing to broaden the workflows, adding new capabilities, expanding our menu and fundamentally offering customization at scale for our customers. That's what we do. You heard about it today. You heard about it from our customers. We'll continue to do that time and time again. And ultimately, what we're driving towards is helping our customers accelerate their flywheel of design, build, test and learn, where we can participate in more elements and expand our offering. But ultimately, what we're doing is we're expanding our wallet share with our customers over time. On the NGS application side of the business, we're the key supplier today for liquid biopsy and a number of other large growing markets, such as in oncology diagnostics, rare disease, and it's because of our unique product quality and rapid onboarding processes that allow us to be successful. We heard about how we're in the early innings of the diagnostics revolution in oncology. With up to 20 million people having cancer by 2029, we see the opportunity for sticky, recurring revenue streams from this area. And more broadly, as cost of sequencing continues to decline, it opens up new markets, new geographies and new opportunities to use NGS that allows us to grow with our customers. Twist isn't just competing in these markets. We're creating and enabling them as we go. And it's that type of growth that will allow us to continue to see the path towards more than doubling our business organically over the next 5 years. Now let's talk about gross margin. When I first joined Twist, I was excited to see how the team operates and understand that it's not just revenue growth, but automation and continuous process improvements as a priority to enabling gross margin expansion. Growing from 2023 at 37% to above 52% in fiscal 2026 was enabled on a number of the capabilities. The #1 foundation of that was our fixed cost. It's like flying the airplane. The more seats you put on the plane, the more profit you drive. So we were able to leverage over the last 3 years, relatively stable fixed cost as the revenue grow. This is best exemplified in the following way. Our revenue grew 8x faster than the people in manufacturing teams across Twist. And you can see that in all of our processes. You heard about process reengineering today as well as automation of the automation. And where that's taking 4x the capacity in the writers by shortening that cycle time. And you think about what that was, that wasn't just a process improvement. That was a capacity expansion, a quality improvement, a reagents reduction and the ability to offer direct synthesis of up to 500 base pairs on the chip, allowing us to introduce new products, all without any capital investment. Looking at the other elements of the business, you spent time today on the tour you saw GeneLab 1, where we make our fragments. We've been able to take that process with automated equipment and automate that automation, taking the footprint of that space and dropping it by 80% to double the capacity of manufacturing in the clonal fragments, the non-clonal fragments. And ultimately, we target all of our products to have a gross margin above 75% of incremental revenue dropping to the gross margin line, particularly as we scale, drive automation and enable continuous process improvements on everything we do. That is what we're defining as operational excellence directly driving gross margin at Twist, and we'll continue to do it time and time again. Let's talk about our facility and our capacity. We're standing in the Wilsonville facility here where we have approximately 210,000 square feet of available space today. And we've got confidence and we're charting a course to having over $1 billion of revenue capacity online by 2030 in these existing facilities. We're here with our -- where our primary manufacturing location for DNA synthesis and Protein Solutions is. We have 4 riders online today. And on the tour, you saw that rider room has capacity to have 16 riders in the room today. And you also saw we have over 65,000 square feet of available expansion space in this facility that we have not yet touched. In South San Francisco, our primary location for NGS applications manufacturing as well as our corporate headquarters, we have the 4 riders online today, more on order. And we -- as we've announced earlier this year, we signed an expansion to take over the third floor of our building, and we did it without adding any cost, but also getting a bunch of TIs to enable that build-out. And finally, as part of that lease expansion, we retained an option to expand further into the fourth floor of our facility, taking over the whole building in the future as we continue to grow, should we need to. So kind of putting the math all together and how do you think about this. We have 8 riders today running across the business. They're running at about 50% capacity. We have 4 more riders on order, and we have the capacity space today to have 22 riders with more room for expansion should we need it. That means we have roughly 3x the rider capacity available in our current facilities than we have online today. Now granted, riders are not the only thing that will drive our capacity needs, but they are the founding principle because everything starts on the chip. So with that, go to the next slide, we'll