AI transcript
0:00:05 The content here is for informational purposes only, should not be taken as legal business
0:00:10 tax or investment advice, or be used to evaluate any investment or security and is not directed
0:00:15 at any investors or potential investors in any A16Z fund. For more details, please see
0:00:21 a16z.com/disclosures. Hi, welcome to the A16Z podcast. This is
0:00:27 Frank Chen. Today’s episode is called “Five Open Problems for the Blockchain Computer.”
0:00:32 It originally aired as a YouTube video. And you can watch all of our YouTube videos at
0:00:39 youtube.com/a16zvideos. Well, welcome to the A16Z YouTube channel. Today,
0:00:45 I’m here with Ali Yahya, our deal partner in the A16Z crypto team. And we’re going to
0:00:49 have a fun conversation. So here’s what we’re going to do. I’m going to pretend Ali to
0:00:54 be a Google software engineer or an Apple software engineer, right? So I’m somebody
0:00:59 who knows how to write software, has been doing it for a while. And then all of a sudden,
0:01:03 I saw my friends start to peel off and go to crypto startups. And I’m looking around
0:01:08 going, “What’s happening? Google’s a great place or Apple’s a great place. Why are people
0:01:13 leaving to go to crypto startups?” And maybe you can help me understand what’s causing
0:01:16 all these smart, talented people to head into crypto land.
0:01:17 I love it.
0:01:22 Fantastic. So maybe let’s just start with the world in which we live today, which is,
0:01:28 you know, I use my iPhone or my Android phone. I happen to use a Google phone, the Pixel.
0:01:35 I use Google Photos. I use Gmail. My carrier is T-Mobile. It’s sort of a centralized world.
0:01:39 And it works pretty well, right? Like, it’s pretty reliable. And Google gets all my photos
0:01:44 and my mail arrives when I want it. And so that’s not a bad world. Is crypto really
0:01:48 trying to overturn that world?
0:01:53 That world does work fine, but it’s not the frontier. So what I would say is the reason
0:02:00 that crypto is so exciting is because it offers a fundamentally new paradigm for computation
0:02:03 that has features that are completely novel and different from the features that enable
0:02:09 applications like social media, as it exists today, like sharing photos, like all of sort
0:02:14 of the centralized services that we know and love today. And so I think with every successive
0:02:22 wave of computation that we’ve seen throughout the history of computing, normally, the new
0:02:28 paradigm tends to suck at first and tends to be pretty bad at most things that the old
0:02:34 paradigm is very good at. But it happens to shine in one or two particular ways that enable
0:02:38 new applications that previously were just not possible to build. And so with, I mean,
0:02:43 I think one of the clearest examples is just the example of mobile phones enabling applications
0:02:49 like Uber, where applications like Instagram, by virtue of having a camera and a GPS bolted
0:02:54 onto the phone, that enable those kinds of behaviors that with a personal computer, you
0:02:57 couldn’t have possibly, couldn’t have possibly built.
0:03:00 Your PC didn’t know where you were necessarily, so it couldn’t enable Lyft.
0:03:04 Exactly. And it would have been also just deeply impractical for one to pull out one’s
0:03:05 laptop.
0:03:08 Hold on, let me get my PC.
0:03:16 And so with crypto, I think the key dimension along which these decentralized computers
0:03:22 that people are building in the world of crypto shine is that of trust. They provide this
0:03:32 new angle that previous computers didn’t have because previous computers are owned and operated
0:03:39 by individuals or by single entities like companies. And so you have to trust that individual,
0:03:43 you have to trust that company to actually run the software that they’re claiming that
0:03:47 they’re running and to actually do what they claim they will do with your data. And with,
0:03:51 we’re basically with the entire interaction between you and them. And so we trust Google
0:03:56 with our photos, we trust Google with our email, we trust Google with just about everything
0:04:01 that entails the kinds of interactions that we have with Google.
0:04:06 This new paradigm of computation is such that you now have a computational fabric that is
0:04:11 not owned and operated by any one person. This is the whole point of decentralization.
0:04:15 When people talk about decentralization in the world of crypto, they mean decentralization
0:04:23 of human control. Not decentralization of computing in a geographic sense. It’s not
0:04:28 decentralization in any other way that you may think. Like the key thing about crypto
0:04:34 is decentralization of human power and human control over systems and figuring out clever
0:04:41 ways to build a system such that it is self policing and such that it’s security and its
0:04:47 trust emerges bottom up from its participants and from individuals as opposed to top down
0:04:54 from like some trusted organization at the top that kind of enforces the rules.
0:05:00 Got it. So instead of trusting Google or Facebook or Apple, I can then trust the collective
0:05:05 of people who have contributed their computing, their storage, power, etc., etc., to deliver
0:05:12 the service that I’m consuming. And that’s the big innovation. And so why don’t we go
0:05:18 through the implications of that by sort of talking through, well, what will we need to
0:05:26 rebuild in crypto land, starting with compute, so that all of the applications that run on
0:05:31 top of this distributed computer sort of will have the power that you’re describing. So
0:05:35 let’s start with distributed compute. What do we need? So I guess maybe to set the stage,
0:05:40 if we think of compute today, we have computers that can perform certain monotransactions
0:05:46 per second. We have visa, which can clear so many financial transactions per second.
0:05:49 And then we compare those things with things like, well, how many Bitcoin transactions
0:05:54 can clear, how many Ethereum smart contracts can clear. So why don’t we talk about how
0:05:57 do we get distributed compute to really sing in crypto land?
0:06:04 Absolutely. So I think so much of the attention in crypto tends to be on this metric of transactions
0:06:08 per second. And I think we would argue that that’s the wrong, I mean, it’s not even the
0:06:13 right framing because we’re not talking about just a ledger that processes payments. We’re
0:06:19 talking about a computer, we’re talking about a decentralized fabric for general computation.
0:06:23 So the right metric is not really transactions per second, it’s really instructions per second.
0:06:28 How many, how many instructions of some computation can you process in any given period of time?
0:06:33 And that’s kind of what people know as, know as as throughput. And so that’s one of the
0:06:38 metrics for scalability that that matter when it comes to compute. The other one is the
0:06:42 latency to finality. It’s like, how do we know that the computation was done and that
0:06:49 it can no longer be reverted, that it can no longer, that its output was final and that
0:06:54 nothing can happen that could reverse it and have it be something different. You can trust
0:06:59 that that outcome is settled. So that’s latency to finality is how much time do you have to
0:07:05 wait before that happens. And people talk all the time about how Bitcoin has terrible latency
0:07:10 to finality, you have to wait 60 minutes before you can be reasonably sure that your
0:07:14 payment is final. So that’s another another access. And then the final one when we talk
0:07:22 about scalability of computation is what is the cost per instruction? How much do I have
0:07:26 to pay for that transaction? How much do I have to pay for just an arbitrary computation
0:07:31 on Ethereum or on some of the more general platforms for for computation? And the reason
0:07:35 obviously that all of this matters is because the kinds of applications that we want to
0:07:44 build will just require far greater scalability and also will require far lower cost to really
0:07:49 to really work. And I think it may be helpful to just exemplify what those applications
0:07:53 are. I think some of the some of the things that we we are seeing already in the world
0:07:59 of of Ethereum kind of the Ethereum ecosystem is maybe the richest so far in terms of actual
0:08:04 developer activity on top. So we’ve seen kind of the emergence of this parallel financial
0:08:12 world where you have things like like stable coins, which are price stable cryptocurrencies
0:08:18 that have some logic that modulate the supply of the of the token to keep it stable to some
0:08:23 external reference like the US dollar. And then on top of that, people build things like
0:08:27 lending platforms and they build they build things like derivatives platforms, they build
0:08:33 things like decentralized exchanges where you can exchange tokens or exchange crypto assets
0:08:38 without depending on some central exchange. So all of these things that’s like one one
0:08:43 example one trend that is already happening among among many other trends that we can
0:08:48 talk about other examples later review if you think it’s helpful. But all of that depends
0:08:59 on far greater throughput, far lower latency to finality and far lower cost per per instruction.
0:09:04 It’s already just with this initial activity, we’re already seeing the limits of the current
0:09:09 of the current technology. So it’s an open problem. How do we increase throughput? And
0:09:13 for that particular question, people people like what one of the things that matters the
0:09:19 most is the delay in propagation of messages in a distributed system. That’s what ends
0:09:28 up dominating the cost of that particular problem. So people talk about the block time
0:09:34 in crypto was like how much time do you have to wait before you can append a new block
0:09:39 to the blockchain and blocks usually contain computations they contain transactions. So
0:09:44 you can lower the amount of time that you have to wait for new blocks to come along,
0:09:50 then you will process more transactions and more computations per per unit time than you
0:09:55 would otherwise. And so propagating messages in the network is is a is the dominant factor
0:10:00 is that’s what makes it slow to sort of finalize a transaction. Exactly. And the reason for
0:10:04 this is that we are building a distributed system. And so if you think about it, what
0:10:09 is the difference between a distributed system and one that’s just centralized? And that
0:10:14 is that there’s distance between the different nodes that are participating in the system.
0:10:18 So the key difference is that now there’s this additional communication cost between
0:10:24 the different nodes in the system. And that cost is also is significant because it’s bounded
0:10:29 is it like the lower bound on it is the speed of light. You cannot get faster than the speed
0:10:35 of light. So so it provides it causes this this kind of lower bound as to how perform
0:10:40 it can possibly be. And you can only get so clever before you reach that that that kind
0:10:46 of lower bound. But it is the case that today we’re still far far from from that lower bound.
0:10:49 There’s still a lot of room for improvement. Yeah, I mean, people in general pretty impatient.
0:10:53 I remember when the chip and pin system started getting deployed here in the United States,
0:10:57 it was just a couple of years ago, right? And then the you didn’t start your credit
0:11:02 card, it would take like five seconds, right? And that was a lot slower than the swipe. And
0:11:06 people were like, this is never going to work. I’m not waiting five seconds for my credit
0:11:12 card to clear. And so maybe talk a little bit about, you know, sort of what is the propagation
0:11:17 delay today? And then what’s practical to get to given sort of speed of light limitations?
0:11:20 And then what’s the target? And how do we get there?
0:11:28 Yeah, for sure. So today, I mean, there’s this there’s enormous tension between, well,
0:11:33 to back up a little bit. So there are two things that matter here. One of them is how
0:11:38 much time does it take to send a message between two two points in space? But then there’s
0:11:46 also the problem of what what influence does the size of the message have on on that amount
0:11:51 of time? And so there’s basically the two angles here are latency and bandwidth, latency
0:11:55 being the amount of time that it takes to send a message bandwidth, being how much how
0:12:00 much data can you actually fit through the pipe per unit of per unit of time. So there’s
0:12:05 a tension in this space, you can see this reflected in in say the Bitcoin like block
0:12:14 size debate. Yeah, between sort of the throughput that you can get out of a network and the
0:12:19 propagation delay that that is caused by say increasing the block size. So in the case
0:12:24 of Bitcoin, people people were talking about doubling the block size from one megabyte to
0:12:29 two megabytes. So that would increase the throughput of the Bitcoin blockchain, because
0:12:35 now you can fit twice as many transactions, and the blocks will still come at a 10 minute
0:12:42 cadence. But that would increase the propagation delay for those blocks, which would cause
0:12:46 certain miners to no longer really be able to participate because they won’t get the
0:12:49 block in time. So they would have to they would eventually end up falling out, you’d
0:12:55 end up with a more centralized system. So we see here there’s a trade off between performance
0:12:59 and decentralization, assuming that you want to keep security constant, you don’t want
0:13:04 to suffer. You still need the trust, right, you can give up on the trust, you can’t allow
0:13:10 double spending, right. So I always thought that the reason say Bitcoin was slow was the
0:13:15 proof of work was so computationally demanding. Is that still the case, or is that is that
0:13:16 a solved problem?
0:13:21 So it’s a very, very good point. So we’ve been talking so far we’ve been talking about
0:13:29 the throughput of of instructions for a blockchain, which is one of the three different dimensions
0:13:35 for scalability of compute. The third one was the cost of instruction of an instruction
0:13:41 how much does it cost to have a transaction be processed. So the cost of proof of work
0:13:49 is what ends up driving the cost of an instruction so far, so so high. Rather than I mean, like
0:13:56 so there are, there are like various different lines of work. There’s the line of work that’s
0:14:01 trying to improve the propagation of messages and to make that more efficient. So there
0:14:07 are companies like blocks route, which are building like a kind of a content delivery
0:14:13 network which has advanced computer networking technology that allows miners to propagate
0:14:18 their blocks to other miners very efficiently. And so that’ll help with the propagation delay,
0:14:19 which will help with the throughput problem.
