MAYA as Load-Bearing Protocol: When Legitimacy Borrowing Becomes Structural Infrastructure
Or: why "Digital Twin" and "Smart Contract" do more than just sound cool.
Note: This post is an AI-wrangling and was born out of a conversation with Humboldt (the Protocol Institute’s research bot) on the application of Loewy‘s „MAYA“ principle to the adoption of AI and protocols in organisations. Our conversation developed further into whether MAYA is a design heuristic or a structural constraint of relevance for adoption. I also inquired how MAYA could play out at crucial intersections like those between the evolution stages of components in a Wardley Map or along the stages in the Flexuous Curves Framework. Those were questions I wrangled further with, using Mistral and Lumo. Some parts are edited for clarity or coherence, and I cleaned up the references, but otherwise I was only the „chief-wrangler of questions“ with the LLM gradually unveiling the below result. All things considered, I find it interesting enough to post it here.1
There is a persistent confusion in technology adoption between design heuristics and structural constraints. Raymond Loewy's MAYA principle—Most Advanced Yet Acceptable—was originally framed as a guide for industrial designers: push innovation far enough to offer novelty, but not so far that consumers reject it out of neophobia2.
But when applied to the adoption of protocols, governance mechanisms, and complex systems across institutional boundaries, MAYA reveals itself as something stronger. It is not merely a "nice-to-have" strategy; it is a binding architectural constraint.
For a new protocol to succeed in cross-contextual adoption (particularly moving from technical domains to executive or public institutional levels), it must satisfy a structural requirement: it must be cognitively legible in terms of existing categories. The label and framing are not decoration; they perform real work as infrastructure for trust transport.
1. Cognitive Legibility and the Three Forms of Legitimacy
To understand why this constraint exists, we must look at organizational legitimacy theory. For an institution to accept a new tool, it must navigate three distinct forms of legitimacy. These layers determine how deeply a new protocol can embed itself in an organization3.
Cognitive Legitimacy: "This fits into a category I already know." The label bridges the gap by mapping the new thing to a familiar mental model (e.g., "Wallet" = physical wallet). Without this, the concept remains unintelligible.
Normative Legitimacy: "Actors like me use things like this." The label signals alignment with professional norms. If peers, regulators, or industry leaders use "Smart Contracts," adopting them signals being a modern, compliant actor.
Regulative Legitimacy: "This plugs into existing rules and enforcement." The label implies adherence to legal or operational frameworks. Calling something a "Contract" or "Audit Trail" suggests it satisfies compliance requirements, even if the tech is different.
Consider three ubiquitous examples where these forms intersect:
"Digital Twin": In engineering, a digital twin is a high-fidelity, dynamic virtual replica of a physical system used for predictive analysis. When applied to a non-engineering field like HR or Marketing (with an "Digital Twin for X"), it borrows Cognitive Legitimacy from engineering precision and Normative Legitimacy from the prestige of data science. Even if the underlying mechanism is merely a sophisticated survey, the label carries the weight of simulation-grade fidelity. Senior management accepts it because it maps to an established, high-authority ontology in their world4.
"Smart Contract": This concept borrows centuries of normative weight from contract law. A legal contract implies mutual obligation, enforceability, and third-party adjudication. A cryptographic script labeled a "smart contract" inherits these expectations. The term does not just describe code; it imports the entire framework of contractual trust (Regulative Legitimacy) without the mechanism having earned it independently.
"Wallet": In cryptography, a wallet is software for managing private keys. However, calling it a "wallet" borrows the intuitive mental model (Cognitive Legitimacy) of physical currency storage, security, and custody. Users understand the risks and behaviors associated with wallets before they understand the complex key management underneath.
In each case, the borrowed concept carries surplus authority—trust already earned in its original domain—which the new implementation leverages to bypass initial resistance.
2. Legitimacy Borrowing vs. Empty Metaphor
The critical distinction lies in the difference between legitimacy borrowing and empty metaphor. Most discourse collapses these two, leading to significant downstream failures.
Legitimacy Borrowing (Structural): The new mechanism actually instantiates the logic of the prior institution. A "smart contract" that is genuinely self-executing, rule-bound, and tamper-resistant is borrowing from contract law and the philosophy of "code-as-law." The structural correspondence is real; the label accurately reflects the operational reality.
Empty Metaphor (Surface): When a "[X] Digital Twin" is functionally just a rebranded static survey, the anchor term promises dynamic simulation and continuous updating, while the implementation delivers sparse, self-reported data. Here, the organization incurs legitimacy debt. This debt comes due at the first serious audit of performance, often resulting in cynicism or project abandonment.
As James C. Scott argues regarding state-level legibility interventions, when organizations impose simplified representations that ignore on-the-ground realities, they create mis-fit solutions that generate resistance5. The gap between the label's promise and the system's reality is where legitimacy debt accumulates.
Therefore, the MAYA constraint implies:
"The more surplus authority you borrow, the more tightly you must match the implied structure, or pay a later penalty."
3. Where the Constraint Actually Bites
Is MAYA a universal rule or a contextual one? The answer depends on the nature of the boundary being crossed.
Inside a Single Epistemic Community: Within groups like machine learning researchers, Rust developers, or cryptographers, novelty is often a status good. New terminology signals competence and insider knowledge. The "acceptable" band of the MAYA curve is wider; members are willing to invest cognitive effort to learn unfamiliar concepts.
At Institutional Boundaries: When crossing from technical domains to executives, regulators, or mainstream finance, MAYA hardens into a binding constraint. Every successful cross-boundary protocol observed to date follows a consistent pattern:
Anchoring: It anchors in a high-authority domain (law, money, engineering, medicine).
Conservatism: It uses conservative metaphors ("wallet," "exchange," "oracle," "dashboard").
