Why Machine Contracts Generally Fail

Contracts are human-first. They exist to fix a shared intention between parties and to give those parties a reliable way to act on that intention together. Mechanisms—paper, software, or code—are tools. They should support, not override, the parties’ will. The posture that “code is law” treats the tool as the authority. It works only until reality changes, which it always does.

The intuition: machines feel rigid for a reason

Automation is built for uniformity and predictability. It removes variation, condenses nuance, and replaces discretion with rules. That is a feature in domains where inputs are stable and the cost of deviation is high. It is less effective when judgment matters, when context shifts, or when values compete.

In commercial relationships, judgment and context are common. Pricing adapts to demand. Deliverables evolve as products and teams evolve. Market shocks reframe priorities. A mechanism that insists the world look like last quarter’s assumptions will be efficient in the narrow sense and brittle in the broader one. It will do exactly what it was told, even when everyone involved knows that continuing on rails would miss the mark.

Contracts in law: the primacy of shared intention

In law, a contract is not the paper; it is the parties’ common intention. The document is evidence of that intention, often the best evidence, but still evidence. Courts privilege the parties’ assent—their mutual agreement to the same material terms, expressed in words or conduct—over formalism when the two diverge.

This primacy of intention explains why context matters: how the parties behaved, what they communicated, and how business norms fill gaps. The written instrument anchors the analysis, yet it does not become an idol. When language is ambiguous, courts look to purpose. When literal wording would produce absurd results, courts avoid them. The law prefers coherence with what the parties actually set out to do.

Change is the norm, not the edge case

People and circumstances change. Most mature relationships build in ways to change the deal while preserving trust and traceability. Amendments restate terms. Waivers excuse strict performance for a time. A side letter clarifies intent or adds a focused accommodation. The course of dealing—how the parties actually perform over time—can shape how ambiguous language is read.

Systems that support contracting should make legitimate change easy, visible, and consensual. They should capture who proposed a deviation, who approved it, and when it took effect. They should keep the historical record intact so that later readers can see what changed and why. And they should help the parties converge on a single source of truth without forcing them to pretend that contingency does not exist.

Where “smart contracts” misfit

“Smart contracts” are valuable for narrow, mechanical commitments where the parties want automated settlement without ongoing discretion. If funds should release when a simple condition is met, code can perform that task reliably.

Problems arise when immutability is celebrated as a universal virtue. The ability to change one’s mind—with the other party’s consent—is not a failure of commitment; it is a basic human freedom and a commercial necessity. Some platforms now allow upgrade paths, pausable modules, or governance-controlled changes. Those are steps in the right direction, but they introduce friction, centralization points, and limits. They also acknowledge the central point: intention evolves.

The slogan “code is law” elevates mechanism over intent. Law gives authority to the contract because the parties chose to bind themselves, not because a machine executed a branch. When the machine’s behavior reflects the parties’ will, it is useful. When it does not, insisting on execution because “that’s what the code does” confuses evidence (the mechanism) with essence (the shared intention).

Evidence vs. essence

Rigid execution can depart from what parties actually meant. Consider mistaken triggers, stale data, or unanticipated edge cases: a pricing routine that fails on a boundary date because of a time-zone shift; an oracle that posts an anomalous price; a split or re-denomination that the mechanism does not contemplate; a notice that lands in a spam filter. A human reader can understand what was supposed to happen and correct course. A machine that is not designed to accommodate correction will amplify the initial error and erode trust.

This is not an argument against precision. It is an argument for faithful execution of intention. The more the system treats the written instrument and the workflow as evidence of what the parties decided—rather than as the decision itself—the more resilient the contract becomes. Evidence should be complete and auditable. Essence should remain with the people who bear the consequences.

What machines should do

Machines should narrow ambiguity, not pretend to abolish it. They should surface inconsistencies early, invite clarification, and record the resulting choices. They should preserve a durable audit trail of intent and consent. When asked to execute, they should do precisely what was authorized—no more, no less.

Two attributes matter in practice. First, reversibility. If an automated action departs from the parties’ intention, the system should support prompt, consensual correction with a clear record of what changed. Second, explicit approvals. The system should collect and authenticate the approvals that matter for a given decision, and make those approvals visible to both sides.

These are table stakes. They keep the machine in its lane: assisting clear thinking, making decisions legible, and carrying out those decisions faithfully. They do not replace the judgments that define the relationship.

The Amicus approach

Amicus treats contracts the way the legal system does: as shared intention. The platform manages conventions that help parties express that intention clearly. It enables clean, scoped amendments, and keeps prior versions and rationales available. It preserves records of proposals, approvals, and effective dates. And it executes what is legitimately and duly intended.

The goal is not to be clever. It is to make intention legible and durable. Amicus supports structured language where structure helps, invites narrative where nuance matters, and ties both to reliable evidence of consent. Where automation is appropriate, the system executes without drama; where judgment is required, it leaves space for deliberation and makes the resulting choice explicit. Like a true friend, it does not presume to be smarter than the parties; it keeps them aligned.

Bottom line

Machine rigidity fails because it ignores living intention. Systems that clarify and memorialize consent make contracts more reliable, not less. Build for shared understanding first, then let mechanisms serve that understanding. When reality changes—and it will—the contract should change with it, on purpose and with proof.