Speaker 1: Pushkin. Every cell in your body is an incredible data
Speaker 1: storage device. Inside the nucleus of almost every cell is
Speaker 1: your entire genetic code, all of your DNA arranged just so,
Speaker 1: providing the complete blueprint of you in every cell. Unbelievable.
Speaker 1: A few decades ago, some scientists considered this fact and thought,
Speaker 1: DNA has stored the data of pretty much every living
Speaker 1: thing on Earth for billions of years. It's incredibly efficient, reliable.
Speaker 1: We know it works. What if we could use it
Speaker 1: to store other kinds of data for certain things? It
Speaker 1: could be profoundly better than the hard drives and tapes
Speaker 1: were currently using for data storage. And now they've figured
Speaker 1: out how to do it. I'm Jacob Goldstein and this
Speaker 1: is What's Your Problem, the show where engineers and entrepreneurs
Speaker 1: talk about how they're going to change the world once
Speaker 1: they solve a few problems. My guest today is Emily Laprust.
Speaker 1: She's the co founder and CEO of Twist Bioscience, one
Speaker 1: of the leading companies working on storing data in DNA.
Speaker 1: Sounds like science fiction, it's just science. Emily's problem, how
Speaker 1: do you make storing data in DNA as cheap as
Speaker 1: storing it on a hard drug. Emily's company, Twist, is
Speaker 1: in the DNA synthesis business. They make and sell custom
Speaker 1: DNA to researchers and healthcare companies, and Twist is also
Speaker 1: part of a group of companies, including Microsoft and the
Speaker 1: data storage company Western Digital, that are trying to bring
Speaker 1: DNA storage to market. It's clear that it can work
Speaker 1: in the lab. The trick now is to make it
Speaker 1: work in the real world and have the economics makes sense.
Speaker 1: We started our conversation by talking about a key problem
Speaker 1: with how data storage works today. Hard drives and flash
Speaker 1: drives and even old fashioned tape which is still used,
Speaker 1: are all based on magnetization, and magnetization just does not
Speaker 1: work for long term storage. It's unreliable, it degrades, and
Speaker 1: so if you want to achive data for a very
Speaker 1: long time, what you have to do is every five
Speaker 1: to seven years, you have to take the data from
Speaker 1: one hob drive to the next, or from one day
Speaker 1: to the next, or from one flash to the next
Speaker 1: every five years, because you just can't trust that the
Speaker 1: data will stay there more than five years. And so
Speaker 1: the system we have now, obviously it's great, right, like
Speaker 1: data storage is this amazing miracle of the modern world.
Speaker 1: But you're saying a flaw with it is it doesn't
Speaker 1: work forever. In fact, it becomes unreliable after five years on.
Speaker 1: One of the big effort that's going on there is
Speaker 1: exactly that it's constantly replacing hard drives that are broken
Speaker 1: with new ones, and so the migration, the maintenance, and
Speaker 1: the energy that you need to do that is a
Speaker 1: big issue for archiving, and archiving is more than sixty
Speaker 1: percent of the market of data. And so that's where
Speaker 1: DNA and totally changings is you could archive data for
Speaker 1: a long time with no migration, no maintenance, no energy,
Speaker 1: and that could be a game changer. So the key
Speaker 1: use case is like, it's not my phone, it's not
Speaker 1: my laptop, it's not anything I'm sort of personally doing.
Speaker 1: It's maybe if I'm saving something to the cloud, you know,
Speaker 1: my whatever, kids, baby pictures, and presumably more significantly big
Speaker 1: institutional sort of corporate or governmental kind of files. I mean,
Speaker 1: it's that the real opportunity that you're talking about here. Yeah, exactly,
Speaker 1: DNA will never be on on your computer. It's not
Speaker 1: for what's called data. So the data will get in
Speaker 1: and out all the time. It's it's more fall cool data.
