How many LTT MCM Arches can hold a PC?
In this article
Andrew — Founder of Signal Reads. Builder, reader, occasional contrarian.
> **Bottom line:** Linus Tech Tips' viral test, demonstrating that just two LTT MCM Arches can support a high-end gaming PC, reveals a pervasive blind spot in how we approach system design and "robustness." The test highlights that over-engineering often masks a lack of understanding of true load paths and failure modes, leading to unnecessary complexity and cost.
This often results in systems that are perceived as strong but are inefficiently built, leaving critical vulnerabilities unaddressed while resources are wasted on redundant, non-contributing elements.
I’ve built enough things to know that reality rarely matches the blueprint.
For years, I subscribed to the gospel of "over-design for safety," adding extra layers of redundancy, padding out specs, and generally building systems that could survive an asteroid strike.
Then I watched a video from Linus Tech Tips (LTT) recently, where they stacked their modular MCM Arches to see how many it would take to support a fully loaded gaming PC.
What happened next wasn't just entertaining; it completely flipped my understanding of what "robust" actually means, exposing a multi-billion dollar fallacy that's costing us all.
This wasn't some academic paper or a theoretical simulation. This was a real-world, highly visible stress test of a simple, modular component.
The conventional wisdom, the gut feeling, the "engineer's intuition" that many of us carry, suggested it would take a significant number of those arches to prevent catastrophic failure.
Yet, through careful bracing and load distribution, LTT demonstrated that just *two* of those arches could reliably hold that heavy PC. Two.
My first reaction was skepticism, then awe, then a deep, uncomfortable realization about how much unnecessary complexity I'd likely baked into my own projects over the years.
The Illusion of Strength in Software and Steel
Why does a test like this, involving essentially glorified desk legs, resonate so deeply?
Because it mirrors a pervasive problem in software and hardware engineering: our tendency to build for perceived strength rather than actual resilience.
We add more microservices, more layers of abstraction, more redundant servers, more complex authentication schemes — all in the name of "robustness." But are we truly building stronger systems, or just more complicated ones that are harder to debug, slower to deploy, and ultimately, just as fragile in unexpected ways?
Consider the modern web application. We've moved from monolithic architectures to distributed microservices, each with its own database, its own deployment pipeline, its own team.
The promise was agility and resilience – if one service fails, the others carry on.
In theory, it's a beautiful, robust design.
In practice, I've seen teams drown in the operational overhead, the cross-service communication failures, and the sheer complexity of tracing an error through a dozen different repositories.
We built *more* pieces, assuming more pieces equaled more strength, but often we just built more *surfaces for failure*.
The Redundancy Paradox: When More Means Less
This brings me to what I call **The Redundancy Paradox**: *The intuitive drive to add more components for perceived safety often leads to diminished overall system understanding and increased complexity, inadvertently creating new, harder-to-diagnose failure modes.* Everyone celebrates the idea of redundancy, but they're missing the crucial point that poorly implemented redundancy is just complexity debt.
The Hidden Cost of Over-Provisioning
When we over-engineer, we're not just adding physical components or lines of code. We’re adding cognitive load, deployment headaches, and maintenance burdens.
Think about cloud infrastructure: spinning up 10 instances when 2 could handle peak load might *feel* safer, but it's costing you money, increasing your attack surface, and creating more logs to sift through when something *does* go wrong.
The LTT Arch test showed that often, the true load-bearing elements are few, and the rest are just along for the ride, adding weight and cost without contributing to core strength.
The Blurry Line Between Robustness and Rigidity
A truly robust system isn't just strong; it's adaptable. Over-engineering often leads to rigidity.
When every component is custom-built and tightly coupled with excessive safeguards, the system becomes incredibly difficult to change or scale in new ways.
Imagine if the LTT arches were welded together in a single, unchangeable structure. It might be strong, but it loses its modularity, its ability to be reconfigured or repaired easily.
In software, this looks like monolithic services that are impossible to refactor, or microservices so interdependent they might as well be one giant monolith.
The Illusion of Control Through Complexity
There's a psychological comfort in complexity. If a system is incredibly intricate, we assume it must be robust because "so much thought went into it." This is a dangerous trap.
The LTT test didn't succeed because of an arbitrarily large number of parts; it succeeded because of a *smart* arrangement of a *minimal* number of parts.
The key wasn't more material; it was understanding how the forces distributed.
In software, this means focusing on clear contracts, well-defined boundaries, and efficient algorithms, not just piling on layers of frameworks and libraries.
We often add complexity to *avoid* understanding the fundamental problem.
What This Means for Your Next Project
So, what are the real-world implications of LTT's Arch test for our daily work?
If you’re a developer, a product manager, or a founder, this isn't just about desk legs; it's about shifting your mindset on what truly constitutes a reliable system.
First, **challenge your assumptions about load and failure points.** Before you add another layer of caching, another database replica, or another circuit breaker, ask yourself: *What is the absolute minimum required to meet the functional and non-functional requirements?* What are the *actual* critical paths?
The LTT test showed that the structural integrity wasn't about the quantity of arches, but about how the load was distributed and braced.
Your system's strength lies in its core logic and its most critical data flows, not necessarily its peripheral fluff.
Second, **prioritize understanding over blind redundancy.** Instead of just adding "more," invest in better monitoring, better observability, and deeper analysis of your system's behavior under stress.
Understand *why* things fail, not just *that* they fail. Use tools like chaos engineering to proactively test resilience, rather than just hoping your over-engineered layers will catch every problem.
By mid-2027, the gap between teams who truly understand their system's failure modes and those who just add more complexity will be astronomical.
Finally, **embrace modularity and simplicity as core tenets of robustness.** The LTT Arch is robust precisely because it’s modular and simple. Each unit performs a clear function.
When building software, strive for components that do one thing well, have clear interfaces, and can be easily replaced or reconfigured.
This isn't about being minimalist for minimalism's sake; it's about building systems that are inherently easier to reason about, maintain, and adapt.
This approach reduces the surface area for unexpected interactions and makes diagnosing issues far more straightforward.
The Bigger Picture: Trusting Design Over Gut Feeling
The LTT Arch test is more than just a fun experiment; it's a stark reminder that our intuition, especially when it comes to engineering, can be deeply flawed.
We often default to "more" because it *feels* safer, because it's easier to justify additional resources than to do the hard work of precise analysis and elegant design.
But true robustness isn't about brute force or endless layers; it's about intelligent design, understanding constraints, and optimizing for efficiency and clarity.
As we move into 2027, with AI tools like ChatGPT 5 and Claude 4.6 increasingly capable of generating complex code, the temptation to simply "add more" will only grow.
It will be easier than ever to build systems that *look* robust on paper but are riddled with hidden inefficiencies and failure points.
Our role as builders isn't just to assemble parts; it's to critically evaluate every part, every layer, every redundancy.
It's about having the courage to strip away the unnecessary and trust in the elegance of a well-engineered, minimal solution.
When was the last time you critically challenged an "over-engineered" solution in your own work, or did you just default to adding more? Let's talk about it in the comments.
---