0:14:23 So the new generation CDN that is optimized for crypto.
0:14:27 And in fact, they call it a blockchain distribution network, a BDN.
0:14:28 Oh, got it.
0:14:31 And so that’s an interesting angle and that operates at layer zero is like the networking
0:14:36 layer below the blockchain layer. And it can help any blockchain project, any blockchain
0:14:42 project that builds on top of it will benefit from faster propagation of messages.
0:14:45 And the classic internet definitely needed this. Like it’s impossible to imagine the
0:14:50 internet without a CDN, right? You’d be waiting a lot longer for almost anything without that
0:14:54 layer of infrastructure. So that makes sense. So there’s sort of a CDN layer.
0:14:57 Yeah. And then, and then there are people who are working on this latency to finality
0:15:01 dimension, which we also talked about, which is how much time do you have to wait before
0:15:10 your message or your update, your computation is final. And so that is a consensus problem.
0:15:16 How do we agree that the update is final? How do we agree that something can no longer
0:15:23 be reversed? So proof of work is a probabilistic consensus algorithm in that there is always
0:15:28 some probability that whatever update to the ledger was performed could be reverted at
0:15:35 some point in time later. And the key aspect there is that the more time passes, the less
0:15:40 likely it becomes that that update gets reverted. But it’s always probabilistic. And this is
0:15:43 why you kind of have to wait 60 minutes before you know that it’s final, because that’s
0:15:48 the point at which that probability becomes so minimal that you can effectively trust
0:15:49 that it won’t be reverted.
0:15:54 But there is innovation in consensus algorithms that are better than that, that are not probabilistic
0:16:01 and that are actually deterministic and are final on a far shorter time span.
0:16:05 Yeah. And you need both, right? You need non probabilistic and you need fast, right?
0:16:10 So I, you know, everybody talks about the, the Ethereum contract things underlying things
0:16:16 like, hey, when you go rent an Airbnb, that lock will open because I know it’s you. There’s
0:16:20 a smart contract that governs the, oh, you’re allowed to stay here tonight. No one’s going
0:16:26 to wait there 60 minutes for that. And so is it feasible? Are we on a path to basically
0:16:31 enable use cases like that where like, I’ve got my smart key and I’m in front of the Airbnb
0:16:35 and like in seconds that thing is going to open because the contract clear, is that possible
0:16:38 or is that not quite possible yet? We don’t know the path.
0:16:43 I think, I mean, we do see, we do see a path. I think that, so given the improvements on
0:16:49 the networking layer with companies like BoxRoute, improvements on the consensus layer with companies
0:16:53 like DFINITY and Ethereum 2.0 and Cosmos and Polkadot, there’s like a large number of people
0:17:00 working at that level. And then finally, improvements on the cost per instruction. Similarly, proof
0:17:05 of stake and other consensus algorithms don’t use the expensive proof of work that the original
0:17:09 blockchain’s used. And so that can also come down as you can see a world where, where this
0:17:15 does come down to a degree that it becomes fairly practical for everyday use for things
0:17:23 like kind of like a quick lighthearted interactions between people or between people and machines.
0:17:29 And so I think that that is certainly possible and I think we’re on our way. But I think
0:17:35 it’s worth noting, decentralized systems will always be more expensive and less performant
0:17:41 than centralized ones. There’s just an inherent tradeoff there and there’s an inherent cost
0:17:47 to decentralizing a computer system. And so it won’t replace everything. There will
0:17:53 be applications that will always make sense for a centralized world, for a centralized
0:17:57 world. And there will be some applications for which decentralization very much does
0:18:03 make sense. And those are the ones where trust is the key differentiator, where trust is
0:18:08 the bottleneck to scale. That’s where decentralized systems will shine.
0:18:12 I want you to give me a couple of examples of sort of applications where trust is the
0:18:18 key as opposed to performance or cost or whatever. But before we do that, I want to talk about
0:18:23 this notion of proof of work, transitioning to proof of stake, because this is super important.
0:18:30 I read all the time that Bitcoin mining is consuming some known fraction of the world’s
0:18:35 electricity because the math is so hard to actually do one of these proof of works, right?
0:18:41 It has to go similar to public key cryptography. And so what’s happening here? Like, how do
0:18:45 we get on a path where we’re not consuming all the world’s electricity doing these proofs?
0:18:51 Yeah. So the key goal for crypto networks is to build trust in a way that is bottom-up
0:18:55 and that does not depend on some central authority. And so as a result, you have to figure out
0:19:01 a way to make the network be self-policing and to kind of have an incentive structure
0:19:07 that makes its members police one another in a way that the entire network kind of works
0:19:12 and sort of proceeds according to people’s expectations. So in a sense, you have to make
0:19:20 it the rational equilibrium to play by the rules of the game rather than to defect and
0:19:26 profit in some way that is against the rules and that kind of figures out a way to game
0:19:31 the system. And so in Bitcoin, one of the key ways this worked was through this proof
0:19:35 of work. So maybe talk a little bit about how did it work? Why did it consume so much
0:19:40 electricity? And then where are we going? So the key problem that crypto networks have
0:19:46 to solve is figuring out who gets to participate because there’s no one central party who is
0:19:50 able to decide who gets to participate and who doesn’t. That’s the entire point we want
0:19:56 to do away with that. And so proof of work does this by requiring every participant to
0:20:01 compute an expensive proof of work. It’s a computation that’s done on top of every block
0:20:05 that they want to add to the blockchain. So that’s an extrinsic resource that they have
0:20:11 to come across, they have to procure to be able to participate and it prevents any one
0:20:18 person from completely monopolizing the system and from having unilateral ability to modify
0:20:22 the underlying blockchain. That of course is very expensive because you have to come
0:20:27 across all of this computational power in order to participate. So proof of stake says
0:20:31 something different is instead of making the resource that you have to come across and
0:20:37 you have to procure be extrinsic to the system, why not make it something that’s intrinsic
0:20:44 to it, namely why not make it a crypto asset, why not make it a token that you have to own
0:20:50 in order to buy your participation in the system. So what proof of stake does is it
0:20:57 says if you own 2% of the tokens in the network, then by and large on average you’ll have 2%
0:21:01 of the say in what blocks get to make it onto the blockchain and which ones don’t. But now
0:21:09 because of the asset itself, the resource that you need to be in procession of in order
0:21:14 to participate, because it’s no longer extrinsic to the system, it’s no longer a resource that
0:21:19 is sort of a physical resource like electricity and rather it’s entirely virtual, now the
0:21:25 cost of actually participating in consensus and making the entire network work in real
0:21:31 terms comes down dramatically. And it’s just secure or at least theoretically can be made
0:21:37 just to secure and that’s a controversial statement but I will sort of stand by it.
0:21:42 But it’s less expensive and so from a cost per instruction, on a cost per instruction
0:21:47 basis it’ll be much more performant than a proof of work system.
0:21:51 So if I could restate that it sounds like in Bitcoin with its proof of work I had to
0:21:56 bring electricity, consume the electricity, do this hard math and that was how I sort
0:22:00 of entered the system and participated and my reward is a minor for burning all this
0:22:06 electricity as I get paid in Bitcoin. In proof of work I’m bringing basically tokens, sorry,
0:22:10 in proof of stake I’m bringing the tokens themselves, I’m not consuming electricity I’m just bringing
0:22:17 the tokens themselves and I’m by virtue of my ownership of the tokens I can participate
0:22:23 in a proof of stake that delivers the same trust properties as proof of work without
0:22:25 burning all the electricity.
0:22:26 Exactly.
0:22:31 Got it. Got it. So it sounds like all of these things need to come together for us to build
0:22:36 sort of distributed compute in this new world, right? We need the new CDNs, we need this transition
0:22:40 to things that look like proof of stake so we’re not consuming all this electricity.
0:22:46 Any other big innovations that need to happen in this space to bring the transaction cost
0:22:48 down and the transaction speed up?
0:22:54 I think those are the big ones. So we’re talking about the three pillars of computation, well
0:23:00 the three pillars of scalability of computation, there’s throughput, there’s latency and then
0:23:03 there’s a cost per instruction and so we kind of addressed all three, there’s companies
0:23:06 that are working in all three and I think these are very much still open problems and
0:23:12 there’s just a lot of green field for exploration and so I will tell you Google engineer, this
0:23:17 is where it’s exciting, this is where your skills as a sort of distributed systems engineer
0:23:23 or machine learning expert can kind of leverage those skills to figure out some of these open
0:23:24 problems.
0:23:29 Got it, so if I’m motivated by doing things like I want to create a better TCP/IP, I want
0:23:34 to create a better HTTPS and oh man I’m just too late to the party, like I arrived when
0:23:38 all those protocols were already settled, like you’re saying this space is for me because
0:23:42 a lot of the problems haven’t been settled yet, right? A thorny problem, unsettled, big
0:23:43 world impact.
0:23:48 Yeah, even if it’s been 10 years since the publishing of the Bitcoin white paper, this
0:23:55 is still very early days, because I think that it’s only recently that people have begun
0:24:00 to conceive of blockchains as computers as opposed to just payment systems, so the emergence
0:24:09 of Ethereum was in 2014 and it’s only really been five years or four years really of people
0:24:14 thinking of blockchains in this way and so it’s very early days, the space is very nascent
0:24:17 and there’s just a lot of work to do, great.
0:24:21 So perfect time, why don’t we sort of move on to part two, so we’ve talked about distributed
0:24:28 compute, let’s talk about distributed storage and so we started with the Google photos example,
0:24:34 I kind of trust Google to have all my storage, all my photos, but to do that they have huge
0:24:38 servers with lots of hard drives in them scattered around the world, it’s pretty expensive and
0:24:44 so if I was a startup trying to mount a frontal assault against that, I kind of only have
0:24:50 two choices, one is raise like a trillion dollars and try to duplicate their infrastructure,
0:24:58 put data centers everywhere, points of presence, or I could do what the distributed crypto community
0:25:02 is trying to do which is convince you to lend me a bit of your hard drive space.
0:25:09 The key reason that we need a decentralized layer of storage is because it itself will
0:25:14 be a foundational building block for this decentralized world computer that we’re talking
0:25:19 about and so in order for some of these applications that we’ve talked about that are not really
0:25:26 possible to build on top of a centralized architecture to really work, we need the full extent of
0:25:31 a computer that works in this way and so if we had a centralized storage layer instead
0:25:38 of a decentralized one then that would be the weakest link, it would dilute the promise
0:25:44 of the decentralized layer of computation if you don’t have all of the pieces themselves
0:25:49 being decentralized and so that’s why it’s important because we want to enable these
0:25:56 applications that kind of depend on decentralization for trust and it’s not so much to compete
0:26:02 head on with Amazon because the economics are different, as we said like decentralizing
0:26:08 a system always comes with the cost and so it won’t make sense for just storing your
0:26:14 photos if storing your photos is something that Amazon/Google can do and if there’s
0:26:19 no trust dimension to doing that, maybe if you care deeply about your photos not ever
0:26:24 being seen by anyone but yourself or by anyone but your close friends then maybe you can
0:26:28 imagine using a different kind of architecture, one that’s maybe more decentralized, but for
0:26:32 that kind of use case I imagine sort of the centralized data center model, it works very
0:26:38 well and they’re not paying the decentralization tax and so it’s always going to be cheaper
0:26:45 for them to store your photos but there are interesting opportunities, so for example
0:26:48 there’s a project called Filecoin, there are a number of others too that are working in
0:26:52 the same space, one of them is called SIA, another one is called StoreJ and they are
0:26:59 trying to build decentralized marketplaces for storage, so the idea is yes I can rent
0:27:05 some of your idle storage space on your laptop and pay you for that storage in Filecoin and
0:27:11 the reason that that’s now possible is because I can now trust that you will actually store
0:27:15 my files and you can trust that I will pay you for that storage even if we’re complete
0:27:20 strangers and reside across the world from one another because of the cryptographic guarantees
0:27:23 of the underlying protocol.
0:27:29 And so that’s important because previously without crypto and without blockchains that
0:27:34 would have been a very difficult interaction to coordinate and it would have been very hard
0:27:39 for us to establish trust from halfway across the world and make that exchange happen.
