Speaker 1: I'm Stacy Marie Ishmael, Managing editor of Crypto for Bloomberg News,
Speaker 1: and this is Bloomberg Crypto at Daily Bloomberg I Heart Podcast.
Speaker 1: It's Thursday, July fourteen. Since the founding document that led
Speaker 1: to the invention of bitcoin first appeared in two thousand
Speaker 1: and eight, Bitcoin's true believers have adopted a philosophy that
Speaker 1: resembles one for all and all for one, or at
Speaker 1: least all who believe in bitcoin. Bitcoin is often mentioned
Speaker 1: as a means to improve financial inclusion or to allow
Speaker 1: people who are oppressed by their governments to maintain or
Speaker 1: reclaim a sense of freedom. In Bitcoin, everyone's equal, or
Speaker 1: so the theory goes, But does that theory match the
Speaker 1: reality of the ecosystem. What we did was a systematic
Speaker 1: origin study of bitcoin, telling the story of how this
Speaker 1: came to be in a very very specific period. That's
Speaker 1: data scientists Alyssa Blackburn. On today's episode, Alyssa and Dr
Speaker 1: Aras Lieberman Eden, an Associate professor of molecular and human
Speaker 1: genetics at Baylor College of Medicine, join me to discuss
Speaker 1: their research into who controlled bitcoin from the very earliest days.
Speaker 1: Alyssa Era's what a pleasure to have you. Thank you
Speaker 1: so much for joining the pod today. Thank you for
Speaker 1: having a son. I don't often get to, you know,
Speaker 1: talk to folks doing the type of research that you
Speaker 1: all have been doing, and I get to talk to
Speaker 1: them even less frequently when it comes to, you know,
Speaker 1: applying that research into the wild world of bitcoin. So
Speaker 1: let's jump right in. Tell me a little bit more
Speaker 1: about your paper and your findings and why you have
Speaker 1: found this to be such a captivating line of inquiry.
Speaker 1: Often times in crypto you hear from kind of two
Speaker 1: camps of people. The first are kind of classic economists
Speaker 1: who do these first principal analyzes um and there's a
Speaker 1: variety of outcomes in that. You have people saying, maybe
Speaker 1: it's I don't really like this term, but like kind
Speaker 1: of a scam, kind of a Ponzi scheme. There's not
Speaker 1: a there's not a there there. And the second group
Speaker 1: tends to be from people who are long on crypto,
Speaker 1: crypto enthusiasts, and there's nothing necessarily wrong with that, but
Speaker 1: both of these are kind of like a priori analyses.
Speaker 1: We are scientists, We are setting the phenomenon of crypto
Speaker 1: and trying to characterize the space, trying to study the
Speaker 1: dynamics of the space and the incentive structure in the space.
Speaker 1: So it's a different kind of analysis that we do,
Speaker 1: and we do not invest in this space as a
Speaker 1: group as scientists, So I kind of want to differentiate
Speaker 1: our work here from kind of other conversations that kind
Speaker 1: of get said about this. So what we did was
Speaker 1: what we sought to do was a systematic origin study
Speaker 1: of bitcoin, telling the story of how this came to
Speaker 1: be in a very very specific period. We kind of
Speaker 1: referred to this as the incipient period of bitcoin, is
Speaker 1: book ended by the launch and when Bitcoin reached price
Speaker 1: periority with the US dollar. In retrospect, this at the time,
Speaker 1: this is not at all foregone conclusion. This is true.