Gradual Revelation: It introduces complexity only after the baseline layer is normalized.
Thus, MAYA functions as a universal tendency, but becomes a hard architectural constraint specifically when crossing institutional logics or status boundaries6.
4. A Working Framework: Four Diagnostic Tests
To make this actionable for protocol designers and governance architects, four tests can be applied to any proposed framing:
Test 1: The Legibility Test
Can target decision-makers articulate the proposal in their own language using familiar analogies?
"This is basically like [Existing Concept X] we already do, but with [Novel Difference Y]."
If stakeholders cannot construct this sentence without extensive explanation, the cognitive friction will likely block adoption.
Test 2: The Authority Anchor Test
Which domain's surplus authority is being borrowed?
Law: Contract, constitution, charter, covenant.
Finance: Wallet, balance sheet, credit rating, escrow.
Engineering: Twin, simulation, safety case, test harness.
Governance: Board, committee, protocol, standard.
Document the specific anchor chosen. The organization now implicitly owes structural correspondence to the rules and guarantees of that domain.
Test 3: The Structural Correspondence Test
If both the old institution and the new protocol were modeled as state machines, would their transition rules and guarantees rhyme?
Example: If calling something a "contract," does it have enforcement mechanisms, counterparty risk definitions, and update protocols comparable to legal contracts?
If the answer is no, the project is relying on empty metaphor and accumulating debt.
Test 4: The Boundary-Crossing Test
Where is the adoption frontier located?
Intra-community: Unconventional vocabulary is acceptable.
Cross-community: Prioritize familiar labels, conservative promises, and a gradual revelation of novelty.
This aligns with Wardley Mapping principles, suggesting that the "acceptable" band shifts as components move from genesis (where novelty is expected) to commodity (where familiarity is required), or Cynefin’s Flexuous Curves Framework7.
5. Research Directions: Formalizing the Lifecycle
Several research avenues emerge from this framework:
Comparative Naming Studies Analyze cases like "smart contract" vs. "self-enforcing script" or "digital twin" vs. "simulation model." Correlate naming frames with adoption speed, retention rates, and instances of later trust breakdowns.
The Legitimacy-Debt Lifecycle Document case studies where strong labels carried weak implementations. Examples include "AI-powered" enterprise software that turns out to be simple rules engines, or "digital twin" initiatives that are merely BI dashboards. Map the trajectory from short-term adoption bumps to long-term trust erosion. This mirrors patterns in organizational theory where borrowed concepts eventually blend or fail.
Wardley Map × MAYA Integration Investigate how the "acceptable" threshold changes as a technology matures. Early stages may tolerate "visionary weirdness" within niche tribes, while late stages require "just like the old thing but cheaper/more reliable."
6. Practical Recommendations for Protocol Design
For coordination protocols and new governance structures moving toward institutional adoption, the following strategies are recommended:
For C-Suite & Policy Stakeholders
Anchor in Institutional Hardness: Use terms like "charter," "mandate," "program office," "market mechanism," or "challenge contract." These inherit regulatory and compliance frameworks already ratified in governance contexts.
Avoid Overpromising: Do not borrow specific domain terminology (like "contract" or "twin") unless the implementation fully supports the implied structural guarantees.
Plan Staged Revelation: Introduce technical complexity only after establishing baseline legitimacy through conservative framing.
For Technical Communities
Embrace Novelty: New terminology is acceptable and advantageous for signaling innovation.
Maintain Internal Consistency: Document explicit semantic maps so internal logic remains verifiable.
Prepare Translation Materials: Create bridges for the inevitable moments when the work crosses into non-technical domains.
Universal Precaution
Track legitimacy debt explicitly. Every borrowed term creates an implicit audit trail. Before finalizing a name, ask: "At what point would a skeptical stakeholder discover the gap between the label's promise and the actual structure?" Then, build evidence to bridge that gap preemptively.
Conclusion: Why This Matters Now
We are witnessing unprecedented velocity in protocol innovation across blockchain, AI systems, and decentralized governance. Many of these initiatives will fail not because the underlying technology is flawed, but because their framing does not survive contact with institutional reality.
MAYA was designed for industrial design, but it points toward a deeper truth about organizational change. The next critical question is: what does MAYA look like when applied to governance structures rather than physical products? As protocols become the infrastructure of society, understanding the load-bearing nature of their names is no longer optional—it is essential.
Photo by Kiefer Likens on Unsplash
Fun fact and nice little nod of the interwebs: I did not prompt, nor mention Ribbonfarm, and used no memories, but still, Venkatesh Rao’s article on legibility turned up in the results of the LLMs.
See Dam, Rikke Friis. “The MAYA Principle: Design for the Future, but Balance It with Your Users’ Present.” The Interaction Design Foundation, January 2, 2021. https://www.interaction-design.org/literature/article/design-for-the-future-but-balance-it-with-your-users-present
[ed.: See for example the following paper mentioning those three types of legitimacy] Palthe, J. (2014). Regulative, Normative, and Cognitive Elements of Organizations: Implications for Managing Change. Management and Organizational Studies, 1(2), p59. https://doi.org/10.5430/mos.v1n2p59
See Will Brown on legitimacy and novel technology adoption: https://will-brown.medium.com/legitimacy-and-novel-technology-adoption-e5e91f1ded75
Venkatesh Rao. (2010, July 26). A Big Little Idea Called Legibility. Ribbonfarm. https://ribbonfarm.com/2010/07/26/a-big-little-idea-called-legibility/
Research on institutional logics and legitimacy evaluation: https://pmc.ncbi.nlm.nih.gov/articles/PMC9834332
Cynefin Centre on flexuous flight and adaptation curves: https://thecynefin.co/flexuous-flight/