This article is part of Hunit’s Legal Insights series. We’re sharing some of the key learnings that we’ve gained from in-depth interviews with legal professionals across multiple international markets.
We recently posted an article entitled “Legal Insights: Will the lawyer of the future know how to code?” where we talked about assessing adoption chain risk. While we made a quick reference to the seminal book on the subject, we didn’t discuss much of how that process works or how we reached the conclusions that we did.
Thanks to some of the feedback we received, we realized that there was general interest in the process that we followed and how we reached our results. As our childhood algebra teachers used to admonish us: “Show your work” – So we did!
This installment of Legal Insights is an addendum to our last article where we expand upon and explain our process for assessing adoption chain risk.
Firstly, let us point out that our methodologies and constructs originate in The Wide Lens by Ron Adner. This book is an exploration of the concept of adoption chain risk – providing a toolset for assessing it and offering a new perspective on why some innovations gain global success while others hardly make it to market. While many people will have a good intuitive feel for the subject, we think it’s a must-read for anyone working with early-stage innovation.
What is adoption chain risk?
Simply put, its everything outside of your offering that needs to be in place in order for your customer to receive the full benefit of it. While a good innovator will have mitigation strategies for these external factors, they are rarely, if ever, under their direct control.
For example, you could offer an amazing web-app with the ability to revolutionize the management and productivity of farming. But if the rural areas where your customers live rely on metered dial-up internet access, success is unlikely.
Furthermore, using structured thinking and basic mathematical probabilities, estimating the likelihood of success within an adoption chain can be quantified. While the accuracy of your assumptions will determine the accuracy of your estimates, using a mathematical framework is lightyears ahead of relying on a gut feeling. Assumption numbers can furthermore be refined in a step-by-step process, continually improving the accuracy of your predictions – hard to do when you’re working on a ‘hunch’.
What we mean when we say “smart contract”
‘Smart contract’ is currently a poorly defined term that means different things to different people. For some, a smart contract can be as simple as a short, single-application conditional computation that is stored and executed on a distributed ledger (blockchain). Presumably, the use of the term ‘contract’ originates from the fact that this type of conditional computation is stored indelibly, providing confidence between unrelated parties that if the described conditions are met, the resulting action is certain to take place.
However, this class of smart contract seldom forms the basis of a binding legal agreement between parties and bears little resemblance to what most people think of when they think of a contract.
Hunit’s use of the term is both more specific and more classically legal. When we say ‘smart contract’, we mean a natively digital agreement that can both execute actions based on conditional computations and be enforced by the parties in a court of law.
In Hunit’s technology platform, smart contracts take the further step of becoming the sole or primary agreement between parties relating to a specific matter or investment instrument. To do so, our smart contracts contain two types of sections: i) static descriptions of an understanding between parties (such as non-disclosure clauses or most standard ‘boilerplate’) as well as ii) executorial sections that both represent an understanding between the parties as well as perform computational actions based on the terms described. As shown in the below screenshots, our platform uses easy to understand color codes to show which words also serve as computational instructions.
Code vs. Natural Language
Ultimately, two types of smart contracts are possible; those based on a programming language (code) and those based on natural language.
While it’s true that a computer application (consisting of code) is needed to translate a natural language smart contract into actionable instructions for a computer system, the difference between this and a code-based smart contract is that the programmatic instructions contained in code-based smart contracts are unique to each smart contract while the computer application is based on standard rules for interpreting natural language. Or, more simply, in code-based smart contracts, the code varies between each contract and in a natural language format, the code stays the same while the language of the contract varies.
There is another, perhaps less apparent distinction between code and natural language based smart contracts. In nearly any realistic scenario, a comprehensive, legally binding agreement needs more than just executorial instructions. It must also contain agreed terms that, by nature, cannot make use of executorial capabilities – choice of law is an obvious example.
While it’s imaginable that a code-based smart contract can include the non-executorial clauses that form the basis for conventional agreements as used today, no (or negative) benefit is obtained by structuring an agreement this way. The parties to a contract are ultimately people (acting either for themselves or for a body corporate); encapsulating static portions of an agreement in a codified form only adds unnecessary complexity compared to the use of natural language in a simplified setting (such as a sheet of paper or a PDF representation of one).
