Fifteen years ago, achieving meaningful computing power required racks of servers, endless cooling and no small amount of hubris. Today, the phone in your pocket outpaces the supercomputers of that era in terms of raw power. And yet, much of the software we build still treats modern hardware like it’s stuck in 2008, tiptoeing around performance as if we’re all still rationing CPU cycles.
Instead of taking advantage of hardware advancements to simplify solutions, we’re often overengineering, creating unnecessarily complex distributed systems that mimic the architectures of tech giants like Google. But here's the secret: Most businesses don’t need Google-scale infrastructure to succeed. In fact, overengineering might be the very thing holding them back.
The Hardware Revolution: Supercomputing In Your Pocket
In the early 2000s, if you wanted to process vast amounts of data or power a globally distributed application, you needed a supercomputer. And even then, "vast" and "global" meant something far less ambitious than today. Those supercomputers were purpose-built, prohibitively expensive and a nightmare to maintain.
Now, nearly everyone has a supercomputer in their pocket. Today’s smartphones, powered by chips like the Apple A18 or Qualcomm’s Snapdragon series, boast capabilities that would have made researchers at Bell Labs weep with envy. Add to this the rise of cloud computing, where vast compute power is available on-demand, and the sheer brute force of modern GPUs and CPUs. You don’t need exotic hardware to solve most problems anymore; commodity hardware is more than enough.
So why do we keep building software as if we’re stuck in 2008?
The Overengineering Trap
Many engineers look to companies like Google, AWS or Netflix as the gold standard. They design software with microservices, load balancers and multi-region failover—even for an app that’s serving 10,000 users, not 10 million.
But the truth is, Google didn’t choose that complexity because it wanted to. It was forced to. At the scale they operate, they couldn’t simply buy better hardware to compensate. They had to invent distributed computing techniques just to keep the lights on.
For the rest of us, mimicking Google’s architecture can feel like using a crane to build a birdhouse. Worse, it creates fragility instead of robustness. Every additional moving part is a potential failure point, and debugging becomes exponentially harder.
Embracing Simplicity In Modern Solutions
Modern hardware capabilities allow for a paradigm shift toward simplicity. By leveraging the power of devices like smartphones, we can offload tasks that previously required dedicated servers. For example, edge computing, which involves processing data locally on devices instead of sending it to a centralized server, enables faster and more efficient operations.
By performing tasks like image recognition, real-time analytics or even security checks directly on devices such as smartphones or IoT sensors, edge computing reduces latency, minimizes bandwidth usage and ensures greater reliability.
Eliminating delays caused by network dependencies doesn’t just streamline operations; it also makes your software look smarter than it probably is.
A Smarter Approach To Scale
If you’re a software developer, entrepreneur or anyone building digital products, scaling your project isn’t about preparing for the most extreme scenarios; it’s about aligning your architecture with the needs of your current users. Overengineering for hypothetical future growth often results in bloated systems that are harder to maintain, more fragile and more expensive to operate—all of which, ironically, slow your product’s adoption in the first place.
Instead, focus on designing for the scale you have today while ensuring the system can evolve as needed. By keeping things simple and practical, you’ll avoid the trap of premature optimization, where complexity grows faster than your user base.
Here are four practical strategies to guide your approach:
- Assess hardware capabilities. Before diving into development, take a hard look at the power of modern devices. If you’ve been in the tech industry for a few years, your intuitions are probably all out of date. Today’s smartphones and tablets are capable of tasks that once required high-end servers. Laptops are 100 times faster—really!—than 10 years ago, and 10,000x faster than 20 years ago. (These figures are a reflection not only of raw CPU advancements but also of the evolution of multithreaded processors, GPUs and parallel processing architectures.) Use that power to handle computation-intensive tasks on a single machine where possible.
- Adopt edge computing. Shift data processing closer to the user by using the devices themselves. Apple, for example, offloads much of its "intelligence" processing, like voice recognition or image analysis, to the device itself instead of relying entirely on the cloud.
- The local hardware is powerful enough for most tasks, and when it isn’t, the system seamlessly falls back on cloud resources. This approach not only eliminates network delays but also improves privacy by keeping sensitive data on the device. Additionally, it reduces processing and transfer costs for both the user and the business.
- Prioritize maintainability. Simplicity is key. A straightforward architecture is not only easier to scale but also simpler to debug and maintain over time. Aim for fewer moving parts, and remember: The best code is often the code you don’t have to write.
- Leverage modern libraries and tools. Don’t reinvent the wheel. Many open-source libraries are optimized for today’s hardware and can save you development time while boosting performance.
In a world where hardware is 10,000 times faster, our solutions don’t need to mirror the complexity of past eras. Instead, we should embrace the opportunity to build systems that are as elegant and powerful as the technology running them. When in doubt, keep it simple: You can always bring in the cranes later if the birdhouse really needs them.
Avery Pennarun