Speaker 1: That's the data that you read that infrequently, but that
Speaker 1: is the majority of the data out there that DNA
Speaker 1: can solve. If you're a bank, if you are an
Speaker 1: insurance company, you have to store the data for a
Speaker 1: very long time. If you're a government, you want you
Speaker 1: need to store that that for a long time. So
Speaker 1: the longer you're going to need to keep the data,
Speaker 1: the more it makes sense to store it in DNA exactly.
Speaker 1: And that's why the first project we're going to launch
Speaker 1: is we call it a century Archive, so it's basically
Speaker 1: you purchase one hundred years of storage. I like that.
Speaker 1: That's good branding. Did a marketing person come up with that?
Speaker 1: Did you come up with that? I did not come
Speaker 1: up with that, but yes, definitely a very clever marketing
Speaker 1: person came up with that. And then after that we'll
Speaker 1: sell the Millennium Archive, the Millennium, the Millennium Archive. But well,
Speaker 1: let's get to one hundred first. We can I can
Speaker 1: have you on a year. We can talk about the
Speaker 1: Millennium Archive, but so are you right now selling a
Speaker 1: product where if a company wants to save something for
Speaker 1: a hundred years, they can give you money and you
Speaker 1: will store their data in DNA. Does that exist? Yeah,
Speaker 1: we've done it before. Right now, we are in the
Speaker 1: early access, so it's not fully broadly available. But someone
Speaker 1: comes today and they have money, will definitely do it
Speaker 1: for them. The one thing though, today it's still is
Speaker 1: still quite expensive, and we're working to make it affordable.
Speaker 1: It will couse a little bit more up front, not
Speaker 1: too much because people don't really want to be a
Speaker 1: premium and this market is the elastic so it's kind
Speaker 1: of ironic. But the lower the price of what we sell,
Speaker 1: the more we're gonna sell. We're gonna sell it. Sure. No,
Speaker 1: that is a very intuitive relationship and a classic issue
Speaker 1: in technology. Right you have this new thing and it's
Speaker 1: really expensive, so nobody's buying it, and the way it
Speaker 1: gets cheaper is lots of people buy it and then
Speaker 1: you figure out the economy of scale and it gets cheaper.
Speaker 1: I mean, it's that the dynamic you're talking about here,
Speaker 1: that's exactly the dynamic. And DNA is amazing for that
Speaker 1: um because DNA is extremely tiny. Yeah, it's extremely dense.
Speaker 1: You could take all the data in the Internet and
Speaker 1: which is youtue, I mean everything, it's the whole world,
Speaker 1: and it will fit in a shoe box if you
Speaker 1: put it in DNA. So I want to understand a
Speaker 1: little bit what that means, because it's so not intuitive.
Speaker 1: So all the data on the Internet means like every
Speaker 1: video that's on YouTube, everything everybody has ever posted to
Speaker 1: the internet, every tweet, every Facebook post, everything, everything, So
Speaker 1: that all exists now on hard drives, right, and giant
Speaker 1: rooms full of computers all over the world, cooled by fans. Right,
Speaker 1: Like all the data on the Internet exists now sort
Speaker 1: of distributed across many, many warehouses. Like just do you
Speaker 1: do you know how much space it takes up? Now?
Speaker 1: I don't know. I don't have any idea. I don't know,
Speaker 1: but it's it's it's millions and minus of square foot.
Speaker 1: What I do know is that one Facebook data center
Speaker 1: in Texas uses two percent of the Texas electricity. When
Speaker 1: we talk about storing data in DNA, can you walk
Speaker 1: me through, actually what happens. What happens somebody wants to
Speaker 1: save whatever, a movie, a Netflix movie, and instead of
Speaker 1: saving it on a little chip, they save it in DNA.
