Eroom’s Law: The “Reverse Moore’s Law” Shaping Drug Discovery

In the world of technology, we’ve all heard of Moore’s Law—the famous prediction that the number of transistors on a microchip doubles every 18 to 24 months, driving faster, cheaper, and more powerful electronics. It’s the force behind the smartphones in our pockets, the laptops on our desks, and the rapid pace of digital innovation. But what if there was a law that ran in the exact opposite direction? Enter Eroom’s Law—a wry, thought-provoking counterpart that reveals a sobering truth about one of humanity’s most critical pursuits: drug discovery.

First, let’s decode the name: “Eroom” is simply “Moore” spelled backwards. Coined by Jack Scannell and his colleagues in a 2012 Nature Reviews Drug Discovery paper, this law flips Moore’s optimistic trajectory on its head. Instead of exponential progress, Eroom’s Law describes an exponential decline: since the 1950s, the number of new drugs approved per billion dollars spent on research and development (R&D) has halved approximately every nine years. In other words, the more we spend on developing new medicines, the fewer breakthroughs we get for our money—a trend that defies the intuition that better technology should make things easier.

To put this in perspective: in the 1950s, a billion dollars (adjusted for inflation) might have yielded several new drugs. Today, that same billion dollars struggles to produce even one. This isn’t for lack of effort or innovation. Pharmaceutical companies now have access to cutting-edge tools like high-throughput screening, computational drug design, and biotechnology—but these advances haven’t reversed the trend. If anything, they’ve highlighted just how stubborn Eroom’s Law can be.

So why is this happening? The 2012 paper that introduced Eroom’s Law identified four key culprits, each painting a picture of a field grappling with increasing complexity. The first is what’s whimsically called the “better than the Beatles” problem: just as it’s hard to write a song better than the Beatles, new drugs now have to outperform already effective treatments (like Lipitor for high cholesterol). This means smaller, harder-to-detect benefits, requiring larger, more expensive clinical trials to prove efficacy.

Then there’s the “cautious regulator” problem. After high-profile safety scandals (such as the thalidomide tragedy), regulatory agencies like the FDA and EMA have raised the bar for new drugs. Today’s medicines must undergo stricter safety and efficacy testing, lengthening trial times and driving up costs. What was once acceptable is no longer enough—and that’s a good thing for patient safety, but it also adds layers of complexity to drug development.

Two more factors compound the issue: the “throw money at it” tendency (adding more researchers and resources doesn’t always boost productivity) and the “basic research–brute force” bias (overestimating the power of new technologies to translate lab discoveries into clinical success). For example, shifting from traditional phenotypic screening to target-based high-throughput screening has made drug discovery faster in theory, but it often fails to account for the complexity of the human body, leading to high failure rates in clinical trials.

While Eroom’s Law was first observed in drug discovery, its lessons extend far beyond pharmaceuticals. It’s a reminder that “low-hanging fruit” doesn’t last forever. In the early days of any field—whether it’s drug development, software, or renewable energy—the easiest problems are solved first. As those problems disappear, the remaining challenges become harder, more complex, and more expensive to tackle. This is true in tech too: as Moore’s Law slows (transistors can only get so small), we’re starting to see hints of Eroom-like inefficiencies in chip design and manufacturing.

But here’s the silver lining: Eroom’s Law isn’t a death sentence for innovation. In recent years, there have been signs that the trend is stabilizing, thanks to new approaches like personalized medicine, adaptive clinical trials (known as MAPPs), and AI-driven drug discovery. These tools are helping researchers navigate complexity more efficiently, reducing trial times and failure rates. They’re not breaking Eroom’s Law outright—but they’re bending it, offering hope that we can strike a balance between safety, cost, and progress.

At its core, Eroom’s Law is a humbling reminder of how hard it is to solve humanity’s most pressing problems. Unlike Moore’s Law, which feels like an unstoppable force of progress, Eroom’s Law teaches us that innovation isn’t always linear—or easy. It’s a call to be smarter about how we invest resources, embrace new methodologies, and collaborate across disciplines.

So the next time you pick up a smartphone and marvel at how far technology has come, remember Eroom’s Law. It’s a quiet counterpoint to Moore’s optimism—a reminder that some of the most important work we do (like finding new medicines) requires patience, creativity, and a willingness to adapt. After all, progress isn’t just about making things faster and cheaper—it’s about making things that matter, even when the odds are stacked against us.