Speaker 1: It was a truly incredible story to us. It's one
Speaker 1: of the most exciting stories of the interface of kind
Speaker 1: of economics and technology. And what we wanted to do
Speaker 1: was paining a very detailed data driven picture of what
Speaker 1: this looked like, who was involved, what did it look like,
Speaker 1: what are the dynamics like, what is the motivation for
Speaker 1: this space, what can we learn from this? And then
Speaker 1: what can we kind of translate and can we make
Speaker 1: this a serious academic story. Can we get beyond kind
Speaker 1: of this like niche crypto conversation and kind of have
Speaker 1: it in a wider, academic, rigorous space. And what did
Speaker 1: you find? First of all, is we did a lot
Speaker 1: of address linking in this space. We used methods that
Speaker 1: may be called DAI leakage fingerprinting to kind of link
Speaker 1: these addresses together. And there are a couple assumptions that
Speaker 1: we were able to make in this very very early
Speaker 1: period that made this process just specific to kind of
Speaker 1: this time horizon. So we were able to recapitulate a
Speaker 1: lot of the early actors. And then once we had
Speaker 1: these users, we were able to do analyses. We were
Speaker 1: able to see how the community changed over time, We
Speaker 1: were able to trace transactions through the network. We were
Speaker 1: able to see what the reality of the system was
Speaker 1: like in contrast to what conversations about the system was like,
Speaker 1: and to kind of explore the gap between those two things.
Speaker 1: The significance of address linking is the following. Right. If
Speaker 1: you look at you know, the Bitcoin blockchain designed to
Speaker 1: be afford identity masking I a pseudonymity. It's the individual
Speaker 1: records relate to addresses pseudonyms. Now, of course, an address
Speaker 1: is not a very interesting thing to study on some level,
Speaker 1: and addresses no incentives and address makes no decisions, etcetera, etcetera.
Speaker 1: If you want to study the sociology of bitcoin, if
Speaker 1: you want to study the economics at bitcoint, you have
Speaker 1: to be able to translate from the space of addresses,
Speaker 1: from collections of addresses to people and the choices that
Speaker 1: people are making and the ways in which people are
Speaker 1: interacting with one another. So that's the role that address
Speaker 1: linking plays in this study. The address linking is not
Speaker 1: there for the purpose of like, gotcha, you know, I
Speaker 1: figured out who you are. In fact, you know, the
Speaker 1: goal is study was not at all two docks anyone, right,
Speaker 1: it's orthogonal is is of no interest to us. But
Speaker 1: the goal of the address linking is to translate the
Speaker 1: blockchain into terms that make it possible to ask questions
Speaker 1: about sociology and economics and game theory and all kinds
Speaker 1: of scientific phenomena that bitpoint can be, but all kinds
Speaker 1: of scientific phenomena that bitcoin can illuminate. And Lissa, one
Speaker 1: of the things that you kind of talk about is
Speaker 1: the like the necessity of persistence in trying to get
Speaker 1: or arrive at those kinds of you know, like insights
Speaker 1: of sociological conclusions that Auras is describing. Can you say
Speaker 1: just a little bit more about how that worked? Sure? Sure.
Speaker 1: So the reality is humans are responsible for coin movements
Speaker 1: on the blockchain. Humans tend to do things differently than computers.
Speaker 1: Humans like round numbers, humans like consolidating their gains. There
Speaker 1: are these little pieces of data um we call them
Speaker 1: data leakage. Some blockchain forens experiment call them like fingerprinting.
Speaker 1: But there are certain behaviors that give insight as to
Speaker 1: who is moving the coins. And if you put these
Speaker 1: all these little pieces of data together, what you get
Speaker 1: our links between addresses. And when you have multiple links
Speaker 1: of different types of leakage from different data sources in aggregate,
Speaker 1: what you can do is put together clusters of addresses
Speaker 1: with a high level of accuracy. And so what that
Speaker 1: means in practices you basically figured out who the first
Speaker 1: sixty four miners were that were responsible. Yes, it means
Speaker 1: we put together the very early mining the agents who
Speaker 1: mind a lot of coin um. The number sixty four
Speaker 1: comes from what is called the Satoshi coefficient, where it's
Speaker 1: the number required for a majority attack to be greater
Speaker 1: than So I want to clarrate that that sixty four
Speaker 1: is over twenty five months, and frequently, at any given
Speaker 1: moment in time, actually a very far far smaller number
Speaker 1: are actually the number of people actively mining and maintaining
Speaker 1: the network at any given point in time. It was
Speaker 1: a very small community. What was the most interesting perhaps
Speaker 1: like behavioral elements of this community, or maybe something that
Speaker 1: neither of you were expecting to find. So one one
Speaker 1: thing that we frequently point out in the paper is
Speaker 1: we do not find evidence of people attacking the chain.