Speaker 1: So storage of data is a bunch of zeros and ones. Right,
Speaker 1: It's the binary code DNA four letters ACGT, And just
Speaker 1: to be super clear, when you say four letters, I
Speaker 1: mean all. All DNA is made up of four basically
Speaker 1: types of molecules, and only four. That's right. If you
Speaker 1: look at any DNA of any leaving organism from the
Speaker 1: biggest to the smoot one, that DNA is made of foditos, acg, NT,
Speaker 1: and the function of that organism is based on in
Speaker 1: which orders the acgs and ts linked together. Emily says,
Speaker 1: to use DNA to store a movie, or to store
Speaker 1: any kind of data, you let each of the four
Speaker 1: letters represent a binary pair. So for example, you could
Speaker 1: say zero zero is A and zero one is C,
Speaker 1: one one is T, and one zero is G. Once
Speaker 1: you've done that, you can take any digital file. You
Speaker 1: can take that movie and on a computer convert the
Speaker 1: zeros and ones that represent the movie into a string
Speaker 1: of the letters ACTG. That string of letters is your blueprint.
Speaker 1: And then the next step is kind of amazing because
Speaker 1: we are so used to everything happening, you know, digitally
Speaker 1: on the computer. The next step is a machine about
Speaker 1: the size of an SUV actually starts printing strands of
Speaker 1: DNA based on that digital blueprint. What we're going to
Speaker 1: actually physically synthesize, We're going to make it from scratch,
Speaker 1: is a bunch of little pieces of DNA that has
Speaker 1: two to three hundred litters. Okay, so is that the
Speaker 1: next step. It's still on the computer, but now the
Speaker 1: computer tells some DNA synthesis machine what molecules to synthesize.
Speaker 1: Where the a's go, where the teas go, where the
Speaker 1: ceas go, where the gee's go. Yeah, so exactly, So
Speaker 1: a twist, we build a DNA three D printer basically,
Speaker 1: ok and we have a we have a piece of silicon.
Speaker 1: It's a citicin chip, and so on. Our citic and chip.
Speaker 1: The first generation that we made at one million pieces
Speaker 1: of DNA. The current generation we're working on as two
Speaker 1: hundred fifty six million. Okay, And so we have a
Speaker 1: we have a cilican chip, and then we have a printer.
Speaker 1: So I want to keep going with our sort of
Speaker 1: how does it work narrative here. So we got the
Speaker 1: movie that was already ones and zeros. The ones and
Speaker 1: zeros became ATCNG, those got split up into little packets. Yeah,
Speaker 1: the computer told the three D printer basically what actual
Speaker 1: ATCNG to print physically the actual chemicals on a silicon chip,
Speaker 1: hundreds of millions of them, and then we got delivered
Speaker 1: that ACGT at the right location at the right time.
Speaker 1: And so we start from the citic ins ship. There
Speaker 1: is no DNA on it. And then we come in
Speaker 1: and we print the first layer. So we put the
Speaker 1: first ACGT on the surface, and then we come in
Speaker 1: again to put the second layer of SEGT again the third.
Speaker 1: We do that two to three hundred times, and so
Speaker 1: we are building DNA up to three hundred letters and
Speaker 1: we do that millions of time on the Citicin chip. Okay,
Speaker 1: so now you've got a silicon chip with DNA on it.
Speaker 1: What happens next? Yeah, So at that point the density
Speaker 1: is not that great. It's kind of like a hard drive.
Speaker 1: But then the beauties. You can remove the DNA from
Speaker 1: the citikinship. I want to keep track of that DNA.
Speaker 1: You just synthesize. Where does it go? You just dry
Speaker 1: it down and it's so tiny what you've made. It's
Speaker 1: a speck of dust. You can't see it. So now
Speaker 1: it goes into a tiny vessel. But it's called the
Speaker 1: DNA shell. Yeah, it's a good name. It's not ours.
Speaker 1: We buy it, and it's a piece of stainless steel
Speaker 1: and there's a glass cutting inside. You put your the
Speaker 1: DNA in it, you dry down the water, you sell it,
Speaker 1: and you put ilium, so there's no oxygen, no water.
Speaker 1: And in that DNAs shell you can put hundreds of
Speaker 1: Google Data Center in that DNA shell, like every movie
Speaker 1: on Netflix. You could pack in a out of DNA
Speaker 1: in it. And and then you see it and that
Speaker 1: is stable for thousands of years. And how big is it?
