Do broken systems always lead to software failures? Well, that would be too straightforward to be true. In most cases, long-held assumptions suddenly collide with real-world complexity to birth the unexpected.
The CrowdStrike incident of July 2024 highlighted this reality. A faulty software update from the cybersecurity firm in question set into motion a worldwide IT outage. The update not only led to system crashes but also disrupted essentials like hospital services and airlines.
The matter subsided only after days of recovery efforts. Do you want to bypass such troubles? Then, steer clear of some system design choices that age like milk. This article will discuss three common choices that look smart on paper. In practice, they unravel poorly over time, which means you need to future-proof your designs against them.
Focusing Only on Immediate Growth
The classic “growth first, questions later” approach is more commonplace than one might think. Startups and product teams often measure success in terms of spikes in adoption and high-engagement metrics.
The reason why these goals feel reasonable is that they come wrapped in deception. Initially, they will validate the product and secure attractive investments. However, the cracks that turn into decay only show up over time.
Shortcuts taken to accelerate delivery keep piling up as technical debt. It’s a lot like borrowing money in the present. Undoubtedly, you get what you want now, but it will all be extracted with interest later.
Similarly, design shortcuts may allow the team to ship a product faster. However, it comes back to bite in the form of future costs in maintenance and debugging. Examples of technical debt include poorly documented code and reliance on outdated architecture.
As baffling as this may seem, why do engineers still choose this path? Given below are the three brutally honest reasons:
- They’re under pressure to launch quickly due to market demands and stiff competition.
- It’s a chase after early stability, even though this is often deceptive.
- Compressed timelines and budget constraints make it seem like the only sane choice.
The problem of tech debt is as pervasive as it can get. Recent research discovered that at least 92% of organizations are burdened with it. What’s more is that 80% even reported delays and cancellations of critical projects as a result.
Something along these lines happened in the early 2010s. Social media startups were prioritizing rapid growth over long-term architectural planning. Did they get what they wanted? Absolutely, but not in the way that was expected.
As the user base expanded, the platforms struggled with database performance and glitches. What seemed like a rational decision at the start ended up eating away time and money in the form of bugs and system crashes.
Designing Never-Ending Engagement Loops
Another design choice that seems to be dangerously common is that of creating engagement loops without any exit conditions. Again, this stems from the desire to keep the metrics growing. The more users stay, the more habitual it becomes, and the more metrics rise, right?
Prima facie, this appears to be a smart move. Gamified elements and variable rewards also benefit users. It may sound good as an argument, but this also keeps the loops running long after they’ve served their original purpose.
In other words, a self-perpetuating cycle is set into motion where the system keeps nudging users to act without ever telling them to take a break. It can’t be a coincidence that internet users worldwide still spend around 141 minutes each day on social media platforms.
From the design viewpoint, it’s important to understand why this choice ages poorly. Here are the most likely reasons:
- Interactions are escalated to an unhealthy extent. Such systems can strengthen user behaviors that extend far beyond the initial intent.
- Even the feedback cannot be trusted. After all, endless interaction only inflates the metrics. Hence, it’s difficult to decipher what’s truly working vs. what’s driven by the loop itself.
- Over time, the risk of user fatigue and burnout increases. At this point, the choice has completely backfired as users can abandon the system entirely.
A concrete, real-world example that may also be a little extreme is that of real-money gaming platforms. They usually leverage reward schedules to keep interactions frictionless.
However, when engagement loops spiral out of control (and they do), even legal exposure is not an impossibility. In fact, users have had to hire a gambling lawyer for gambling addiction claims against popular betting platforms.
As TruLaw explains, plaintiffs have alleged that the platforms are intentionally engineered with features that keep users engaged. These include VIP programs and targeted incentives. What’s shocking is that the behavioral profiling was used to track users most likely to continue wagering.
While this contributed to profitable compulsive behaviors, it also created ripples that affected users and brand reputations. This could have been avoided with proper system boundaries.
Ignoring Rare Edge Cases
It’s said that sometimes, the greatest risk of all is not taking a risk. This is quite relevant in cases where system design choices only focus on the ‘happy, safe path.’ In other words, only the most commonly known scenarios are taken into account, not the rare ones.
On the surface, it even seems to make sense. You would want the system to work smoothly for most people most of the time, right? However, not anticipating the rare edge cases is like building a bridge for fair weather. It may work, but the minute there’s a rare storm, the bridge will collapse without recovery.
First, let’s go into a little more detail as to why developers fail to ditch this choice. In most cases, it has to do with time and resource constraints given how fast-paced development landscapes are. Testing every conceivable edge case can feel like a bit of a stretch.
Plus, the complexity of predicting all unusual combinations further discourages extensive testing. Despite the concerns sounding valid, they come at a heavy price. The latter becomes clear in light of the reasons why this choice ages poorly:
- Despite seeming insignificant, rare edge cases are capable of triggering a cascade of costly software errors hidden initially from sight.
- Technical debt may compound as handling exceptions requires more work, thereby tempting patchwork solutions.
- Even if a fraction of users encounter edge cases, the impact can still be disproportionately negative.
As an example, consider the infamous Knight Capital Incident of 2012. It involved a system designed to handle typical trading orders accurately. Unfortunately, a rare combination of untested conditions caused the algorithm to malfunction. This resulted in a shocking $440 million in losses.
The lesson is clear, although it may not be easy to follow through. Rigorous testing against edge cases and unusual data patterns is a must. You don’t want your system to gain accolades only to crash later in a spectacular manner.
If your focus is on the choices that look brilliant today, they will likely age like milk. However, you cannot ignore the present entirely. This is why balance is the key. Plan for immediate victories, but do not sacrifice long-term results in the process.
In case of shortcuts, ensure they are thoughtful and within boundaries. Having a myopic view only lands developers in the headlines for all the wrong reasons. You can only build solutions that age like wine when foresight and moderation guide your design choices.