Speaker 1: And one Sutoshi originally described this currency in the initial
Speaker 1: emails to the cryptographic Lesser, in the initial white paper,
Speaker 1: on the original bitcoin website and frequently on the forums
Speaker 1: as not relying in a third party, is being decentralized
Speaker 1: and trustless, and is being anonymous. And some opinions on
Speaker 1: the perceived anonymity of bitcoin have changed over time. I
Speaker 1: mean probably people who are very very familiar with the
Speaker 1: Bitcoin protocol now would not describe as anonymous, They would
Speaker 1: described as studonymous. But Bitcoin was designed at least partially
Speaker 1: to have these pseudonyms to effectuate some level of privacy.
Speaker 1: Bitcoin was also designed to not rely on a third party.
Speaker 1: But what we find frequently, because you have these very
Speaker 1: large minors, not many mining at one time, there's frequently
Speaker 1: in a minor that's functioning as the trusted arbiter of
Speaker 1: the network. We find about five blocks minduring this period
Speaker 1: our mind and majority streets where one minor does in
Speaker 1: fact have a majority control of the network. So what
Speaker 1: I'm hearing you say is that is the literal opposite
Speaker 1: of the idea of decentralization when there isn't supposed to
Speaker 1: be a single centralized entity that's making those kinds of coals. Yes, exactly.
Speaker 1: And what I want to focus on here is the
Speaker 1: gap between the computational ideals and how this was described
Speaker 1: and the actual state of the system, because it's it's
Speaker 1: a little bit easy to say, now, well this is
Speaker 1: a ten year old data set. What are you telling
Speaker 1: me about the block chain today? And what I want
Speaker 1: to highlight is the tension in that gap. I only
Speaker 1: studied these two years. I cannot tell you like a
Speaker 1: blanket statement about the system today, and I'm not going to,
Speaker 1: but I will say that at this time that decentralized
Speaker 1: trousis mechanism was not functioning. Yeah, I mean, I think
Speaker 1: it's It's also worth noting, right that like today we think, oh, bitcoint,
Speaker 1: it's worth a trillion dollars, and if it's not worth
Speaker 1: a trillion dollars, you know, there's a bit of a
Speaker 1: temptation to be like, oh, you know, before it was
Speaker 1: worth a trillion dollars, it kind of wasn't worth anything. Look,
Speaker 1: you know, there's no doubt that the value of you know,
Speaker 1: bitcoin and it's importance have changed greatly over the last
Speaker 1: ten years. There's no doubt that the state of that
Speaker 1: community has changed greatly over the last ten years. At
Speaker 1: the same time, it's worth noting that by the end
Speaker 1: of the period that we examined, this twenty five months
Speaker 1: long period, uh, bitcoin is a functioning currency. Right the
Speaker 1: end of the twenty five month long period, at this point,
Speaker 1: Quiki Leaks has already invested in bitcoint to get around
Speaker 1: the payment blockade, the financial blockade that was enacted against
Speaker 1: it by the government in the world. It's after the
Speaker 1: Electronic Frontier Foundation is starting to solicit donations in bitcoin um.
Speaker 1: It's after bitcoin transactions start. There's a market price for
Speaker 1: bitcoin that's a dollar. Um. It is coincident with the
Speaker 1: launch of the Silk Road, you know, which became, you know,
Speaker 1: a massive online black market denominated in bitcoin. So it
Speaker 1: was at this point was it what it is today?
Speaker 1: It was not, But was it functioning as a currency. Yes.
Speaker 1: We'll be right back with more from Melissa Blackburn and
Speaker 1: Dr Eras Lieberman Eden. I also want to point out
Speaker 1: the importance of studying the difference between the sort of
Speaker 1: ideals that you're hoping a cryptocurrency can achieve and the
Speaker 1: reality of what it does achieve, which and be quite different.
