On paper, AI disclosure sounds straightforward.
Tell people when AI is being used.
In reality, it is anything but simple.
As AI becomes part of everyday products, disclosures stop being a legal checkbox and turn into a deeply operational problem. They affect product design, engineering workflows, platform governance, and eventually trust. Most discussions today focus on what disclosures should say. Far fewer talk about whether they can realistically work at scale.
AI Is No Longer a Feature
One of the biggest disconnects in the AI disclosure debate is the assumption that AI is a visible feature.
In modern products, AI usually sits in the background. It ranks content, nudges decisions, rewrites drafts, flags risks, or improves speed. Often, it is only responsible for a small part of the final output.
A single user action may pass through multiple systems, some generative, some predictive, some purely analytical. From an operational standpoint, it becomes unclear what qualifies as “AI-generated” or even “AI-assisted.”
When disclosures rely on binary labels, they immediately break in these grey zones.
Defining AI Involvement Is Harder Than It Sounds
At scale, the first challenge is definition.
If a human writes something but uses AI to refine it, is that AI content?
If AI suggests ten options and a human picks one, should it be disclosed?
If AI is used only to rank or filter, does the user need to know?
If definitions are too broad, everything ends up labeled AI and the disclosure loses meaning. If they are too narrow, they are easy to bypass. Most large platforms end up somewhere in the middle, not because they want to hide anything, but because rigid definitions do not survive real-world complexity.
Disclosure Fatigue Is a Real Risk
Even when disclosures are present, users stop noticing them very quickly.
We have already seen this with cookie notices, sponsored tags, and privacy pop-ups. Over time, they fade into the background.
At scale, excessive disclosure creates visual noise and false reassurance. Users think they are informed, but they are not actually processing anything. In some cases, this can reduce trust rather than build it.
The problem is not lack of transparency. The problem is too much low-value transparency.
Enforcement Weakens Once Content Leaves the Platform
Disclosures are easiest to enforce inside tightly controlled platforms. They fall apart once content starts moving.
A label does not survive a screenshot.
Metadata does not survive a repost.
Watermarks do not survive compression and cropping.
Most AI content today travels across platforms, private groups, and messaging apps. Enforcement assumes centralized control, but the internet does not work that way. Once content spreads, the original disclosure often disappears, even if it was implemented correctly at the source.
The Open Model and API Gap
Another major challenge comes from open models and API-based ecosystems.
When developers build their own tools on top of platforms like OpenAI, responsibility for disclosure becomes fragmented. The model provider does not control the interface. The interface does not control how outputs are reused. The end user may not even know what model was involved.
In these cases, enforcement is not just difficult. It is unclear who should be held accountable in the first place.
Global Rules, Local Execution
AI products operate globally, but regulations are local.
What counts as sufficient disclosure under the EU AI Act may not align with user expectations or enforcement capacity in other regions. For global companies, this often leads to designing for the strictest regime and applying it everywhere, even when it does not fit local context.
Operationally, this creates complexity and inconsistency. From a user’s perspective, it can feel confusing and arbitrary.
Human Oversight at Scale Is Not What People Imagine
Many disclosure frameworks lean heavily on the idea of human oversight.
In practice, humans do not review every output. They review systems, samples, and edge cases. Oversight exists, but it is statistical, not personal.
Labeling something as human-reviewed can be technically accurate while still creating the wrong mental model for users. Disclosure language struggles to capture this nuance, and enforcement often ignores it entirely.
Trust Is Built Through Behavior, Not Labels
The uncomfortable truth is that disclosures alone do not create trust.
Users trust products that behave predictably, correct mistakes visibly, and take responsibility when things go wrong. They do not build trust from small labels or carefully worded disclaimers.
At scale, trust is earned through repeated interactions and consistent behavior. Disclosures can support that trust, but they cannot replace it.
A More Practical Way Forward
Instead of trying to label every instance of AI use, a more realistic approach would focus on three things.
First, contextual disclosure. Disclose AI use when it meaningfully affects outcomes, decisions, or accountability.
Second, system-level transparency. Explain clearly what AI is used for, where it is not used, and what its limitations are.
Third, accountability over attribution. Users care less about whether AI was involved and more about who is responsible when something breaks.
Closing Thought
AI disclosures are struggling not because of bad intent, but because we are trying to apply static rules to dynamic systems.
At scale, AI is not a single tool you switch on or off. It is a layer woven into how modern products work. Expecting simple labels to capture that reality is unrealistic.
The future of trust in AI will not come from better wording or stricter badges. It will come from better systems, clearer accountability, and honest product behavior. Disclosures should support that goal, not pretend to solve it on their own.
(Views are personal)
