Databased
Blockchains are a new type of database that increase the scale and complexity of what we can build.
Imagine trying to govern a country without the ability to create written records. You’d have to:
- Make sure that any law that you decreed was heard by everyone, regardless of where they were.
- Make sure that nothing got lost in translation as people relayed the law to each other.
- Ensure that people who were asleep during the time of the decree could somehow hear the correct thing at a later time.
Achieving all of those goals would be almost impossible without having a series of written documents that everyone could reference to confirm what was said, and by whom. Ideally, this reference point could be shared and distributed easily so that everyone across your jurisdiction could come to know your laws.
Your ability to effectively legislate, coordinate, and offer critical public services is limited by how well you can record and manipulate the information you produce and receive. You can run a family of five with just word of mouth — we certainly don’t need laws written on tablets to know not to take cookies out of the jar — but as you scale the size and complexity of your organization, how you record and manipulate information becomes a key limiting factor.
Throughout history, the “success” of societies has been defined in part by their sophistication of documentation.
Written-tradition societies came to dominate over oral-tradition societies in part because coordination, consistency, and scale were beneficial advantages, versus the flexibility and lower “bandwidth” of oral-tradition societies. Each had different benefits and drawbacks, but mastery over information was key to being able to coordinate a larger society.
As we became more effective at recording and disseminating information, we began to build more complex institutions.
Double-entry bookkeeping — a system of keeping track of what someone owned, versus what they owed, enabled the birth of more sophisticated financial structures such as banks and credit lines. These capital allocation tools provided the infrastructure for us to explore new lands, start new nations, and touch the surface of the moon.
When the personal computer arrived, one of the immediate applications used was spreadsheet software, allowing large organizations to do “business intelligence”. Companies no longer needed to hire hundreds of typewriters to tabulate information on separate sheets of paper and devise convoluted schemes to do analysis.
As a result, we began to compress the amount of information that we were recording — how many apples sold, at what price, etc. — into a 10” diagonal screen rather than have a room full of typists generate the same information.
Over time this digital information began to be stored in databases; a central place for all the information about a particular entity. Sometimes that entity was the number of soldiers to be deployed, sometimes it was the number of refugees who needed supplies in an area, and other times it was the number of apples sold. Databases would hold whatever information people wanted to put in them; numbers, text, images, and more.
Businesses who began to do analysis on top of their databases found that they could forecast the number of apples sold better than competitors. This spawned an industry race to apply scientific approaches to manage business metrics; each player in search of new ways to increase profitability, enter into new markets, or acquire complementary firms.
This industry was popularized by James O’McKinsey who took this “Management engineering” approach to businesses and formalized it, founding what is now the world’s largest consultancy — McKinsey & Company — to help businesses analyze the data they recorded.
While the information that was captured in databases was increasingly sophisticated, it was difficult to find relationships between them.
For example, if I have a tabular database of apples and attributes about their color, size, and weight, and another table of apples and attributes about how many I’ve sold and at what price, I would need to manually create a new table that could answer questions like “what’s my best selling apple?”, “what apple brings in the most revenue?” and more.
Without being able to relate databases, the quality of one’s insights, the sophistication of one’s business, and their ability to do strategy is limited. These informational speed limits didn’t just apply to businesses either. Imagine the questions that governments, NGOs, and other organizations were unable to answer quickly or at scale because of this fundamental limit.
It wasn’t until the 1970s when Edgar F. Codd invented the relational database while at IBM that the informational speed limit was broken. Now new questions, new insights, and new capabilities were possible at scale, all on computers.
With the birth of the internet and the rise of personal computing, the amount of data — both structured and unstructured — that we began to capture provided rich sandboxes from which we could generate insights. As the ways in which we captured this data, first in relational databases, and later on in non-relational databases — amongst other database types — scaled and were deployed via the internet, the innovations of the modern technology sector became possible.
Page Rank, Spotify, Tinder’s (alleged) Elo scores, Uber, Waze, etc. are all algorithmic manifestations of questions like — “What song will they most likely like?”,“Who is this person most likely to match with?”, “What is the best route?” — that can’t be answered unless the underlying data can be rapidly manipulated and connected at scale. The global supply chains and logistics networks of Apple and Amazon would be impossible to run on paper alone or separate tables. Without sophisticated databases, the technological advancement of the last 30+ years may have never come.
While these sophisticated databases have provided the underpinning for the digital experiences we experience today, they still separate entities from attributes.
In other words, one still can’t store physical, real-world apples in databases, they can only represent them with the data types that we have.
In the case of the business that sells apples, the attribute *number of apples* would be represented as the data type of an integer — 17,000; and the attribute *type of apples* can only be represented as a string of text — “Red Delicious”. The database can’t actually hold the entity real-world apples, only representations.
So too is it the case for our broader financial services system.
Representative movements of money are siloed in databases behind gatekeepers who are entrusted to ensure that identity, KYC, AML, and other protocols are followed. These gatekeeper functions are critical. The tradeoff is a financial infrastructure that has limited navigability.
When you ask why sending a check takes ~3 days to settle, why sending a remittance abroad is expensive, or why a bank that does your mortgage and your checking can’t offer you a combined, personalized rate and experience, it is in part because all of these systems need to authenticate between each other to share and manipulate the information in each others’ databases.
But what if you could do more? What if the very item stored in all of these distributed databases was in fact money, not just a representation that had to be authenticated every time a change needed to be made?
In 2009 Satoshi Nakamoto published the Bitcoin whitepaper, which introduced a new type of database, one that was built by consensus, was extremely difficult to change and that everyone could publically see.
For a transaction to be successful, a whole network of computers around the world must concurrently agree that the transaction did in fact happen, and through consensus, “publish” the record of that transaction to a ledger, printed in blocks. This makes it hard to falsify or hack a transaction, and these mechanisms are fundamental to the new data type that blockchains enable — that of money.
Blockchains are a new database that promise the ability to put *real* money in a digital record. And in the same way that we’ve come to understand that the ability to manipulate information with ease defines the scale and complexity of what can be built in a society — blockchains promise to make the movement of money as easy as sending a tweet. This step-change promises to radically change our relationship with money.
In the real world, my money (generally) can’t be deleted out of thin air, be in more than one place at a single time (i.e. you and I can’t own the same dollar bill — it’s either in your hand or in my mine), or move without my active consent (i.e. I give you the dollar). As these properties are enforced in blockchains, the data type that they store effectively emulates the real-world properties of money.
Native money in silicon benefits from the most important technological trends of the past several decades. Compute is now embedded in a new context. Moore’s law, cloud computing, machine learning, etc. are several vectors of growth that can now be leveraged in a world with native money-as-bits. Money becomes programmatic; able to be manipulated as easily as we can software, but with full preservation of its essence. Money becomes internet-native, embedded in everything that we do on the web, and beyond. Money can now be a substrate for identity, bringing authentication to the client-side, enabling new types of user experiences.
This reduction of friction in organizing money will have significant cultural downstream effects, not all of which will be positive. We are just now coming to terms with a world in which information is moved and manipulated freely; are we ready for one in which money can as well?
As always, if you’re working on any of these spaces — or want to partner to refine our thinking, subscribe below and reach out at info@mutablematter.com
