The Great Acceleration
Moore’s Law: An introduction to acceleration
A transistor is a tiny device that controls the flow of electrical signals. Put lots of them together, and you can do some really complex things. Your computer is full of transistors. So is your phone. And so is your TV, your washing machine, the MRI in your doctor’s office, the cell tower outside, and so are a million other devices. But transistors didn’t exist before 1947, which meant that electronic devices had to be pretty simple, and often really huge.
When transistors came along, everything changed. Electronic devices could be small and portable. Transistors were much cheaper than the technologies used before, so lots more were available, which led to an explosion in electronics. Because of the size and availability of transistors, devices could contain huge numbers of them, which made it possible for a machine to do lots of different calculations and tasks. This capability led to the development of fast, cheap, powerful computers. Scientists suddenly had new tools for research, and consumers had access to life-changing technology. Over a handful of decades, transistors became cheaper and smaller. In 1965, the cofounder of Intel Corporation, Gordon Moore, famously predicted that this trend—transistors getting cheaper and cheaper and smaller and smaller—would continue. He forecast that the number of transistors that could be put on a single microchip would double every two years, meaning that technological change would just keep accelerating. Known as Moore’s Law, this prediction has proven to be largely true for the last 50 years.
The Great Acceleration
Why begin an article for a history course by talking about transistors and Moore’s Law? Well, Moore’s Law is connected to a hugely important trend that started in the twentieth century, called the Great Acceleration.
The Great Acceleration is a name for the period that began in 1945. It was coined by researchers JR McNeill and Peter Engelke. However, McNeill and Engelke used this term to describe changes in the environment, not technology. They noticed that human impact on the environment had become much more significant in the years after the Second World War. “Within the last three human generations,” they wrote, “three-quarters of the human-caused loading of the atmosphere with carbon dioxide took place. The number of motor vehicles on Earth increased from 40 million to 850 million. The number of people nearly tripled… In 1950 the world produced about 1 million tons of plastics but by 2015 that rose to nearly 300 million.”1
Yet environmental impact and technological innovation—two different expressions of human activity, one largely negative and the other mostly positive—have both increased dramatically since 1945. And they haven’t just increased, they’re increasingly increasing—or accelerating—like a car entering the highway or a plane getting ready to lift off into the sky.
Actually, cars and planes are two useful metaphors for talking about the Great Acceleration, because they’re part of the way we experience it—just like transistors are. You see, just like in every period before 1945, the Great Acceleration was shaped by humans inventing, sharing, and making things. But the new technologies pioneered after 1945 made it possible for all of that to happen much faster than ever before. Think about it:
- New technologies, especially those featuring transistors, helped us to do things faster and better than we previously could. Computers, robots, and other new tools dramatically sped up research, innovation, and production.
- A different set of tools, backed up by the same technologies, let us share our ideas faster than ever before. From the fax machine to the firewire, we could suddenly communicate faster and collaborate much more efficiently.
- Other technologies, like improved crops and medical procedures, drove human populations to grow rapidly, creating more innovators and creators than ever before.
- And if we needed to move ourselves or the things we made, improved vehicles, also featuring transistors, let us travel faster than ever.
Sociologist Manuel Castells wants us to think about these changes as resulting from the continued growth of networks in human history, which only increased in speed after 1945 (see a theme here?). Castells describes a network as a series of interconnected “nodes” such as people, organizations, and systems. Information and resources flow along the lines that connect these nodes. But networks are flexible and adaptable. Castells believes that the rise of digital systems and the internet—both equipped with transistors—have broken down the geographical and other boundaries that made it hard for people to work with each other. With new technologies such as instantaneous texting, sharing of files, and translation software, this kind of collaboration can happen faster and more widely than ever before.2 And Castells believes that it is this expansion of networks, as much as the technology itself, that is making change happen faster and faster.
The future of the Great Acceleration
Back in 2001, computer scientist Ray Kurzweil predicted that change would continue to speed up. He wrote, “so we won't experience 100 years of progress in the 21st century—it will be more like 20,000 years of progress (at today's rate).”3 Looking back over the first quarter-century of this time period, it certainly does feel like change has been happening very fast. For example, in 2001, the development of generative AI (artificial intelligence) seemed very far away. In 2024, AI seems to be everywhere!
Other scholars doubt that acceleration can continue at these rates. They point out that it seems as though not all technologies are changing at very fast speeds. For example, we seem to be making only slow progress on an important potential source of energy: nuclear fusion. And energy is an important factor in all of this. Generative AI, for example, requires huge amounts of electricity, which in turn emits huge amounts of carbon into the atmosphere. A shortage of energy might soon slow down the acceleration in AI and computing technologies.