0:27:44 So it’s a marketplace that now emerges where previously it couldn’t have and it gives us
0:27:54 this property that no one controls this sort of layer of storage and we can use it in conjunction
0:27:58 with a computation layer to build applications that are fully decentralized and that are
0:28:07 unstoppable and kind of run in their own right and therefore command greater trust than
0:28:09 applications that are centralized.
0:28:13 So I remember before the crypto craze there were definitely startups that were trying
0:28:17 to do that do this exact thing which is sort of let’s share hard drive space.
0:28:22 I remember there were backup companies that basically say your price would be I’ll make
0:28:26 up a price $20 per gig per month but if you contribute your own hard drive space your
0:28:31 price is $10 per gig a month or whatever and making up those numbers but they never really
0:28:32 got to scale.
0:28:39 So what are the advantages of doing this with sort of a cryptographic protocol as their
0:28:43 intermediary as opposed to just hey there’s a company and there’s a service and there’s
0:28:48 a price chart and please participate right and we’re going to sort of transact value
0:28:50 and fiat currency.
0:28:55 Yeah, well I think that the key difference is that those companies were operating on
0:29:03 the assumption that the value at here is an economic that you actually that is kind of
0:29:12 like Uber you’ll tap into all of this unused storage space that previously was inaccessible
0:29:17 that you’ll offer that at a cheaper rate but I think that in the end because storage is
0:29:24 the most commoditized of computational resources and because there’s just so strong economies
0:29:31 of scale that benefit companies like Amazon as they build data centers the economic argument
0:29:32 just doesn’t doesn’t work.
0:29:37 So the reason that we need decentralized storage networks is not because they’re going to reduce
0:29:43 the price of storage by orders of magnitude at least for most kinds of files and for most
0:29:47 use cases I don’t believe that that’ll be the case.
0:29:53 The value proposition is that again we now no longer have this central entity that’s
0:29:57 controlling the storage on this network and so for the kinds of applications that depend
0:30:02 on that the kinds of applications that really cannot be built unless you have that you just
0:30:04 have no other option.
0:30:05 Yeah.
0:30:09 So you would pay the additional cost you pay a higher price for storing your files and
0:30:12 file coin because that matters.
0:30:17 Is there a privacy argument here which is the because it’s decentralized for instance
0:30:23 there’s nobody to give a government subpoena to to say I want to see your files.
0:30:29 I think privacy comes into it to some extent but I think it’s a little bit orthogonal because
0:30:33 you can imagine encrypting your files before storing them in a centralized service.
0:30:34 Yeah.
0:30:35 Fair.
0:30:40 So you there are ways of building privacy into into sort of existing centralized storage
0:30:41 networks.
0:30:42 Okay.
0:30:46 And then what are some of the challenging computer science things about building these
0:30:51 and so if there was proof of work for computation what are the proof of in the space.
0:30:52 Yeah.
0:30:55 Well the biggest one is trusting that the people who are claiming to be storing your
0:30:58 files actually are storing your files.
0:31:04 So there’s this line of work that’s been spearheaded by the people at Filecoin and by
0:31:11 Stanford’s cryptography lab, Dan Bonet’s lab and sort of people like Confisch and Benedict
0:31:18 Boone’s underneath him have done a lot of work on figuring out how to create cryptographic
0:31:20 proofs of retrievability.
0:31:26 How can I prove to you that I actually am storing the files that I’m claiming that I
0:31:27 am storing.
0:31:28 Right.
0:31:29 And it’s super interesting.
0:31:33 It’s extremely cutting edge and it’s basically at the heart of how you make a system like
0:31:34 this work.
0:31:35 Yeah.
0:31:39 So you basically have to catch the pretenders which is you don’t want somebody to be able
0:31:44 to say yes I’ll store your files and then not actually store them right because it would
0:31:49 be cheaper for them not to store them right and so these sort of proofs of retrievability
0:31:51 are basically ways to catch the pretenders.
0:31:52 Exactly.
0:31:53 Right.
0:31:54 And it’s sort of a mathematical fashion.
0:31:56 So yeah, you actually had a conversation with Ben Fisch on this.
0:32:01 So for people who are interested in exploring this topic further, there’s a couple YouTube
0:32:02 videos.
0:32:03 Awesome.
0:32:04 So, okay.
0:32:05 So we’ve talked about distributed compute.
0:32:07 We’ve talked about distributed storage.
0:32:10 I guess the third leg is now networking like what’s happening here.
0:32:14 Actually this kind of reminds me, do you remember the company Phone?
0:32:20 This is sort of back in 2006 and the idea was I could buy a Wi-Fi router from this company
0:32:23 called Phone and then I could sort of do one of two things with it.
0:32:29 One, I could sort of offer it in Linus mode which is I gave it away free Wi-Fi access
0:32:30 right.
0:32:34 Anybody that came to my house with my Wi-Fi router, you could access my Wi-Fi for free
0:32:40 and then in exchange I could access anybody else’s Wi-Fi access point for free.
0:32:44 So that’s mode one or I could be in bill mode and in bill mode basically I would say look
0:32:52 my Wi-Fi is available to you but you’re going to rent it for two bucks and in exchange for
0:32:56 that I would have to pay to access other people’s Wi-Fi.
0:33:01 So I could be in open source mode or I could be in rent seeking mode and it was this attempt
0:33:08 to basically create a distributed ISP out of millions and millions of wireless.
0:33:09 So it’s something wireless access points.
0:33:12 Is something similar going on in crypto today?
0:33:13 Absolutely.
0:33:19 So the difficulty with those efforts has often been just the problem of density and the problem
0:33:20 of incentives.
0:33:26 So how do you get enough people to offer up hardware that forwards packets and provides
0:33:32 bandwidth within a particular geographic region to make it make sense and to make it work
0:33:39 and to be at all competitive with sort of the more kind of centralized top-down internet
0:33:41 backbone infrastructure that we rely on.
0:33:46 And so there are a number of projects that are, it’s very early because this is actually
0:33:48 probably one of the hardest problems in this space to tackle.
0:33:52 How do you decentralize even the networking layer, the communication between different
0:33:58 nodes and to not have it depend on centralized internet infrastructure.
0:34:05 So people are talking about incentivized mesh networking protocols where you can earn cryptocurrency,
0:34:13 you can earn the asset that’s native to a particular protocol by setting up a router.
0:34:17 This router can be a normal router that just forwards packets but it could also be wireless
0:34:24 and provide a different layer of connectivity that otherwise, that essentially makes the
0:34:31 networking layer more robust and more resistant to censorship and perhaps even more performant
0:34:35 if you have just greater connectivity to the people you want to interact with.
0:34:40 So yeah, I think this is one of these problem areas that’s fairly far out because it kind
0:34:46 of depends on the other two building blocks and it has its unique challenges because now
0:34:49 we’re talking about bringing hardware into the picture.
0:34:51 That’s always a whole other kind of worms.
0:34:56 But it is very interesting and I think in the end, it’ll also be a piece of the puzzle.
0:35:03 So that’s one angle to it, it’s decentralizing networking and then the other angle is making
0:35:08 networking itself just more performant for the use cases of decentralization.
0:35:12 So we talked a little bit about the CDNs for blocks.
0:35:16 So that kind of falls into that category, into this category as well.
0:35:17 Got it.
0:35:20 So those are the key ingredients that you need to build a computer, right?
0:35:24 You need compute, you need network, you need storage and it looks like there’s sort of
0:35:27 efforts underway in all of these things.
0:35:32 Let’s assume for a second that time has gone by and protocols and sort of Darwinian fashion
0:35:38 have competed and a couple winners have emerged and these things look more like solved problems.
0:35:43 So now the exciting opportunity is, okay, now we can build killer apps on top of the
0:35:47 blockchain computer and so maybe talk to me about what is the community most excited about?
0:35:50 What kinds of apps are you going to build?
0:35:53 Because as you’ve been pointing out, it’s not going to be the straightforward replacements
0:35:55 for the things that we know and love today.
0:36:01 It’s not like instantly the replacement for Airbnb or Google Photos or Lyft because those
0:36:04 systems don’t have to pay the decentralization tax.
0:36:08 It’s probably going to be another class of apps at least to begin with.
0:36:12 That is the killer question and I think as with any new technology it is very difficult
0:36:16 to predict what applications will be the most impactful.
0:36:22 I think one reason to believe that the kind of innovation that we’ll see will be enormous
0:36:28 is that everything happens, all of the code that’s written in the space ends up being
0:36:29 open source.
0:36:31 And so as a result the ideas are out there.
0:36:36 People share their ideas with other teams, other teams sort of build on top of one another’s
0:36:37 ideas.
0:36:43 And so the kind of innovation that we’re likely to see is just combinatorial in nature and
0:36:49 likely more explosive and will accelerate more quickly than it has for previous waves
0:36:52 of computing and previous waves of technology.
0:36:59 We do have this kind of decentralized world computer that is a kind of computational fabric
0:37:04 on top of which applications can run and it is unified in that one application can easily
0:37:06 talk to another.
0:37:10 Then we have the possibility of composability of applications.
0:37:16 So not only do we have the sharing of ideas that are just available to people because
0:37:22 by virtue of being open source, but we also have the actual composability of running code.
0:37:25 Code that runs on top of this computational fabric that builds on top of the code that
0:37:27 other people have built.
0:37:32 And this kind of composability will just fuel the flame of combinatorial innovation even
0:37:33 further.
0:37:38 So I feel like the kinds of applications that we’ll see as a result are fundamentally
0:37:40 impossible to predict.
0:37:44 But I will say, I think the kinds of things that we’ve started to see, the kinds of applications
0:37:48 that seem to be working so far, and it’s still very early and they’re working only in kind
0:37:56 of niche, within niche communities, are ones where trust is the bottleneck to scale.
0:37:59 So I think the most obvious one began with Bitcoin.
0:38:08 It’s attempting to be money and the only way that you would trust a program that maintains
0:38:15 a ledger of tokens that claims that those tokens should be money is if that ledger isn’t
0:38:20 in the control of any one entity or any one individual.
0:38:27 I guess you would trust the central government, maybe, but you would not trust a company to
0:38:28 do that.
0:38:31 So you wouldn’t have been able to build Bitcoin on top of Amazon.
0:38:35 Bitcoin is like one example of an application that you can build and a bunch of the applications
0:38:41 that have worked so far in the Ethereum ecosystem primarily have been financial in nature, have
0:38:47 been things that build on top of that initial idea, so things like lending platforms, derivatives,
0:38:54 exchanges, things that depend on trust for them to really take off.
0:38:59 But we’ve also started to see other applications that benefit from this feature.
0:39:05 I think gaming is an interesting one where you can imagine taking the existing world
0:39:09 of gaming, you can imagine, for example, a world of Warcraft where people have significant
0:39:14 investment in their character and in the gear that they have and in the lives that they
0:39:22 live within these games, taken to a whole other level where you actually own your character
0:39:27 and you own the gear for your character and you can take your character and gear out of
0:39:33 the game and maybe into another game because you now have this interoperable trustworthy
0:39:36 fabric of computation that other developers can build on top of.
0:39:43 So that kind of investment in your personality and in your character in the game is unlike
0:39:44 what we’ve seen in gaming before.
0:39:48 So this could take gaming just to a whole other level and that could be a very interesting
0:39:49 set of applications.
0:39:53 But it very much depends on these three building blocks, we need scalability before gaming
0:39:55 can really take off.
0:40:00 And we’ve seen examples of this, I think CryptoKitties is one where people became very invested
0:40:08 in owning this digital collectible, which is something that is fundamentally new.
0:40:13 Never before would you be able to directly own something that’s digital is the first
0:40:14 time that that’s possible.
0:40:18 I love this idea of being able to take sort of a high level character that I’ve developed
0:40:23 in one game and moving it to another because, you know, look, essentially a high level character
0:40:27 in say, World of Warcraft is the ultimate proof of work, right, which is I had to do
0:40:31 a lot in order to get this character to be super high level.
0:40:34 And now I’m kind of stuck in World of Warcraft, which is great if I want to play more World
0:40:38 of Warcraft, but like it’d be awesome if I could take my proof of work and move it to
0:40:39 another system.
0:40:40 Absolutely.
0:40:41 Yeah.