talk a little bit about our capital discipline and capital allocation strategy. Twist has been an organic growth engine, driven by our NPI machine. That trajectory will not change as we go forward. We're continuing to focus on organic growth, and we plan to self-fund our innovation by investing into our NPI machine as long as the returns justify it. We'll continue to stay close to our customers, understanding the science that they need to help enable them to make new products, enter new markets and drive new capabilities. And we also believe we can expand our footprint and continue to expand our offering using a sustaining level of CapEx investment generally in line with depreciation today. That's been our criteria, and that will continue to be our criteria for how we think about driving growth and investment. We've also used M&A opportunistically, focusing on bolt-on technology opportunities that are accretive to our business. The best example of this is recently is in Invenra, where you heard about the B-Body technology platform we licensed earlier this year that's giving us access to capabilities and bispecifics we didn't have before. And we're committed to making investments in agreements to support our long-term free cash flow trajectory and margin profile. In short, our central criteria is fairly straightforward. First, will this bring more volume onto our silicon platform And second, do we believe we can improve the unit economics over time with it. Ultimately, we're not looking to buy companies. We look for opportunities to bring more volume onto our platform that can accelerate growth and deliver improved unit economics. Okay. Now let's bring it all together. Medium- and long-term financial summary. Revenue growth, more than doubling our revenue from 2027 to 2031 organically. And again, this is the floor -- a floor, not a ceiling for growth. It's going to be driven by the NPIs, our market share growth and the category expansions we have in our existing markets. We see multiple opportunities for upside, whether it be AI drug discovery, MRD, mRNA technology and therapeutics and nucleic acid therapeutics and other areas as well. And we expect our long-term growth to be driven both by DSPS and NGS. Looking at adjusted EBITDA, we've committed to being profitable adjusted EBITDA positive in Q4 of this fiscal year. And once we cross that, we plan to expand that in 2027 and beyond. And we have a clear path to free cash flow positive with our available capital on hand. Our gross margin trajectory is strong, having improved over 15 points in the last 3 years. And we're driving towards a path with revenue expansion that will get us to over 60% gross margin as we continue to grow and mature. And of course, this is all based on our ability to continue to automate and drive the continuous process improvements we've seen in the past, and we know we can continue to apply to every step in our process. Our capital allocation and CapEx strategy has been consistent and clear and is to self-fund our growth and our organic growth through driving our NPI machine and CPIs. And we have a clear path to over $1 billion of revenue capacity with sustaining levels of capital investment. In summary, now that we've built the platform, Twist is entering a new phase of profitable growth, where every dollar of growth is more profitable than the one that came before. Our platform and scale will continue to drive robust profit, top line growth with multiple opportunities for upside. Thank you for the time. I look forward to the Q&A later today. I'm going to hand the mic back to Emily. [p id=242921299 name=Emily Leproust type=E ] Thank you very much, Adam. We have one more presentation before we go into Q&A. You've seen we have great technology. We have great software, great patents, great products, amazing customer, and we have people -- business of people. And Paula is going to tell us about our culture advantage. [p id=382973279 name=Paula Green type=E ] Thank you, Emily. So I stand between Q&A and wine. So I'll get the show on the road. Thank you so much for being here today. I think I have one of the luckiest jobs in the world. I'm going to go back one slide. And just let you know, I am Paula Green, as this picture actually shows. I've been with Twist for 10 years, and I am responsible not only for HR, but also for the facilities here at Twist. You guys got a flavor of our culture today from all of my colleagues who spoke about that. But I'm going to spend a little bit more time speaking in depth because really, for us, our culture is our moat, and it is an extreme advantage to us. Without it, we are a great -- still a great company, but we are an even better company with it. So our mission and our vision are really -- I went the wrong way, so sorry. Our mission and our vision are simple, yet extremely powerful. So we make synthetic DNA to improve health and sustainability. And really, we see our customers as our heroes who are changing the world for the better. But it's our mission that really attracts highly driven people who really want to make a difference on solving the world's problems. So we all know that purpose only matters if you really operationalize it. And so it's how we succeed through that mission because employees are deeply connected on the execution of the day-to-day