Other critics suggest that humans may not want change to keep happening so fast. In many places, we see people working to preserve traditions and slow down the pace of change, partly because it doesn’t seem healthy or beneficial to their communities.
Moore’s Law, with which we began this article, is a great example of the dangers of predicting an accelerating future. Moore predicted that we would continue to see more, smaller, cheaper transistors at an incredible rate, and he was right—for a time. But we seem to have hit a limit to how tiny and cheap we can make transistors. They are so small now that engineers are facing some real physical challenges in reducing them further. The materials we have just can’t work at a such tiny scales, at least not without sacrificing some performance. That doesn’t mean we won’t see more innovation in computing, but it’s likely most of the savings or new efficiencies won’t come from transistors. We will need some new type of innovation.
What does this mean to people like you and me? Well, I’m not a microchip designer or engineer, and you probably aren’t either, so I’m not really concerned about Moore’s Law itself. But the success of Moore’s prediction over the past 60 years tells me something: I should be aware that things are changing quite rapidly, and I should try to remain flexible so I can adjust to that change. On the other hand, the fact that Moore’s Law is now hitting new boundaries tells me that sometimes change slows down, and I shouldn’t rely on accelerating change in my preparations for the future.
The Great Acceleration isn’t over. Changes, both good and bad, are still happening at incredible rates. As long as we still have active networks that rapidly connect people around the world, this might continue to be the case. But there’s a case to be made for slowing things down, as well. I know I could use a bit of a rest. How about you?
1 JR McNeill and Peter Engelke, The Great Acceleration: An Environmental History of the Anthropocene since 1945, Cambridge, MA: Belknap, 2014, 4
2 Manuel Castells, The Rise of the Network Society. 2nd ed. Malden, MA: Blackwell Publishers, 2010, 469.
3 Ray Kurzweil, “The Law of Accelerating Returns,” March 7, 2001, https://www.karmak.org/archive/2003/01/art0134.html.
Trevor Getz
Trevor Getz is a professor of African and world history at San Francisco State University. He has been the author or editor of 11 books, including the award-winning graphic history Abina and the Important Men, and has coproduced several prize-winning documentaries. Trevor is also the author of A Primer for Teaching African History, which explores questions about how we should teach the history of Africa in high school and university classes.
Image credits
This work is licensed under CC BY 4.0 except for the following:
Cover image: Robot - stock illustration. © CSA Images / Getty Images.
The “portable” telephone of the pretransistor era. Imagine carrying this around in your pocket. Public domain. https://commons.wikimedia.org/wiki/File:Healthy_living_(1917)_(14596736569).jpg
The first transistor (left) was small—much smaller than existing electronic components in 1947. But they quickly got smaller, like the 1956-era transistors (center). Still, these early transistors are huge compared to the hundreds of billions of tiny transistors found on today’s microprocessors (right). Photos © Science & Society Picture Library/SSPL/Getty Images (left), © OFF/AFP via Getty Images (center), By Pixabay, public domain https://pixabay.com/photos/pcb-logic-board-transistors-1137492/ (right).
Sweet, sweet acceleration! By Joint Base San Antonio, public domain. https://commons.wikimedia.org/wiki/File:220424-F-FD742-0182_(52027918558).jpg
The first “Internet,” Arpanet, digitally connected researchers in places like New York University, Stanford University, and other locations. This is a map of the connections linking the “nodes” of that network. By ARPANET - The Computer History Museum, public domain. https://commons.wikimedia.org/wiki/File:Arpanet_logical_map,_march_1977.png
A server room. AI tools can help us do a lot of things, but they consume vast amounts of energy, something humans just don’t have enough of. In the process, of course, AI also contributes to pollution and climate change—yet another accelerating problem! Public domain. https://pixabay.com/photos/server-room-datacenter-network-90389/
Articles leveled by Newsela have been adjusted along several dimensions of text complexity including sentence structure, vocabulary and organization. The number followed by L indicates the Lexile measure of the article. For more information on Lexile measures and how they correspond to grade levels: www.lexile.com/educators/understanding-lexile-measures/
To learn more about Newsela, visit www.newsela.com/about.
The Lexile® Framework for Reading evaluates reading ability and text complexity on the same developmental scale. Unlike other measurement systems, the Lexile Framework determines reading ability based on actual assessments, rather than generalized age or grade levels. Recognized as the standard for matching readers with texts, tens of millions of students worldwide receive a Lexile measure that helps them find targeted readings from the more than 100 million articles, books and websites that have been measured. Lexile measures connect learners of all ages with resources at the right level of challenge and monitors their progress toward state and national proficiency standards. More information about the Lexile® Framework can be found at www.Lexile.com.