Chapter 1 in a series "How to Think About Blockchains"
Introduction
In part 1, I laid out a structure for thinking about blockchains. Not how they work in detail, but understanding some of the concepts in a way that makes that knowledge applicable to whatever your situation is. I broke things down into six chapters, and now I am outlining what each chapter looks like. I think each sub heading in the chapters could be an article on its own, so let me know in the comments, or by getting in touch direct, which pieces you would like me to unpack in more detail at some point.
This chapter has the least to do with blockchains of all of them; it is about the way that we think about and understand things, and in particular about our response to disruptive innovation. A change to something that affects us personally is usually seen as a threat, partly because it triggers a neurological reaction, and partly because it makes us think. Our thinking models are the way we interpret the world, and changing them is rewarding but can also be very difficult. Innovations from new technology are particularly hard because after a point, as Douglas Adams pointed out, they feel like an affront to the natural order of things. At a personal level, it is hard to conceive of new possibilities, and then the potential impact they have, which is why true innovation is relatively rare. Large companies struggle when those innovations do appear, and governments' responses are usually to figure out how to tax and whether to regulate them. These factors are summed up by Everett Rogers' work around the diffusion of innovation, which brings multiple factors together.
Amara's Law says that we tend to overestimate the short term impact of a new technology, while underestimating its long term effects. The point of using quotes like this is twofold. Firstly, the right quote captures precisely the concept that you are trying to put across in a memorable way. The second point is that the quote itself is a form of mental model. If you understand that principle in another context, then all you have to do is bring that understanding to a new context. Some of the nuance translates nicely. We overestimated the impact of - cars, cellphones, the internet - initially, because we didn't recognise how it could be used, or we didn't see what would happen when behaviour changed to accommodate it, or how it could be developed. The same will be true of blockchains.
The thing with blockchains is that it started with Bitcoin. This created individual level interest because of the combination of freedom and money, large financial companies saw threat and opportunity, and governments saw an existential threat to their authority. It was always going to be difficult. Let's dive into all these things in a bit more detail.
Human reaction to change
People are hard wired to be resistant to change. Those are the genes that made it through to us, because if I got this far without getting killed, then I know the old stuff is safe. This persists to this day, hence all the change management stuff that consultants sell, and hence the need to hard-sell all the new things. In change management, I think there is only one model you need to know, and it's called SCARF. It's based on the science that social threats and rewards have the same effect on our brains as physical ones. This applies to both positive and negative, but reactions to threats are faster, tend to be more severe, and last longer. There are 5 types of threat, and this is where the SCARF acronym comes in. Any threat to these social parameters has a large effect on an individual's ability to think and perform well. S is Status, the position in the group, their importance. C for miles - sorry - C for Certainty, of knowledge about the future. Then A is Autonomy, the ability to influence one's own situation. R is Relatedness, a slightly awkward word for feeling part of the group, tribal membership. Finally F is Fairness: people must feel that they have been treated fairly. You can think of any change situation, for example any significant work conversation about roles or promotion, and this model explains the reactions you will see. How anyone perceives blockchain can often be seen in their reaction according to this model: maybe it threatens their status in the financial system, or perhaps it creates a lack of certainty about the future of their business. If you get to the root problem, then it helps to deal with it.
Another famous change model comes from "On Death and Dying" by Elizabeth Kubler-Ross. Originally describing the five stages of grief, it can be (probably mis-) applied to the death of an idea or concept as well as to an actual death. If the SCARF model deals with the reason behind change responses, Kubler-Ross addresses the actual response. This is almost comically accurate in how large banks have responded to blockchains. First it was denial: "crypto is only used by criminals", soon followed by anger: "it should be illegal, there is no regulation". The corner is turned at bargaining: "maybe we could use blockchain (not bitcoin!) to save money". This leads naturally to depression: "it's cheaper and faster, but we can't make sense of it". Finally comes acceptance: "blockchains are the answer, tokenisation is the future!". This is mostly tongue in cheek, but you will notice this progression from many people and organisations, and it has been applied to bitcoin, blockchain, crypto, and so on. First they ignore you, then they laugh at you, then they fight you, then you win.
Luckily for us as a species, and as innovators, there is another human urge that counters the resistance to change, and that is the eagerness to learn. It is sadly not as widely distributed, but it is out there. We learn by updating our thinking frameworks.
Thinking frameworks and innovation
We all work with our own set of mental models of how the world works. A model might be literally how the world works - if you're like me, you have a mental image of a set of varying sized fruits and balls all orbiting a giant fireball - or it may be the ways that things work, in a given context. We use these models to predict what will happen in a given situation. They often represent quite complex systems, and the way that we build up nuances of understanding on a particular topic is by refining these models. One of the most familiar quotes about how we change these models came from Henry Ford: "If I had asked my customers what they wanted, they would have asked for faster horses". At that point, the common mental model for personal transportation had not been updated to include cars. The process of learning and building expertise in a given area is one of continually creating and refining new models. This is to an extent what I hope to do in this series of articles, by describing ways of thinking about blockchains.