responsibilities that they have across the company. So when we go back to Twist's history and the foundation, what we stand on is not just the scientific innovation. So from the beginning, when Emily and the 2 bills founded Twist, they were willing to rethink how DNA could be written, manufactured and commercialized. And that mindset still defines us to today. So we move quickly, we iterate quickly, and we are comfortable making bold decisions in pursuit of our long-term advantage. We work like a sports team, not a family. And that distinction really matters. We are really focused on execution, delivery and pushing the envelope forward. Our competition is outside of the company. And that means we have 0 interest in the political games, in the backstabbing, in doing anything other than making sure we are successful and executing with kindness. So that means we have 0 tolerance for brilliant jerks. We just don't have time for it. We demand solid performance. It is our guiding principles that actually drive our execution. So these are not just symbolic words. They actually are operational for us. Our 4 guiding principles are grit, impact, service and trust. Most importantly, they are not posters on a wall. They directly shape how decisions get made within our organization. So for us, grit means resilience and accountability. Impact means focusing on outcomes, not just activity. And service means deep connection and customer obsession, both internally and externally. And for us, trust means transparency and integrity and accountability at scale. So we live and breathe these principles, which enable us to create an organization that moves fast without creating chaos. And that balance continues to be important for our global enterprise. So here's where we get our competitive advantage. It's the combination of our cross-functional collaboration with speed and enterprise discipline. Most companies are good at one or another, but we believe we've built the ability to do both, maintain the precision and the quality and the scalability while still moving with urgency. We will always maintain customer centricity. We teach our teams to forge and create long-term scientific and commercial partnerships with our customers. And you saw that today. So the mindset is really how we create those deeper relationships, so we build stronger retention and better insight into emerging opportunities. So this is one of my favorite, favorite slides, and this is the people philosophy. We demand a lot from our employees. And in return, we give them a lot. It is very common to hear our employees say, this is the toughest job they've ever, ever had, but it is the most rewarding job for them. So our people philosophy is quite simple. We hire exceptional people. We build high-performing teams. We create that accountability through transparency and we reward impact. So as we've grown to over 1,000 employees, maintaining this culture becomes increasingly intentional. We remain highly focused and disciplined on preserving this culture as we continue to scale. Commercial execution with kindness. It's one of the clearest reflections of how Twist competes. So as Paddy shared earlier today, we are highly ambitious commercially. We believe the best long-term outcomes, they really come from combining urgency with trust. So our teams are encouraged to listen intently to our customers' needs, bring them back into the organization, and then we work cross-functionally to solve the problems quickly. Most importantly, we go beyond simply selling products. It is really our desire to continuously implement, troubleshoot, scale and succeed. So that creates a different kind of customer relationship. What we're asking for is one that's built around partnership and reliability rather than a transactional relationship. Customer centricity is at the heart of everything that we do. So for us, the customer sits front and center. We listen first, we serve them proactively. We focus relentlessly on consistency, quality and speed. And we really work side-by-side with them in their growth and therefore, ours. Here's my last slide. We have the ability to maintain precision, quality and scalability all the while moving with urgency. And at the same time, scaling successfully for us requires discipline. So one of our core beliefs is say what you're going to do and do what you say. That commitment drives accountability across the entire organization. So automation and software infrastructure, operational efficiency and cost discipline are really embedded into how we build products and processes. We pair that discipline with start-up velocity. So we believe in launching, learning, iterating and then improving rapidly, and this is continuous. We fail fast. We fix fast. We understand that because of this, when our markets are evolving really quickly, speed of learning becomes a major advantage for us. So in closing, when we talk about Twist's future, we're not only talking about technology platforms or product pipelines. We're really an organization that's designed to execute. We're built around high performance, customer obsession and most importantly, operational rigor and speed. So ultimately, we believe that our culture is one of the most durable competitive advantages to continuing to scale Twist in the future. Thank you very much.