0:40:46 Yeah, I mean, I think there’s a story about how Vitalik, part of Vitalik’s inspiration
0:40:51 for starting Ethereum is having, I’m not sure which game it was, but it was like some
0:40:58 gaming platform that revoked his ownership over, over like a key, a key item in the game.
0:40:59 There it is.
0:41:02 That made him like, made him like so, so mad, right.
0:41:06 This is the problem with centralization, right, which is you have a company operating
0:41:08 a game and they can do whatever they want with the game, right.
0:41:13 One change to the terms of service and all of a sudden your proof of work is basically,
0:41:14 it’s invalid.
0:41:15 Exactly.
0:41:16 Yeah.
0:41:17 That would make you mad.
0:41:20 Well, if you think about sort of trust being the key feature, I mean, there’s so many
0:41:26 sort of, you know, properties that we think about on the internet that are essentially
0:41:27 sort of brokers of trust, right.
0:41:33 So LinkedIn is sort of the trusted entity to manage your resume and present your resume.
0:41:37 And eBay is sort of the trusted marketplace where the sellers or Etsy is sort of the trusted
0:41:42 place where you sort of send money and expect stuff, right.
0:41:49 There’s Airbnb and Lyft, and so like trust seems like a super powerful primitive for
0:41:50 creating killer apps.
0:41:51 Yeah, definitely.
0:41:58 And I think the web 2.0 world has figured out how to bootstrap trust in a way that
0:42:05 depends on things like identity and reputation, where there’s social capital associated with
0:42:08 your track record on the internet.
0:42:16 So things like reviews on Yelp or things like reviews on or stars on Uber or number of likes
0:42:21 on Twitter and number of followers on just generally social media.
0:42:28 These things are, this is like the mechanism for trust that’s used in web 2.0.
0:42:30 And I think crypto is orthogonal to that.
0:42:36 Crypto today has no sense of identity, that people are pseudonymous, people can create
0:42:38 multiple addresses and pretend to be different people.
0:42:43 People can abandon identities that maybe have a bad reputation and move over to new identities
0:42:45 that don’t.
0:42:53 And the entire fabric of trust, therefore, depends not on social capital but rather on
0:42:54 financial incentives.
0:43:00 So it’s this orthogonal layer where you’re incentivized to behave honestly because there
0:43:03 is real money at stake.
0:43:08 And if you lie or if you behave in a way that’s not in accordance to the rules of the protocol,
0:43:10 then there’s something that you will lose as a result.
0:43:17 So there’s sort of financial capital and financial incentives as a way of bootstrapping trust
0:43:20 and then there’s social capital as a way of bootstrapping trust and I think that’s one
0:43:29 of the key differences between the web 2.0 world and the now web 3.0 crypto-enabled world.
0:43:34 And what will be very interesting to see is the two models coming together.
0:43:36 So that’s something to kind of look out for.
0:43:41 And it seems like Keybase is sort of an early attempt at that, which is on the one hand
0:43:46 you had all of these private keys that represented you in these cryptographic networks and on
0:43:50 the other hand you had sort of your Twitter profile and your LinkedIn profile and your
0:43:56 Facebook profile and Keybase sort of bridged them.
0:44:00 What other things do you think we will see in this space of sort of making identity more
0:44:01 seamless?
0:44:02 Yeah.
0:44:04 I think Keybase is a key one.
0:44:13 A key problem is how do you map a real human individual to a public key in a way that is
0:44:15 trustworthy and in a way that you can rely on.
0:44:21 So Keybase is this and I think it’s a very apropos example where you can use your existing
0:44:26 web 2.0 world identity to bootstrap your web 3.0 identity.
0:44:31 You can use your Twitter account and your Facebook account and your GitHub account and
0:44:39 your website and point them all to this cryptographic identity that you can then use to interact
0:44:43 with other people in the sort of crypto anonymous world.
0:44:47 And they can verify that that really is you because of the cryptographic assurances of
0:44:54 those connections between Twitter and so on and your public key.
0:44:55 You can take that further though.
0:45:00 I think once you do have identity in the crypto world and I think it is an unsolved problem,
0:45:05 Keybase is the first kind of attempt, but there’s still a lot to do there.
0:45:10 Once you have a solid layer of identity within crypto, that also doesn’t sacrifice privacy.
0:45:15 So it’s worth noting there’s a big trade-off there, like if you have strong identities
0:45:19 and you have less privacy and it’s kind of difficult to come to the right balance between
0:45:22 the two and it will vary per application.
0:45:26 But once you have a good system for that, then you can start building reputation systems.
0:45:30 You can even imagine like a page rank style algorithm for reputation.
0:45:41 If I trust Frank and Frank trusts Joe, then I kind of indirectly trust Joe.
0:45:46 And you can imagine kind of taking this to the whole other level to really enhance the
0:45:53 kind of trust that emerges from financial incentives with social capital and with reputation.
0:45:54 That’s very powerful.
0:45:58 There’s some of this even in things like the Facebook marketplace today, which is you see
0:46:02 somebody listing cheese or a bike or whatever and you’ll see, “Oh, this is a friend of Ali.”
0:46:06 And then that brings a level of trust to that transaction that wouldn’t otherwise exist.
0:46:07 Exactly.
0:46:12 And one of the reasons this is so important for crypto is that today, every interaction
0:46:16 in the world of crypto tends to be very transactional.
0:46:20 You don’t even know who you’re dealing with and so it really is about that one transaction.
0:46:27 It’s one-off and whenever there’s conflict, it’s a one-off prisoner/dilemma style game.
0:46:30 Whereas if you had identity and if you had reputation, you could turn all of those one-off
0:46:35 prisoner/dilemma style games into iterated prisoner/dilemma style games, which are far
0:46:36 easier to solve.
0:46:38 You have like the long view of relationships.
0:46:45 You can have a track record and rapport with the people that you interact with if you only
0:46:47 had that other layer.
0:46:50 So so many of the problems, the game theoretical problems that need to be solved for crypto
0:46:54 to work that are so hard to solve will become easier once you have this additional lever
0:46:55 to play with.
0:46:56 Yeah, that’s super interesting.
0:46:57 Right.
0:47:01 So every prisoner/dilemma type game is sort of assume perfect strangers go in and now
0:47:05 we have to sort of mathematically model what will happen, not knowing anything about them.
0:47:06 Exactly.
0:47:09 But if you threw me and you into a prisoner’s dilemma, right, like we’ll have much higher
0:47:14 fidelity predictions about what each other like, “I’m not going to squelch on you, Ali.
0:47:15 He’s a friend of mine.”
0:47:18 Especially if we know that we’re going to be in a similar kind of game in the future.
0:47:21 And it’s like if we cooperate now, then we’ll build rapport and then it’ll be easier for
0:47:24 us to cooperate in the future.
0:47:29 And if we cheat each other now, then we will kind of ruin that opportunity later on and
0:47:32 make it harder for us to cooperate down the line.
0:47:33 Super interesting.
0:47:38 So one thing before we go, I want to talk a little bit about governance, because today
0:47:43 it seems like there’s a lot of conversation in the tech community about, “Gee, maybe the
0:47:47 tech giants have gotten too big,” right, because with the stroke of a pen and one change in
0:47:52 terms of service, like all of a sudden, the rules of engagement or the winners and losers
0:47:55 in that environment are dramatically different.
0:48:00 In crypto land, the idea would be let’s not have one company which completely owns their
0:48:05 terms of service control that, there’s going to be sort of a decentralized community.
0:48:10 But we end up with some of the same questions, right, like who gets to change the terms of
0:48:11 service?
0:48:14 How do those changes come about, who proposes them?
0:48:19 So maybe talk to me a little bit about what’s happening in the community as we iterate on
0:48:20 systems of governance.
0:48:25 Yeah, you’re hitting at one of the most fundamental questions in this space, which is that if
0:48:32 you do build a system that is decentralized and that control over it does not rest with
0:48:36 any one individual, then there’s a question, well, how do you go about updating it?
0:48:39 How do you go about changing it in any meaningful way?
0:48:43 If it is going to be a complex system that adapts and evolves over time, this question
0:48:46 most certainly has to be answered in order for any of this to work.
0:48:53 So this is a question of sort of governance of protocols, and there are enormous number
0:48:57 of experiments that people are running, like different kind of approaches.
0:49:04 The canonical and sort of initial approach was out of Bitcoin, which is that essentially
0:49:07 you do just have to coordinate with all of the stakeholders, all of the people who are
0:49:14 running the Bitcoin node software in order to change the protocol.
0:49:18 And in this case, that would be that all miners, all of the people who are running the code
0:49:23 to mine Bitcoin, have to modify their software, and this is a human level process.
0:49:28 You have to call them up or you have to sort of issue an announcement saying that the protocol
0:49:31 is being upgraded and get that to work.
0:49:35 There are other approaches that people are exploring with that are more sort of they’re
0:49:38 formalized and are built into the protocol.
0:49:41 So there’s the idea of being able to vote with tokens.
0:49:50 So if I own a certain stake, a certain amount of the network, then I can use the tokens
0:49:55 that constitute that stake to vote in favor or against proposals that may be made by the
0:49:56 community.
0:50:01 It’s just another approach to decentralize governance that tries to lower the barrier
0:50:03 and tries to make it a little bit more seamless.
0:50:07 There’s an enormous set of challenges associated with that because there are possible attacks
0:50:10 where you can bribe people.
0:50:12 There is the issue of voter participation.
0:50:17 And all of the issues that you see in governance systems outside of the world of crypto, just
0:50:18 an offline government.
0:50:19 Offline?
0:50:20 Who’s going to the election?
0:50:21 How do they vote?
0:50:22 Exactly.
0:50:24 How do we prevent dead people from voting?
0:50:25 Exactly.
0:50:30 These problems have become replicated in crypto as well and they are therefore like fundamentally
0:50:33 difficult problems that have been unsolved for millennia.
0:50:39 So it’s not as if crypto will solve any of that, it’ll just have to figure out the right
0:50:44 mechanisms and the right structures to be good enough and to enable systems that are
0:50:51 decentralized to adapt and to change and evolve while striking a balance between sort of
0:50:53 evolvability and decentralization.
0:50:59 And actually, we did two podcasts specifically on this question, the question of governance
0:51:03 and crypto, that will go much, much deeper and talk about all of the challenges.
0:51:07 So if you’re interested in that topic, I recommend checking those out.
0:51:08 Perfect.
0:51:12 We’ll throw the links into the YouTube video so you can follow them easily.
0:51:15 Well, Ali, this has been super interesting.
0:51:20 There’s so many problems to be solved, there’s so many meeting computer science things to
0:51:25 be had, like how do you prove that I’m actually storing your photos instead of just pretending
0:51:28 to store your photos and collecting the money.
0:51:33 And so I guess the way I think about it is like if you have ever wished that you could
0:51:41 have been like a semiconductor engineer at Bell Labs in the 1950s or a PC enthusiast in
0:51:47 the 1970s and you were like, “I missed the 50s and then I missed the 70s.”
0:51:53 And then like if you wished you were at UIUC with Mark at the dawn of the internet in the
0:51:55 90s, like, “Look, here it is.
0:51:57 This is the new computing platform.
0:51:59 Here is your opportunity.
0:52:00 It’s not too late.”
0:52:05 And these are the times to exactly insert yourself into that conversation if sort of that’s
0:52:09 what you wish you had the opportunity to do is influence some of these protocols, these
0:52:12 incentive systems at the ground level.
0:52:13 Absolutely.
0:52:14 Awesome.
0:52:15 Fantastic.
0:52:18 So that’s it for this episode.
0:52:22 And if you liked what you saw, go ahead and subscribe to the list.
0:52:25 If you have comments, go ahead and leave them down below.
0:52:28 Maybe you could pick one thing that you were super excited about, like what problem do
0:52:32 you wish you could solve as an engineer.
0:52:33 And we will see you next episode.
0:52:42 [BLANK_AUDIO]
0:00:10 tax or investment advice, or be used to evaluate any investment or security and is not directed
0:00:15 at any investors or potential investors in any A16Z fund. For more details, please see
0:00:21 a16z.com/disclosures. Hi, welcome to the A16Z podcast. This is
0:00:27 Frank Chen. Today’s episode is called “Five Open Problems for the Blockchain Computer.”
