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Visualizing Moore’s Law in Action (1971-2019)

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Animation: Visualizing Moore’s Law in Action (1971-2019)

The pace of technological progress keeps accelerating.

There are many ways to show this, but perhaps the simplest way is to create a visual representation of Moore’s Law in action.

Today’s animation comes to us from DataGrapha, and it compares the predictions of Moore’s Law with data from actual computer chip innovations occurring between 1971 to 2019.

Defining Moore’s Law

Moore’s Law was originally derived from an observation by Gordon Moore, the co-founder of Fairchild Semiconductor and later the co-founder and CEO of Intel.

In 1965, Moore wrote that the number of components in a dense integrated circuit (i.e., transistors, resistors, diodes, or capacitors) had been doubling with every year of research, and he predicted that this would continue for another decade.

Later on in 1975, he revised his prediction to the doubling occurring every two years.

Like the animation, the following chart from Our World in Data helps plot out the predictions of Moore’s Law versus real world data ⁠— note that the Y Axis is logarithmic:

Moore's Law in Action

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The prophetic prediction of Moore’s Law has led to exponential progress in computing — as well as for everything else touched by computers.

It’s no surprise then, especially given that the modern information age is largely driven by increasingly efficient computing, that this law has had a trickle down effect on nearly every significant aspect of global innovation.

An Accelerated Pace of Change

Moore’s Law has translated into a faster rate of change for society as a whole.

A new idea, like the smartphone, can get immediate traction because of instantaneous communication, increased global connectivity, and the ubiquity of information. New tech advancements can now change business or culture in a heartbeat:

The accelerating rate of technology adoption

Further, since software is a “layer” built upon the foundation of computing, it means that digital products can be replicated at almost no marginal cost. This is why a phenomenon like Pokémon Go was able to captivate 50 million users in just 19 days.

Imagine this kind of scalability, when applied to things like artificial intelligence or virtual reality.

Is Moore’s Law Dead or Alive?

As with any enduring prediction, there are always naysayers out there that will boldly forecast an imminent end to the trend.

Since the 2000s, there has been an ongoing debate within the semiconductor community on whether Moore’s Law will continue its reign, or if progress will ultimately sputter out as certain physical limitations catch up with the process of miniaturization.

Earlier in 2019, Nvidia CEO Jensen Huang declared that Moore’s Law is no longer possible. For what it’s worth, Intel still says technology in chipmaking always finds a way to advance — while TSMC has recently said the law is actually alive and well.

Regardless of who is right, Moore’s Law has held true for close to 50 years, and its repercussions will continue to be felt in almost every aspect of life and society going forward.

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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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