Beta Stories — Episode 08
In 1974, Donald Knuth wrote one sentence that would later be deployed to justify rather a lot of mediocre software:
"Premature optimisation is the root of all evil."
He meant it about hand-tuning loops nobody had measured. The full quote, which everyone forgets to include, runs differently:
"We should forget about small efficiencies, say about 97% of the time: premature optimisation is the root of all evil. Yet we should not pass up our opportunities in that critical 3%."
The 3% disappeared. Only the 97% survived.
Combined with Moore's Law and an invisible cloud bill, a generation of developers grew up assuming computational waste was a solved problem. Hardware, after all, is cheap.
The Decay
Median desktop web page: 500 KB in 2010, 2.9 MB in 2025. A 5.3x inflation for the same paragraph of text and a photograph.
Slack, built to send short messages, consumes well above 1 GB of RAM. Discord did one better: until 2020, their Go-based Read States service hit garbage-collector spikes that fleets of boxes barely contained. They rewrote it in Rust. Memory dropped 40%. Latency improved 6.5x best case, 160x worst. Same product. Less hardware.
In November 2022, Elon Musk dismissed roughly 80% of Twitter's staff and demanded $1 billion in annual infrastructure cuts. Twitter exited its Sacramento data centre, decommissioning 5,200 racks and 148,000 servers. Cloud spend dropped 60-75%. The site continued to function. One does pause at this.
WhatsApp, for context, served 900 million users in 2015 with fifty engineers. A ratio of one engineer per eighteen million users. The ratio was deliberate. Most companies do not aim for it; most companies do not consider that they could.
The Mechanism
The cloud abstracts cost away from the developer. An additional instance is a single API call; the bill arrives at someone else's desk. By 2010, the mechanism was complete: Moore's Law on the hardware side, AWS on the procurement side, "premature optimisation" on the cultural side.
Every generation since has trained on frameworks, never the layers underneath. React, Next.js, an npm tree of 1,400 packages. The runtime cost is invisible until the cloud bill is.
Bootcamps, in particular, optimise for time-to-employability. That means teaching the framework, not the model underneath it. The student ships, the company hires, and the question of what happens at runtime is deferred indefinitely. By the time the engineer is senior enough to ask it, the system is large enough that nobody else wants to.
The Signal
Your build script needs 4 GB of RAM. Your editor consumes 800 MB to display text. The architecture review concludes "we'll scale horizontally". A senior engineer suggests the data structure could be smaller, and is told this is premature optimisation.
The single most reliable signal is the question that never gets asked: how much does this cost to run?
The Bill
Globally, data centres consumed 415 TWh of electricity in 2024, about 1.5% of all electricity produced on the planet. In 2025 the figure rose 17%, against 3% growth in general electricity demand. The IEA projects 945 TWh by 2030: roughly twice today's figure, four times the rate of all other growth combined. AI is responsible for 5-15% of data centre power today, projected to reach 35-50% by 2030.
These numbers are not abstract. They translate into transformer stations, transmission lines, cooling water, and the carbon budget of countries that made political commitments years ago and now find themselves in an arithmetic problem.
Hardware is not free. It was simply cheap enough that nobody bothered to look at the bill. The bill, as it turns out, is paid by the grid, the cooling, the climate, and the laptop fan currently spinning under your fingertips.
Read the full article on vivianvoss.net →
By Vivian Voss — System Architect & Software Developer. Follow me on LinkedIn for daily technical writing.