0:00:32 It originally aired as a YouTube video. And you can watch all of our YouTube videos at
0:00:39 youtube.com/a16zvideos. Well, welcome to the A16Z YouTube channel. Today,
0:00:45 I’m here with Ali Yahya, our deal partner in the A16Z crypto team. And we’re going to
0:00:49 have a fun conversation. So here’s what we’re going to do. I’m going to pretend Ali to
0:00:54 be a Google software engineer or an Apple software engineer, right? So I’m somebody
0:00:59 who knows how to write software, has been doing it for a while. And then all of a sudden,
0:01:03 I saw my friends start to peel off and go to crypto startups. And I’m looking around
0:01:08 going, “What’s happening? Google’s a great place or Apple’s a great place. Why are people
0:01:13 leaving to go to crypto startups?” And maybe you can help me understand what’s causing
0:01:16 all these smart, talented people to head into crypto land.
0:01:17 I love it.
0:01:22 Fantastic. So maybe let’s just start with the world in which we live today, which is,
0:01:28 you know, I use my iPhone or my Android phone. I happen to use a Google phone, the Pixel.
0:01:35 I use Google Photos. I use Gmail. My carrier is T-Mobile. It’s sort of a centralized world.
0:01:39 And it works pretty well, right? Like, it’s pretty reliable. And Google gets all my photos
0:01:44 and my mail arrives when I want it. And so that’s not a bad world. Is crypto really
0:01:48 trying to overturn that world?
0:01:53 That world does work fine, but it’s not the frontier. So what I would say is the reason
0:02:00 that crypto is so exciting is because it offers a fundamentally new paradigm for computation
0:02:03 that has features that are completely novel and different from the features that enable
0:02:09 applications like social media, as it exists today, like sharing photos, like all of sort
0:02:14 of the centralized services that we know and love today. And so I think with every successive
0:02:22 wave of computation that we’ve seen throughout the history of computing, normally, the new
0:02:28 paradigm tends to suck at first and tends to be pretty bad at most things that the old
0:02:34 paradigm is very good at. But it happens to shine in one or two particular ways that enable
0:02:38 new applications that previously were just not possible to build. And so with, I mean,
0:02:43 I think one of the clearest examples is just the example of mobile phones enabling applications
0:02:49 like Uber, where applications like Instagram, by virtue of having a camera and a GPS bolted
0:02:54 onto the phone, that enable those kinds of behaviors that with a personal computer, you
0:02:57 couldn’t have possibly, couldn’t have possibly built.
0:03:00 Your PC didn’t know where you were necessarily, so it couldn’t enable Lyft.
0:03:04 Exactly. And it would have been also just deeply impractical for one to pull out one’s
0:03:05 laptop.
0:03:08 Hold on, let me get my PC.
0:03:16 And so with crypto, I think the key dimension along which these decentralized computers
0:03:22 that people are building in the world of crypto shine is that of trust. They provide this
0:03:32 new angle that previous computers didn’t have because previous computers are owned and operated
0:03:39 by individuals or by single entities like companies. And so you have to trust that individual,
0:03:43 you have to trust that company to actually run the software that they’re claiming that
0:03:47 they’re running and to actually do what they claim they will do with your data. And with,
0:03:51 we’re basically with the entire interaction between you and them. And so we trust Google
0:03:56 with our photos, we trust Google with our email, we trust Google with just about everything
0:04:01 that entails the kinds of interactions that we have with Google.
0:04:06 This new paradigm of computation is such that you now have a computational fabric that is
0:04:11 not owned and operated by any one person. This is the whole point of decentralization.
0:04:15 When people talk about decentralization in the world of crypto, they mean decentralization
0:04:23 of human control. Not decentralization of computing in a geographic sense. It’s not
0:04:28 decentralization in any other way that you may think. Like the key thing about crypto
0:04:34 is decentralization of human power and human control over systems and figuring out clever
0:04:41 ways to build a system such that it is self policing and such that it’s security and its
0:04:47 trust emerges bottom up from its participants and from individuals as opposed to top down
0:04:54 from like some trusted organization at the top that kind of enforces the rules.
0:05:00 Got it. So instead of trusting Google or Facebook or Apple, I can then trust the collective
0:05:05 of people who have contributed their computing, their storage, power, etc., etc., to deliver
0:05:12 the service that I’m consuming. And that’s the big innovation. And so why don’t we go
0:05:18 through the implications of that by sort of talking through, well, what will we need to
0:05:26 rebuild in crypto land, starting with compute, so that all of the applications that run on
0:05:31 top of this distributed computer sort of will have the power that you’re describing. So
0:05:35 let’s start with distributed compute. What do we need? So I guess maybe to set the stage,
0:05:40 if we think of compute today, we have computers that can perform certain monotransactions
0:05:46 per second. We have visa, which can clear so many financial transactions per second.
0:05:49 And then we compare those things with things like, well, how many Bitcoin transactions
0:05:54 can clear, how many Ethereum smart contracts can clear. So why don’t we talk about how
0:05:57 do we get distributed compute to really sing in crypto land?
0:06:04 Absolutely. So I think so much of the attention in crypto tends to be on this metric of transactions
0:06:08 per second. And I think we would argue that that’s the wrong, I mean, it’s not even the
0:06:13 right framing because we’re not talking about just a ledger that processes payments. We’re
0:06:19 talking about a computer, we’re talking about a decentralized fabric for general computation.
0:06:23 So the right metric is not really transactions per second, it’s really instructions per second.
0:06:28 How many, how many instructions of some computation can you process in any given period of time?
0:06:33 And that’s kind of what people know as, know as as throughput. And so that’s one of the
0:06:38 metrics for scalability that that matter when it comes to compute. The other one is the
0:06:42 latency to finality. It’s like, how do we know that the computation was done and that
0:06:49 it can no longer be reverted, that it can no longer, that its output was final and that
0:06:54 nothing can happen that could reverse it and have it be something different. You can trust
0:06:59 that that outcome is settled. So that’s latency to finality is how much time do you have to
0:07:05 wait before that happens. And people talk all the time about how Bitcoin has terrible latency
0:07:10 to finality, you have to wait 60 minutes before you can be reasonably sure that your
0:07:14 payment is final. So that’s another another access. And then the final one when we talk
0:07:22 about scalability of computation is what is the cost per instruction? How much do I have
0:07:26 to pay for that transaction? How much do I have to pay for just an arbitrary computation
0:07:31 on Ethereum or on some of the more general platforms for for computation? And the reason
0:07:35 obviously that all of this matters is because the kinds of applications that we want to
0:07:44 build will just require far greater scalability and also will require far lower cost to really
0:07:49 to really work. And I think it may be helpful to just exemplify what those applications
0:07:53 are. I think some of the some of the things that we we are seeing already in the world
0:07:59 of of Ethereum kind of the Ethereum ecosystem is maybe the richest so far in terms of actual
0:08:04 developer activity on top. So we’ve seen kind of the emergence of this parallel financial
0:08:12 world where you have things like like stable coins, which are price stable cryptocurrencies
0:08:18 that have some logic that modulate the supply of the of the token to keep it stable to some
0:08:23 external reference like the US dollar. And then on top of that, people build things like
0:08:27 lending platforms and they build they build things like derivatives platforms, they build
0:08:33 things like decentralized exchanges where you can exchange tokens or exchange crypto assets
0:08:38 without depending on some central exchange. So all of these things that’s like one one
0:08:43 example one trend that is already happening among among many other trends that we can
0:08:48 talk about other examples later review if you think it’s helpful. But all of that depends
0:08:59 on far greater throughput, far lower latency to finality and far lower cost per per instruction.
0:09:04 It’s already just with this initial activity, we’re already seeing the limits of the current
0:09:09 of the current technology. So it’s an open problem. How do we increase throughput? And
0:09:13 for that particular question, people people like what one of the things that matters the
0:09:19 most is the delay in propagation of messages in a distributed system. That’s what ends
0:09:28 up dominating the cost of that particular problem. So people talk about the block time
0:09:34 in crypto was like how much time do you have to wait before you can append a new block
0:09:39 to the blockchain and blocks usually contain computations they contain transactions. So
0:09:44 you can lower the amount of time that you have to wait for new blocks to come along,
0:09:50 then you will process more transactions and more computations per per unit time than you
0:09:55 would otherwise. And so propagating messages in the network is is a is the dominant factor
0:10:00 is that’s what makes it slow to sort of finalize a transaction. Exactly. And the reason for
0:10:04 this is that we are building a distributed system. And so if you think about it, what
0:10:09 is the difference between a distributed system and one that’s just centralized? And that
0:10:14 is that there’s distance between the different nodes that are participating in the system.
0:10:18 So the key difference is that now there’s this additional communication cost between
0:10:24 the different nodes in the system. And that cost is also is significant because it’s bounded
0:10:29 is it like the lower bound on it is the speed of light. You cannot get faster than the speed
0:10:35 of light. So so it provides it causes this this kind of lower bound as to how perform
0:10:40 it can possibly be. And you can only get so clever before you reach that that that kind
0:10:46 of lower bound. But it is the case that today we’re still far far from from that lower bound.
0:10:49 There’s still a lot of room for improvement. Yeah, I mean, people in general pretty impatient.
0:10:53 I remember when the chip and pin system started getting deployed here in the United States,
0:10:57 it was just a couple of years ago, right? And then the you didn’t start your credit
0:11:02 card, it would take like five seconds, right? And that was a lot slower than the swipe. And
0:11:06 people were like, this is never going to work. I’m not waiting five seconds for my credit
0:11:12 card to clear. And so maybe talk a little bit about, you know, sort of what is the propagation
0:11:17 delay today? And then what’s practical to get to given sort of speed of light limitations?
0:11:20 And then what’s the target? And how do we get there?
0:11:28 Yeah, for sure. So today, I mean, there’s this there’s enormous tension between, well,
0:11:33 to back up a little bit. So there are two things that matter here. One of them is how
0:11:38 much time does it take to send a message between two two points in space? But then there’s
0:11:46 also the problem of what what influence does the size of the message have on on that amount
0:11:51 of time? And so there’s basically the two angles here are latency and bandwidth, latency
0:11:55 being the amount of time that it takes to send a message bandwidth, being how much how
0:12:00 much data can you actually fit through the pipe per unit of per unit of time. So there’s
0:12:05 a tension in this space, you can see this reflected in in say the Bitcoin like block
0:12:14 size debate. Yeah, between sort of the throughput that you can get out of a network and the
0:12:19 propagation delay that that is caused by say increasing the block size. So in the case
0:12:24 of Bitcoin, people people were talking about doubling the block size from one megabyte to
0:12:29 two megabytes. So that would increase the throughput of the Bitcoin blockchain, because
0:12:35 now you can fit twice as many transactions, and the blocks will still come at a 10 minute
0:12:42 cadence. But that would increase the propagation delay for those blocks, which would cause
0:12:46 certain miners to no longer really be able to participate because they won’t get the
0:12:49 block in time. So they would have to they would eventually end up falling out, you’d
0:12:55 end up with a more centralized system. So we see here there’s a trade off between performance
0:12:59 and decentralization, assuming that you want to keep security constant, you don’t want
0:13:04 to suffer. You still need the trust, right, you can give up on the trust, you can’t allow
0:13:10 double spending, right. So I always thought that the reason say Bitcoin was slow was the
0:13:15 proof of work was so computationally demanding. Is that still the case, or is that is that
0:13:16 a solved problem?
0:13:21 So it’s a very, very good point. So we’ve been talking so far we’ve been talking about
0:13:29 the throughput of of instructions for a blockchain, which is one of the three different dimensions
0:13:35 for scalability of compute. The third one was the cost of instruction of an instruction
0:13:41 how much does it cost to have a transaction be processed. So the cost of proof of work
0:13:49 is what ends up driving the cost of an instruction so far, so so high. Rather than I mean, like
0:13:56 so there are, there are like various different lines of work. There’s the line of work that’s
0:14:01 trying to improve the propagation of messages and to make that more efficient. So there
0:14:07 are companies like blocks route, which are building like a kind of a content delivery
0:14:13 network which has advanced computer networking technology that allows miners to propagate
0:14:18 their blocks to other miners very efficiently. And so that’ll help with the propagation delay,
0:14:19 which will help with the throughput problem.
0:14:23 So the new generation CDN that is optimized for crypto.
0:14:27 And in fact, they call it a blockchain distribution network, a BDN.
0:14:28 Oh, got it.
0:14:31 And so that’s an interesting angle and that operates at layer zero is like the networking
0:14:36 layer below the blockchain layer. And it can help any blockchain project, any blockchain
0:14:42 project that builds on top of it will benefit from faster propagation of messages.
