Platform antitrust in 2026 is shaped by ongoing cases against major tech platforms, state and federal actions, and emerging concerns around algorithms and data. While classic theories (e.g., tying, exclusive dealing) remain, several specific theories of harm are receiving heightened attention in policy debates, litigated cases, and new legislation.

Key Theories Gaining Traction

1. Self‑preferencing and discriminatory access on platforms

• Theory: A dominant platform that operates a marketplace or ecosystem and competes on that same platform can illegally favor its own products or services over third‑party rivals (e.g., ranking, access, interoperability, default settings) [1][2].

• Evidence of traction:

• Investigations and enforcement actions focused on app stores, marketplaces, and adtech stacks continue to revolve around allegations of self‑preferencing and discriminatory treatment of rivals on the platform [1].

• Remedies discussions in major search and app‑store cases emphasize behavioral constraints (e.g., fair ranking, anti‑retaliation, interoperability requirements) as ways to address this harm [3].

• Implications:

• Platforms face scrutiny of default settings, ranking algorithms, and internal conflict‑of‑interest structures.

• Structural separation between platform and its own offerings remains debated but behavioral self‑preferencing restrictions are common.

2. Data‑driven and algorithmic coordination (including pricing algorithms)

• Theory: Platforms and intermediaries use shared data, algorithms, or pricing software to facilitate tacit or explicit coordination—“hub‑and‑spoke” or algorithm‑enabled cartels—without a traditional written agreement [4].

• Concrete developments:

• The RealPage rental‑pricing case: enforcers alleged that sharing competitively sensitive pricing data and using common algorithms facilitated landlord coordination; settlement restricted such data sharing and algorithmic recommendations [4].

• State‑level bans such as New York’s S.7882 (effective Dec 15, 2025) prohibit operating software/data analytics services that facilitate algorithmic rent coordination [4].

• Over 50 bills across US states have targeted algorithmic or data‑driven pricing, especially in housing, travel, and consumer services [4].

• Implications:

• Platforms providing shared pricing tools or data‑analytics services risk being treated as hubs enabling collusion.

• New rules may restrict algorithmic pricing tools that ingest rivals’ data, raising compliance costs and altering pricing strategies.

3. Data as an essential input / foreclosure via data advantages

• Theory: A dominant platform leverages control over data (e.g., user data, transaction data, clickstream) as an essential input to entrench its dominance and foreclose rivals, even without traditional price‑based exclusion [4].

• Signals:

• In RealPage, regulators treated pricing‑related data as a key competitive input enabling collusive outcomes, emphasizing its centrality to market competition [4].

• Algorithmic pricing and surveillance‑pricing studies highlight concerns that personal data (location, browsing history) is used to engage in discriminatory pricing and can support coordination [4].

• Implications:

• Platforms’ data‑gathering, sharing, and preferential internal use are scrutinized for exclusionary effects.

• Remedies may require data access, unbundling of datasets, or restrictions on how platform data is used to compete with dependent firms.

4. Algorithmic and AI‑enabled conduct

• Theory: AI and algorithmic tools, especially for pricing and recommendation systems, can implement classic anticompetitive strategies—collusion, tying, steering, margin squeezes—at scale while obscuring human intent [1][3][5].

• Examples:

• Enforcement outlooks for 2026 emphasize algorithmic pricing and AI‑enabled coordination as priority areas, including at state‑AG level [1][3][4].

• Global antitrust enforcement outlooks highlight “algorithm‑enabled coordination, shared data platforms, and automated pricing tools” as a dedicated focus area [5].

• Implications:

• Platforms must document and monitor algorithm design, data inputs, and outcomes to avoid unintended anticompetitive effects.

• Regulators may demand algorithm impact assessments, transparency, or design constraints.

5. Labor‑market and non‑compete theories applied to platforms

• Theory: Platforms with significant labor‑market power (e.g., gig platforms, online intermediaries) can engage in anticompetitive practices in labor markets: no‑poach agreements, algorithmic wage suppression, or exclusionary practices that harm workers [1][6].

• Evidence:

• 2026 antitrust previews highlight continued enforcement in labor markets, including platform‑mediated work [1][6].

• Implications:

• Platforms face scrutiny not only for consumer‑side prices but also for how they set terms for gig workers, creators, and suppliers.

6. Multi‑sided market and dynamic‑competition theories (contested but evolving)

• Some economists and courts are re‑examining dynamic competition paradigms and network effects for large platforms, especially given generative‑AI‑driven disruption [7].

• While there is pushback against overly structural theories that ignore innovation and cross‑market competition, platform‑specific analyses of multi‑sided markets (user vs. developer vs. advertiser sides) remain central to cases against big tech [1][7].

Countervailing Trends and Limits

• In some major cases (e.g., search), courts have favored behavioral over structural remedies, citing rapid innovation and emerging competition from generative AI [4].

• Efforts to build new platform‑specific statutes (e.g., digital market regulations) in some jurisdictions may partially shift away from litigating novel theories under traditional antitrust and into ex ante regulation [5].

• First‑Amendment concerns arise for theories tying antitrust to content‑moderation or editorial choices on social media platforms [8].

Implications for Platforms

• Platforms should expect:

• Ongoing focus on self‑preferencing and discriminatory access.

• Expanding risks around algorithmic pricing, shared data platforms, and AI‑driven conduct.

• Growing attention to labor‑market effects and data‑centric foreclosure narratives.

• Practical responses:

• Implement competition‑law review of ranking, default, and access policies.

• Limit shared pricing tools and data exchanges among competitors.

• Document algorithm design and governance; monitor for collusive or exclusionary patterns.

• Evaluate data‑access and interoperability policies to mitigate essential‑facility‑type allegations.