Bicameral Thesis Part 1: The Road to The Archetype of 1
Bear with me here, this is a long one and something that I’ve been thinking about for quite a while! I’ve given this talk now in Vegas and London with great results, so excited to break it down in all it’s permanent, non-loud, on the streetcar appropriate wordiness here!
Humans are fascinating creatures, give us a system with constraints and frictions and we’ll pretty quickly figure out how to maximize within that system. Individually we are panicky, irrational, emotion monsters, but put a bunch of us together and our ability to optimize is pretty amazing. Individually, few of us think about ourselves as maximizing machines at all and even fewer actually do the mental calculus that we’ll walk through explicitly, but when you put large groups of us together, for better or worse, we tend to operate exactly as the system of incentives tell us to. Generally what we’re optimizing for is profit and we can argue forever about whether that is the right goal, but assuming that is what has been the end goal for thousands of years we can work backwards and imply some fascinating things about human systems over time.
The Calculus of Trust
When humans first started doing commerce, they were limited to trading with those in their own tribe because the physical frictions inherent in walking over to the next tribe made it completely uneconomic and something not worth pursuing from a profit perspective. From a mental calculus perspective:
When horses, carts etc began to arrive on the scene the cost of the physical frictions began to reduce and the incentive grew to seek out new profits farther afield. However, a new layer of frictions began to introduce itself. It was still a second order effect given the people we were trading with were still in relatively close proximity, but whereas the likelihood of a bad actor not paying you from within your own tribe was exceedingly low, it grew meaningfully with the farther you ventured afield. A second new friction that arose was that while you were exceptionally aware of what products or commodities were required within your own tribe, the risk of making a bad product also rises meaningfully with the farther you venture out.
For example if you are manufacturing red scarfs and the tribe 3 over considers red to be unlucky, then the loss from that shipment is a total as if there is a bad actor who refuses to pay you. While there are some important differences, we will consider these two frictions together as “trust frictions” and assume that they increase with the physical size of the economy within which you transact. Introducing these two trust frictions into the equation leads to
Four important points to make here:
1. There is a rational equilibrium here that kept commerce expanding as fast as technology could reduce physical frictions (cars, ships, phones, fax, internet), but with every quantum leap outwards, there is another level of risk from bad actors and bad products that keeps it constrained
2. All foreign transactions are not equal, the tribe two over will have a marginally higher profit potential, but also only marginally higher trust frictions as compared to trading across an ocean
3. We’re at the stage now where the internet has reduced physical frictions to the point where trust risks have become the dominant constraint
4. The internet presents us with extremely profitable universal commerce, but the trust frictions inherent in transacting with global, effectively anonymous counterparties are also enormous
You Centralize, I Centralize, We All Centralize!
The other critical angle here is that physical frictions are known and predictable (it costs $100 in gas plus maintenance to drive from here to there), trust frictions are unknown and potentially infrequent, but the loss they generate is total. Physical frictions are operational costs and require ongoing low level expenditure to overcome, trust frictions require a set aside float of capital to offset those total losses. Therefore the higher the risk of total loss from a trust event, the higher the amount of capital required to “support” that operation.
This is a key point, physical frictions are operational costs, trust frictions are capital events. When there is less trust or a greater trust risk the amount of capital required to support it increases. Capital is expensive and in order to generate or buy it in sufficient quantities to offset trust risk, prices need to be at a certain level and if that is higher than the market is willing to pay, then you will be constrained to either less risky activities or some other method of reducing capital required. There will always be some inherent risk that requires a certain amount of capital, but a substantial portion of capital required is from offsetting risk from a lack of trust.
Given this, an individual entrepreneur likely won’t be in a position to generate enough capital internally to be able to handle this type of total loss trust risk. Therefore they would only take on the transactions where the trust risk is lower (closer to home). But as mentioned, humans are creative and where there are profitable honeypots, we’ll figure out ways to get’em so two options emerged:
Option 1: People came up with ways of proxying for the trust of a transaction in the absence of a personal relationship. For instance if you transacted with someone from that tribe before and they were honest then there is a higher likelihood that their peer is as well. Or if someone you know has sold red scarfs to the people there then your bad product risk reduces. However there is a cost to both acquiring information and processing it, so the effectiveness of this method is inversely proportional to these costs. This method also still incurs a total risk of loss if the proxy is wrong
Option 2: One day someone comes along and says “for a 10% fee on all of your transactions I’ll pay you back if someone screws you” or “pay me x thousand dollars and I’ll tell you exactly what colour of scarves the people in that tribe like”. Then you turn that capital event into an ongoing operating event and allow smaller players to execute transactions farther from home
In an analog and “early data” world, option 2 quickly became the dominant method for solving the trust gap and cued the rise of what I call “centralized trust proxiers” (“CTP”). Bodies who pool risk and the capital required to support it centrally for a fee (insurance companies and banks for bad actor risk). Bad product risk had it’s CTPs as well in the form of large ad agencies who would charge a fee to better target your product and reduce bad product risk.
