The Preventing Algorithmic Collusion Act of 2025 targets pricing algorithms that can facilitate collusion by using nonpublic competitor data. It creates a competition-law enforcement audit tool, requires reporting to the Attorney General and the FTC when requested, and permits civil actions with penalties for violations.
The Act also imposes transparency requirements for large- revenue entities and commissions a federal study on the use and effects of pricing algorithms. The goal is to deter covert price coordination while preserving legitimate algorithmic pricing and competition.
For compliance professionals, the Act defines pricing algorithms and nonpublic data with concrete clarity, establishes the mechanism and timeline for audits, and sets out specific penalties and disclosure obligations. It does not create new antitrust theories without precedent; instead, it codifies a framework to enforce existing antitrust laws in the context of algorithmic pricing and data-driven terms.
At a Glance
What It Does
Prohibits pricing algorithms that use nonpublic competitor data and establishes a mandatory audit/reporting regime. It also creates transparency requirements for large- revenue entities and lays out enforcement mechanisms under the Sherman Act and FTC Act.
Who It Affects
Firms that develop, distribute, or use pricing algorithms; companies with interstate pricing operations; the FTC and DOJ as enforcement bodies; employees and independent contractors subject to disclosure requirements.
Why It Matters
Addresses the rise of algorithmic pricing by creating a clear enforcement pathway, reducing the risk of covert price fixing, and increasing market transparency for regulators and market participants.
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What This Bill Actually Does
The bill defines pricing algorithms as computational processes, including machine-learning systems, that set or suggest prices or other commercial terms across interstate or international markets. It prohibits using such algorithms if they are trained with or rely on nonpublic competitor data.
To enforce this prohibition, the act grants the Attorney General or the FTC authority to request reports on any identified pricing algorithm; recipients must provide a detailed description of the data, development, and decision rules within 30 days (or a later date approved by the requesting agency). The reports must be certified as accurate under penalty of perjury by a corporate officer and treated as confidential trade secrets.
The law also allows information sharing between the DOJ and the FTC, and with the National Institute of Standards and Technology for technical assistance, while safeguarding the confidentiality of the data.
Separately, the act makes it unlawful to use or distribute pricing algorithms that rely on nonpublic competitor data, with civil penalties that start at not less than $10,000 per day of violation or the price of products sold using the offending algorithm, whichever is greater, and sets a 90-day clock for the act to take effect after enactment. It creates a presumption of illegal agreement for pricing that is algorithmically coordinated when the algorithm is distributed to multiple parties or used by multiple parties in the same or related markets, and it imposes joint and several liability on distributors who knew or should have known about the nonpublic data inputs.
To promote transparency, the bill requires entities with $5 million or more in annual revenue to disclose, before a sale, that pricing is algorithm-driven to customers and, for employees or independent contractors, that pricing is algorithm-driven for services rendered. False disclosures would be treated as unfair or deceptive practices, with civil penalties and injunctive relief available.
Finally, the bill tasks the FTC with a two-year study to map how pricing algorithms are used, their benefits and harms, and potential regulatory needs. This combination of prohibitions, audits, disclosures, and a high-level study aims to curb algorithmic price collusion without stifling legitimate pricing innovation.
The Five Things You Need to Know
Pricing algorithms that use nonpublic competitor data become unlawful to use or distribute.
A Competition Law Enforcement Audit tool requires reports within 30 days of agency requests, with detailed data, training sources, and governance disclosures.
A presumption of agreement under the Sherman Act applies when an algorithm is distributed to or used by multiple parties to set or fix prices in the same or related markets.
Transparency rules require disclosing algorithm-driven pricing to customers and workers for firms with $5M+ in annual revenue, with penalties for noncompliance.
The FTC must complete a two-year study on pricing algorithms, their effects, and implications for policy and further regulation.
Section-by-Section Breakdown
Every bill we cover gets an analysis of its key sections.
Short title
This Act may be cited as the Preventing Algorithmic Collusion Act of 2025.