Speaker 1: It's uh so, you know, I live in Montana. Everybody
Speaker 1: has guns here, so it's the size of a small
Speaker 1: caliber bullet. That's nice. Okay, So about like I live
Speaker 1: in Brooklyn where fewer people have guns at least as
Speaker 1: far as I know. So would it be about like
Speaker 1: the size of like a like a bean. It would
Speaker 1: be the size of two black beans. Okay. You can
Speaker 1: put every movie on Netflix in there, that's right, and
Speaker 1: then you can store it your room. Tompret show um
Speaker 1: in the sun. You know, it's it's extremely stable for forever.
Speaker 1: And then when you want the data bag. So now
Speaker 1: it's fifty years later and I want to watch that
Speaker 1: movie again. Because I forgot that it wasn't very good.
Speaker 1: Well oh you oh, let me say, jeez, you know
Speaker 1: there's something happened to to the Netflix that are center,
Speaker 1: and you know that movie is gone. We need to
Speaker 1: get it back. That's the real practical use for this.
Speaker 1: I would imagine it's that like, at least for now,
Speaker 1: it's like a backup. It's like a super safe backup,
Speaker 1: even if all of the data centers get hacked or
Speaker 1: blown up or whatever. You have this volt Yeah, yeah,
Speaker 1: it's a very it's it's the vault exactly so exactly
Speaker 1: that that's the ultimate vault. And the beauty is DNA.
Speaker 1: You can make a copy very easily. Let's go back.
Speaker 1: So we have our all of Netflix in a little
Speaker 1: cylinder size of two beans. Well, now we want to
Speaker 1: get the data. How do we get the data off there?
Speaker 1: So you you crack open the shell and you put
Speaker 1: it in what's called a sequencer. So it's a machine
Speaker 1: that reads DNA. And the sequencer is not fast. It's
Speaker 1: twelve hours. So twelve hours later you'll get your data.
Speaker 1: And now it's still ACGT in the computer and you
Speaker 1: have to do the reverse decoding. You have to turn
Speaker 1: the ACGT in two zeros on one and that's the
Speaker 1: end of the story, right, and then you can watch
Speaker 1: the movie that you encoded today exactly exactly. Sounds amazing,
Speaker 1: DNA all of Netflix and a cylinder the size of
Speaker 1: two black beans. But DNA storage is not yet a
Speaker 1: thing that is really happening in the world at scale.
Speaker 1: In a minute, the problem Emily has to solve for
Speaker 1: that to happen, for DNA storage to become a real
Speaker 1: thing in the world. Now, let's get back to the show.
Speaker 1: So it sounds amazing. I'm sold the Internet in a shoebox.
Speaker 1: It sounds incredible. Why Why isn't it really a thing yet?
Speaker 1: Why does Why why aren't people doing it? Yeah, it
Speaker 1: goes back to economics. You can do it today, but
Speaker 1: today it's too expensive. And it's too expensive because today
Speaker 1: we only pack a million pieces of adigos on the
Speaker 1: civic and chip. It works. We've done dozens of administrations
Speaker 1: with again Netflix, Microsoft, University of Washington, with the Montro
Speaker 1: Jazz Festival, with the United Nations, with the Olympic Games. Right,
Speaker 1: so it works, but it is expensive, and that's why
Speaker 1: at Weiss we are pushing the technology further we're packing
Speaker 1: more and more pieces of DNA on the same citkinship. Again,
Speaker 1: the state of the art used to be one hundred
Speaker 1: pieces of DNA. The first Twist product was a million
Speaker 1: pieces of DNA. Right now we're working working on a
Speaker 1: chip that we have two hundred and fifty six millions.
Speaker 1: So okay, so you're packing more and more a DNA
Speaker 1: on a chip to make DNA storage cheaper. Right, that's
Speaker 1: the fundamental problem. You have to solve. How much cheaper
Speaker 1: have you made it so far? And how much cheaper
Speaker 1: does it have to get. We've already done a thousand
Speaker 1: times less and were we're in field of and those
Speaker 1: a thousand times cost reduction and have to add by
Speaker 1: packing more sequences. You do that by packing more pieces
Speaker 1: of DNA on the city Kin chip. And so it's weird.