0:14:45 And the classic internet definitely needed this. Like it’s impossible to imagine the
0:14:50 internet without a CDN, right? You’d be waiting a lot longer for almost anything without that
0:14:54 layer of infrastructure. So that makes sense. So there’s sort of a CDN layer.
0:14:57 Yeah. And then, and then there are people who are working on this latency to finality
0:15:01 dimension, which we also talked about, which is how much time do you have to wait before
0:15:10 your message or your update, your computation is final. And so that is a consensus problem.
0:15:16 How do we agree that the update is final? How do we agree that something can no longer
0:15:23 be reversed? So proof of work is a probabilistic consensus algorithm in that there is always
0:15:28 some probability that whatever update to the ledger was performed could be reverted at
0:15:35 some point in time later. And the key aspect there is that the more time passes, the less
0:15:40 likely it becomes that that update gets reverted. But it’s always probabilistic. And this is
0:15:43 why you kind of have to wait 60 minutes before you know that it’s final, because that’s
0:15:48 the point at which that probability becomes so minimal that you can effectively trust
0:15:49 that it won’t be reverted.
0:15:54 But there is innovation in consensus algorithms that are better than that, that are not probabilistic
0:16:01 and that are actually deterministic and are final on a far shorter time span.
0:16:05 Yeah. And you need both, right? You need non probabilistic and you need fast, right?
0:16:10 So I, you know, everybody talks about the, the Ethereum contract things underlying things
0:16:16 like, hey, when you go rent an Airbnb, that lock will open because I know it’s you. There’s
0:16:20 a smart contract that governs the, oh, you’re allowed to stay here tonight. No one’s going
0:16:26 to wait there 60 minutes for that. And so is it feasible? Are we on a path to basically
0:16:31 enable use cases like that where like, I’ve got my smart key and I’m in front of the Airbnb
0:16:35 and like in seconds that thing is going to open because the contract clear, is that possible
0:16:38 or is that not quite possible yet? We don’t know the path.
0:16:43 I think, I mean, we do see, we do see a path. I think that, so given the improvements on
0:16:49 the networking layer with companies like BoxRoute, improvements on the consensus layer with companies
0:16:53 like DFINITY and Ethereum 2.0 and Cosmos and Polkadot, there’s like a large number of people
0:17:00 working at that level. And then finally, improvements on the cost per instruction. Similarly, proof
0:17:05 of stake and other consensus algorithms don’t use the expensive proof of work that the original
0:17:09 blockchain’s used. And so that can also come down as you can see a world where, where this
0:17:15 does come down to a degree that it becomes fairly practical for everyday use for things
0:17:23 like kind of like a quick lighthearted interactions between people or between people and machines.
0:17:29 And so I think that that is certainly possible and I think we’re on our way. But I think
0:17:35 it’s worth noting, decentralized systems will always be more expensive and less performant
0:17:41 than centralized ones. There’s just an inherent tradeoff there and there’s an inherent cost
0:17:47 to decentralizing a computer system. And so it won’t replace everything. There will
0:17:53 be applications that will always make sense for a centralized world, for a centralized
0:17:57 world. And there will be some applications for which decentralization very much does
0:18:03 make sense. And those are the ones where trust is the key differentiator, where trust is
0:18:08 the bottleneck to scale. That’s where decentralized systems will shine.
0:18:12 I want you to give me a couple of examples of sort of applications where trust is the
0:18:18 key as opposed to performance or cost or whatever. But before we do that, I want to talk about
0:18:23 this notion of proof of work, transitioning to proof of stake, because this is super important.
0:18:30 I read all the time that Bitcoin mining is consuming some known fraction of the world’s
0:18:35 electricity because the math is so hard to actually do one of these proof of works, right?
0:18:41 It has to go similar to public key cryptography. And so what’s happening here? Like, how do
0:18:45 we get on a path where we’re not consuming all the world’s electricity doing these proofs?
0:18:51 Yeah. So the key goal for crypto networks is to build trust in a way that is bottom-up
0:18:55 and that does not depend on some central authority. And so as a result, you have to figure out
0:19:01 a way to make the network be self-policing and to kind of have an incentive structure
0:19:07 that makes its members police one another in a way that the entire network kind of works
0:19:12 and sort of proceeds according to people’s expectations. So in a sense, you have to make
0:19:20 it the rational equilibrium to play by the rules of the game rather than to defect and
0:19:26 profit in some way that is against the rules and that kind of figures out a way to game
0:19:31 the system. And so in Bitcoin, one of the key ways this worked was through this proof
0:19:35 of work. So maybe talk a little bit about how did it work? Why did it consume so much
0:19:40 electricity? And then where are we going? So the key problem that crypto networks have
0:19:46 to solve is figuring out who gets to participate because there’s no one central party who is
0:19:50 able to decide who gets to participate and who doesn’t. That’s the entire point we want
0:19:56 to do away with that. And so proof of work does this by requiring every participant to
0:20:01 compute an expensive proof of work. It’s a computation that’s done on top of every block
0:20:05 that they want to add to the blockchain. So that’s an extrinsic resource that they have
0:20:11 to come across, they have to procure to be able to participate and it prevents any one
0:20:18 person from completely monopolizing the system and from having unilateral ability to modify
0:20:22 the underlying blockchain. That of course is very expensive because you have to come
0:20:27 across all of this computational power in order to participate. So proof of stake says
0:20:31 something different is instead of making the resource that you have to come across and
0:20:37 you have to procure be extrinsic to the system, why not make it something that’s intrinsic
0:20:44 to it, namely why not make it a crypto asset, why not make it a token that you have to own
0:20:50 in order to buy your participation in the system. So what proof of stake does is it
0:20:57 says if you own 2% of the tokens in the network, then by and large on average you’ll have 2%
0:21:01 of the say in what blocks get to make it onto the blockchain and which ones don’t. But now
0:21:09 because of the asset itself, the resource that you need to be in procession of in order
0:21:14 to participate, because it’s no longer extrinsic to the system, it’s no longer a resource that
0:21:19 is sort of a physical resource like electricity and rather it’s entirely virtual, now the
0:21:25 cost of actually participating in consensus and making the entire network work in real
0:21:31 terms comes down dramatically. And it’s just secure or at least theoretically can be made
0:21:37 just to secure and that’s a controversial statement but I will sort of stand by it.
0:21:42 But it’s less expensive and so from a cost per instruction, on a cost per instruction
0:21:47 basis it’ll be much more performant than a proof of work system.
0:21:51 So if I could restate that it sounds like in Bitcoin with its proof of work I had to
0:21:56 bring electricity, consume the electricity, do this hard math and that was how I sort
0:22:00 of entered the system and participated and my reward is a minor for burning all this
0:22:06 electricity as I get paid in Bitcoin. In proof of work I’m bringing basically tokens, sorry,
0:22:10 in proof of stake I’m bringing the tokens themselves, I’m not consuming electricity I’m just bringing
0:22:17 the tokens themselves and I’m by virtue of my ownership of the tokens I can participate
0:22:23 in a proof of stake that delivers the same trust properties as proof of work without
0:22:25 burning all the electricity.
0:22:26 Exactly.
0:22:31 Got it. Got it. So it sounds like all of these things need to come together for us to build
0:22:36 sort of distributed compute in this new world, right? We need the new CDNs, we need this transition
0:22:40 to things that look like proof of stake so we’re not consuming all this electricity.
0:22:46 Any other big innovations that need to happen in this space to bring the transaction cost
0:22:48 down and the transaction speed up?
0:22:54 I think those are the big ones. So we’re talking about the three pillars of computation, well
0:23:00 the three pillars of scalability of computation, there’s throughput, there’s latency and then
0:23:03 there’s a cost per instruction and so we kind of addressed all three, there’s companies
0:23:06 that are working in all three and I think these are very much still open problems and
0:23:12 there’s just a lot of green field for exploration and so I will tell you Google engineer, this
0:23:17 is where it’s exciting, this is where your skills as a sort of distributed systems engineer
0:23:23 or machine learning expert can kind of leverage those skills to figure out some of these open
0:23:24 problems.
0:23:29 Got it, so if I’m motivated by doing things like I want to create a better TCP/IP, I want
0:23:34 to create a better HTTPS and oh man I’m just too late to the party, like I arrived when
0:23:38 all those protocols were already settled, like you’re saying this space is for me because
0:23:42 a lot of the problems haven’t been settled yet, right? A thorny problem, unsettled, big
0:23:43 world impact.
0:23:48 Yeah, even if it’s been 10 years since the publishing of the Bitcoin white paper, this
0:23:55 is still very early days, because I think that it’s only recently that people have begun
0:24:00 to conceive of blockchains as computers as opposed to just payment systems, so the emergence
0:24:09 of Ethereum was in 2014 and it’s only really been five years or four years really of people
0:24:14 thinking of blockchains in this way and so it’s very early days, the space is very nascent
0:24:17 and there’s just a lot of work to do, great.
0:24:21 So perfect time, why don’t we sort of move on to part two, so we’ve talked about distributed
0:24:28 compute, let’s talk about distributed storage and so we started with the Google photos example,
0:24:34 I kind of trust Google to have all my storage, all my photos, but to do that they have huge
0:24:38 servers with lots of hard drives in them scattered around the world, it’s pretty expensive and
0:24:44 so if I was a startup trying to mount a frontal assault against that, I kind of only have
0:24:50 two choices, one is raise like a trillion dollars and try to duplicate their infrastructure,
0:24:58 put data centers everywhere, points of presence, or I could do what the distributed crypto community
0:25:02 is trying to do which is convince you to lend me a bit of your hard drive space.
0:25:09 The key reason that we need a decentralized layer of storage is because it itself will
0:25:14 be a foundational building block for this decentralized world computer that we’re talking
0:25:19 about and so in order for some of these applications that we’ve talked about that are not really
0:25:26 possible to build on top of a centralized architecture to really work, we need the full extent of
0:25:31 a computer that works in this way and so if we had a centralized storage layer instead
0:25:38 of a decentralized one then that would be the weakest link, it would dilute the promise
0:25:44 of the decentralized layer of computation if you don’t have all of the pieces themselves
0:25:49 being decentralized and so that’s why it’s important because we want to enable these
0:25:56 applications that kind of depend on decentralization for trust and it’s not so much to compete
0:26:02 head on with Amazon because the economics are different, as we said like decentralizing
0:26:08 a system always comes with the cost and so it won’t make sense for just storing your
0:26:14 photos if storing your photos is something that Amazon/Google can do and if there’s
0:26:19 no trust dimension to doing that, maybe if you care deeply about your photos not ever
0:26:24 being seen by anyone but yourself or by anyone but your close friends then maybe you can
0:26:28 imagine using a different kind of architecture, one that’s maybe more decentralized, but for
0:26:32 that kind of use case I imagine sort of the centralized data center model, it works very
0:26:38 well and they’re not paying the decentralization tax and so it’s always going to be cheaper
0:26:45 for them to store your photos but there are interesting opportunities, so for example
0:26:48 there’s a project called Filecoin, there are a number of others too that are working in
0:26:52 the same space, one of them is called SIA, another one is called StoreJ and they are
0:26:59 trying to build decentralized marketplaces for storage, so the idea is yes I can rent
0:27:05 some of your idle storage space on your laptop and pay you for that storage in Filecoin and
0:27:11 the reason that that’s now possible is because I can now trust that you will actually store
0:27:15 my files and you can trust that I will pay you for that storage even if we’re complete
0:27:20 strangers and reside across the world from one another because of the cryptographic guarantees
0:27:23 of the underlying protocol.
0:27:29 And so that’s important because previously without crypto and without blockchains that
0:27:34 would have been a very difficult interaction to coordinate and it would have been very hard
0:27:39 for us to establish trust from halfway across the world and make that exchange happen.
0:27:44 So it’s a marketplace that now emerges where previously it couldn’t have and it gives us
0:27:54 this property that no one controls this sort of layer of storage and we can use it in conjunction
0:27:58 with a computation layer to build applications that are fully decentralized and that are
0:28:07 unstoppable and kind of run in their own right and therefore command greater trust than
0:28:09 applications that are centralized.
0:28:13 So I remember before the crypto craze there were definitely startups that were trying
0:28:17 to do that do this exact thing which is sort of let’s share hard drive space.