Thank you very much, Paula. We are at the time for Q&A. We have people online, and we will take questions both from the room where we have microphones and runners as well as online. Angela is our moderator, and then I will send the question either to myself or to a member of the management team.

Indeed. All right. I'll start on this side of the room and head to that.

Excellent day, Emily. First one for you on -- given the focus on of the day, AI, obviously, front and center, a key question on AI is really the growth velocity here in the near to midterm. You're showing triple-digit order growth fiscal '26 versus '25. I mean, is that fair to assume that you'll do $50 million in orders this year? And if that's the floor? And maybe could you talk about maybe the ceiling, what's the upper bound to that? And can you -- how should we think about the revenue conversion there from those orders? Should it imply a triple-digit revenue growth as well for the AI business?

Thank you. That's a great question. Maybe I'll start -- I'll take this one. I love when you talk about ceiling because we talk about ceiling with our customers, and we tell our customers, back up the truck. You can't break up. You're going to run out of money before you're going to break us, right? And so go out, bankrupt yourself. We are here for it. So there's no problem for the ceiling. Shatter that. And then in terms of the floor, right, it's trying to achieve 2 things at the same time. One is absolutely crushing the next quarter and delivering continuous growth, the 14th quarter and 15th and so on. At the same time, we are not into the flash in the pan building. We are here to build a durable business. And so that's why we are, investing in the technology, in the product lineup and listening to customers. As the science evolves, as things change, we'll be able to adjust and adapt because we have the nimbleness and the platform enables that flexibility. In terms of yes, order growth, orders were $25 million last year for AI-driven drug discovery. We think we're going to get triple-digit growth this year, and that could be more than $100 million. And in terms of the revenue capture, you heard from Colby. A lot of those experiments is 20, 30 days. That is the whole point of AI is that it's faster. And that's why AI is going to become the first path for the discovery broadly, we believe, because in vivo in vitro are fantastic. You can find amazing drugs, but they are slower. So that's the great thing for us is that the to book the revenue is very quick. And last year, we mentioned the orders because the order actually came kind of late in the fiscal year.

So I'm going to take one -- Subu, you're next. I'm going to take one from the webcast and then you're next, Subu. All right. From the webcast, fiscal '27 to '31 revenue doubling. Does it mean fiscal '27 revs to double by 31%, meaning rev CAGR is 19%. If I use 26% as new rev base, it implies 16% CAGR. What's being assumed for NGS versus DSPS segment growth relative to the 19% CAGR for overall Twist?

Adam, do you want to take this one?

Thank you, Emily. In terms of the revenue numbers, yes, if you look at the exit number for 2026, $442 million to $447 million. We plan to double that by -- or more by 2031 fiscal. And so you look at the growth rates on that. You say that's the floor, not the ceiling, and that we have a number of areas of opportunity for upside. looking at the growth rates across the 2 areas of the business, in my comments, we talked about essentially having the ability to see growth contributing roughly equally from both sides of the business. That won't necessarily be the case in every cycle or every quarter. But over the course of time, we like to say we love our babies equally, and we see the opportunity for long-term growth in both sides of the business.

Thank you. And that's a very important point is, again, we're very diversified. It all comes back to the silicon chip, whether it's a DSPS or NGS, but we have opportunity for broad-based. And I'm going to say the same thing with the different it will depend on the cycle. Some quarters will be more one and some others will be the other. And that's what we like about I think thousands of SKUs, hundreds of applications and many, many customers.

Actually, can I ask a follow-up to that question. If you actually do meaningfully better than that, Emily, is it likely via a source we talked about today or any other adjacencies that won't showcase today?

That's a very good question. So we all heard how C1 and Colby are amazing at launching new products. We can do 2026 without any new products. We probably can do 2027 without many substantial new products, but we are an API machine, right? The products that are going to sustain us in 2030, they are not launched yet. We have an idea, however, have ideas about what we want to do. Actually, we have more ideas than we have time and capital to do. And so we have to force rank our opportunities. We routinely force rank and then force rank by the biggest return on investment, so biggest opportunity with the least amount of effort. And then we make deliberate decisions that we're going to do the top 2 or the top 5, and we do nothing on the rest. And that discipline is very important. But we are nowhere near running out of ideas.