Speaker 1: I mean, we're used to thinking about this kind of thing.
Speaker 1: I think because of Moore's law, right, because this thing
Speaker 1: has happened with computers for whatever it is, fifty years now.
Speaker 1: That exactly what you're talking about in DNA, right, every
Speaker 1: two years you get twice as many transistors on a
Speaker 1: chip or whatever. But it's not really a law. It
Speaker 1: didn't happen naturally. Right, It happened because like many, many, many,
Speaker 1: really really really smart people kept coming up with clever
Speaker 1: ways of getting more transistors on a chip. Right, it
Speaker 1: doesn't have to keep getting better, getting more efficient. So
Speaker 1: how do you do that? Like, is there some way
Speaker 1: without getting too technical, that we can talk about some
Speaker 1: of the problems you have to solve to keep doubling
Speaker 1: to get a thousand times cheaper. Yeah, it's it's a
Speaker 1: very it's a very difficult engineering problem. And I love
Speaker 1: that it's difficult because if it was easy, every idiots
Speaker 1: would be doing it. And so it is really really hard,
Speaker 1: and you have to be very good at sumer conductor,
Speaker 1: at silicon engineering, you have to be extremely good at
Speaker 1: electrical engineering, at mechanical engineering, and chemical engineering at computation.
Speaker 1: It just a What you have to do is build
Speaker 1: a team where everybody is the best at what they
Speaker 1: do in their own field and have those people be
Speaker 1: extremely great team players, and then you have to demand performance.
Speaker 1: So in some ways you have to run this lack
Speaker 1: of sports team. I always say at Twist, we're not
Speaker 1: a family. Right, in the family, you tolerate bad behavior.
Speaker 1: We out sports team. We hire the best athlete for
Speaker 1: each position to demand performance, and you demand that they
Speaker 1: work as a team and if you give them an
Speaker 1: amazing tough problem, they'll find a way. So you have
Speaker 1: this team, how do they actually solve the problem of
Speaker 1: you know, building denser chips to make DNA storage cheaper. Yeah,
Speaker 1: so first you have to design the Citicans ship where
Speaker 1: it's it's a design a computer that cost millions of
Speaker 1: dollars just to do design right, And then you press
Speaker 1: the button and you say, get me that chipmate. That's
Speaker 1: another set of millions of dollars. And then you get
Speaker 1: the chip in and you literally hook it up to
Speaker 1: a giant amount of set of flectionics and you literally
Speaker 1: boot the chip like you boot your computer, and you
Speaker 1: know the chip doesn't work. It's like, oh, now I
Speaker 1: have to go back either redesign it, which you know
Speaker 1: it's money and time that you want to do. Oh,
Speaker 1: you have to debug it. See what, See what happens.
Speaker 1: And when you finally get the chip to work, that's
Speaker 1: just a Citicans chip. Now you have to bring the
Speaker 1: ACGT on it. Okay, yeah, you have to. You have
Speaker 1: to build the DNA and that's very hard as well.
Speaker 1: And that's why we be one experiment and we have
Speaker 1: to do the the engineering principle of design, build tests.
Speaker 1: You design an expense, you build the DNA, you test it,
Speaker 1: you learn a little bit, you go back and you
Speaker 1: do it again and again, and every cycle of learning
Speaker 1: is time and money and again, if you have the
Speaker 1: right team, that cycle of learning will be faster than
Speaker 1: the competition, will be cheaper on a competition, and that's
Speaker 1: how we win. It is extremely, extremely difficult, and again
Speaker 1: thankfully it's difficult. That that means we have a shot
Speaker 1: at greatness. So yes, the good news is it's super
Speaker 1: hard and nobody knows how to do it. So the
Speaker 1: goal ultimately is to be able to store things in
Speaker 1: DNA for a price that's comparable to what you could
Speaker 1: put it on a hard drive for now, and in
Speaker 1: that universe it would be cheaper in the long run
Speaker 1: in the DNA. When do you think that'll happen? So
Speaker 1: I know the answer, and I get that question from
Speaker 1: all our investors all the time. Unfortunately I can't. I
Speaker 1: can't tell you, but it's it's it's very soon. And
Speaker 1: the chip we're working on now, that is the chip
Speaker 1: that we're going to go commercial with and so the
Speaker 1: chip is designed, the ship is build, and now we're
Speaker 1: in the debugging phase and when when that is finished,
Speaker 1: then we'll be able to launch commercially. And so does
Speaker 1: that mean in a year? Does that mean in five years?