0:28:22 I remember there were backup companies that basically say your price would be I’ll make
0:28:26 up a price $20 per gig per month but if you contribute your own hard drive space your
0:28:31 price is $10 per gig a month or whatever and making up those numbers but they never really
0:28:32 got to scale.
0:28:39 So what are the advantages of doing this with sort of a cryptographic protocol as their
0:28:43 intermediary as opposed to just hey there’s a company and there’s a service and there’s
0:28:48 a price chart and please participate right and we’re going to sort of transact value
0:28:50 and fiat currency.
0:28:55 Yeah, well I think that the key difference is that those companies were operating on
0:29:03 the assumption that the value at here is an economic that you actually that is kind of
0:29:12 like Uber you’ll tap into all of this unused storage space that previously was inaccessible
0:29:17 that you’ll offer that at a cheaper rate but I think that in the end because storage is
0:29:24 the most commoditized of computational resources and because there’s just so strong economies
0:29:31 of scale that benefit companies like Amazon as they build data centers the economic argument
0:29:32 just doesn’t doesn’t work.
0:29:37 So the reason that we need decentralized storage networks is not because they’re going to reduce
0:29:43 the price of storage by orders of magnitude at least for most kinds of files and for most
0:29:47 use cases I don’t believe that that’ll be the case.
0:29:53 The value proposition is that again we now no longer have this central entity that’s
0:29:57 controlling the storage on this network and so for the kinds of applications that depend
0:30:02 on that the kinds of applications that really cannot be built unless you have that you just
0:30:04 have no other option.
0:30:05 Yeah.
0:30:09 So you would pay the additional cost you pay a higher price for storing your files and
0:30:12 file coin because that matters.
0:30:17 Is there a privacy argument here which is the because it’s decentralized for instance
0:30:23 there’s nobody to give a government subpoena to to say I want to see your files.
0:30:29 I think privacy comes into it to some extent but I think it’s a little bit orthogonal because
0:30:33 you can imagine encrypting your files before storing them in a centralized service.
0:30:34 Yeah.
0:30:35 Fair.
0:30:40 So you there are ways of building privacy into into sort of existing centralized storage
0:30:41 networks.
0:30:42 Okay.
0:30:46 And then what are some of the challenging computer science things about building these
0:30:51 and so if there was proof of work for computation what are the proof of in the space.
0:30:52 Yeah.
0:30:55 Well the biggest one is trusting that the people who are claiming to be storing your
0:30:58 files actually are storing your files.
0:31:04 So there’s this line of work that’s been spearheaded by the people at Filecoin and by
0:31:11 Stanford’s cryptography lab, Dan Bonet’s lab and sort of people like Confisch and Benedict
0:31:18 Boone’s underneath him have done a lot of work on figuring out how to create cryptographic
0:31:20 proofs of retrievability.
0:31:26 How can I prove to you that I actually am storing the files that I’m claiming that I
0:31:27 am storing.
0:31:28 Right.
0:31:29 And it’s super interesting.
0:31:33 It’s extremely cutting edge and it’s basically at the heart of how you make a system like
0:31:34 this work.
0:31:35 Yeah.
0:31:39 So you basically have to catch the pretenders which is you don’t want somebody to be able
0:31:44 to say yes I’ll store your files and then not actually store them right because it would
0:31:49 be cheaper for them not to store them right and so these sort of proofs of retrievability
0:31:51 are basically ways to catch the pretenders.
0:31:52 Exactly.
0:31:53 Right.
0:31:54 And it’s sort of a mathematical fashion.
0:31:56 So yeah, you actually had a conversation with Ben Fisch on this.
0:32:01 So for people who are interested in exploring this topic further, there’s a couple YouTube
0:32:02 videos.
0:32:03 Awesome.
0:32:04 So, okay.
0:32:05 So we’ve talked about distributed compute.
0:32:07 We’ve talked about distributed storage.
0:32:10 I guess the third leg is now networking like what’s happening here.
0:32:14 Actually this kind of reminds me, do you remember the company Phone?
0:32:20 This is sort of back in 2006 and the idea was I could buy a Wi-Fi router from this company
0:32:23 called Phone and then I could sort of do one of two things with it.
0:32:29 One, I could sort of offer it in Linus mode which is I gave it away free Wi-Fi access
0:32:30 right.
0:32:34 Anybody that came to my house with my Wi-Fi router, you could access my Wi-Fi for free
0:32:40 and then in exchange I could access anybody else’s Wi-Fi access point for free.
0:32:44 So that’s mode one or I could be in bill mode and in bill mode basically I would say look
0:32:52 my Wi-Fi is available to you but you’re going to rent it for two bucks and in exchange for
0:32:56 that I would have to pay to access other people’s Wi-Fi.
0:33:01 So I could be in open source mode or I could be in rent seeking mode and it was this attempt
0:33:08 to basically create a distributed ISP out of millions and millions of wireless.
0:33:09 So it’s something wireless access points.
0:33:12 Is something similar going on in crypto today?
0:33:13 Absolutely.
0:33:19 So the difficulty with those efforts has often been just the problem of density and the problem
0:33:20 of incentives.
0:33:26 So how do you get enough people to offer up hardware that forwards packets and provides
0:33:32 bandwidth within a particular geographic region to make it make sense and to make it work
0:33:39 and to be at all competitive with sort of the more kind of centralized top-down internet
0:33:41 backbone infrastructure that we rely on.
0:33:46 And so there are a number of projects that are, it’s very early because this is actually
0:33:48 probably one of the hardest problems in this space to tackle.
0:33:52 How do you decentralize even the networking layer, the communication between different
0:33:58 nodes and to not have it depend on centralized internet infrastructure.
0:34:05 So people are talking about incentivized mesh networking protocols where you can earn cryptocurrency,
0:34:13 you can earn the asset that’s native to a particular protocol by setting up a router.
0:34:17 This router can be a normal router that just forwards packets but it could also be wireless
0:34:24 and provide a different layer of connectivity that otherwise, that essentially makes the
0:34:31 networking layer more robust and more resistant to censorship and perhaps even more performant
0:34:35 if you have just greater connectivity to the people you want to interact with.
0:34:40 So yeah, I think this is one of these problem areas that’s fairly far out because it kind
0:34:46 of depends on the other two building blocks and it has its unique challenges because now
0:34:49 we’re talking about bringing hardware into the picture.
0:34:51 That’s always a whole other kind of worms.
0:34:56 But it is very interesting and I think in the end, it’ll also be a piece of the puzzle.
0:35:03 So that’s one angle to it, it’s decentralizing networking and then the other angle is making
0:35:08 networking itself just more performant for the use cases of decentralization.
0:35:12 So we talked a little bit about the CDNs for blocks.
0:35:16 So that kind of falls into that category, into this category as well.
0:35:17 Got it.
0:35:20 So those are the key ingredients that you need to build a computer, right?
0:35:24 You need compute, you need network, you need storage and it looks like there’s sort of
0:35:27 efforts underway in all of these things.
0:35:32 Let’s assume for a second that time has gone by and protocols and sort of Darwinian fashion
0:35:38 have competed and a couple winners have emerged and these things look more like solved problems.
0:35:43 So now the exciting opportunity is, okay, now we can build killer apps on top of the
0:35:47 blockchain computer and so maybe talk to me about what is the community most excited about?
0:35:50 What kinds of apps are you going to build?
0:35:53 Because as you’ve been pointing out, it’s not going to be the straightforward replacements
0:35:55 for the things that we know and love today.
0:36:01 It’s not like instantly the replacement for Airbnb or Google Photos or Lyft because those
0:36:04 systems don’t have to pay the decentralization tax.
0:36:08 It’s probably going to be another class of apps at least to begin with.
0:36:12 That is the killer question and I think as with any new technology it is very difficult
0:36:16 to predict what applications will be the most impactful.
0:36:22 I think one reason to believe that the kind of innovation that we’ll see will be enormous
0:36:28 is that everything happens, all of the code that’s written in the space ends up being
0:36:29 open source.
0:36:31 And so as a result the ideas are out there.
0:36:36 People share their ideas with other teams, other teams sort of build on top of one another’s
0:36:37 ideas.
0:36:43 And so the kind of innovation that we’re likely to see is just combinatorial in nature and
0:36:49 likely more explosive and will accelerate more quickly than it has for previous waves
0:36:52 of computing and previous waves of technology.
0:36:59 We do have this kind of decentralized world computer that is a kind of computational fabric
0:37:04 on top of which applications can run and it is unified in that one application can easily
0:37:06 talk to another.
0:37:10 Then we have the possibility of composability of applications.
0:37:16 So not only do we have the sharing of ideas that are just available to people because
0:37:22 by virtue of being open source, but we also have the actual composability of running code.
0:37:25 Code that runs on top of this computational fabric that builds on top of the code that
0:37:27 other people have built.
0:37:32 And this kind of composability will just fuel the flame of combinatorial innovation even
0:37:33 further.
0:37:38 So I feel like the kinds of applications that we’ll see as a result are fundamentally
0:37:40 impossible to predict.
0:37:44 But I will say, I think the kinds of things that we’ve started to see, the kinds of applications
0:37:48 that seem to be working so far, and it’s still very early and they’re working only in kind
0:37:56 of niche, within niche communities, are ones where trust is the bottleneck to scale.
0:37:59 So I think the most obvious one began with Bitcoin.
0:38:08 It’s attempting to be money and the only way that you would trust a program that maintains
0:38:15 a ledger of tokens that claims that those tokens should be money is if that ledger isn’t
0:38:20 in the control of any one entity or any one individual.
0:38:27 I guess you would trust the central government, maybe, but you would not trust a company to
0:38:28 do that.
0:38:31 So you wouldn’t have been able to build Bitcoin on top of Amazon.
0:38:35 Bitcoin is like one example of an application that you can build and a bunch of the applications
0:38:41 that have worked so far in the Ethereum ecosystem primarily have been financial in nature, have
0:38:47 been things that build on top of that initial idea, so things like lending platforms, derivatives,
0:38:54 exchanges, things that depend on trust for them to really take off.
0:38:59 But we’ve also started to see other applications that benefit from this feature.
0:39:05 I think gaming is an interesting one where you can imagine taking the existing world
0:39:09 of gaming, you can imagine, for example, a world of Warcraft where people have significant
0:39:14 investment in their character and in the gear that they have and in the lives that they
0:39:22 live within these games, taken to a whole other level where you actually own your character
0:39:27 and you own the gear for your character and you can take your character and gear out of
0:39:33 the game and maybe into another game because you now have this interoperable trustworthy
0:39:36 fabric of computation that other developers can build on top of.
0:39:43 So that kind of investment in your personality and in your character in the game is unlike
0:39:44 what we’ve seen in gaming before.
0:39:48 So this could take gaming just to a whole other level and that could be a very interesting
0:39:49 set of applications.
0:39:53 But it very much depends on these three building blocks, we need scalability before gaming
0:39:55 can really take off.
0:40:00 And we’ve seen examples of this, I think CryptoKitties is one where people became very invested
0:40:08 in owning this digital collectible, which is something that is fundamentally new.
0:40:13 Never before would you be able to directly own something that’s digital is the first
0:40:14 time that that’s possible.
0:40:18 I love this idea of being able to take sort of a high level character that I’ve developed
0:40:23 in one game and moving it to another because, you know, look, essentially a high level character
0:40:27 in say, World of Warcraft is the ultimate proof of work, right, which is I had to do
0:40:31 a lot in order to get this character to be super high level.
0:40:34 And now I’m kind of stuck in World of Warcraft, which is great if I want to play more World
0:40:38 of Warcraft, but like it’d be awesome if I could take my proof of work and move it to
0:40:39 another system.
0:40:40 Absolutely.
0:40:41 Yeah.
0:40:46 Yeah, I mean, I think there’s a story about how Vitalik, part of Vitalik’s inspiration
0:40:51 for starting Ethereum is having, I’m not sure which game it was, but it was like some
0:40:58 gaming platform that revoked his ownership over, over like a key, a key item in the game.
0:40:59 There it is.
0:41:02 That made him like, made him like so, so mad, right.
0:41:06 This is the problem with centralization, right, which is you have a company operating
0:41:08 a game and they can do whatever they want with the game, right.
0:41:13 One change to the terms of service and all of a sudden your proof of work is basically,
0:41:14 it’s invalid.
0:41:15 Exactly.
0:41:16 Yeah.
0:41:17 That would make you mad.
