Algorithmic Visibility Economies and Creator Dependency

1. Introduction

Algorithmic visibility economies describe markets in which creators, sellers, publishers, influencers, app developers, musicians, and other participants depend upon algorithmically controlled platforms to obtain attention, ranking, recommendations, discoverability, traffic, and ultimately revenue.

In traditional markets, a producer could compete primarily through price, quality, distribution, advertising, and reputation. In platform markets, an additional competitive resource has become crucial:

Visibility — the ability of an algorithm to expose a creator's content to users.

Platforms such as search engines, social-media services, video platforms, app stores, marketplaces, and content-distribution systems can determine visibility through ranking, recommendation, personalization, moderation, monetization, engagement optimization, and advertising algorithms.

This creates a potential form of creator dependency. A creator may technically remain independent but become economically dependent upon the platform because the platform controls access to audiences.

The competition-law problem therefore shifts from simply asking:

“Who controls the market?”

to also asking:

“Who controls the algorithmic gateway through which market participants reach customers?”

2. Meaning of Algorithmic Visibility Economy

An algorithmic visibility economy exists where economic value depends substantially upon an intermediary's algorithmic allocation of attention.

A simplified model is:

Creator → Platform → Algorithm → Visibility → Audience → Engagement → Revenue

For example:

• a YouTube creator depends upon recommendation and search ranking;
• an Instagram creator depends upon feed and recommendation systems;
• an app developer depends upon App Store or Play Store discovery;
• an online seller depends upon marketplace ranking;
• a publisher depends upon search and news recommendation;
• a musician depends upon playlist and recommendation algorithms.

Visibility therefore becomes an economic input.

Important characteristics

1. Algorithmic ranking
2. Personalized recommendations
3. Search-result positioning
4. Content recommendation
5. Trending systems
6. Monetization eligibility
7. Platform-controlled advertising
8. Automated moderation
9. Account or content restrictions
10. Data-driven audience allocation

A creator may therefore compete not merely against other creators but also against the platform's own algorithmic rules.

3. Creator Dependency

Creator dependency occurs when creators become economically reliant upon a platform because the platform controls essential or difficult-to-replicate access to audiences.

Dependency can arise through:

A. Audience dependency

The creator's followers or customers cannot easily be reached outside the platform.

B. Ranking dependency

Traffic depends upon placement in search results, recommendations, feeds or trending lists.

C. Revenue dependency

Advertising, subscriptions, commissions, tips or platform payments constitute a substantial part of creator income.

D. Data dependency

The creator lacks equivalent access to:

• audience data;
• engagement information;
• demographic information;
• recommendation analytics;
• conversion data.

E. Switching dependency

Moving to another platform may cause:

• loss of followers;
• loss of historical engagement;
• loss of reputation;
• loss of accumulated reviews;
• loss of algorithmic ranking;
• loss of monetization history.

Thus, multi-homing may exist formally while effective switching remains expensive.

4. The Competition-Law Problem

Creator dependency becomes particularly relevant when a platform possesses substantial market power.

The principal legal questions are:

4.1 Can algorithmic demotion constitute exclusion?

Potentially, depending upon the circumstances.

A dominant platform could theoretically disadvantage creators by:

• lowering rankings;
• suppressing recommendations;
• changing eligibility criteria;
• reducing monetization;
• limiting discoverability;
• withholding data;
• imposing discriminatory algorithmic conditions.

But poor algorithmic performance alone does not establish an antitrust violation.

The analysis generally requires consideration of:

1. dominance;
2. relevant market;
3. discriminatory or exclusionary conduct;
4. effects on competition;
5. objective justification;
6. consumer/creator welfare;
7. proportionality of the platform's conduct.

5. Algorithmic Visibility as a Competitive Input

Traditional competition law often examines access to physical or commercial inputs.

Algorithmic visibility introduces a different category:

Consequently, visibility can function as a quasi-distribution infrastructure.

6. Self-Preferencing

One of the most significant risks arises where a platform competes with the creators who depend upon it.

For example:

Platform operates marketplace + sells its own products

or

Platform hosts creators + promotes its own content

or

Platform operates app distribution + owns competing applications.

The platform may theoretically have an incentive to modify ranking algorithms in favour of its own services.

This raises the competition-law concept of self-preferencing.

7. Algorithmic Opacity

Creators generally cannot observe precisely how ranking systems work.

