Picture this: you’re searching for an apartment in a bustling city. You find that rents across multiple complexes seem oddly similar, even though these properties are managed by different companies. Coincidence? Maybe not. A lawsuit against Yardi Systems, a property management software company, alleges that its algorithmic pricing system facilitated price-fixing, inflating rents for millions of tenants. On 4 December 2024, the U.S. District Court for the Western District of Washington denied the defendants’ motion to dismiss the ongoing case of alleged algorithmic price collusion, signaling a shift in how courts might view algorithm – not as neutral tools, but as potential conspirators in anti-competitive behavior.
From a legal perspective, this case is a significant forewarning for what’s to come in the legal AI space. Yardi’s software allegedly enabled property managers to share sensitive data and coordinate pricing strategies, driving up rents without explicit communication between competitors. The Department of Justice and Federal Trade Commission have long warned that shared AI pricing tools could lead to tacit collusion. Now, the judiciary appears ready to entertain this theory, and not only in the realm of real estate.
Algorithms like Yardi’s are designed to optimize efficiency. They adjust prices, predict demand, and maximize revenue – all tasks that, on the surface, seem beneficial. But when multiple competitors rely on the same system, the algorithm becomes a silent middleman, steering the market in ways that harm consumers. It’s like a conductor leading an orchestra where the players didn’t realize they were part of the same band. They might not be in direct communication, but they’re all playing the same tune.
The Yardi case is just one example.
Consider ride-hailing platforms like Uber and Lyft, which use algorithms to determine surge pricing. While these systems are intended to balance supply and demand, they also create a data-driven moat. The more rides these companies facilitate, the better their algorithms become, leaving little room for smaller competitors. In digital advertising, Google and Meta’s algorithms are so effective at targeting audiences that they’ve seemingly created a duopoly, with smaller players struggling to carve out a niche.
The question we need to ask isn’t just whether these algorithms are efficient – it’s whether they are fair. Are they having certain competitive outcomes because they’re the best tools available, or because they’ve amassed so much data and power that no one else can effectively compete? And when does their success cross the line into anti-competitive behavior?
Antitrust laws were built for a different era. They’re traditionally designed to catch bad actors conspiring in smoky backrooms and, while they have undergone certain reforms over the years, they may not be equipped to uncover algorithms quietly coordinating across servers. When pricing tools like Yardi’s lead to market distortions, proving intent becomes nearly impossible. Yet the outcome – a less competitive market – is just as damaging.
As a lawyer, it’s hard not to see the complexities here. Algorithms don’t have intent; they follow the instructions they’re given. If their creators design them to prioritize profit over fairness, is that illegal, or just smart business? The law struggles with these nuances, and perhaps it’s time to rethink regulation – that is, if these types of contraventions are what we should be sanctioning.
One potential solution is mandatory transparency. Companies using algorithms to set prices or allocate resources could be required to disclose how those systems work. Not the proprietary code of course – no one is asking for trade secrets – but the logic behind their decisions. What factors does the algorithm consider? How does it weigh them? This wouldn’t just help regulators; it would empower consumers and competitors to understand what they’re up against.
Another approach might be to treat data as a shared resource. If dominance in an industry stems from access to data, then perhaps we need to democratize that access. Imagine a system where companies contribute anonymized data to a public repository, leveling the playing field for smaller firms. Yes, this raises privacy concerns, but with the right safeguards, it’s a conversation worth having.
There’s also the idea of real-time oversight. Could regulators deploy their own algorithms to monitor markets? These systems could flag suspicious patterns – like sudden, synchronized price increases – before they spiral out of control. It’s a futuristic concept, but not out of reach, and one that aligns with the challenges we’re facing.
Overregulation could stifle innovation, punishing companies for being too successful and disincentivizing risk-taking
At the same time, we must be cautious. Overregulation could stifle innovation, punishing companies for being too successful and disincentivizing risk-taking. Algorithms, after all, aren’t inherently bad. They’ve made our lives easier in countless ways, from streamlining online shopping to improving public transportation. The goal isn’t to eliminate their use but to ensure they don’t harm the very markets they’re meant to improve.
The Yardi case will likely set a precedent for how we approach these questions. If the court finds that its software facilitated price-fixing, it could open the floodgates for similar lawsuits across industries. But even if Yardi is cleared, the case has already sparked a critical debate about the role of algorithms in modern markets.
Are we optimizing efficiency, or are we unknowingly enabling restrictive practices? The answer isn’t clear-cut, and maybe it never will be. But as lawyers, regulators, and consumers, we owe it to ourselves to keep asking the question – and to ensure that progress doesn’t come at the cost of fairness.
After all, an algorithm might not have a conscience, but we do.
