TLDR

1. A data asset is not an intelligence moat unless the firm can observe outcomes, reconnect them to the action and system version, judge them independently, and convert the lesson into a controlled change.
2. Feedback rights are a bundle: capture, linkage, evaluation, improvement, retention and portability. A supplier contract can grant dashboard access while withholding the rights that matter.
3. Context gives an observation meaning. Without population, workflow, authority, intervention and version data, more records can make a wrong lesson look statistically impressive.
4. Learning speed is end to end. Measure time from consequential event to validated change, including outcome delay, joining, adjudication, approval and safe release.
5. The Feedback-Rights Audit tests whether a proposed loop is defensible, merely useful, a telemetry trap or incapable of supporting any learning claim.

Imagine two insurers launching the same document model on the same morning. They use an identical interface, serve comparable claim types and begin with the same measured accuracy. Firm A records prompts, completions and a thumbs-up signal. Its supplier retains most logs, aggregates them across clients and offers a polished dashboard. Firm B records the model version, extracted evidence, adjuster edit, decision reason, policy wording, claim outcome and later appeal. Its contract permits evaluation and internal improvement, while preserving exportable event records and provenance.

Six months later, Firm A has more interactions. Firm B knows which interventions prevented leakage in storm claims, which apparent savings produced successful appeals, where a new policy clause broke retrieval and which corrections remain reliable across offices. It can change its workflow, evaluation set and retrieval rules within days. The firms did not begin with different intelligence. They acquired different abilities to learn.

This thought experiment exposes a loose habit in AI strategy. Leaders call proprietary data a moat, then count rows, users or conversations. Yet a record has strategic value only relative to a decision that it can improve. Bajari and colleagues found diminishing forecast gains from more history for a product and an essentially flat effect from adding products across many retail categories.1 More is not the same as more informative. The relevant asset is the production system that repeatedly converts distinctive experience into validated operational change.

Figure 1. The moat surrounds a learning circuit, not a data pile

Core mechanism

01 · Strategic claimA moat is a rate advantage in warranted learning

An intelligence moat is the persistent ability to reduce decision error from proprietary experience faster than rivals, because the firm controls the rights, context and conversion routines required to learn. This definition makes three distinctions. The outcome is better decisions, not a larger warehouse. The input is experience that rivals cannot reconstruct cheaply, not any private file. The mechanism is a repeatable rate advantage, not a one-off model improvement.

Learning through use has an older economic foundation. Arrow modelled experience as a source of technical change rather than treating productivity as the result of capital accumulation alone.16 The AI version needs a stricter qualification. Repetition creates exposure, but exposure becomes learning only when the organisation can discriminate a better decision from a merely more familiar one. Reliable outcomes, challenge cases and an authorised change route are the bridge between doing more work and knowing more about how the work should be done.

Classical strategy asks whether a resource is valuable, scarce and difficult to reproduce. AI changes the object of analysis. A snapshot of labelled data often depreciates as models, policies, populations and work practices change. A live learning circuit can replenish itself. Teece's account of complementary assets helps explain why technical invention alone may not capture value: commercial outcomes depend on the surrounding capabilities and contracts.2 For deployed AI, those complements include outcome access, reliable identifiers, domain judgement, experiment design, release authority and a safe path to alter work.

The loop must also be warranted. A rapid sequence of model outputs training later models may be fast yet epistemically empty. Shumailov and colleagues show that indiscriminate recursive training on generated data can make learned distributions lose their tails and eventually collapse; they note the rising value of genuine human interaction data.3 Human origin alone does not guarantee truth, but the result punctures the belief that self-produced volume automatically compounds intelligence.

Figure 2. A feedback packet preserves the causal address

Data anatomy

02 · Feedback rightsDashboard access is not the right to learn

Feedback rights are distributed across law, contract, product design and internal authority. The right to capture an event does not necessarily include the right to link it to a person, retain it, reuse it for evaluation, train with it or transfer it when switching suppliers. Personal data principles such as purpose limitation, minimisation and storage limitation constrain convenient accumulation. The UK Information Commissioner's Office stresses that organisations cannot collect extra personal data merely because it may become useful later.4 A defensible loop starts with a stated purpose and necessary fields, not a speculative lake.