This creates an information asymmetry:

Platform → knows algorithmic rules

Creator → observes only outcomes

The creator may therefore be unable to determine whether declining visibility results from:

• genuine quality assessment;
• changing user preferences;
• ordinary algorithmic experimentation;
• moderation;
• commercial incentives;
• discrimination;
• retaliation;
• self-preferencing.

This makes algorithmic discrimination particularly difficult to prove.

8. Algorithmic Changes and Dependency

A platform can change its algorithm without renegotiating individual creator relationships.

For example:

Old algorithm → Creator receives 1 million impressions

↓

Algorithmic change

↓

Creator receives 200,000 impressions

↓

Advertising revenue declines

↓

Creator becomes economically vulnerable

The platform may therefore exercise substantial quasi-regulatory power over creators even without formally terminating their accounts.

9. De-Ranking as a Competition Concern

De-ranking can take several forms:

• removal from recommendations;
• lower search position;
• exclusion from trending lists;
• reduced feed distribution;
• reduced advertising eligibility;
• reduced monetization;
• suppression of external links;
• restricted recommendation eligibility.

The legal significance depends upon why the conduct occurred and its competitive consequences.

A platform may legitimately de-rank content because of:

• fraud;
• copyright infringement;
• harmful content;
• spam;
• manipulation;
• user-safety concerns.

Therefore:

Algorithmic de-ranking is not inherently anticompetitive.

The competition issue arises where ranking power is used in a manner that unlawfully excludes rivals or exploits dependent business users.

10. Six Major Case Laws

The following cases are particularly useful for constructing the legal framework. Some directly concern algorithmic ranking or digital-platform visibility; others establish competition-law principles that can be applied to creator dependency.

Case 1: Google Search (Google Shopping) — European Commission / General Court

Facts

Google operated a dominant general search engine while also operating its own comparison-shopping service.

The European Commission found that Google systematically positioned and displayed its own comparison-shopping service more prominently than competing comparison-shopping services.

The General Court substantially upheld the Commission's decision.

Principle

The case is highly relevant to algorithmic visibility because the competitive problem concerned how a dominant search intermediary treated competing services within its ranking and display architecture.

The important distinction was between:

• Google's general search results; and
• preferential treatment of Google's own competing service.

Relevance to creator dependency

The case demonstrates that where a platform controls a critical visibility mechanism, manipulation of placement can have competitive consequences.

The conceptual chain is:

Dominant platform → ranking mechanism → preferential visibility → reduced rival visibility → competitive harm

This is closely analogous to creator markets where a platform controls recommendation or search exposure.

Legal significance

The case is particularly important for:

• self-preferencing;
• algorithmic ranking;
• discriminatory visibility;
• platform gatekeeping;
• exclusionary effects.

Case 2: Google Search (Shopping) — Google v Commission, C-48/22 P

The later appellate litigation concerning Google's Shopping conduct is important because it examines the relationship between dominance, preferential treatment and competition on digital platforms.

The broader legal significance lies in recognising that conduct involving a dominant intermediary's infrastructure can affect competitors even where the platform does not completely deny access.

Creator-economy relevance

A creator may technically remain:

“allowed on the platform”

while simultaneously becoming substantially less visible.

Thus:

Access ≠ effective access

A creator who remains technically present but is algorithmically buried may experience a commercially significant form of exclusion.

Case 3: Google Android — Google and Alphabet v Commission

The Google Android proceedings concerned Google's conduct surrounding the Android ecosystem, including contractual arrangements involving search, browsers and application distribution.

The European Commission examined how Google's position within one part of the ecosystem could reinforce its position in another.

Relevance to creator dependency

The case illustrates the concept of ecosystem leverage.

A platform can accumulate power across interconnected layers:

Operating system

↓

App distribution

↓

Search

↓

Advertising

↓

Data

↓

User access

Creators and developers may consequently become dependent upon a broader ecosystem rather than a single product.

Competition-law lesson

Algorithmic visibility should therefore sometimes be examined as part of a multi-sided ecosystem, rather than as an isolated ranking function.

Case 4: Amazon Marketplace — European Commission

The European Commission investigated Amazon's use of non-public marketplace seller data.

Amazon operated simultaneously as:

1. a marketplace intermediary; and
2. a retailer competing with marketplace sellers.

The Commission's concerns centred on Amazon's access to commercially sensitive seller information and the potential use of such information in competition with those sellers.

Creator-dependency relevance

The analogy is particularly strong.

A platform may possess information unavailable to dependent participants, such as:

• sales;
• engagement;
• audience behaviour;
• conversion rates;
• demand;
• competitor performance.