What Are They Even DOING BACK THERE??
When you can lay off your entire risk on someone else, it is easy to turn off your brain as to the actual behind the scenes operations of that counterparty. These CTPs are rational as well and they make money when these trust risk events don’t happen. It is highly in their interest to have the most accurate reading of the risk of something happening.
The earliest forms of these were based on webs of relationships. International banking families like the Medicis or the Rothschilds who wove complicated network of webs based on personal relationships to vouch for people and underwrite transactions, and fashion houses who would know what was happening from family members in different countries and sell this information for a price. For hundreds of years these CTPs created a new paradigm of global commerce and made absolute pantsloads of money. However this is not scalable and every additional degree of separation from the end decision maker reduces the effectiveness of their trust proxying. As physical frictions kept reducing and the sphere of accessible commerce kept growing, even this business model needed to evolve.
The Road to the Archetype of 1
How to take the model from needing to know someone who knows someone who knows someone to being able to have an ability to generate some kind of information about any potential counterparty? The answer goes back to option 1 from section 1, start using data to create profiles (or archetypes) of people to create a model of their likely behavior based on people similar to them. So CTPs evolved and began to move to creating archetypes of a person when there was no personal relationship. This allows them to move from having more total trust in a limited number of people, to having some level of trust in everybody!
The models were crude at first (people from Canada like this, people from Australia like this), but with maximization spurring them forward and the cost of data collection and processing reducing, the models began to become more granular and the proxies tighter (tailored credit scoring, granular insurance profiles, suspicious transaction reports, targeted advertising etc). The end goal for these CTPs is to have the tightest, most granular archetypes to maximize their ability to proxy trust (and monetize it) on an infinitely scalable basis. Effectively they want to generate an archetype of 1….you!
But there is hope! The same trends that allow CTPs to gather sufficient data to generate these archetypes are also leading to a revolution of peer to peer trust and self sovereign data, which can lead to a return to an online version of the direct, decentralized trust we once had when we were interacting with people in our tribe. An Archetype of 1, totally controlled by its creator and where the creator can reveal only the exact pieces of information required to secure trust for that given transaction or product without revealing everything about yourself to a 3rd party sounds infinitely better than one controlled and monetized to the benefit of the internet behemoths of today.
Soooooooo……
Key challenges remain in moving towards this Archetype of 1 (privacy, siloed data, untrustworthy data, exclusion due to scale, uneconomical data) and with those stated that brings us roughly to the world we have today defined by 10 key traits:
1. A fully digital world where frictions are almost entirely trust based and focused around bad actors and bad products
2. CTPs have evolved their moat from being generated due to a web of personal relationships to having the best web of behavioural archetypes
3. Capital requirements are a key driver of scale and trust gaps are a key driver of capital requirements
4. Reducing capital requirements by reducing trust risk leads to rational reductions in scale requirements and ability for smaller companies to compete
5. More granular archetypes created by better data lead to a reduction in trust risk and the ability to operate at reduced scale
6. However there are some limits to this reduction in scale as there are still risks unrelated to bad product and bad actors that require capital (eg. Earthquake)
7. Better data and better data processing has led to an influx of entrepreneurs and products chasing more granular and better defined archetypes that previously were uneconomic
8. CTP’s goal is to create an “archetype of 1” which allows for perfect trust in a hypergranular archetype that is purely based on an individual instead of a group
9. Many many others share this goal, but want the archetype to be controlled by the person it represents and to be completely anonymous
10. There are still substantial roadblocks to creating the archetype of 1
So now we understand the rational landscape that we’re in and that decentralization is not the goal but a byproduct of the real goal: to perfectly replicate digital trust and free up as much capital as possible to reduce the cost of everything for everyone!
Part 2 we’ll dive into specifically what steps forward Fintech has made already, why blockchain is the catalyst for the next quantum leap forward, why Aion is the key to that and specifically how this feeds our thesis at Bicameral!!
Alex McDougall
Bicameral CIO
About Bicameral Ventures
Bicameral Ventures is an innovative Venture Capital fund which both raises and deploys capital directly in cryptocurrency and invests in early stage projects leveraging the Aion interoperability blockchain platform. Bicameral Ventures was co-founded by Kesem Frank, co-founder of Aion and Nuco Inc. and Alex McDougall, fintech and M&A investment banking veteran with Bank of Montreal Capital Markets.