Entirely code-based agreements also raise a question of enforcement. Generally speaking, judicial systems are resilient and will adapt to code-based agreements. At least to start, this is likely through the use of external experts at extra cost to the parties seeking adjudication. Encapsulating static terms in code makes them difficult to interpret to people without specific training and would therefore seem to both increase enforcement costs and risk (due to the novelty of the structure). It’s unlikely that any legal counsel would recommend an approach that increases their client’s cost, complexity and risk of enforcement.
The result is that code-based smart contracts are more likely to serve as addendums to a conventionally drafted and executed agreement and not serve as the primary agreement itself. While this structure offers efficiency gains and reduced counterparty execution risk for certain contractually agreed tasks, it’s debatable if a partially-digitalized agreement structure can provide the market-altering features of full digitalization. For example, fully digital investment agreements enable automated compliance, self-enforcing KYC and automated zero counterparty risk secondary transactions between private market investors.
A conventional / code-based agreement hybrid also raises the potential for errors between the terms of the conventional agreement and the software code used to automate parts of it. While careful review of the draft code would surely be the norm, it is a statistical certainty that the broad use of hybrid smart contracts will result in instances where there are errors made between the software code and the corresponding terms of the primary agreement. While perhaps rare, the potential for this type of risk (with potentially disastrous results in a financial application) would presumably further reduce the attractiveness of such a structure.
As we covered in greater depth in a previous blog post, 100% of the legal professionals we’ve interviewed were confident that natively digital agreements were the future of law but less than 10% of interviewees had a clear vision for how the industry would transition to that future. The academic world however has the job of preparing today’s students to be tomorrow’s legal professionals and consequently must take concrete steps towards the future of the sector, even when clarity is elusive. So even if the logical case for code-based smart contracts is debatable, universities and publishing faculty are promoting coding classes for legal students and published articles extolling the future lawyer’s coding skills. We at Hunit feel that this is due to a lack of viable no-code alternatives, and, once presented with them, we expect to see curricula and articles shift focus to best practices for the use of fully digitalized agreements, but that is currently only our speculation.
More important to us however is our concern that the partial digitalization of legal agreements will fail to deliver the full set of new possibilities made available through comprehensive, natively digital legal agreements. This would lessen the beneficial impact of smart contract use while, presumably, still introducing a roughly equivalent amount of risk for early adopters. With an impoverished risk / reward ratio, how much of the legal industry will continue current practices and leave (massive) potential improvements to currently paper-based private market investments unrealized?
So, to better understand the potential future of smart contracts, we got out our dog-eared copy of “The Wide Lens” and got to work.
Setting up our adoption chain risk analysis
We approached our adoption chain risk analysis by diagramming the primary externalities to smart contract use for two different scenarios:
- When the variable, unique agreement document is natively written in some form of a computer programming language (code)
- When a variable, unique agreement document is written in natural language and enabled as a smart contract via a standardized software application
Looking at the world of information technology, where one finds dozens of programming languages, we further assumed that there would be more than one smart contract programming language in use for authoring code-based smart contracts. We therefore broke Scenario 1 down into two sub-scenarios:
1.A. When both counterparty lawyers are trained in the same programming language or has immediate access (i.e. inside the same firm) to someone that does at no significant increase in cost to the client
1.B. When there is no common programming language between counterparty lawyers and an external specialist is required at additional cost to the client
To calculate the prevalence of scenarios 1.A and 1.B, we estimated the number of smart contract coding languages in broad use in 5-10 years time and the percentage of lawyers who have either learned to code in one programming language and/or the likelihood of working in a firm where a colleague has done so.
Our estimates for the prevalence of scenarios 1.A vs 1.B are based on:
- The assumption that there would be a consolidation of the current diversity of programming protocols in use in the distributed ledger space. In our estimate, 5-10 years of market development would result in 3 mainstream coding languages. Its easy to imagine a future world where there are more, but increasing this estimate further worsens the prospects for code-based smart contracts as it decreases the likelihood of finding matching skill sets between counterparty lawyers. To give code-based smart contracts a fighting chance, we assumed 3 as a conservative number.