In creator markets, equivalent information may include:

• watch-time data;
• audience retention;
• recommendation performance;
• engagement rates;
• monetization information.

Competition principle

A platform's dual role as:

intermediary + competitor

creates incentives and opportunities for competitive exploitation of platform-generated information.

Case 5: Facebook / Bundeskartellamt — Meta Platforms

The German competition proceedings involving Facebook concerned the combination of user data obtained from different sources and the relationship between data practices and market power.

The German Federal Cartel Office's case examined Facebook's position in the social-networking market and its ability to combine data across services.

The matter ultimately produced important European litigation concerning the relationship between competition law, data and platform power.

Creator-economy relevance

Creator visibility depends heavily upon data.

Platforms may possess extensive information about:

• users;
• interests;
• engagement;
• viewing patterns;
• creator performance;
• audience preferences.

This creates a potential data-visibility feedback loop:

More users → more data → better prediction → better recommendations → more users → greater creator dependency

This can strengthen network effects and entry barriers.

Case 6: Epic Games v Apple

The dispute between Epic Games and Apple concerned Apple's App Store rules, payment system and restrictions affecting application developers.

Although the case was not a conventional creator-ranking case, it is important for understanding platform gatekeeping and dependence.

Central issue

Apple controlled a major distribution channel for applications on iOS devices.

Developers therefore depended upon Apple's:

• App Store;
• distribution rules;
• payment architecture;
• review process;
• contractual conditions.

Creator-economy relevance

The same structural concept applies to creator platforms:

Developer dependence on app distribution

is analogous to:

Creator dependence on audience distribution.

In both situations, the intermediary can determine important commercial conditions for participants who need access to the platform's users.

Case 7: FTC v Amazon

The U.S. Federal Trade Commission's antitrust action against Amazon provides another important framework for understanding marketplace power and alleged conduct affecting sellers.

The case concerns Amazon's role as both:

• marketplace intermediary; and
• participant competing within the marketplace.

Relevance

The creator economy can produce a similar dual-role problem.

A platform may simultaneously be:

• hosting creators;
• selling advertising;
• operating recommendation systems;
• promoting its own content;
• producing original content;
• collecting creator data.

The competition question becomes whether platform control over infrastructure can be used to disadvantage dependent participants.

11. Case 8: Apple — European Commission App Store Proceedings

European competition proceedings involving Apple's App Store rules are relevant to the economics of digital intermediaries.

The broader principle concerns the ability of a platform operator to impose commercial conditions on businesses that need access to the platform's user base.

Creator relevance

A platform may control:

• distribution;
• payment;
• discoverability;
• monetization;
• advertising;
• data.

When those functions are vertically integrated, creators can become dependent upon several platform-controlled gateways simultaneously.

12. A General Legal Test

Algorithmic creator dependency can be analysed through a structured competition-law test.

Step 1 — Define the relevant market

Possible markets include:

• social-media services;
• online video platforms;
• digital advertising;
• app distribution;
• creator monetization;
• online marketplaces;
• search services.

The relevant market depends upon substitutability and the actual competitive constraints.

Step 2 — Establish platform power

Relevant indicators may include:

• market share;
• network effects;
• user numbers;
• switching costs;
• multi-homing;
• data advantages;
• ecosystem integration;
• entry barriers;
• control over distribution.

Step 3 — Identify the visibility mechanism

The investigation should identify:

• ranking algorithm;
• recommendation algorithm;
• search algorithm;
• moderation system;
• monetization algorithm;
• advertising allocation mechanism.

Step 4 — Identify the affected creators

The analysis should determine whether affected participants are:

• independent creators;
• professional influencers;
• publishers;
• sellers;
• developers;
• musicians;
• app providers;
• commercial content producers.

Step 5 — Identify the conduct

Potential conduct includes:

• discriminatory ranking;
• self-preferencing;
• retaliation;
• arbitrary de-ranking;
• tying;
• exclusionary conditions;
• discriminatory monetization;
• discriminatory access to data;
• manipulation of recommendations.

Step 6 — Determine competitive effects

Possible effects include:

• foreclosure of rival creators;
• reduced innovation;
• reduced entry;
• increased creator switching costs;
• reduced quality;
• higher advertising costs;
• reduced audience choice;
• increased platform concentration.

13. Creator Dependency and Essential-Facility Theory

An important theoretical question is whether a dominant platform's visibility infrastructure could constitute an essential facility.

Traditional essential-facility doctrine generally requires stringent conditions, and not every commercially important platform qualifies.