And a quick one. Are there scenarios, especially as you work with earlier-stage AI-focused companies where you would lower upfront cost for -- in exchange for downstream economic participation?

Maybe I'll send this one to Paddy.

Yes. I mean it's a great answer. It depends. I mean the goal here is to make an incredibly low barrier to interact with Twist. And we don't make kings or queens. We're here to enable the entire community. So for the moment, the deals we're seeing just get on the platform, get the crank turning. And for the moment, it will be business as usual. And there's plenty of opportunity to grow into.

Okay. And we'll take a question from the room. Any...

All right. So no surprise. Well, actually, before I ask my question, thanks to the team. Really fantastic day. I appreciate the effort and the time. So I too want to talk about the AI opportunity. And again, I'm trying to get at how to model this. You did a great job today with the customer presentations. I certainly have a better understanding of the role that Twist plays with a variety of customers. But again, I'm still struggling with how to frame the opportunity. So given it's an AI question, I used AI and looked up a few data points. And there's over 500 companies globally working on antibody research, development and manufacturing. Close to 200 of them are active in developing mabs in their drug pipelines. But of those, only about a dozen are considered largely dominant and hugely and broadly active. So when you look at those numbers, -- what's your real target customer size over the next few years? That's the first question. I know you're going to say all of them. So the second question is how do we define success given that, that does seem to be the customer base? And maybe this is an Adam question. I'm not sure, but like what's the revenue potential realistically on a customer-by-customer basis?

Yes. Sumit, maybe I'll start, and it's great that you can do your own question and answer. And you're correct. We want all of them with a caveat. And the caveat is not all companies want to do AI drug discovery. We have an internal joke with each other, which is how many top 20 pharmas are there? There are 20. I mean it's 100 pharmas out there, 100. Do we have all of them doing AI drug discovery? And the answer is no. It's not because we haven't knocked on the door. It's because they don't all believe in AI drug discovery. There is even customers we go visit them, going the whole way you go right to talk to the AI drug discovery team, and they love it, they buy from us, you exit, you go left to the traditional drug discovery and they tell you that AI drug discovery does not work, right? So even in the same company, different groups differ. And so we are not here to tell customers how to do science. We are here to have a full menu of products. We are here to meet them where they are. And if they want to do AI drug discovery, we're going to provide them with the best tools possible. If they want to do in vivo in vitro, we are going to do the same. And so ultimately, probably everybody infinitely will get to AI drug discovery, but not all of them are ready to do so. At the same time, back, I think, to your second question in terms of the contribution of AI drug discovery to the revenue growth. We given a guidance for the year. We just gave you what we think will happen in terms of revenue growth for the next 5 years. We think that AI drug discovery will be an important part of it. But by no means, it would be the only thing. I think one of -- hopefully, one of the things that you get out of the day is all the many levers that we have and make a guess that if tomorrow AI discovery was forbidden, out loud, can't be done, I think we still find a way to make it happen because there's many other things to do. There was a table in C1's presentation about all the different format of DNA that are needed that we could get to. Just right there, there's a lot of white space that we're not in yet.

Maybe to get at that another way, maybe Colby maybe help. You gave the customer hearing example. and sort of the iterations working through a model to ultimately get to a lead. And you gave a variety of numbers. I couldn't quite do the math in my head. But just how would that compare to your typical engagement with a non-AI program in terms of the work that you do to get them to a lead?

Colby, do you want to come on stage here? So Colby, our CSO, is our best salesperson because he is a scientist, so he designs everybody. So you can tell us how much more can you extract from an AI company than from an in vivo in vitro project?