0:41:20 Well, if you think about sort of trust being the key feature, I mean, there’s so many
0:41:26 sort of, you know, properties that we think about on the internet that are essentially
0:41:27 sort of brokers of trust, right.
0:41:33 So LinkedIn is sort of the trusted entity to manage your resume and present your resume.
0:41:37 And eBay is sort of the trusted marketplace where the sellers or Etsy is sort of the trusted
0:41:42 place where you sort of send money and expect stuff, right.
0:41:49 There’s Airbnb and Lyft, and so like trust seems like a super powerful primitive for
0:41:50 creating killer apps.
0:41:51 Yeah, definitely.
0:41:58 And I think the web 2.0 world has figured out how to bootstrap trust in a way that
0:42:05 depends on things like identity and reputation, where there’s social capital associated with
0:42:08 your track record on the internet.
0:42:16 So things like reviews on Yelp or things like reviews on or stars on Uber or number of likes
0:42:21 on Twitter and number of followers on just generally social media.
0:42:28 These things are, this is like the mechanism for trust that’s used in web 2.0.
0:42:30 And I think crypto is orthogonal to that.
0:42:36 Crypto today has no sense of identity, that people are pseudonymous, people can create
0:42:38 multiple addresses and pretend to be different people.
0:42:43 People can abandon identities that maybe have a bad reputation and move over to new identities
0:42:45 that don’t.
0:42:53 And the entire fabric of trust, therefore, depends not on social capital but rather on
0:42:54 financial incentives.
0:43:00 So it’s this orthogonal layer where you’re incentivized to behave honestly because there
0:43:03 is real money at stake.
0:43:08 And if you lie or if you behave in a way that’s not in accordance to the rules of the protocol,
0:43:10 then there’s something that you will lose as a result.
0:43:17 So there’s sort of financial capital and financial incentives as a way of bootstrapping trust
0:43:20 and then there’s social capital as a way of bootstrapping trust and I think that’s one
0:43:29 of the key differences between the web 2.0 world and the now web 3.0 crypto-enabled world.
0:43:34 And what will be very interesting to see is the two models coming together.
0:43:36 So that’s something to kind of look out for.
0:43:41 And it seems like Keybase is sort of an early attempt at that, which is on the one hand
0:43:46 you had all of these private keys that represented you in these cryptographic networks and on
0:43:50 the other hand you had sort of your Twitter profile and your LinkedIn profile and your
0:43:56 Facebook profile and Keybase sort of bridged them.
0:44:00 What other things do you think we will see in this space of sort of making identity more
0:44:01 seamless?
0:44:02 Yeah.
0:44:04 I think Keybase is a key one.
0:44:13 A key problem is how do you map a real human individual to a public key in a way that is
0:44:15 trustworthy and in a way that you can rely on.
0:44:21 So Keybase is this and I think it’s a very apropos example where you can use your existing
0:44:26 web 2.0 world identity to bootstrap your web 3.0 identity.
0:44:31 You can use your Twitter account and your Facebook account and your GitHub account and
0:44:39 your website and point them all to this cryptographic identity that you can then use to interact
0:44:43 with other people in the sort of crypto anonymous world.
0:44:47 And they can verify that that really is you because of the cryptographic assurances of
0:44:54 those connections between Twitter and so on and your public key.
0:44:55 You can take that further though.
0:45:00 I think once you do have identity in the crypto world and I think it is an unsolved problem,
0:45:05 Keybase is the first kind of attempt, but there’s still a lot to do there.
0:45:10 Once you have a solid layer of identity within crypto, that also doesn’t sacrifice privacy.
0:45:15 So it’s worth noting there’s a big trade-off there, like if you have strong identities
0:45:19 and you have less privacy and it’s kind of difficult to come to the right balance between
0:45:22 the two and it will vary per application.
0:45:26 But once you have a good system for that, then you can start building reputation systems.
0:45:30 You can even imagine like a page rank style algorithm for reputation.
0:45:41 If I trust Frank and Frank trusts Joe, then I kind of indirectly trust Joe.
0:45:46 And you can imagine kind of taking this to the whole other level to really enhance the
0:45:53 kind of trust that emerges from financial incentives with social capital and with reputation.
0:45:54 That’s very powerful.
0:45:58 There’s some of this even in things like the Facebook marketplace today, which is you see
0:46:02 somebody listing cheese or a bike or whatever and you’ll see, “Oh, this is a friend of Ali.”
0:46:06 And then that brings a level of trust to that transaction that wouldn’t otherwise exist.
0:46:07 Exactly.
0:46:12 And one of the reasons this is so important for crypto is that today, every interaction
0:46:16 in the world of crypto tends to be very transactional.
0:46:20 You don’t even know who you’re dealing with and so it really is about that one transaction.
0:46:27 It’s one-off and whenever there’s conflict, it’s a one-off prisoner/dilemma style game.
0:46:30 Whereas if you had identity and if you had reputation, you could turn all of those one-off
0:46:35 prisoner/dilemma style games into iterated prisoner/dilemma style games, which are far
0:46:36 easier to solve.
0:46:38 You have like the long view of relationships.
0:46:45 You can have a track record and rapport with the people that you interact with if you only
0:46:47 had that other layer.
0:46:50 So so many of the problems, the game theoretical problems that need to be solved for crypto
0:46:54 to work that are so hard to solve will become easier once you have this additional lever
0:46:55 to play with.
0:46:56 Yeah, that’s super interesting.
0:46:57 Right.
0:47:01 So every prisoner/dilemma type game is sort of assume perfect strangers go in and now
0:47:05 we have to sort of mathematically model what will happen, not knowing anything about them.
0:47:06 Exactly.
0:47:09 But if you threw me and you into a prisoner’s dilemma, right, like we’ll have much higher
0:47:14 fidelity predictions about what each other like, “I’m not going to squelch on you, Ali.
0:47:15 He’s a friend of mine.”
0:47:18 Especially if we know that we’re going to be in a similar kind of game in the future.
0:47:21 And it’s like if we cooperate now, then we’ll build rapport and then it’ll be easier for
0:47:24 us to cooperate in the future.
0:47:29 And if we cheat each other now, then we will kind of ruin that opportunity later on and
0:47:32 make it harder for us to cooperate down the line.
0:47:33 Super interesting.
0:47:38 So one thing before we go, I want to talk a little bit about governance, because today
0:47:43 it seems like there’s a lot of conversation in the tech community about, “Gee, maybe the
0:47:47 tech giants have gotten too big,” right, because with the stroke of a pen and one change in
0:47:52 terms of service, like all of a sudden, the rules of engagement or the winners and losers
0:47:55 in that environment are dramatically different.
0:48:00 In crypto land, the idea would be let’s not have one company which completely owns their
0:48:05 terms of service control that, there’s going to be sort of a decentralized community.
0:48:10 But we end up with some of the same questions, right, like who gets to change the terms of
0:48:11 service?
0:48:14 How do those changes come about, who proposes them?
0:48:19 So maybe talk to me a little bit about what’s happening in the community as we iterate on
0:48:20 systems of governance.
0:48:25 Yeah, you’re hitting at one of the most fundamental questions in this space, which is that if
0:48:32 you do build a system that is decentralized and that control over it does not rest with
0:48:36 any one individual, then there’s a question, well, how do you go about updating it?
0:48:39 How do you go about changing it in any meaningful way?
0:48:43 If it is going to be a complex system that adapts and evolves over time, this question
0:48:46 most certainly has to be answered in order for any of this to work.
0:48:53 So this is a question of sort of governance of protocols, and there are enormous number
0:48:57 of experiments that people are running, like different kind of approaches.
0:49:04 The canonical and sort of initial approach was out of Bitcoin, which is that essentially
0:49:07 you do just have to coordinate with all of the stakeholders, all of the people who are
0:49:14 running the Bitcoin node software in order to change the protocol.
0:49:18 And in this case, that would be that all miners, all of the people who are running the code
0:49:23 to mine Bitcoin, have to modify their software, and this is a human level process.
0:49:28 You have to call them up or you have to sort of issue an announcement saying that the protocol
0:49:31 is being upgraded and get that to work.
0:49:35 There are other approaches that people are exploring with that are more sort of they’re
0:49:38 formalized and are built into the protocol.
0:49:41 So there’s the idea of being able to vote with tokens.
0:49:50 So if I own a certain stake, a certain amount of the network, then I can use the tokens
0:49:55 that constitute that stake to vote in favor or against proposals that may be made by the
0:49:56 community.
0:50:01 It’s just another approach to decentralize governance that tries to lower the barrier
0:50:03 and tries to make it a little bit more seamless.
0:50:07 There’s an enormous set of challenges associated with that because there are possible attacks
0:50:10 where you can bribe people.
0:50:12 There is the issue of voter participation.
0:50:17 And all of the issues that you see in governance systems outside of the world of crypto, just
0:50:18 an offline government.
0:50:19 Offline?
0:50:20 Who’s going to the election?
0:50:21 How do they vote?
0:50:22 Exactly.
0:50:24 How do we prevent dead people from voting?
0:50:25 Exactly.
0:50:30 These problems have become replicated in crypto as well and they are therefore like fundamentally
0:50:33 difficult problems that have been unsolved for millennia.
0:50:39 So it’s not as if crypto will solve any of that, it’ll just have to figure out the right
0:50:44 mechanisms and the right structures to be good enough and to enable systems that are
0:50:51 decentralized to adapt and to change and evolve while striking a balance between sort of
0:50:53 evolvability and decentralization.
0:50:59 And actually, we did two podcasts specifically on this question, the question of governance
0:51:03 and crypto, that will go much, much deeper and talk about all of the challenges.
0:51:07 So if you’re interested in that topic, I recommend checking those out.
0:51:08 Perfect.
0:51:12 We’ll throw the links into the YouTube video so you can follow them easily.
0:51:15 Well, Ali, this has been super interesting.
0:51:20 There’s so many problems to be solved, there’s so many meeting computer science things to
0:51:25 be had, like how do you prove that I’m actually storing your photos instead of just pretending
0:51:28 to store your photos and collecting the money.
0:51:33 And so I guess the way I think about it is like if you have ever wished that you could
0:51:41 have been like a semiconductor engineer at Bell Labs in the 1950s or a PC enthusiast in
0:51:47 the 1970s and you were like, “I missed the 50s and then I missed the 70s.”
0:51:53 And then like if you wished you were at UIUC with Mark at the dawn of the internet in the
0:51:55 90s, like, “Look, here it is.
0:51:57 This is the new computing platform.
0:51:59 Here is your opportunity.
0:52:00 It’s not too late.”
0:52:05 And these are the times to exactly insert yourself into that conversation if sort of that’s
0:52:09 what you wish you had the opportunity to do is influence some of these protocols, these
0:52:12 incentive systems at the ground level.
0:52:13 Absolutely.
0:52:14 Awesome.
0:52:15 Fantastic.
0:52:18 So that’s it for this episode.
0:52:22 And if you liked what you saw, go ahead and subscribe to the list.
0:52:25 If you have comments, go ahead and leave them down below.
0:52:28 Maybe you could pick one thing that you were super excited about, like what problem do
0:52:32 you wish you could solve as an engineer.
0:52:33 And we will see you next episode.
0:52:42 [BLANK_AUDIO]
Do you sometimes wish you had been born in a different decade so you could have worked on the fundamental building blocks of modern computing? How fun, challenging, and fulfilling would it have been to work on semiconductors in the 1950s or Unix in the 1960s (both at Bell Labs) or personal computers at the Homebrew Computer Club in the 1970s or on the Internet browser at the University of Illinois at Urbana-Champaign (and later Mountain View, CA) in the 1990s?
Good news: it’s not too late. There’s a new computing platform being built today by a vibrant and rapidly growing cryptocurrency community. You might have noticed some of your coworkers and friends leaving big stable tech companies to join crypto startups.
In this episode, which originally appeared on YouTube, a16z crypto partner Ali Yahya (@ali01) talks with Frank Chen (@withfries2) about five challenging problems the community is trying to solve right now to enable a new computing platform and a new set of killer apps:
*Scaling decentralized computing
*Scaling decentralized storage
*Scaling decentralized networks
*Establishing trusted identities and reputation
*Establishing trusted governance models
If you’re a software engineer, product manager, UX designer, investor, or tech enthusiast who thrives on the particular challenges of building a new computing platform, this is the perfect time to join the crypto community.
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
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