Speaker 1: Not every cryptocurrencies as long in the tooth as Bitcoin, right,
Speaker 1: if you look at other cryptocurrencies, you know, you look
Speaker 1: at Luna terra ust that that's an example of a
Speaker 1: cryptocurrency that's only a couple of years old. In that
Speaker 1: period of time, it became clear that there were significant
Speaker 1: you know, and especially recently, it's become very clear that
Speaker 1: there were significant deviations between the ideals what people said
Speaker 1: their intentions were, and you know, the reality of how
Speaker 1: that community worked, the reality of you know, even the
Speaker 1: attention of the founder and founders of that community. I
Speaker 1: think a measured response to This is not to say
Speaker 1: that the sky is falling and distant applies, you know,
Speaker 1: wader catastrophe for bitcoin. That wouldn't be a measured response,
Speaker 1: but also wouldn't be a measured response to say that
Speaker 1: that this period, because it was ten years ago, can't
Speaker 1: teach us valuable lessons for bitcoin and for other cryptocurrency communities.
Speaker 1: We kind of used the term a little bit informally
Speaker 1: internally of like stress testing and cryptocurrency eco system And
Speaker 1: I mean, Bitcoin is far in a way the most
Speaker 1: popular with the highest market cap, but the reality is
Speaker 1: there a lot of young, you know, crypto ecosystems that
Speaker 1: perhaps have not been as tested as thoroughly, and they're
Speaker 1: likewise maybe a gap between how they're being described how
Speaker 1: the founders may describe them in the actual state of
Speaker 1: the system. And I think it's kind of important to
Speaker 1: go into this kind of space with some degree of
Speaker 1: healthy skepticism. Also, in your description of you know, your
Speaker 1: attempts to even get to the point of being able
Speaker 1: to ask questions, you describe some challenges that you encountered,
Speaker 1: like getting kicked off of your university network because they
Speaker 1: thought that you were mining mining crypto. If if there
Speaker 1: are other academics out there who are hopefully listening to
Speaker 1: this podcast and thinking, I too would be very interested
Speaker 1: in engaging in some deep dives into the stuff. What's
Speaker 1: one thing that you would tell them that's interesting. I mean, frankly,
Speaker 1: the data is all public. I'm mean, if you have
Speaker 1: enough compute power, you have like a graph database, anybody
Speaker 1: can honestly get started. It's public data, or at least
Speaker 1: the parts that I mean, we we assessed. But what
Speaker 1: you said actually made me think of something else. And
Speaker 1: in those frustrations that are kind of like bureaucratic in nature,
Speaker 1: it made me think of kind of the information asymmetry
Speaker 1: out there between people who are very very familiar with
Speaker 1: bitcoin and the client and the protocol and those who
Speaker 1: are less familiar with the client the protocol but may
Speaker 1: still even be in the crypto space. And I think
Speaker 1: that's something that we are trying to address kind of
Speaker 1: that junction and like have a better seal there between
Speaker 1: kind of taking this space seriously as an academic an
Speaker 1: incredibly rich sociological data set, and not necessarily require you
Speaker 1: to be a bitcoin software developer in order to kind
Speaker 1: of engage in conversations here, but in terms of getting
Speaker 1: involved in this case, I mean, I think there's just
Speaker 1: a significant startup costs in terms of information. It's it's
Speaker 1: hard to simply begin. You have to know how this works,
Speaker 1: you have to know the use cases of it. I mean,
Speaker 1: and I think academics are free to reach out to us,
Speaker 1: actually if there's kind of interest in this area where
Speaker 1: we're happy to have those types of conversations. It can
Speaker 1: be tricky for academics to do this sort of work.
Speaker 1: Like a great example is you start looking at this
Speaker 1: data set. It's you know, it's it's a large data
Speaker 1: set and you want to analyze it. So good, good
Speaker 1: thing is your academic you have these big computer clusters
Speaker 1: and resources available through the university. So Lissa starts to
Speaker 1: analyze the data, and you know, within short order she's
Speaker 1: thrown off the cluster and band because they see, you know,
Speaker 1: the word bitcoin is sort of associated with her code,
Speaker 1: and they say, oh, you must be and you're using
Speaker 1: all this compute, you must be bitcoin mining. Alyssa was
Speaker 1: never able to prevail on them that she could possibly
Speaker 1: have any other interest in bitcoin other than to mind it.