Separate six questions. Can the firm observe the consequential event? Can it join the event to the exact action and version? Can qualified people evaluate it? Can the result be used to improve the model, retrieval layer, policy or workflow? Can the necessary record be retained for the relevant outcome period? Can raw and derived records, including metadata, leave the supplier in a usable form? A no at one layer can immobilise all the others.

The European Union's Data Act illustrates why access and usability must be distinguished. It creates rules for user access to data from connected products and related services, including relevant metadata and machine-readable delivery, and addresses switching between data-processing services.5 It does not create a general legal basis for personal-data processing. A contract and architecture therefore need to specify the actual data, roles, purpose and transfer route rather than rely on the slogan that the user owns the data.

Consider a manufacturer buying an AI maintenance assistant. The supplier permits prompt export but withholds retrieval traces, model-version identifiers and corrected equipment taxonomy. The manufacturer sees that a recommendation was accepted, yet cannot tell which manual passage supported it, whether the engineer changed it or whether the repair prevented recurrence. It has interaction rights but not learning rights. If the supplier improves from pooled customer data while each customer receives only a dashboard, the supplier may own the stronger loop.

Figure 3. Six rights form a serial circuit

Rights stack

Write the rights schedule before the model schedule

A useful procurement schedule names event classes rather than claiming all data. It defines the identifiers and version fields required for linkage; the party authorised to adjudicate outcomes; the permitted evaluation and improvement purposes; retention and deletion triggers; derived artefacts such as error slices or embeddings; and export formats with tested delivery times. It also says what the supplier may learn from the customer's activity and whether that learning reaches competitors.

This is not an argument for maximal exclusivity. Exclusive data access may reduce switching and competition, while pooled learning may improve safety. The OECD notes that specialised data and feedback effects can concentrate downstream AI markets, and that portability, interoperability and contractual terms shape contestability.6 Strategy must choose which learning needs to remain distinctive, which should be shared for assurance and which cannot be processed at all.

03 · ContextA label without its decision setting is a counterfeit lesson

Suppose a contract-drafting assistant suggests a liability clause and the lawyer accepts it. Is acceptance positive feedback? Perhaps the lawyer had no time, intended to revisit the clause or accepted it only because the counterparty had already fixed the commercial position. The contract may later be amended, disputed or perform perfectly. An acceptance click records an action. It does not establish the quality of the recommendation.

Context supplies the conditions under which an observation can travel. At minimum, record the population, task, constraints, available evidence, human role, intervention, model and retrieval versions, policy state and outcome window. Some fields are sensitive and should not be centralised. They can be transformed into authorised slices, evaluated within controlled environments or represented by privacy-preserving summaries. The strategic requirement is not to hoard context, but to preserve enough structure to avoid false transfer.

Sambasivan and colleagues interviewed 53 practitioners across high-stakes domains and reported data cascades in 92 per cent of the cases they studied: delayed, compounding downstream problems caused by undervalued data work.7 The finding is not a population estimate for all AI projects. It is evidence that missing context, weak documentation and fragmented responsibilities can make defects both pervasive and hard to see.

Production systems also change the data they later observe. A triage model routes easy cases away from specialists, so the specialists' case mix becomes harder. A recommendation system affects what users encounter. A fraud model changes adversary behaviour. Sculley and colleagues identify feedback loops and unstable data dependencies among the sources of hidden technical debt in machine-learning systems.8 Treating post-deployment data as if it came from an untouched world teaches the system from consequences it helped create.

Perdomo and colleagues formalise this difficulty as performative prediction: deploying a predictor can alter the distribution on which its later performance is assessed.17 The issue is not confined to advertising or ranking. In a healthcare simulation, Adam and colleagues show that when predictions influence the labels used in later updates, full adoption of a predicted label can increase false-positive rates across retraining cycles.18 This is a negative control for the easy flywheel story. A loop may be active, frequent and technically closed while moving steadily away from the outcome its operator intended.

An intervention record is therefore essential. It states what the model proposed, what a person or policy changed, what alternative was available and which unit was actually exposed. Where a counterfactual cannot be observed, the record should say so and narrow the inference. A later success cannot be credited to the model when an expert replaced its recommendation, nor can a failure be attributed to the expert when the workflow prevented an override. Separating these effects is slower than counting acceptance, but it is the difference between evidence and institutional folklore.

