The epistemic asymmetry of optimization-first approaches

Most optimisation strategies begin from a simple premise: if AI systems are shaping decisions, then influencing those systems is the rational response.

What this framing overlooks is that intervention changes the very environment organisations later attempt to evaluate.

Once optimization begins:

• the original answer state is lost
• subsequent outputs are path-dependent
• and comparison against a “natural” baseline becomes speculative

At that point, teams are no longer observing system behaviour. They are observing the interaction between the system and their own interventions.

This asymmetry creates a structural blind spot: the more aggressively optimization is pursued, the harder it becomes to determine whether observed changes reflect improvement, noise, or distortion.

Why AI systems are uniquely sensitive to contamination

Traditional optimization domains tolerate intervention because their outputs are relatively stable and reconstructable. AI systems do not share these properties.

Large language models:

• are probabilistic rather than deterministic
• are updated, tuned, and policy-adjusted continuously
• generate outputs that vary across runs, prompts, and contexts

As a result, once content or signals are introduced into the system, there is no reliable way to replay the prior state under identical conditions.

In forensic terms, this is equivalent to modifying a scene before documenting it.

The third-party contamination problem

Optimisation-first approaches also obscure the role of third-party activity.

AI systems do not distinguish between:

• first-party optimization
• competitor-led optimization
• incidental amplification via unrelated sources

All signals are ingested and weighted without provenance transparency.

This means organizations may:

• inherit distorted answer environments
• misattribute changes to their own actions
• or respond to competitor activity they cannot observe directly

When intervention is already underway, separating these influences becomes analytically impossible.

Forensic consequences of acting first

From a governance perspective, the most significant risk is not that optimization fails, but that it succeeds in ways that cannot later be explained.

Once optimization contaminates the environment:

• there is no durable record of what influenced the decision
• causality cannot be reconstructed
• and post-hoc explanations rely on inference rather than evidence

This creates problems not just for marketing accountability, but for:

• internal decision review
• regulatory scrutiny
• and reputational defence

In regulated or risk-sensitive contexts, the inability to demonstrate what information was presented at the time a decision was influenced is itself a failure condition.

Why “measure while optimizing” doesn't resolve the issue

Some approaches attempt to mitigate contamination by measuring outputs during optimization.

This doesn't solve the problem.

Measurement conducted after intervention has begun captures:

• the combined effect of system behaviour and intervention
• not the underlying system behaviour itself

Without a pre-intervention record, these measurements lack a reference point. They may be useful for directional tuning, but they cannot support forensic reconstruction.

The distinction matters. Optimization metrics are not evidence.

The governance implication

The core issue is not whether optimization should occur, but when.

Observation after intervention answers the question:

• “What is happening now?”

It cannot answer:

• “What would have happened otherwise?”

That counterfactual gap is where governance, accountability, and forensic clarity break down.

As AI systems increasingly function as decision infrastructure, the absence of uncontaminated observation becomes a systemic risk.

Observation before intervention

The solution implied by this analysis is not to abandon optimization, but to sequence it.

Observation without influence must occur:

• before optimization begins, or
• after intervention has already occurred, as a diagnostic of the current environment

Only then can subsequent actions be evaluated against something other than assumption.

This observational layer does not improve outcomes.
It improves understanding.

And in governance contexts, understanding precedes legitimacy.

Conclusion

Optimization-first approaches are attractive because they promise action.
They are risky because they erase evidence.

As AI-mediated representations increasingly shape decisions, organizations will need to treat uncontaminated observation not as a luxury, but as a prerequisite.

Without it, optimization may deliver short-term movement at the cost of long-term explainability.

In many cases, the most consequential decision is not how to act, but when to refrain.

Editorial note

This commentary examines a structural limitation in optimization-first approaches. A subsequent Journal piece will explore the observational capabilities required to address this limitation without influencing outcomes.