What You Need to Learn to become an Expert in Blockchain Tech
TL;DR — Study Mechanism Design, Distributed Computing, and Cryptography
A lot of people are going to lose a lot of money when the crypto bubble pops. To be honest it’s not worth the time to study the market and try to figure out when it will pop because frankly, no one knows.
A better approach would be to look at the underlying fundamentals that are driving this global phenomenon. It is truly a revolutionary technology, and changing your career and entering the blockchain space will pay dividends long term, but you need to be careful about how you approach it.
If you start day trading, researching ICOs, or doing anything else that is short-sighted, your success will not last. Everyone looks intelligent in a bull market. Even if you’ve heard this saying before, you’ll probably still experience some amount of confirmation bias that your trading ability is above average. It is how we humans are wired.
You’re probably thinking what you should be doing to get involved in the blockchain space, and it is quite simple. Study the underlying computer science fields that gave birth to blockchain technology. These fields are Cryptography, Distributed Computing, and Mechanism Design. Alone they have all been studied for years, but when Satoshi Nakamoto brought them together it created a new revolution.
Studying these will greatly mitigate your risk if you get involved in this space. It is a more robust knowledge base to have compared to being a crypto investor, or even a Solidity developer, as it is less certain that Solidity will be around in 10 years, compared to these computer science fields (see Lindy Effect). Knowledge in these fundamental fields is highly sought out in the blockchain space, and the amount of people putting in the hard work to learn these topics are disproportionally skewed to the amount of money flying into the space, meaning it will definitely be worth your time to study them. These fundamental topics have been around for decades, and have not changed because of some hype cycle. Wether the bubble crashes or falls, this tech is here to stay.
Cryptography is one of the most essential concepts to understand of our day and age. Consider for a second that the logic of violence determines the structure of our society.
In short, what this implies is that as our assets are becoming more and more digitized (think about how much data is worth, and how the largest companies in the world are all data aggregators) we are going to have to secure them with more advanced cryptography to keep them safe.
Comparing this to a past with a large amount of physical assets, we can look at medieval Europe. Feudal lords worked extremely hard to protect land, which involved physical protection mechanisms such as a horse riding knight that strong-arms peasants into plowing the fields.
To take it a step further, consider the fact that the encryption algorithm behind bitcoin secures over $300,000,000,000. (December 17th, 2017). This single encryption algorithm is securing more value than the amount of gold in Fort Knox . This digital trend will continue to overtake how our society secures what we consider valuable.
When bitcoin brought the methods of encryption, mechanism design, and distributed computing together, we created the first valuable distributed trusted network across the globe with little to no centralization. Before bitcoin existed the best comparison of a global distributed peer to peer network we had were torrenting websites. They worked well enough, but they were missing the incentives for peers to be good actors in the network. Anyone could post anything, including malicious software, and go unpunished. There were also no good incentives for peers to volunteer their own computer to upload data to the network.
Now with economic incentives built into blockchains, we are seeing full scale distributed computing systems, and we can now test new distributed computing problems at scale with real skin in the game. Many of the new Proof of Stake models (such as Ethereum’s Sharding) are actually highly experimental, and in general Ethereum is going up against some hard, well known computer science problems.
It is both extremely exciting that we have the opportunity to study distributed peer to peer networks at such a large scale, but it is also dangerous how much trust we have put into this relatively new software. Expect a ton of research to continue to be done in this area of computer science, now that we have the ability to attack it at a global scale.
In summary, we are going to need more distributed computing engineers!
A simple definition of mechanism design would be “if Computer Science and Game Theory had a baby, it would be named Mechanism Design.”
This is another piece of the puzzle that is essential to blockchains. A cryptographer alone can secure data, but there has to be incentive to own that data for it to have any worth (and to prove it can’t be double spent). And a distributed computing engineer can connect a network of computers to distribute data, but if there is an imbalance in the incentives built in, it makes for less secure and effective network (like that of torrenting sites). Mechanism design glues these fields together to create working blockchains, and the field is constantly evolving, as we can see by studying Bitcoin.
Bitcoin had an amazingly inventive design from the start. But a large part of the mechanism got upturned with the invention of ASIC miners. With the creation of these miners, it created a concentration of mining power in the hands of people with capital to buy ASICs and build large mining farms. The ability to mine bitcoin with a personal computer CPU was lost, and the decentralization of Bitcoin was weakened. I imagined Satoshi thought long and hard about how to prevent something like this from happening, but a global decentralized network is always open to continually evolving attack vectors. This is the nature of mechanism design when securing digital assets.
Continued testing and building of blockchains will lead to more robust and secure algorithms that help humans engage in a global trust network.
Comparing blockchain networks to our own biology can help explain how small iterations over time will improve upon Bitcoin, Ethereum, and the other blockchains we have today.
The responsive biological mechanisms that our bodies adhere to come from billions of years of life replicating itself on earth, and improving by minuscule amounts each time. We inherit these improvements through the passage of DNA (biological code), which modifies itself at an extremely slow, but robust pace, through a distributed network of humans spread across the globe.
Looking at it through a biological lens gives us an interesting perspective, and the more you study mechanism design, the better you’ll be able to design blockchain systems that work effectively and efficiently.
Resources to Learn These Fields
By now you are wondering what you can do next to study these topics, and luckily listed below are some good places to start.
If you teach yourself any of these fields, you will be of massive value for years to come. These skills are what distinguish a “blockchain developer” from a “smart contract developer” (which is worthy of another discussion).
Each place to start has a different learning approach, so that one can fit into your own style of digesting information.
- https://www.coursera.org/learn/crypto — This is a great Cryptography class, taught online by Dan Boneh, professor at Stanford. This is more geared towards those who love a structured class setting. In my experience taking this class having a math background would help. But it isn’t necessary to know calculus, most of it is just algebraic equations.
- Going from a more blockchain focused approach, check out the current research happening at Ethereum with Sharding, Cosmos with their “Network of Blockchains”, and Blockstack with their “build a new decentralized internet” approach. They are all applying interesting areas of computer science research at a global scale with skin in the game.
- This book by Matt Ridley is a must read for game theory, which will give you a good starting point for mechanism design. It isn’t focused centrally on computers, but you will see how game theory attaches itself to markets and systems such as biology, computer networks, the stock market, etc. This is a good place to start if you are a little intimidated by the other two because of a lack of computer science knowledge (… but don’t be intimidated, I knew nothing of all three of these fields 9 months ago!).
Let me know if you have any questions about getting started with this stuff on twitter @davekajpust!