Figure 4. The telemetry quarry is a negative control

Negative control

Worked example: compressor-failure triage

An industrial service network uses an assistant to rank likely causes of compressor failure. The first dashboard looks excellent: engineers open the top-ranked manual section in 71 per cent of cases. The product team calls this relevance. A feedback-packet review reveals that senior engineers open the section to disprove it, rural sites have different sensor calibration, and the eventual repair code is entered days later in another system.

The firm creates a bounded join. For one equipment family, it links the assistant event to sensor lineage, model and retrieval versions, technician edit, replacement part, repeat visit within 30 days and an independently reviewed failure code. It has authority to use those fields for the stated maintenance purpose and expires the identifiable linkage after adjudication. The join shows that opening rate is positively associated with familiarity, not repair success. A small retrieval change improves first-visit resolution for one sensor generation but harms another, so the release remains sliced.

The moat is not the new ranking. A rival can imitate it. The harder-to-copy asset is the firm's ability to obtain delayed repair outcomes, connect them across operational systems, enlist technicians in adjudication, recognise a sensor-specific boundary and release a controlled change. This capability can improve later models from multiple suppliers. It is therefore less brittle than dependence on a single model family.

Figure 5. Context determines whether experience transfers

Worked example

04 · Learning speedCount the whole journey from consequence to controlled change

Teams often measure model latency in milliseconds and feedback latency in quarters. For strategy, the second is usually more important. Define cycle time from the moment a decision-relevant consequence becomes observable to the moment a validated change reaches its authorised population. Include outcome delay, data availability, joining, quality checks, adjudication, decision, implementation, assurance and release. A fast annotation queue cannot compensate for a six-month wait for the real outcome.

Organisational learning research distinguishes accumulated experience from the deliberate work of articulating and codifying what experience means. Zollo and Winter argue that dynamic capabilities evolve through the interaction of experience accumulation, knowledge articulation and knowledge codification, with their usefulness depending on task frequency, homogeneity and causal ambiguity.9 This matters because AI teams can automate accumulation while leaving interpretation and conversion episodic.

Prior knowledge also shapes what a firm can absorb. Cohen and Levinthal describe absorptive capacity as the ability to recognise, assimilate and apply external information, and argue that it is path-dependent.10 A competitor can license the same frontier model but still lack the firm's failure taxonomy, adjudication practice, historical comparisons and authority network. Those capabilities reduce the time needed to identify a meaningful discrepancy and act on it.

Field evidence shows how unevenly such gains can arrive. Brynjolfsson, Li and Raymond studied a staggered deployment to 5,172 customer-support agents and found a 15 per cent average increase in issues resolved per hour, with larger gains for less experienced and lower-skilled workers and evidence consistent with learning from the tool.19 One firm and occupation cannot establish a universal AI learning curve. The heterogeneity is the useful strategic fact: the same system can interact differently with existing knowledge, so a defensible loop must preserve worker experience, task type and intervention context rather than average them away.

Do not optimise the sum blindly. Shortening adjudication by replacing experts with the model being assessed creates circular evidence. Releasing daily may swamp operations and erase the stable comparison needed to learn. The target is the shortest cycle that preserves independent judgement, a valid comparison and recoverability. Microsoft research on controlled online experiments shows both the power of causal tests and the cultural and technical infrastructure required to trust them.11 Telemetry loss alone can bias experiment results and lead to wrong conclusions.12

Figure 6. Learning speed has five clocks and one truth gate

Cycle-time model

A better-model thought experiment

Suppose a rival buys a model that is ten percentage points better on today's evaluation. Does the feedback moat disappear? If the task distribution is stable, the evaluation represents value and the superior model is available at a sustainable price, perhaps it does. A learning loop is not magic protection from a large level advantage.

Now let policy language, customer mix and workflow change monthly. The rival cannot connect disputes to model versions for eight weeks; the incumbent can adjudicate a representative slice and alter retrieval within ten days. The incumbent may recover, but only if the improvement is real, transfer is bounded and switching to the better base model remains possible. The most robust strategy combines an open model option with a proprietary learning circuit. It does not defend yesterday's model to protect the story of a moat.

05 · Decision instrumentThe Feedback-Rights Audit

Apply the audit to one decision and one defined population. Inspect evidence that exists now. A score describes the loop, not the importance of the use case, legality of processing or probability of advantage. Attach a document, system test, owner and expiry date to each non-zero answer.

