Adapt or Disappear: The Ethical Question of AI Gatekeeping

Not so long ago, we knew who decided what we saw. A newspaper’s editor, TV producers, or a search engine algorithm. It was sort of opaque, but at least we understood better that Google made its choices based on links, keywords, and authority signals we could more accurately study and influence.

Nowadays, LLMs are the gatekeepers. And the truth is that nobody –not even their creators– can 100% explain how they decide which brands to mention, facts to cite, or which voices to amplify. This is not only a technical shift, but an ethical one that we are not discussing enough.

The Illusion of Objectivity

When ChatGPT recommends three project management tools and yours isn't one of them, that's not a neutral outcome. It's an editorial decision. One made by an algorithm trained on data curated by humans, optimized for engagement metrics chosen by a big company, and deployed at a scale that makes traditional media gatekeeping look quaint by comparison.

So what changed? Traditional gatekeepers (journalists, editors, producers, directors) worked under certain frameworks of accountability, like ethical codes, editorial standards, or correction policies. When a newspaper got something wrong, there were mechanisms in place to correct that.

When an AI model excludes your brand from an answer seen by ten million people, there's no correction policy or a transparent decision-making process you can audit. Just a black box that "decided" you don't exist in this context.

Who Writes the Rules Nobody Can Read?

Here's where it gets uncomfortable: AI systems can be manipulated by something as simple as a fake timestamp. Researchers at Waseda University in Japan proved that adding a recent date to existing content, without changing a single word, caused it to jump higher in AI rankings.

Every major AI model they tested fell for it. ChatGPT, LLaMA, Alibaba's Qwen. The freshness signal outweighed everything else: expertise, accuracy, depth, or peer review. A simple 2025 blog post could potentially beat a comprehensive 2020 research paper, simply because it looks newer.

This isn't a bug. It's a design choice.

Someone at OpenAI, Meta, or Google decided that recency ranks the highest. They encoded that preference into the algorithm. And now every brand, publisher, and creator on the internet is playing a game where the rules are not entirely clear, but the stakes are existential.

The ethical question isn't "Can AI be fooled?" but "Who decided these were the right priorities?"

The Concentration of Power

Let’s be as honest as we can about the current situation. Just a handful of big companies (OpenAI, Google, Meta, etc.) control or curate what millions and millions of people learn, discover, or believe.

They came and dominated so fast that good regulations are still not in place, even if being debated thoroughly at all by lawmakers and governments in general. They exercise more influence over information access than any media company we have ever seen, with virtually no accountability.

Here's the truly troubling part: traditional news organizations are now dependent on these same platform companies for the AI tools they use to produce content. Publishers outsource content moderation to Google's APIs. They use OpenAI's models for summarization. They optimize for visibility in systems they don't control and can't fully understand.

The gatekeepers are now gatekeeping the previous gatekeepers.

The Questions Nobody Wants to Answer

If AI is now the primary gatekeeper of information, we need to ask ourselves some uncomfortable questions:

Who should have the power to decide what's visible? Right now, it's engineers optimizing for engagement metrics and shareholders optimizing for profit. Is that who we want making editorial decisions that shape public knowledge?

What happens to voices that can't afford to optimize? Small businesses without SEO budgets. Non-profits without marketing teams. Independent researchers without institutional backing. If AI visibility requires resources most people don't have, we're not democratizing information, we're privatizing it.

Can we even audit these systems? Proprietary algorithms and training data are secret. Decision-making processes are unclear. How do we hold gatekeepers accountable when we can't see what they're doing?

What if AI is wrong? When ChatGPT hallucinates facts about your brand, misstates your pricing, or associates you with negative sentiment from outdated Reddit threads, what do you do? There's no correction mechanism. Just algorithmic reputation damage at scale.

What Should Happen

If we took the ethics of AI gatekeeping seriously, then we should start demanding a few things:

Transparency. Full disclosure of ranking factors, training data sources, and decision-making logic. If AI is making editorial choices, those choices should be auditable.

Accountability. Clear mechanisms for challenging AI-generated misinformation about brands, people, and facts. Correction policies. Appeals processes. Legal liability for harmful inaccuracies.

Public oversight. Regulatory frameworks treating AI platforms like the publishers they functionally are, with corresponding responsibilities for accuracy, fairness, and harm prevention.

User control. Options to choose and customize which sources AI should prioritize, adjust freshness vs authority tradeoffs, and see why certain content was included or excluded.

Some of this requires regulatory pressure. Some require platform accountability. But some can happen now through tools that bring transparency to an unclear process, helping brands understand and navigate this landscape strategically.

The Strategic Response

The brands thriving in this environment aren't waiting for full clarity, they are building visibility strategies based on what we do know:

• AI systems prioritize third-party validation over self-promotion: Invest in authentic community presence and customer advocacy programs that generate real mentions.
• AI systems need semantic clarity to recommend you: Use clear, specific positioning and descriptions that explains who you serve and what problem you solve, not just vague buzzwords.
• AI systems cross-reference multiple sources: Apply consistent messaging across all platforms (your website, reviews, community discussions, and press coverage).

This is about ensuring quality gets recognized in an AI-mediated landscape. The brands that will thrive won't be the ones changing timestamps or making fake Reddit posts. They'll be the ones building genuine authority, creating products worth talking about, and understanding the systems well enough to ensure their quality gets recognized.

You can engage with AI visibility ethically. You can demand better from platforms. And you can build a competitive advantage by understanding these systems faster than your competitors.

Making the Invisible Visible

You can't fix what you can't see. You can't compete fairly in a game where you don't know the score. That's why we, at HatchEcom, work towards giving brands visibility into how AI systems actually perceive them. Not to game the system, but to compete fairly in it.

Understanding AI perception is about having the information you need to build better signals (authentic reviews, quality content, consistent messaging) that AI should reward.

The brands that will shape this landscape aren't the ones resigned to algorithmic fate. They're the ones measuring their AI visibility as rigorously as they measure SEO, building strategies based on data instead of guesses, and refusing to accept that visibility should be a black box.

The real choice isn't between adapting and fighting. It's between informed strategy and uninformed guessing.

Do you want to see how AI systems currently perceive your brand? Get in contact with us at HatchEcom and discover what signals you're sending before your competitors figure this out.