Nevertheless, the theory raises an important question:

If a creator cannot realistically reach consumers without access to a dominant platform's recommendation or distribution infrastructure, should that infrastructure receive special competition-law scrutiny?

The answer depends on the applicable jurisdiction and doctrine.

Mere importance is generally insufficient.

14. Network Effects

Creator platforms frequently exhibit strong network effects.

Direct network effects

More users attract more creators.

Indirect network effects

More creators attract users, while more users increase the value of the platform to creators.

The cycle becomes:

Users ↑

→ Creators ↑

→ Content ↑

→ Engagement ↑

→ Data ↑

→ Algorithmic accuracy ↑

→ Users ↑

This can make market entry increasingly difficult.

15. Data and Visibility Feedback Loop

One of the most important characteristics of algorithmic visibility economies is the data-feedback mechanism.

A dominant platform may obtain more:

• behavioural data;
• engagement data;
• search data;
• transaction data;
• creator-performance data.

That information can improve recommendation algorithms.

Improved recommendations increase user engagement.

Greater engagement produces additional data.

Thus:

Data → algorithmic improvement → visibility → users → more data

This can create a self-reinforcing competitive advantage.

16. Switching Costs

Creator dependency becomes stronger when switching platforms is expensive.

A creator leaving a platform may lose:

• followers;
• subscribers;
• accumulated reviews;
• reputation;
• historical engagement;
• monetization status;
• algorithmic history;
• audience relationships.

Therefore, even if alternative platforms technically exist, the creator may not be able to migrate economically.

17. Multi-Homing Does Not Always Eliminate Dependency

A creator may simultaneously maintain accounts on:

• YouTube;
• Instagram;
• TikTok;
• Facebook;
• X;
• Twitch;
• Patreon.

This is multi-homing.

However, multi-homing does not necessarily eliminate dependency.

A creator may use five platforms but still receive:

• 70% of traffic from one platform;
• 80% of revenue from another;
• most new audience discovery from another.

The competition analysis should therefore examine effective dependence, not merely account ownership.

18. Algorithmic Opacity and Evidentiary Problems

Algorithmic cases present significant evidentiary difficulties.

A creator may demonstrate:

“My views declined by 80%.”

But this does not automatically establish:

“The platform unlawfully manipulated the algorithm.”

The decline could result from:

• changing consumer preferences;
• increased competition;
• seasonality;
• content quality;
• platform-wide algorithmic changes;
• moderation;
• fraud detection;
• technical problems.

Consequently, competition authorities may need:

• internal documents;
• algorithmic audit evidence;
• A/B testing records;
• ranking data;
• communications;
• source-code evidence where appropriate;
• economic analysis;
• counterfactual analysis.

19. Algorithmic Retaliation

A particularly serious theoretical problem is retaliatory ranking.

Suppose a creator:

1. criticises the platform;
2. joins a competing service;
3. promotes an alternative payment method;
4. challenges platform terms;

and subsequently experiences unexplained visibility reduction.

If evidence establishes that the reduction was retaliatory and exclusionary, competition-law concerns could arise depending on the platform's market power and applicable legal framework.

The important distinction is between:

legitimate content governance

and

commercially motivated exclusion.

20. Algorithmic Monetization Dependency

Visibility and monetization are closely related.

A platform may control:

Visibility → engagement → advertising → revenue

Consequently, creators can become dependent upon a platform twice:

First dependency

Access to audiences.

Second dependency

Access to monetization.

This gives the platform considerable contractual and economic leverage.

21. Possible Competition Remedies

If unlawful conduct is established, possible remedies may include:

Structural remedies

• divestiture;
• separation of platform functions;
• restrictions on vertical integration.

Behavioural remedies

• non-discrimination requirements;
• transparency obligations;
• interoperability;
• data portability;
• access obligations;
• restrictions on self-preferencing.

Procedural remedies

• explanation of ranking changes;
• appeal mechanisms;
• notice before significant monetization changes;
• independent review of de-ranking decisions.

Data-related remedies

• portability;
• interoperability;
• controlled access to relevant data;
• restrictions on combining datasets.

22. Algorithmic Transparency

Transparency does not necessarily require disclosure of the complete source code.

A competition-law transparency framework could instead require disclosure of:

• principal ranking factors;
• material algorithmic changes;
• reasons for significant restrictions;
• monetization criteria;
• appeal procedures;
• categories of prohibited manipulation.

The objective is to reduce the information asymmetry between platform and creator.