Feedback-Rights Audit

The audit asks whether consequential experience can become portable, context-valid and controlled improvement before it loses relevance.

Right to observe consequential outcomesNo lawful or practical outcome routeProxy or occasional manual reportNamed outcomes for a bounded populationReliable outcomes with stated purpose

Right and ability to reconnect eventsActions cannot be joined to outcomesManual or ambiguous matchingStable identifiers with known gapsTested lineage across action and outcome

Evaluation and improvement permissionUse prohibited or purpose absentEvaluation only, no change rightInternal improvement within scopeExplicit purposes, roles and controls

Retention and portabilitySupplier lock or unusable exportPartial export without metadataUsable records with tested limitsPortable packets and derived artefacts

Decision-context fidelityOutcome detached from decision settingCoarse segment or missing versionsMaterial context and versions retainedTransfer boundaries independently tested

Independent signal integrityModel output labels later outputClicks or preferences onlyReviewed proxy with negative casesConsequential outcome or valid test

Outcome-to-change cycleSlower than context remains relevantEpisodic and person-dependentOwned cadence with release controlsFast, repeatable and recoverable

Conversion capabilityNo owner or change authorityInsight without release pathCross-functional owner and backlogAdjudication, decision and release routine

Audit loopDefensible caseContestable caseTelemetry trapBlocked case

Assumptions, test cases, implications and limits

• Assumptions: one decision and population are named; legal roles and purposes have separate review; system versions are identifiable; and the proposed improvement can be released and reversed.
• Defensible test: explicit rights, context-valid outcomes, independent signal and a fast controlled conversion routine should support a learning-rate advantage.
• Contestable test: useful rights and context with slow or ordinary conversion should improve operations without supporting a strong moat claim.
• Telemetry test: large interaction volume with clicks, missing outcomes and weak version linkage must not be described as compounding intelligence.
• Blocked test: absent outcome or use rights prevents a learning claim regardless of data volume, model quality or executive confidence.
• Limits: ordinal scores can be gamed. The audit does not establish data protection, intellectual-property rights, safety, fairness, security, causal validity or competitive durability.

Figure 7. Rights and conversion speed locate the learning position

Decision field

Turn the audit into a feedback-rights schedule

For each low dimension, write a repair or a boundary. If outcome capture is weak, define the smallest valid outcome and population rather than substituting engagement. If linkage is weak, test the identifier path and quantify unmatched records. If context is weak, freeze the transfer claim. If signal is circular, introduce independent adjudication and a negative set. If conversion is slow, assign an owner with authority to change the smallest controlled unit.

Then express the strategic claim in falsifiable terms: “For commercial claims in regions A and B, we can connect reviewed outcomes to the responsible model and workflow version within five days, adjudicate a representative error slice weekly, and safely release or reverse retrieval changes within ten days. The required packets are exportable if the supplier changes.” This claim can be tested. “Our proprietary data flywheel” cannot.

06 · Rival and boundaryFeedback is not the only moat, and sometimes it is no moat

The strongest rival explanation is distribution. A trusted brand, regulated licence, installed workflow, scarce expert network or privileged route to customers may protect value even when competitors learn at the same rate. Scale can lower acquisition cost; integration can raise switching cost; exclusive rights to a transaction can shape the market before model improvement matters. This rival is often correct. The feedback mechanism should be claimed only when outcome-linked learning changes performance or economics in a way rivals cannot reproduce on comparable terms.

Test that claim with a matched comparison. Identify two populations with similar starting performance and exposure, but different access to the proposed feedback mechanism. Pre-specify the decision-quality measure, learning window and plausible confounders such as brand, channel, staffing and switching cost. Then ask whether the population with stronger feedback rights improves faster on held-out or controlled cases. If the difference disappears after a model upgrade reaches both groups, the advantage was a temporary model gap. If performance remains equal while retention differs, distribution or workflow friction is the more credible moat.

A static, well-specified task may not need a proprietary loop. If a commodity model already clears the requirement and errors have little consequence, building elaborate feedback infrastructure destroys value. A superior external model may improve faster than any internal adaptation. Low-frequency outcomes may arrive too late to guide the current system. Rare harms may never produce enough local observations. In each case, buy evaluation and portability, not a mythology of compounding data.

