Nic Carter's On-Chain Data Story
Today’s letter is a guest post from Nic Carter, Partner at Castle Island Ventures and Co-founder of Coin Metrics. He is one of the most thoughtful investors / operators in the crypto industry, while also consistently surfacing new and interesting data to analyze. I hope you enjoy his perspective below about working with on-chain data for the last few years. If you would like to receive this email daily, please subscribe to join 38,000 other investors.
In 2016, I was at business school at the University of Edinburgh, in the midst of an exploration of the investment case for Bitcoin from a traditional finance perspective. I had a serious problem — I wanted high quality data with which to build models relating to Bitcoin and other cryptoassets, but I couldn’t find it anywhere. I had a strong intuition that there was a fundamental relationship between the usageof public blockchains and their valuations. Additionally, I needed this data to be reliable, free, and comparable between blockchains. The issue was: it didn’t exist. Anywhere.
This was puzzling to me. In a public blockchain, every transaction is posted for all to see, and registered to the ledger forever (in theory). Acquiring economic data could be done by simply making RPC requests to one’s full node. In practice, extracting and understanding this data is lot more complex.
So I got in touch with a friend and we created the first version of Coin Metrics. We spun up a bunch of full nodes, developed some tooling to extract the relevant data, and figured out how to translate it to something that made sense. The v1 was an ugly, bare-bones site with a simple charting functionality and a page where you could download all the data we had for a handful of blockchains. I didn’t expect anyone aside from a few niche hobbyists to look at it. To my surprise, it started getting traction on Crypto Twitter, and our data began to see usage from academics and industry research desks. People were actually finding out shoestring-budget, bootleg data product useful! Over time, we started getting requests for more assets and more metrics. By simply honoring these requests, our sample grew from a dozen cryptoassets to hundreds.
We came to realize that a standard of rigor was required, if we were to meet the lofty expectations that were being laid out for us. Over time, we devised new metrics like Realized Capitalization, which was an effort to solve some of the issues we had identified with market cap, and we did things like figuring out how to adjust transaction volume to exclude spam. As it turned out, the cryptocurrency community had an unquenchable demand for high-quality, well-documented data on the network usage of their favorite coins. Just by being receptive to what the public wanted, we built something entirely new: a one-stop shop for economic data relating to public blockchains.
We have been working on this problem for about three years now, and we have learned a lot. It’s still early — and there’s still only a handful of individuals looking at this data full time. I consider on-chain data to be a largely untapped resource; it is used by hedge funds and investors, but outside of the trading use case it is mostly ignored by economists and researchers trying to understand these assets.
Therein lies the opportunity. Having access to the full audit log and transaction history of an entire mini economy is an unprecedented resource. Not to de-anonymize individuals, but to understand the nature of the relationship between the turnover of money and commerce, to granularly assess velocity, and to experiment with some of these economic theories that until now haven’t been tested in sandbox environments. We’ve simply never had data this good in one place before. I wouldn’t be surprised to see a Nobel prize materialize from inferences made from these on-chain economies.
But the most interesting findings, to me, have been the times where the ledger itself, the ground truth, has revealed something that issuers may have wanted to keep quiet, or were not even aware of. In these cases, you have third parties (like us) simply taking a careful look at publicly available blockchain data and surfacing new information. I’ll briefly detail some of our favorite case studies:
Covert Bitcoin Private inflation
This one was dramatic. We were conducting a routine audit of supply for all of our covered assets to make sure that the supply as found in the ledger matched what the issuers had claimed. We were looking at this exotic asset, Bitcoin Private, which was a merger of the UTXO sets of Bitcoin and of Zclassic (itself, a fork of Zcash). At one point, BTCP was worth over $1B. Lo and behold, during that UTXO import process, someone (the developers claim it was a malicious third party) managed to covertly inflate supply by 10%, crediting themselves with the additional coins. This is what it looked like:
Y-axis is BTCP blocks, X-axis is BTCP supply
Amazingly, no one had noticed this! In the nine months between the launch and our report, no one had bothered to check the supply. We published our findings on Christmas eve 2018. I have to say I felt a little bad about ruining Christmas for a few BTCP owners, but once we were confident in our findings, we had no option but to publish them. Of course, Coin Metrics has a strict policy prohibiting employees from trading on any material information that we uncover.
One of the most interesting discoveries came from our analysis of the pattern of randomness in the numbers miners used to create blocks. To briefly refresh you on mining, miners essentially try and pick a random number (called a “nonce”) that qualifies them to propose a valid block, and these numbers can only be obtained by brute force (i.e. by trying over and over again). Because of the properties of hash functions, there’s no shortcuts to finding these numbers. You have to randomly search around the “nonce space” to find the right number. Now, when you visualize all the nonces used to mine blocks on Bitcoin, or some other chain, they should appear random. They should look like static on the TV screen. In practice, they are decidedly nonrandom, with distinct bands being more popular than others.
