A good contract is a fair contract. A contract is fair when it is willfully entered into with full and informed consent. Fairness is not just a matter of how the contract is drafted, but how it is understood. Parties need to understand how their interests are served and need to feel confident that they will be protected under most reasonable circumstances. However, since all contracts are currently expressed as static documents, it can be difficult to capture in text dynamic, evolving relationships that are triggered by potential, future events. In order to account for such scenarios, contracts must enumerate various contingencies and remedies, often times resulting in hundreds of pages of complex, technical prose that even legal professionals find daunting. Signatories, founders and entrepreneurs in particular, are often told to “sign at the dotted line,” without actually reading, much less comprehending the full import of the contract. Even relatively simple contracts, like a standard venture capital term sheet, are difficult to understand when they account for just standard business contingencies. This can hardly be considered full and informed consent.
Contracts written as static documents are inherently limited by their inability to capture and generalize the intent of the drafters. Intent by definition cannot be literal, because it represents a principle of how to act rather than a particular kind of action. While such a principle or rule might be described in text, it cannot be implemented as text, since text is inherently descriptive whereas rules are procedural, that is, actions that are triggered by conditions, in short, algorithms.
Given these inherent limitations in textual contracts, might it not be possible to algorithmically represent and protect the interests of parties to a contract without knowing in advance what events or actions might impinge on those contracts? Instead of writing a contract, it may be more effective, efficient, and fair to program a contract to act in a way that transparently, exhaustively, and fairly preserves the interests of the parties for circumstances not previously contemplated. Clearly, there will be novel circumstances that cannot be anticipated, and hence, the algorithm would have to be amended, but for the vast majority of cases there could be empirical data to support the likelihood of different scenarios and how the contract would handle them in a highly transparent manner.
With this understanding, the Law Lab developed a kind of evolvable contract spreadsheet using the Wilson Sosini standard venture capital term sheet as a first use case. We focused on a venture capital term sheet because it is sufficiently complex to be representative and compelling, but sufficiently simple to be tractable. The evolvable contract spreadsheet allows users to explore in graphical terms the “what-if” implications of different provisions and clauses in a term sheet that are typically negotiated between venture capitalists and entrepreneurs. Moreover, by making it possible to explore different funding and ownership structures, what is in the trade called capitalization or “cap” tables, users are able to have a fairly clean definition of what the “fitness function” would be for the modeling. In this case, the fitness function is the preferences the different parties have for cap tables (e.g. percentages for different types of stock and their value at liquidation).
But what makes the evolvable contract spreadsheet highly novel is that it uses genetic algorithms to generate alternative combinations of term sheets to meet all the fitness conditions and preferences of the different parties. This contracting model allows us to demonstrate simply the implications of the various terms for all parties under different capitalization and business scenarios, making the contract far more transparent and comprehensible. Another key advantage is that by being automated, the drafting process becomes far more independent and inexpensive than traditional contracting.
Robustness in biology refers to the ability of an organism to survive and replicate under a variety of adverse and diverse conditions. Similarly, robust fairness in a contract refers to a contract’s ability to be both transparent and durable in protecting the original intent of the contracting parties.
Evolvable contract spreadsheets could lead to more transparent, less costly, and more durable contracting. Given these new possibilities in modeling contracts and generating alternatives, the traditional approach to term sheets and to a wide variety of contracting may no longer be necessary or desirable.