Why Software Feels Slower Every Year

Your Computer Gets Faster. Your Software Gets Heavier.

There is a specific frustration that comes from opening an app you've used for years and noticing it now takes three seconds to load where it once took one. Nothing about your workflow changed. The task is identical. But the software feels sluggish in a way that's hard to articulate and easy to dismiss — until it happens with every app, every update, every year.

This phenomenon even has a name: Wirth's Law, named after computer scientist Niklaus Wirth, who observed in 1995 that software gets slower faster than hardware gets faster. The observation has only grown more accurate. A modern Slack desktop client routinely consumes 500–1,000 MB of RAM to display a list of text messages — a task that would have been trivially lightweight two decades ago. Microsoft Teams, at idle, can consume more memory than the entire operating system did in 2005. These aren't edge cases; they're the norm across productivity software, browsers, and creative tools.

What makes this problem distinct from ordinary technical debt is that it's largely invisible to the people shipping the software. Performance regressions rarely trigger a bug report. No one files a ticket saying "this feels 12% slower than six months ago." The degradation is cumulative and gradual, which means it almost never reaches the threshold that would prompt a fix — even as users feel it every single day.

The Architectural Choices That Trade Speed for Convenience

Software slowness isn't random. It's the predictable output of a set of structural decisions made by developers, product teams, and platform vendors — each of which made sense in isolation and compounds into the sluggishness you feel.

Abstraction layers multiply silently. Modern software is rarely written close to the hardware. A typical web app runs JavaScript inside a browser engine (itself a massive runtime), which runs on an operating system abstraction layer, which talks to hardware drivers. Each layer adds overhead. Electron — the framework behind Slack, VS Code, Discord, and dozens of other mainstream apps — works by bundling an entire Chromium browser engine with every application. This is why each of those apps individually consumes RAM that would have seemed absurd for a standalone program in 2010. The tradeoff is real: abstraction layers let small teams ship cross-platform apps quickly. But users pay the performance cost on every launch, every keystroke, every scroll.

Cross-platform frameworks prioritize reach over efficiency. Writing separate native apps for Windows, macOS, iOS, and Android is expensive. Frameworks like React Native, Flutter, and Electron allow one codebase to run everywhere. The efficiency loss is the price of that portability. A native macOS app written in Swift can call system APIs directly; a React Native app routes the same call through a JavaScript bridge, a native module interface, and then the system API. Each hop adds latency. At human timescales, a few milliseconds of extra latency per interaction accumulates into a feeling of unresponsiveness that users notice even when they can't name it.

Feature accumulation is structurally rewarded; optimization is not. Product roadmaps are driven by features because features are legible — they can be announced, marketed, and measured in user adoption. Performance improvements are largely invisible to the people who approve budgets. A 30% reduction in startup time doesn't appear in a press release. This creates a systematic bias: engineering time flows toward new capabilities and away from the optimization work that would make existing capabilities feel better. Over a product's lifetime, this compounds. Each release adds more code paths, more telemetry, more integrations — and the performance baseline quietly erodes.

Telemetry and background processes have become standard overhead. Contemporary software frequently runs processes that have nothing to do with the task you're using it for. Analytics pings, crash reporters, auto-update checkers, cloud sync daemons, and email capture and engagement hooks all run alongside the core application. These are individually small costs, but across a system running a dozen modern apps simultaneously, the aggregate CPU and memory overhead is substantial. The user experiences this as general system sluggishness rather than attributing it to any single application — which means no single application ever gets blamed or fixed.

Why Optimization Has Become a Low-Priority Engineering Task

The forces making software slower are self-reinforcing. As hardware has grown more powerful, the industry's tolerance for inefficient code has grown with it. In the early PC era, developers optimized obsessively because they had no choice — a few kilobytes of wasted memory could crash a system. Today, a developer shipping code that wastes 200 MB of RAM faces no immediate consequence. Users upgrade hardware. Companies provision more cloud compute. The feedback loop between poor code and real-world cost has been severed.

This dynamic is especially visible in the browser, which has become the default runtime for an enormous share of modern software. Web technologies were designed for documents, not applications. Running complex software inside a browser means carrying the full weight of a rendering engine, a JavaScript virtual machine, and a security sandbox — for every tab, every app, every time. The browser's dominance as a platform was driven by deployment convenience (no installation required, instant updates), not by performance characteristics. Yet because job security in tech increasingly depends on shipping product fast rather than shipping it lean, browser-based and Electron-based deployment has become the default even for applications that would benefit enormously from native code.

There's also a market structure problem. Once a slow application achieves sufficient user lock-in — through network effects, data portability barriers, or workplace mandates — there is little competitive pressure to improve performance. Slack's users aren't going to abandon it because it uses too much RAM if their entire company is on Slack. The switching cost insulates the product from the performance feedback that a competitive market would otherwise provide. The result is a landscape where the most widely used professional software is often the most bloated, precisely because its dominance removes the incentive to optimize.

Getting Usable Performance Without Waiting for Developers to Care

The most effective first step is substitution: replacing Electron-based or web-wrapped apps with native or lightweight alternatives where they exist. For email, native clients like Apple Mail or Thunderbird consume a fraction of the resources of browser-based equivalents. For messaging, lighter clients or even browser tabs (rather than dedicated Electron wrappers) often perform better than the "official" desktop app. Tools like the increasingly credential-heavy software industry have produced a counter-movement of developers who deliberately optimize — finding and using their work is often a direct performance gain.

For software you can't replace, managing background processes is high-leverage. On both Windows and macOS, startup item managers and task monitors can identify which applications are consuming resources while idle. Disabling auto-launch for apps that don't need to run continuously — Spotify, Slack, Teams, creative suite daemons — can meaningfully reduce baseline system load. Browser hygiene matters too: each open tab in Chrome or Edge runs its own renderer process; reducing tab count or switching to a browser with more aggressive memory management (Firefox's "tab unloading," for instance) has measurable effects.

The broader pattern here is that software performance has become a hidden externality. The costs of bloated, slow software are paid entirely by users — in time, in hardware upgrade cycles, in cognitive friction — while the benefits of shipping fast and feature-rich accrue entirely to the companies doing the shipping. This is the same structural mismatch that appears in many modern system frustrations: the people who bear the cost of a design decision are not the people who made it. Understanding that the slowness is structural, not accidental, is itself useful. It explains why waiting for the next update rarely helps, and why the most reliable path to faster software is often choosing software whose developers had a reason to care about performance from the start.