Yes. It's a good one because it is true that we have multiple platforms for a reason. So a number -- my favorite example is the last one I gave, which was this was a can't fail campaign for that customer. This was -- they have a very small pipeline because they're a smaller company, but they don't mind spending because it's they can't fail. So they had to do all 3 techniques. They said, I want to do in vivo, I want to do in vitro, I want to do AI. So we have a number of companies that come to us to do that because we can offer each of those platforms. So those do end up being 3x the campaign size, both in terms of amount of work and dollars. Now the other campaigns, you'll go to a company like you're saying, you turn left in the hallway, you turn right. Some people like in vivo because of their background. Their background is an in vivo immunizations, and so they choose to go down that path because that's the data they're comfortable with. And so those campaigns, I'd say, amount of effort, you say amount of effort, amount of dollars spent, they're all fairly similar. It's just what method are people comfortable with or people have a background. And now we have this emerging contingent of AI/ML companies that weren't there 3 or 4 years ago. So that's just one additional group that's emerging from these contingents for us to now serve. So again, similar amount of effort overall, just faster time line for them, maybe some different demands, larger volumes. But no matter how you slice it, it's adding another piece to the pie for these companies. And to your point, I think every company eventually will do AI. It's just how long does it take all of them to get into it? And what's that long tail of how many companies emerge from that.

Yes. And that's why when we showed the market that we project that we will be serving in 2030, we showed that there was growth in antibody discovery service and growth in protein expression as well as on top, putting $1 billion of AI-based drug discovery. So we see both the industry growing and adding extra dollar on top, thanks to AI because there's an extra flux of capital going into those AI companies.

Emily, congrats to the broader team on a great day. In terms of the AI order book, how do you actually construct it? Is this -- do you have visibility from customers directly saying we're going to place x amount of orders in x amount of days? Or are you doing sort of an estimation based on the expected scope of the project and how much has already been ordered? What's the process there? And then in the same breath, that order book is really one, the speed is a huge differentiator for you guys. So is it fair to say that, that order book in and of itself is likely to be converted into revenues within either the same quarter or the next?

We need to hear it from the person who's actually whipping that order book. Paddy?

Treatment Inspirational environment would be a better rephrase. If you remember the slide on commercial Execution. If you're going to work at Twist, I expect you to have tip the spear activities. You need to know your customer. You need to know what they're doing, you need to know when they're doing. You absolutely need to know how much. And the unit of mass I like there is dollar bill. And then to help Mark and the team out from a sales and ops standpoint, we really do need to understand the timing. So as far as I'm concerned for the commercial team that put up against anybody else in the industry, those are table stakes in terms of how we function. So we've got good eyes on what's going on. We obsess about that. It's a little bit -- I think the word intensity was maybe missing from the slide, but just don't get between either of us in a purchase order. That's a very dangerous place.

In terms of NGS and your regulated clinical customers, how do I think about the tail of being spec-ed in on a revenue basis? And given, I guess, you're earlier in adoption, would you expect your growth to exceed, I guess, liquid biopsy combined with rare disease?

Paddy?

Yes. So I mean the platform resonates for any of the segments, okay? Super fast design, build, test, learn iterations, super cost effective at small scale and absolutely scales up beautifully to when you're successful. If you remember the picture of research, development, verification, validation, it's a very, very powerful platform. I think the things that maybe kept us away from 100.0% market share essentially is entrenched, right? You've got maybe got an assay that's validated or under some sort of control. But all I can tell you is we're through the door, okay? Because if you're not on the Twist platform, you're economically compromised in your assay. So we're just getting going.

This is Alex [indiscernible] from Canaccord Genuity here for Kyle Mikson. Congratulations on the successful event. So you noted you're looking for platform accretive technologies and potential bolt-on offerings that could support your free cash flow and margin aspirations. Can you just dive a bit more deeply into that? What aspects of the portfolio do you feel would be best served by bolt-on offerings at this juncture versus perhaps things you believe you could end up developing in-house?