The failure boundary is sharper where feedback collection is impermissible, deceptive or harmful. Do not expose people to consequential errors merely to obtain labels. Do not turn appeals into free annotation labour. Do not retain personal context beyond the necessary purpose. NIST's AI Risk Management Framework calls for post-deployment monitoring, feedback and appeal mechanisms, field evidence and documented performance changes.13 These are governance obligations and risk controls before they are sources of advantage.

Nor does monitoring solve itself. A 2026 NIST report highlights fragmented logging, difficulties detecting drift, scaling human monitoring and insufficient research on human-AI feedback loops.14 Production readiness work likewise treats tests and monitoring as core concerns rather than optional analytics; Breck and colleagues propose 28 specific tests across ML systems.15 A firm that cannot maintain trustworthy measurement should stop claiming that deployment automatically makes it smarter.

Figure 8. Four cliffs end the intelligence-moat claim

Failure boundary

07 · ConclusionOwn the ability to learn, not the appearance of learning

A firm does not gain an intelligence moat merely by deploying widely, logging everything or negotiating generic ownership language. It gains the possibility of a moat when it can lawfully observe consequences, reconnect them to the responsible action and context, judge them with an independent signal, and convert the result into a controlled change. The moat becomes real only when that circuit improves decisions faster than rivals can reproduce it.

This shifts executive attention from data quantity to feedback architecture. Ask where the real outcome appears, who may see it, which identifiers preserve its causal address, what context defines transfer, who can adjudicate it, which change unit is authorised, how quickly a safe release can occur and whether the entire packet remains portable. Each answer is inspectable. Together they reveal who actually holds the learning rights in a vendor relationship.

The discipline also prevents overclaiming. Some loops are useful but ordinary. Some telemetry is valuable for audit but cannot teach. Some contexts make learning too slow, too circular or too intrusive. Some competitors win through distribution, licence or workflow position. The appropriate response is not to force every AI initiative into a flywheel story. It is to reserve the intelligence-moat claim for a demonstrated rate advantage in warranted learning, and to build the smallest rights-and-context system capable of proving it.

SourcesReferences

1. Bajari, P., Chernozhukov, V., Hortaçsu, A. and Suzuki, J. (2019). The Impact of Big Data on Firm Performance: An Empirical Investigation. AEA Papers and Proceedings, 109, 33–37.
2. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.
3. Shumailov, I. et al. (2024). AI models collapse when trained on recursively generated data. Nature, 631, 755–759.
4. Information Commissioner's Office (2023, current guidance). Guidance on AI and data protection. Used for purpose limitation, minimisation, retention and accountability principles.
5. European Union (2023). Regulation (EU) 2023/2854 on harmonised rules on fair access to and use of data. Official Journal of the European Union.
6. OECD (2024). Artificial intelligence, data and competition. OECD Artificial Intelligence Papers, No. 18.
7. Sambasivan, N. et al. (2021). “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. CHI 2021.
8. Sculley, D. et al. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems, 28.
9. Zollo, M. and Winter, S. G. (2002). Deliberate Learning and the Evolution of Dynamic Capabilities. Organization Science, 13(3), 339–351.
10. Cohen, W. M. and Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128–152.
11. Kohavi, R. et al. (2009). Online Experimentation at Microsoft. Microsoft Research.
12. Gupchup, J. et al. (2018). Trustworthy Experimentation Under Telemetry Loss. CIKM 2018.
13. NIST (2023). Artificial Intelligence Risk Management Framework Core. Used for contextual measurement, post-deployment monitoring, feedback, appeal and change management.
14. NIST Center for AI Standards and Innovation (2026). Challenges to the Monitoring of Deployed AI Systems. Summary of practitioner workshops and literature review.
15. Breck, E. et al. (2017). The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. IEEE Big Data 2017.
16. Arrow, K. J. (1962). The Economic Implications of Learning by Doing. The Review of Economic Studies, 29(3), 155–173.
17. Perdomo, J. et al. (2020). Performative Prediction. Proceedings of the 37th International Conference on Machine Learning, PMLR 119, 7599–7609.
18. Adam, G. A. et al. (2020). Hidden Risks of Machine Learning Applied to Healthcare. Proceedings of Machine Learning for Healthcare, PMLR 126, 444–464.
19. Brynjolfsson, E., Li, D. and Raymond, L. R. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–942.