A mental model is something that we have created from our observation or understanding of the world that helps us to predict its behaviour. The first step is creation of the model, the second is using it. For something new, we often start with an old model, then adapt it to our use: anchor and adjust. This is also a good way of getting people to understand something new. The shape of table lamps derives from candlesticks; the save icon looks like an old floppy disk. Nowadays we call this skeuomorphism, which probably has something to do with Steve Jobs, but we used to just call it analogy. It is such a basic concept that we do it automatically in all sorts of situations. "Well, it's a bit like baseball, but there are three stumps between the batter and the catcher. And the ball bounces on the way to the batter. And ..."
Usually, the world around us changes gradually, especially the base layers of our systems. While the world changes gradually, we can relatively quickly learn something new about a given aspect of it. When that happens, we create a new mental framework, or a new way of thinking about something. This leads to a step change in our understanding - an "aha moment". As Churchill said, none of us likes being taught, but we all enjoy learning. One reason that blockchains, and particularly digital forms of money, are so unpopular in some quarters is because it feels too much like being taught when we are forced to take on a new concept that challenges our understanding. One way of dealing with this is to start by unpacking our existing model down to the foundations so that we truly understand where it came from, then building it up again. This approach of going back to the fundamentals is a recurring theme in thinking about new technology, and blockchains in particular.
If you take how you pay for something as an example, there are different levels to it. This is true of any field: there are things that change often, and there are things closer to the core that are pretty persistent. In payments, there have been many innovations in recent years that enable us to pay in what seems like different ways. You may scan a QR code, or you can pay with Google or Apple Pay, or your watch. All of these feel new, but the underlying credit card rails are the same; the watch or phone is just a new way to access those rails. Beneath the credit card rails is the commercial and central banking system, and beneath that is the system of global currencies. Beneath the system of currencies is, well, that's another story. It used to be gold, but that hasn't been the case since 1971. Bitcoin, and much that built on its insights, challenges the fundamental layers of the payments system, and indeed financial services more broadly. The evangelists of Bitcoin therefore tend to have thought a lot about money and banking and how they work. Those whose careers have been built on those foundations are often less willing to dig into them. This disruption from new technology is not a new phenomenon, but disruption to money and mechanisms of value is a big one.
Technology driven disruption
Clayton Christensen's theories about disruptive innovation are another example of a known model that can be applied to blockchain. He describes disruptive innovations, often using new technology, that start out looking worse than the current one in all but one or two respects. What happens over time is that these innovators improve, find a market, fix the problems and add features. An industry thus reaches a point where the new way of doing things becomes the mainstream, and the old way then dies off. This process may take years, but we can see the patterns in crypto and blockchains already. It starts out "looking like a toy", as Chris Dixon describes. Blockchains certainly had this problem, with it being hard to identify the "killer" use cases, but fringe uses are emerging. Bitcoin hasn't yet taken over from any traditional currency or asset, but crypto and blockchains are filling gaps that exist in the current system. The best example is probably the use of stablecoins, in the form of Tether (USDT), for payments in countries like Argentina. The same mechanism isn't yet used widely, but in the edge case of Argentina, stablecoins solve a very specific problem with currency volatility and devaluation.
There is a natural resistance to new technology, particularly if it is not immediately obvious that it is much better than the existing one. We also tend to have an inherent bias against novelty. Douglas Adams wrote in the 1990s about a set of rules that define our reaction to technology:
"Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.
Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.
Anything invented after you’re thirty-five is against the natural order of things."
Like much of what Adams wrote, this is brilliant, true and funny. New technology can feel like witchcraft, as Arthur C Clarke pointed out, and the effects can be similarly difficult to explain. Technology has always been a key driver of innovation, from the steam engine to cars to computers, and the compounding effects mean that the rate of change accelerates. This isn't just how it feels as you get older, it's the way it works. Another interesting quirk is that we tend to highlight the negatives of new technology: it will scare the horses (cars), or it's used by criminals (Bitcoin), or it's going to kill us all (AI). This is another human preservation mechanism that's probably hard-wired. Fear of the unknown is a good way to stay alive in the savannah.
The source of disruptive technology is another factor. In most cases in the last few decades, disruptive technology has come from startups. The concept of a startup is in itself a fairly new one; "The Lean Startup" wasn't published until 2010. Coming at an existing market with a business that is built around new technology confers a big agility advantage. Startups have the ability to do things that large companies cannot, either because they are restricted by their existing business model or practices, or they just don't have the skills. The lack of skills is certainly the case with blockchain, although the tide is now changing. Many of the current crop of blockchain based startups have founders who came out of large organisations, leaving to set up their own businesses because they were frustrated by corporate constraints. A final point to make here is that many blockchain based businesses have had a fundraising advantage over more 'conventional' startups, since the nature of the business made it easy for them to raise funds via cryptocurrency, and/or the founders got in early to Bitcoin and other opportunities, and thus can afford to fund their own businesses.
