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How Many Millions of Lines of Code Does It Take?

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How Many Millions of Lines of Code Does It Take?

How Many Millions of Lines of Code Does It Take?

Today’s data visualization comes from David McCandless from Information is Beautiful. Buy their awesome book called Knowledge is Beautiful – we own the physical version, and it’s full of great data visualizations.

How many millions of lines of code does it take to make the modern program, web service, car, or airplane possible?

The range is extraordinary: the average iPhone app has less than 50,000 lines of code, while Google’s entire code base is two billion lines for all services. And interestingly, the code behind machines such as fighter jets, popular video game engines, and even the Large Hadron Collider fall somewhere in between these two extremes.

Increasing Complexity

A million lines of code, if printed, would be about 18,000 pages of text. That’s 14x the length of War and Peace.

It’s more than what was needed to run old technologies like the Space Shuttle, a pacemaker, or even the game engine of Quake 3 – but it’s not enough to be the driving force behind the modern software that’s used in everyday life today.

  • The control software to run a U.S. military drone uses 3.5 million lines of code.
  • A Boeing 787 has 6.5 million lines behind its avionics and online support systems.
  • Google Chrome (browser) runs on 6.7 million lines of code (upper estimate).
  • A Chevy Volt uses 10 million lines.
  • The Android operating system runs on 12-15 million lines.
  • The Large Hadron Collider uses 50 million lines.
  • Not including backend code, Facebook runs on 62 million lines of code.
  • With the advent of sophisticated, cloud-connected infotainment systems, the car software in a modern vehicle apparently uses 100 million lines of code. This is according to Wired magazine.
  • All Google services combine for a whopping 2 billion lines.

Applying the math above – that means it would take 36,000,000 pages to “print out” all of the code behind all Google services. That would be a stack of paper 2.2 mi (3.6 km) high!

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Charted: The Jobs Most Impacted by AI

We visualized the results of an analysis by the World Economic Forum, which uncovered the jobs most impacted by AI.

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Charted: The Jobs Most Impacted by AI

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

Large language models (LLMs) and other generative AI tools haven’t been around for very long, but they’re expected to have far-reaching impacts on the way people do their jobs. With this in mind, researchers have already begun studying the potential impacts of this transformative technology.

In this graphic, we’ve visualized the results of a World Economic Forum report, which estimated how different job departments will be exposed to AI disruption.

Data and Methodology

To identify the job departments most impacted by AI, researchers assessed over 19,000 occupational tasks (e.g. reading documents) to determine if they relied on language. If a task was deemed language-based, it was then determined how much human involvement was needed to complete that task.

With this analysis, researchers were then able to estimate how AI would impact different occupational groups.

DepartmentLarge impact (%)Small impact (%)No impact (%)
IT73261
Finance70219
Customer Sales671617
Operations651817
HR57412
Marketing56413
Legal46504
Supply Chain431839

In our graphic, large impact refers to tasks that will be fully automated or significantly altered by AI technologies. Small impact refers to tasks that have a lesser potential for disruption.

Where AI will make the biggest impact

Jobs in information technology (IT) and finance have the highest share of tasks expected to be largely impacted by AI.

Within IT, tasks that are expected to be automated include software quality assurance and customer support. On the finance side, researchers believe that AI could be significantly useful for bookkeeping, accounting, and auditing.

Still interested in AI? Check out this graphic which ranked the most commonly used AI tools in 2023.

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