Definitions
Defines key terms: antitrust laws; commercial terms; Commission (FTC); distribute; nonpublic competitor data; nonpublic data; person; price; pricing algorithm. Sets the scope for what counts as data used to set prices and what constitutes an algorithmic pricing decision.
Competition Law Enforcement Audit
Gives the Attorney General or the FTC authority to request a report on pricing algorithms within 30 days (or a later date approved). Reports must identify developers, data sources, whether the algorithm operates autonomously, data used to train, data collection methods, any price discrimination, and changes since the request. Officers must certify the report under penalty of perjury. Information is treated as confidential trade secret material, with limited sharing among DOJ, FTC, and NIST for technical assistance.
Prohibiting collusive activity in pricing algorithms
Makes it unlawful to use or distribute any pricing algorithm that uses nonpublic competitor data. It provides civil penalties (not less than $10,000 per day or the price of products sold using the algorithm, whichever is greater) and allows for injunctive relief. The prohibition takes effect 90 days after enactment.
Algorithmic price fixing
Creates a presumption that using or distributing an algorithm to fix prices in the same or related markets constitutes an agreement under the Sherman Act and a violation of the FTC Act, if certain conditions are met (distribution to two or more parties or use by multiple parties). Establishes joint and several liability for those who knew or should have known that nonpublic data was used.
Transparency in pricing algorithms
Requires entities with $5M+ in annual revenue using pricing algorithms to disclose to customers and to employees/contractors that pricing is algorithm-driven. Disclosures may include information on price discrimination and the identity of third-party algorithm developers. Noncompliance triggers FTC penalties and potential civil actions.
FTC study
Requires the FTC to publish a two-year study on the prevalence of pricing algorithms, their use in price discrimination, potential harms and benefits, and recommendations for further legislation or regulation.
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Explore Economy in Codify Search →Who Benefits and Who Bears the Cost
Every bill creates winners and losers. Here's who stands to gain and who bears the cost.
Who Benefits
- U.S. consumers in markets where pricing algorithms influence prices, who would benefit from reduced discrimination and more competitive outcomes.
- The Federal Trade Commission and Department of Justice Antitrust Division, which gain enhanced enforcement tools and data for monitoring algorithmic pricing.
- In‑house compliance teams at large, price‑sensitive firms that will need to implement audit, disclosure, and governance processes.
- Employees and independent contractors who receive pricing-related disclosures and related protections under the new framework.
Who Bears the Cost
- Firms that develop or distribute pricing algorithms using nonpublic competitor data, facing audits, reporting requirements, and potential penalties.
- Firms with $5M+ in annual revenue that must implement pricing-algorithm disclosures, creating ongoing compliance costs.
- Third‑party algorithm developers and data providers who supply inputs or algorithms used by others, due to potential joint liability.
- Companies that collect or distribute nonpublic competitor data, increasing data governance and privacy‑risk management costs.
- Regulators and legal departments that must implement and administer new enforcement and reporting regimes.
Key Issues
The Core Tension
The central dilemma is whether aggressive enforcement of algorithmic pricing rules (to prevent collusion) can coexist with legitimate, data-driven pricing optimization that benefits competition and efficiency.
The act provides a comprehensive framework for regulating pricing algorithms, but several tensions are implicit. First, defining nonpublic competitor data and the boundary between useful data for pricing optimization and prohibited inputs could prove contentious in enforcement.
Second, the 90-day effective date paired with a broad audit and disclosure regime imposes rapid compliance demands, which may be challenging for smaller firms or complex pricing ecosystems. Third, the presumption of agreement in Section 5 relies on the distribution and use patterns of pricing algorithms, which could raise questions about intent and knowledge in multi-party tech ecosystems.
Finally, the transparency requirements in Section 6 create potential competitive concerns for firms about exposing proprietary pricing logic or training data, even when the data is disclosable under the statute. The act relies on existing antitrust authorities to interpret and resolve these tensions, but the practical balance between enforcement and innovation will depend on agency guidance and case outcomes.
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