The Algorithmic Drive-Thru: McDonald's, Deloitte, and the Price-Fixing Machine

The Digitalization of Collusion

The classic image of price-fixing involves corporate executives meeting in a dimly lit room to write numbers on a napkin. Today, that napkin has been replaced by an API, and the room is a cloud-based server hosted by a third-party consultant. What is branded to consumers as "dynamic pricing" is increasingly functioning as a highly coordinated, algorithmic price-fixing scheme. This shift raises fundamental questions about how antitrust law functions in an era of automated commerce.

In traditional retail markets, independent businesses operating in the same geographic area are legally required to compete. If two neighboring business owners agree to set a similar price, it is a per se violation of antitrust law. However, when a centralized entity introduces a unified pricing algorithm across these independent businesses, the competitive landscape is quietly neutralized. Instead of making independent pricing decisions, businesses feed proprietary, real-time transaction data, local competitor pricing, and inventory levels into a single, centralized software system. The system then calculates and feeds back optimized price recommendations to each location, replacing independent decision-making with a coordinated, algorithmic response.

The Mechanics of the Hub-and-Spoke Conspiracy

Antitrust lawyers increasingly categorize this structure as a "hub-and-spoke" conspiracy. The hub is the central actor—the technology provider or corporate coordinator—while the spokes are the individual, nominally independent businesses. Under this model, coordination does not require the spokes to communicate directly with one another. Instead, they all agree to deal with the same hub, knowing that their competitors are doing the exact same thing. The algorithm acts as a digital clearinghouse, ensuring that no competitor undercuts the others, thereby stabilizing and inflating prices across the board.

The "Recommendation" Defense

The primary defense in these cases relies on the concept of recommendation. Software providers argue that their systems merely provide suggestions, and that individual businesses retain the ultimate authority to set their own prices. However, critics point out that the structural incentives and software design make these recommendations effectively mandatory.

Businesses that deviate from the algorithmically generated prices risk underperforming according to centralized metrics, which can damage their commercial relationships or access to corporate support. Furthermore, because the predictive accuracy of these algorithms depends entirely on compliance, the systems are engineered to encourage, if not coerce, strict adherence. The technology is not a neutral tool for independent decision-making, but a mechanism for enforcing uniformity across a highly fragmented market.

Upstream Liability for Tech Vendors and Consultants

Historically, technology vendors and consulting firms escaped liability in antitrust cases by claiming they were merely passive service providers building tools to client specifications. That immunity is eroding. When a professional services firm actively designs, implements, and maintains a pricing system that pools competitor data to harmonize prices, they are increasingly treated as active participants in the conspiracy.

This shift represents a major challenge to the business model of large consulting firms that have spent the last decade selling digital modernization and algorithmic optimization to competing giants across every major industry. If a system is engineered to eliminate price variance among competitors, the designer of that system cannot easily claim neutrality.

The Public-Private Conflict of Interest

The intersection of public policy, corporate consulting, and government procurement introduces a profound structural conflict. While regulatory bodies are actively prosecuting algorithmic price-fixing, other branches of government are paying the same consulting firms billions of dollars to design and deploy automated decision-making systems for public administration, national security, and regulatory oversight.

Federal procurement databases show that major consulting firms hold massive contracts with key government agencies. This creates a disturbing feedback loop: a multinational consulting firm helps write the regulatory guidelines for AI safety, builds the automated systems that the government uses, and meanwhile designs pricing engines for private corporations that test the boundaries of competition. This raises an unresolved question: can the state effectively regulate the algorithmic economy when it relies on the chief architects of that economy to build its own technical and regulatory infrastructure?

Economic Extraction and the Erosion of the Franchise Model

For the consumer, the immediate economic reality of algorithmic coordination is highly regressive. Legitimate dynamic pricing is driven by individual market actors responding to supply and demand (e.g., airlines adjusting prices based on their own capacity). Algorithmic coordination, however, is a network of competitors using a single, shared tool to eliminate price competition and maximize "surplus extraction."

An advanced pricing algorithm, fueled by vast amounts of localized data, is designed to identify the absolute maximum price a consumer is willing to pay at any given moment—detecting if a nearby competitor is closed, if local traffic is heavy, or if local payrolls have just been distributed—and adjusting prices upward in real time. Because fast food is a staple for millions of working-class families, paying a premium calculated to exploit immediate hunger and a lack of alternatives has a severe social impact.

This centralization also strips away the autonomy of the franchise model. Historically, franchising allowed local entrepreneurs to tailor their operations and prices to their communities. The centralization of pricing through automated algorithms reduces the franchisee to a local manager of a corporate-controlled node, fully subordinated to the centralized pricing machine.