Large organisations and innovation
The difficult relationship between large companies and innovation is well documented. The classic framing is that companies race to innovate before innovators can get distribution at scale. The partnerships between companies looking for innovation and innovators looking for distribution are often fractious. The main problem is that the cultures are so different, because innovators and corporates come from completely different backgrounds and mindsets. These dynamics are illustrated by blockchain. Blockchains require multiple parties to use the same blockchain: the shared ledger is critical to the whole idea. The message for companies is therefore that they must share a set of data with their peers or partners, and they must change their processes to accommodate this. This is a hard thing to sell. It leads to a recurring theme in blockchain, in that it works when there are ready made groups. If you look at blockchain successes, then there is often a pre-existing group behind them. A large retailer gets its suppliers to use their blockchain, or a central bank convenes its local banking industry for a CBDC experiment. I have tried to get industry peers to collaborate on blockchain based solutions, and it is very hard to do. In situations where blockchains have achieved retail scale, it has worked because of a shared set of beliefs - Bitcoin's libertarian angle for example - or because it just plays to basic human greed. Going with human nature is a good way to bet.
Startups are searching for new business models, and blockchains give them new tools with which to build those models. This gives them an advantage over incumbent players, who are faced with the familiar challenges of corporate innovation. In these cases, there seem to be three main approaches: buy, build or partner. There are difficulties with all three, and there are overlaps between them, but that 3 bubble Venn diagram has 7 areas that cover the possible outcomes. The skills and understanding currently required to build blockchain based products and services are currently scarce. Incumbents are therefore forced into the 'buy' or 'partner' options, and the majority of relationships at the moment are partnerships. The market dynamics have created a cohort of blockchain based companies building products and services for incumbents. Particularly in financial services, this is down to regulation.
I wrote above that the 'anger' stage of dealing with the grief of crypto was a desire to regulate this new technology. In financial services particularly, regulations provide a moat to defend against potential new entrants. This is right and proper: consumer protection is critical, and banks are inherently illiquid, so regulations are there to protect consumers and stabilise the industry. Blockchains threaten some of the business models of financial services, and regulatory models are racing to catch up. Regulation always lags innovation, and with crypto and blockchains I think there are four stages. The first thing regulators have done is to apply the old rules directly to crypto and blockchains. This is still where the US is stuck, arguing over how the Howey test applies to complex tokens. The second model is to adapt existing rules slightly to encompass crypto. This is what South Africa have done, for example getting Crypto Asset Service Providers to register with the authorities, so that they can be held to account for KYC and anti money laundering procedures. The third iteration of regulation is the creation of rules that are specific to crypto and tokens, such as the European MiCA regulation. This is the most advanced state that anyone has yet got to as far as I am aware, and MiCA is one of the best examples, even though it still parks NFTs and DeFi for later consideration. The final, fourth, stage is when the regulations are programmed into the infrastructure. With blockchain networks and programmable assets, there are elements of regulation that can be automated, so there are rules that it is impossible to break. As an example, it is easy to imagine a situation where a wallet cannot do a certain transaction unless it already contains a valid KYC token, or an accredited investor NFT. Crypto is already a terrible way to launder money compared cash and large banks; it will only become worse with time and development.
Diffusion of innovation
The time it takes, and the process involved in adoption is the subject of Everett Rogers' work on the diffusion of innovation. Rogers proposes that five main elements influence the spread of a new idea: the innovation itself, adopters, communication channels, time, and a social system. If we think of blockchains and crypto as a new idea, then we can see how this works. The innovation itself can be summarised as the ability for a wallet to control a unique digital asset. The applicability of that innovation in so many areas might be expected to speed up adoption overall as it works on many fronts, but in opposition to this accelerator, the complexity and novelty may slow things down. The early adoption of crypto has happened rapidly because of its link to money. Many individuals are thus attracted to it, but organisations less so, given the problems they see with new types of money. Communication channels and a social system are now the same thing when applied to crypto, with the ability of the internet to spread memes and ideas around groups on X / Twitter and Discord. Social networks as we currently understand them are probably not what Rogers have in mind, but the internet has changed the way we connect. It has also changed the speed that things happen, affecting the time it takes for innovations to spread. Just as there are some people who still don't use the internet, there will be those who don't use blockchains, but it will become increasingly difficult not to do so as, like the internet, the technology vanishes into the infrastructure layer of many of the things we do.
The process of adoption is one that fascinates me, and I have written about it before; I may go back and update that article once I have made my initial pass through these chapters. The incentives for adoption are easier to understand and manipulate when forms of money are involved, and I will start that explanation with a first look at Bitcoin in chapter 2.
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