Speaker 1: So if it's not done in five years, I'll definitely
Speaker 1: get a lot of love letters from our investors. So
Speaker 1: it's definitely a lot less than five years. In a minute,
Speaker 1: it's the lightning round, including Emily's advice for solving hard
Speaker 1: problems and what she actually does to solve hard problems.
Speaker 1: Now let's get back to the show. The last thing
Speaker 1: is a lightning round, a bunch of questions, but but
Speaker 1: we'll do them fast. What is one piece of advice
Speaker 1: you'd give to someone trying to solve a hard problem?
Speaker 1: If I first, you don't succeed, try again, Right, you
Speaker 1: have a choice. Right when you don't succeed, you can
Speaker 1: call mom, you can order a pizza, you can drink
Speaker 1: a beer. You try again. You have a favorite gene.
Speaker 1: I do not have a favorite gene. I love all
Speaker 1: the genes. They're like children, right, I guess you're their children.
Speaker 1: We're their children. As a scientist, As a geneticist, do
Speaker 1: you feel like you understand something about the world or
Speaker 1: about people that most people don't really understand. I don't
Speaker 1: think so. Yeah, I'm not very to shephilia as a person.
Speaker 1: I'll probably somewhere on on some spectrum. So I know,
Speaker 1: to make DNA and or to write, you know, to sell,
Speaker 1: to do fundraising. But those are probably the four things.
Speaker 1: Do you think you'll ever leave twist? You think they're
Speaker 1: all kind of time when you want to go do
Speaker 1: something else, I would be in the seat until I
Speaker 1: get fired by by the board. So this is probably
Speaker 1: my last job. I've read that you play the piano
Speaker 1: when you're thinking about work. Is there some something in
Speaker 1: particular you've been enjoying playing lately? A favorite piece these days?
Speaker 1: So I practice the piano every day. I don't know
Speaker 1: if I'm really playing it. Some people make coll it
Speaker 1: butchering or murdering the piano, but I totally enjoy it.
Speaker 1: What I love about the piano is that you know,
Speaker 1: the left hand is easy, the right hand is easy.
Speaker 1: But putting the two hands together, that's a total mind
Speaker 1: bending experience. And it's you have to use one hundred
Speaker 1: percent of your conscious mind. And when I find is
Speaker 1: if I have a hard problem at work, I can't
Speaker 1: figure that out, I go up to the piano. I'm
Speaker 1: one hundred percent. I'm using one hundredercent of my brain
Speaker 1: and somehow, in the background somewhere I think of a
Speaker 1: solution and that that is quite exciting. And yeah, all
Speaker 1: my tough problem. I never get them resolved sitting on
Speaker 1: by desk, routing on my computer. It's I get them
Speaker 1: resolved either playing piano or taking the dogs out for
Speaker 1: a walk. So maybe that's your real advice for how
Speaker 1: to solve a hard problem is don't think about the
Speaker 1: problem or do something else. Yeah, take your prim out
Speaker 1: of the building, take a hike. Emily Laprus is the
Speaker 1: co founder and CEO of Twist Bioscience. Today's show was
Speaker 1: produced by Edith Russlo, edited by Robert Smith, and engineered
Speaker 1: by Amanda K. Wong. You can reach us at problem
Speaker 1: at pushkin dot fm, or you can find me on
Speaker 1: Twitter at Jacob Goldstein. I'm Jacob Goldstein and I'll be
Speaker 1: back next week with another episode of What's Your Problem.