23. Difference Between Transparency and Trade Secrets

Platforms may legitimately argue that detailed algorithmic disclosure could expose:

• trade secrets;
• cybersecurity vulnerabilities;
• anti-fraud mechanisms;
• proprietary technology.

Therefore, the legal challenge is to balance:

creator transparency

against

legitimate confidentiality.

Possible solutions include:

• confidential regulatory access;
• independent audits;
• trusted third-party auditors;
• protected disclosures;
• aggregated explanations.

24. Competition Harm Versus Individual Creator Harm

An important doctrinal distinction must be maintained.

A creator losing visibility is not automatically an antitrust injury.

Competition law generally focuses on competition in the market, rather than simply protecting every individual competitor from commercial loss.

Thus:

Individual economic harm ≠ necessarily competition harm.

The stronger case arises where algorithmic conduct produces broader effects such as:

• foreclosure;
• exclusion of rival services;
• raising barriers to entry;
• reduction in innovation;
• suppression of competing business models;
• exploitation of platform dependency.

25. Creator Dependency as a New Form of Intermediation Power

Traditional intermediaries controlled:

distribution channels.

Digital platforms increasingly control:

distribution + visibility + data + monetization + audience access.

This produces a more sophisticated form of intermediary power.

The platform does not necessarily say:

“You cannot compete.”

Instead, the platform may determine:

“How many users will see you.”

That distinction is central to algorithmic competition law.

26. Key Legal Principles From the Case Law

27. Emerging Competition-Law Theory

Algorithmic visibility economies suggest a broader concept:

Visibility Power

A platform possesses visibility power where it can materially determine which market participants receive access to consumer attention.

Visibility power may arise from control over:

1. search;
2. recommendations;
3. feeds;
4. rankings;
5. playlists;
6. trending systems;
7. advertising;
8. monetization.

This potentially creates a new dimension of market power beyond conventional price-based analysis.

28. Hypothetical Example

Suppose Platform X has 90% of the market for short-form video discovery.

Creator A develops a competing subscription service.

Platform X then changes its recommendation algorithm.

Before the change:

Creator A → 5 million monthly impressions

After the change:

Creator A → 100,000 monthly impressions

Platform X simultaneously promotes its own subscription service.

The legal investigation would ask:

1. Is Platform X dominant?
2. What is the relevant market?
3. Did the algorithm materially change?
4. Did the change disproportionately affect competing creators?
5. Did Platform X promote its own service?
6. Was Creator A's external service disadvantaged?
7. Was there a legitimate technical or consumer justification?
8. Did the conduct foreclose competition?
9. Did it reduce consumer choice or innovation?
10. Is there evidence of exclusionary intent or effect?

The algorithmic change itself is not sufficient. The surrounding evidence and competitive effects matter.

29. Relationship With Digital Markets Regulation

Modern digital competition regulation increasingly recognises that platform power can arise from:

• gatekeeper status;
• ecosystem effects;
• data accumulation;
• interoperability restrictions;
• self-preferencing;
• platform dependency.

Consequently, traditional abuse-of-dominance analysis is increasingly complemented by ex ante digital-platform obligations in some jurisdictions.

This is particularly important for creator markets because individual creators often lack bargaining power comparable to the platform.

30. Conclusion

Algorithmic visibility economies transform attention into an economically controlled resource.

Creators may depend upon platforms not merely because platforms host their content, but because platforms control:

• discovery;
• ranking;
• recommendation;
• audience access;
• data;
• advertising;
• monetization.

The central competition-law concern is therefore the conversion of algorithmic control into economic dependency.

The most important analytical distinction is:

A platform's algorithmic control is not itself unlawful. The competition-law question is whether a platform possessing substantial market power uses that control in a manner that unlawfully excludes competitors, exploits dependency, distorts competition, or advantages its own competing activities.

The Google Shopping, Google Android, Amazon Marketplace, Facebook/Bundeskartellamt, Epic Games v Apple, FTC v Amazon, and Apple App Store matters collectively provide a useful doctrinal foundation for examining this emerging problem, even though several concern adjacent forms of platform power rather than creator algorithms specifically.

Exam-ready proposition

Algorithmic visibility should increasingly be understood as a form of digital distribution power. Where creators depend upon a dominant platform for access to audiences, algorithmic ranking and recommendation can become economically equivalent to control over a critical distribution channel. Competition law must therefore distinguish legitimate content and quality management from exclusionary manipulation, self-preferencing, discriminatory access, and exploitation of platform-created dependency.