These bands, when nonces are visualized on a scatterplot, signify idiosyncratic behavior on the part of miners. They mean that, for whatever reason, miners are searching only a subset of the space they should be exploring. The best explanation for this? Specific behavior patterns attributable to hardware, namely ASICs. So the more nonrandom behavior you see, the more mining is dominated by ASICs. As you might imagine, this is incredibly useful information — just by looking at the distribution of randomness in block data, you can determine whether ASICs are active on the chain.
This is evident in Bitcoin: you can see how early on, miners just incremented nonces upwards from 0, because it was very easy to mine a block and you didn’t need to search the entire nonce space to find one. Then, as difficulty rose, nonces became more random. Around block 300,000 (mid 2014) ASICs started to dominate mining, and you can clearly see their imprint in the chain, as those white bands appear.
A more interesting chart is the Monero nonce chart. Monero maintains a commitment to being “ASIC-resistant,” which involves changing the hash function whenever ASICs are discovered. You can see an entertaining tug of war between the Monero developers and the ASIC manufacturers in the following chart:
The red lines denote hard forks, and the solid black line is difficulty (a proxy for hashrate). Thus you can see solid blue lines (nonrandom nonce data) periodically emerging, and getting halted by hard forks that abruptly obsolete ASICs built for the hash function. This has happened on multiple occasions! Interestingly, using this data, we were able to identify the rise of ASICs on certain chains before the manufacturers publicly shipped them. This means that they were effectively front-running their own customers.
The other interesting thing about on-chain data is that you can evaluate the effect of economic experiments in real time. One such class of experimentation is airdrops. In their heyday, UBI proponents spoke of airdrops in reverential tones as a potential way of achieve fairer systems of wealth allocation. These proposals weren’t without their detractors. Many saw historical parallels in the voucher privatization schemes in the aftermath of the Soviet Union, in which normal citizens were granted equity in local enterprises. Not understanding the value of this equity, most individuals sold their claims for pennies on the dollar, with the lions share ending up in the hands of hedge funds or oligarchs. The voucher ‘airdrops’, designed to distribute equity into the hands of individuals, ended up being counterproductive, causing a concentration of wealth.
Mindful of these historical lessons, the Coin Metrics team set out to evaluate one of the most prolific airdroppers, Stellar. One such airdrop took place in late 2018, with users of blockchain.com’s wallets being entitled to some free XLM. So what did users do with their newfound cash? We looked on chain and found that the vast majority of it was more or less instantly transferred to exchanges and sold. A disappointing, albeit predictable, result.
One of our engineers, Antoine Le Calvez, calls this field “on-chain archeology.” And that’s really what it is — peering into layers and layers of publicly available transactional data to find something interesting buried deep within. Suffice to say, I find this field of study absolutely fascinating. Not only is it possible to unearth potentially scandalous information like hidden premines and covert ASICs, but you can directly feel the pulse of the on-chain economy in real time. Moreover, developers can obtain immediate feedback about the effect of protocol changes on the on-chain economy. This is a valuable information signal which can and should guide policy. We just need to ensure that the relevant information is being surfaced in a consumable form.
Today, Coin Metrics is a commercial entity with venture financing and real clients. But I will never forget our open source roots. As long as I have influence at the company, I will ensure that they publish troves of data for free, for anyone to use however they like.
In contrast to the traditional model of capital markets data, I firmly believe that any successful data company in the crypto industry will have to be serious about open-sourcing large tranches of its data. Of course, the bills have to be paid, and CM makes money by licensing proprietary insights to clients. But I wouldn’t want to be in this line of work if we weren’t generating far more value for the world than we were capturing. The way I see it, the commercial side of Coin Metrics subsidizes the real objective: scraping as much useful data from as many chains as possible and posting it online for anyone to use. This includes data for blockchains many would consider meritless. Sunlight is the best disinfectant, and we plan to illuminate the whole cryptoverse.
This installment of Off The Chain is free for everyone. I send this email to our investors daily. If you would also like to receive it every morning, join the 38,000 other investors today.
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Rong Chen is building Elastos, a decentralized internet powered by blockchain. He has had a long career building network infrastructure, operating systems, and internet services, which has given him an unique perspective on why decentralization matters so much. I really learned a lot from this conversation and found Rong to be equally as entertaining to speak with as educational.
In this conversation, Rong and I discuss:
Working at Microsoft in the early days
Building network operating systems
Why the decentralized internet is important
How Elastos is pushing the pace of innovation
Where Rong thinks dApps may have gone wrong
I really enjoyed this conversation with Rong. Hopefully you enjoy it too.
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