Maybe I can start and if you want to add anything. We love all of our applications. As has been mentioned a few times, we don't make kings or Queens. We don't force, we think our technology view on the market, except maybe in the area of tumor-informed MRD, where we don't tell people how to do science. So actually, we don't necessarily have deep plans that are preconceived there. I think what we want to do is to follow the market. We want to wait for the phone calls from customers saying, can you do this? And that is -- definitely informs our product road map, informs our future. And the answer sometimes is yes and sometimes the answer is no, which means not yet, that eventually, we'll get there. So we -- in some ways, we are very opportunistic to see how the market is evolving and then take advantage of our technology. The best example is AI-driven drug discovery. 18 months ago, we were selling some protein. We are not selling characterization high throughput. We got a call and we realized that, that was the beginning of a new trend. That was not on our road map. We adjusted the road map very quickly. We serve that customer really well on the second one, the third one, just reinstant repeat. So that's how we're going to move forward. Anything you want to add?

Adam, can I ask a cleanup question? We know how to do math, but by 2031, is the revenue supposed to be $900 million? Or is it supposed to be $1 billion? Literally, I'm getting so many e-mails on it.

Yes. It is doubling to $900 million.

$900 million by 2031.

By 2031. That's the floor, and that's organic growth only, and that's by 2031.

Emily, Puneet Souda, Leerink Partners. So just you have a scalable platform. You have a miniaturized silicon platform that has enabled all these applications. And from the early days of the company, you've been -- you found these markets first in kits, then NGS diagnostics, now AI, you're scaling into that. The question is what's next? And I think Paddy alluded to that -- started to allude to that. And then I think there was some incination towards therapeutics oligos. Obviously, there is a large competitor that was acquired for, I think, $9 billion plus in that market. So maybe just educate us what are the new applications and the real next one is therapeutic oligos.

Yes. I mean the nucleic acid therapeutics, we see it as a definite growth opportunity for us, both in enabling the discovery of antisense oligonucleotide as well as we are being dragged by customers saying, we are getting DNA from you and every one sequence is going to 1 patient or 1 cluster going to one patient. We heard it from onco RNA today. So we think that the drive towards personalized medicine is inevitable. And that is one definitely big, big growth opportunity for us. There's others, but that definitely is one of them that we are following closely, and we think as legs. Anything you want to add, Paddy?

So over the years, you've done a great job reducing a key component of the bill of materials in a lot of different areas, clinical as well as others. That said, you're only one, maybe a little bit more than one in some instances, but you're only, we'll say, one component of a customer's bill of materials. You can build efficiencies there, you can lower cost, but you can't control the cost of others. So where I'm going with this is, are there instances where you would consider forward integrating to make sure that opportunities that could be great for Twist and markets that should be open but aren't open because of cost constraints. Essentially, would you forward integrate to make it happen because others are holding you back?

Take the first part, Paddy?

Good one. It's a good question. I don't want to get my enzyme slides out again, but that's a second component of workflow that we've helped them improve efficiencies on. I think our role in the community, the customers the hero or Batman to our Robin to borrow your joke, it's very good. And I think for the foreseeable future, that's the right place for us to play. And I think it's just really that relentless focus and attention on serving the customer most effectively. If there's product we can add to help with that enablement, that's certainly something as long as it leverages our advantage in DNA synthesis, I think that's something we would look at.

Paddy, can I ask a quick follow-up? Have you demonstrated with your new methylation enzyme, some of the recent issues where they compared bisulfite and enzymatic conversion? Does your enzyme actually address that error issue?

We are cautiously optimistic that Dr. Chen's good luck in making enzymes continues. So we're in early access. I trust him implicitly when he judges the molecule, but we're out there sensing with the customer how well we're doing. But the way that enzyme is designed and developed, we have a cautious sense of optimism at this point, if it's okay to leave it there.

Okay. We'll take two more questions. I have one spoken for. So if you have our last question, get ready. In addition, before we take Robbie's question, don't forget to take your swag. It's on either side. There are some super cool little pockets for all your little chargers. It will be very useful for all of you who travel a lot, which is all of you. All right. Robbie, you up.