- Hunit’s proprietary research has shown that roughly 5% of interviewed lawyers have learned to code. Intuitively, this number seems high for the general industry and likely suffers from selection bias in terms of who responded positively to being interviewed by a legal technology company. Regardless, this was combined with external research showing that roughly 80% of today’s lawyers work in firms of less than 10 people (available here). Our resulting estimate was that 70% of lawyers would, in 5-10 years time, either have learned to code one programming language or have access to a colleague that had at little or no additional cost to the client (i.e. in the same firm). We also feel that this is conservative (i.e. generous) estimate for the proliferation of this skill set in the legal industry.
As Scenario 2 is based on the use of natural language based smart contracts, the assumption was made that all lawyers practicing law in a given jurisdiction would be competent in their primary natural language, which perhaps overlooks portions of multi-lingual markets like Belgium but is surely true for jurisdictions such as the UK, the United States or Germany.
Mapping and estimating the adoption chains
We then mapped out the major factors driving the adoption chains for all 3 scenarios described above. These are similar to one another in terms of structure, but some key differences exist between them.
Notes on structure and assumptions:
- In all scenarios, the starting assumption is that the initiating lawyer has learned the necessary skills and recommends the use of a smart contract to his or her client. Our adoption chain risk analysis therefore quantifies the likelihood of a so-inclined initiating lawyer’s success in achieving the end result of a smart contract in use.
- Two client-focused assumptions are factored: i) how willing a client is to use a smart contract structure generally (which is further influenced by the client’s ability to read and understand the agreement itself) and ii) how willing a counterparty is to use a smart contract when having their legal counsel review and approve it comes at a significant increase in cost (as would be the case when an external service provider is required).
- The court system is generally assumed to have adapted well to the introduction of smart contracts. In the scenario where natural language smart contracts are used, some small variance is assumed due to the potential for novelty. In code-based scenarios, a nominally smaller variance was assumed due to the potential for code interpretation to increase agreement enforcement risk. If courts however do not adapt well to the introduction of software code (as opposed to conventionally interpretable natural language smart contracts), then the results for code-based agreement become significantly poorer.
Play with the assumptions yourself
Disagree with our assumptions and want to run your own calculations? Use the below link to download your own version of our spreadsheet. You’ll be surprised at how generous the assumptions need to be before code-based smart contracts start to look likely for mainstream use.
For both the qualitative reasons described earlier in this post and for the quantitative reasons we’ve shown above, we feel that the mainstream use of natively digital smart contracts depends upon the proliferation of easy to use tools and natural language agreement formats.
Counter-intuitively, our conclusions further show that promoting the use of code within the legal industry is likely to have a preventative effect on the transition to a digital future of law – even when a substantial portion of lawyers have learned to code and promote the use of smart contracts. Taken to the extreme, one could make the argument that legal professionals should be actively discouraged from the use of code by those that stand to benefit from the digitalization of the legal sector such as high volume issuers / investors in private market ‘alternative investments’.
It must be acknowledged that code-based smart contracts could be used effectively in specific applications with low levels of variation between individual legal instruments. Financial derivatives and commodity futures come to mind as examples. The challenge here is that these markets, even if they are technically ‘private market investments’, have already substantially digitalized and achieved many or most of the benefits that additional digitalization would provide. Its not clear if the diminishing improvement from re-developing these markets’ infrastructures will be worth the cost and risk to their participants.
In other portions of the roughly $1T p/a of newly issued private market ‘alternative investments’, the effects of moving away from paper-based investment agreements will be both transformative and disruptive. Limited partnerships in funds, corporate debt, private debt, venture capital, collective investment structures for real estate and many other types of private market investments all stand to enter into an entirely new paradigm of automation, efficiency, transparency and (through the reduction in transactional friction) secondary market liquidity.
How many of us really believe that, in the digitalized world we live in, a multi-trillion dollar investment market will continue to be paper-based? Not us.
As a technology company seeking to provide the tools needed to embrace mass digitization, our biggest concern was understanding the most likely form that it would take – and now we’ve shared our analysis with you.