Speaker 1: So then we were actually in worse shape now because
Speaker 1: you have this based and massive data set, and you
Speaker 1: don't have access to all the clusters usually used. So
Speaker 1: who actually got a computer dedicated to this, which we
Speaker 1: called hail Mary since we thought the odds were perhaps
Speaker 1: against us. So there are challenges. Look, there's challenges to
Speaker 1: approach this from from the academic sector. But I also
Speaker 1: want to highlight that for the reasons that a Lista
Speaker 1: pointed at the beginning, right, you know, you have a
Speaker 1: lot of people with a lot of really strong views,
Speaker 1: but there's a real need for folks who you know,
Speaker 1: don't have financial interest in the matter, who are working
Speaker 1: the public interests and the way that scientists do taking
Speaker 1: data and looking at the data and digesting the data
Speaker 1: and drawing lessons from that data in the public interest.
Speaker 1: And so I actually think that this is an extraordinary
Speaker 1: opportunity for scientists. Says hail Mary is still running. Hail
Speaker 1: Mary was actually cannibalized into one of our other clusters,
Speaker 1: but in theory she's she's in note in one of
Speaker 1: our other conversational clusters. Now we're going to probably need
Speaker 1: to ye, I don't know if you have any proposals
Speaker 1: or your listeners a proposals as to what to name
Speaker 1: that machine. But we're looking okay, great, I will, we will.
Speaker 1: This is our first question to our audience. What should
Speaker 1: these two scientists game their next computer for their next
Speaker 1: research project. Send us your answers to Crypto at Bloomberg.
Speaker 1: What is it computing computer faces that I feel like
Speaker 1: that one's played out? Yeah, that's yeah. Sorry, sorry, I'm
Speaker 1: dating myself. Well amazing. Thank you both for you know,
Speaker 1: sharing your your time and your research interest and just
Speaker 1: giving our our listeners the opportunity to hear a little
Speaker 1: bit more of like the technical on their pinnings and
Speaker 1: as you describe the gap that exists between theory and
Speaker 1: reality that is, you know, so often overlooked or at
Speaker 1: least less well understood. Thank you so much for having
Speaker 1: a son and giving us a chance to talk about
Speaker 1: our work a little bit. Thanks so much. You can
Speaker 1: find Alyssa on LinkedIn and on her website, and you
Speaker 1: can find Eras on Twitter. He is at Eras a
Speaker 1: terras that is e er easy, a t e R easy,
Speaker 1: don't forget. If you have suggestions for what they should
Speaker 1: name their next computer, send us a note to Crypto
Speaker 1: at Bloomberg dot net. On the next episode of Bloomberg
Speaker 1: Crypto every day around the world. The reporters and editors
Speaker 1: who work on Crypto Bloomberg wake up often at the
Speaker 1: crack of dawn and start figuring out how we'll tackle
Speaker 1: the biggest and most important news events of the day.
Speaker 1: Crypto is a twenty four seven asset class. It doesn't
Speaker 1: stop trading and it doesn't take holidays, so it's our
Speaker 1: job to assess both the big trends and the small moments,
Speaker 1: and to figure out how those translate into stories and
Speaker 1: of course, podcast episodes. On the next episode, you'll get
Speaker 1: to meet three of Bloomberg's Crypto editors, folks who are
Speaker 1: making decisions all day long about how we approached this
Speaker 1: asset class. You'll hear from Beth Williams and Dave Litka,
Speaker 1: who are both based in New York, and from Philip Blagerkranzo,
Speaker 1: who's based in Zurich. I'm Stacy Marie Ishmael, and this
Speaker 1: is Bloomberg Crypto, a daily podcast from Bloomberg and I
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Speaker 1: the show to Crypto at Bloomberg dot net and you'll
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Speaker 1: Bloomberg Crypto is Vicky very Galina. Our senior producer is
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