Yes. Just wondering about the headcount plan right now, you're about 1,000 employees with all the efficiencies that you are -- have been talking about. wondering what assumptions are baked in through 2030, 2031.

Adam?

Thank you for the question. And if you kind of look at where we are today in terms of OpEx, if you kind of fast forward to kind of what we projected out by the end of this year, Q4, we'll be growing revenue probably about double the growth rate of OpEx year-on-year. And so looking forward in terms of headcount, not all headcount are created equal, a $25 an hour operator versus a PhD scientist of 30 years' experience has a slight difference. And so we take that into consideration. So really, we're optimizing for the OpEx line. But I think what we've seen is we can continue to drive investments in the business where they matter and at the same time, drive efficiencies in the business to offset that. And so whether that be through automation or that be through process improvements inside manufacturing and out, we see opportunities to be balanced such that we can see that continuous leverage into the P&L all the way through. And so we're not so much targeting a specific headcount number as much as we're targeting that continued OpEx leverage.

Okay. Who's going to close this out? I know I could count on you.

All right. No, I just want to say congrats to the team. I know it's been several years of hard work. I mean there were times,, I won't want to say what the gross margin was at that time, but a remarkable success getting here and good luck ahead.

Thank you so much. Much appreciated. And I will close. I just have a very few slides before we go to some wine testing. Coming back to this slide, which is the evolution of our product road map from 2021 to 2026. This is key to what we do. You heard from C1 on oligo synthesis on clonal gene ultra complex gene, our MRD 10K. You heard from Colby on our in vivo in vitro IgG antibody characterization. You heard from our customers about oligo pools. You heard from LC about something that is not on the chart, right? So remind -- remember that, that if you need DNA that's not there, call C1. If you need protein or data that's not there, call Colby, and we'll work on it. We heard about clonal gene customer, where -- from Jimi about our UDIs, about our MRD Express, from Paddy around our enzyme from John about our exome, from Alpha about our Flex prep and from OncoDNA about our custom DNA and RNA panels as well as our library prep, right? And so we did a kind of a full view or we did a view, but there's a lot of products we haven't touched on, right? And so that's something to remember is this was not by any means an exhaustive list. You will be very, very tired of us if we went through everything. And that road map is really key to what's important to us and what's important to us is the durable top line growth. We're very happy with what we've accomplished over the last few years, but it's all about what have you done for me lately. And so it's all about the future. That future is going to come through revenue growth from different products. At JPMorgan, we share the growth of a few products. Here, we are showing the growth of different products, but it really shows that, that growth is multifaceted. It's not one lever. It's many, many levers. In terms of our market, those are updated market size that we believe will be in 2030. On the NGS application on the left, most of the growth is coming from our oncology diagnostic that's going to grow much faster than the entire market. And that is why we are singularly -- not singular, but we are very focused on that market. We are very focused on providing solutions that are differentiated and that going to enable the revenue growth. And then on the right, with DNA synthesis and protein [indiscernible]. We've already gone through the market for DNA synthesis antibody discovery service and protein expression. And now we are adding one more SAM for us to go after with nucleic acid therapeutics. So overall, not only we have a history of building an NPM machine on top of our silicon chip, but also that NPM machine enables us to increase our SAM. So last slide, I think, for me, kind of the take-home message. We have built an industrialized platform with extremely precise measuring prowess that thanks to our automation, our software, and we're leveraging that into -- by building multiple monetization layers to build a diversified list of customers that are leveraging our product portfolio. We want -- soon we'll have profitable growth, and we want to turn that profitable growth into durable growth. And at the end of the day, what enables us to do that is the unique culture that we have built and that we're going to leverage in the future. So with that, I want to thank everybody for -- on the East Coast that have waited late in the day. I want to thank the entire Twist team. I want to thank the customers that made the effort to come here and thank the investors and analysts that also have done the trip. And for those of you that have done the trip, you'll have a special reward of getting on a bus to a winery. So with that, thank you so much.