Amazon is the Standard Oil of the 21st century. Its business operations and global reach dwarf those of virtually every other company on the planet — and exceed the GDP of more than a few countries — illustrating the vital importance innovation has on the modern economy. In his latest book, The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society, author Azeem Azhar examines how the ever-increasing pace of technological progress is impacting, influencing — and often rebuilding — our social, political and economic mores from the ground up.
Excerpted from The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society by Azeem Azhar. Copyright © 2021 Azeem Azhar. Printed with permission of the publisher, Diversion Books. All rights reserved.
In 2020, Amazon turned twenty-six years old. Over the previous quarter of a century, the company had transformed shopping. With retail revenues in excess of $213 billion, it was larger than Germany’s Schwarz Gruppe, America’s Costco, and every British retailer. Only America’s Walmart, with more than half a trillion dollars of sales, was bigger. But Amazon was, by this time, far and away the world’s largest online retailer. Its online business was about eight times larger than Walmart’s. Amazon was more than just an online shop, however. Its huge operations in areas such as cloud computing, logistics, media, and hardware added a further $172 billion in sales.
At the heart of Amazon’s success is an annual research and development budget that reached a staggering $36 billion in 2019, and which is used to develop everything from robots to smart home assistants. This sum leaves other companies — and many governments — behind. It is not far off the UK government’s annual budget for research and development. The entire US government’s federal R&D budget for 2018 was only $134 billion.
Amazon spent more on R&D in 2018 than the US National Institutes of Health. Roche, the global pharmaceutical company renowned for its investment in research, spent a mere $12 billion in R&D in 2018. Meanwhile Tesco, the largest retailer in Britain — with annual sales in excess of £50 billion (approximately $70 billion) — had a research lab whose budget was in the “six figures” in 2016.
Perhaps more remarkable is the rate at which Amazon grew this budget. Ten years earlier, Amazon’s research budget was $1.2 billion. Over the course of the next decade, the firm increased its annual R&D budget by about 44 percent every year. As the 2010s went on, Amazon doubled down on its investments in research. In the words of Werner Vogels, the firm’s chief technology officer, if they stopped innovating they “would be out of business in ten to fifteen years.”
In the process, Amazon created a chasm between the old world and the new. The approach of traditional business was to rely on models that succeeded yesterday. They were based on a strategy that tomorrow might be a little different, but not markedly so.
This kind of linear thinking, rooted in the assumption that change takes decades and not months, may have worked in the past—but not anymore. Amazon understood the nature of the Exponential Age. The pace of change was accelerating; the companies that could harness the technologies of the new era would take off. And those that couldn’t keep up would be undone at remarkable speed.
This divergence between the old and the new is one example of what I call the “exponential gap.” On the one hand, there are technologies that develop at an exponential pace—and the companies, institutions, and communities that adapt to or harness those developments. On the other, there are the ideas and norms of the old world. The companies, institutions, and communities that can only adapt at an incremental pace. These get left behind—and fast.
The emergence of this gap is a consequence of exponential technology. Until the early 2010s, most companies assumed the cost of their inputs would remain pretty similar from year to year, perhaps with a nudge for inflation. The raw materials might fluctuate based on commodity markets, but their planning processes, institutionalized in management orthodoxy, could manage such volatility. But in the Exponential Age, one primary input for a company is its ability to process information. One of the main costs to process that data is computation. And the cost of computation didn’t rise each year; it declined rapidly. The underlying dynamics of how companies operate had shifted.
In Chapter 1, we explored how Moore’s Law amounts to a halving of the underlying cost of computation every couple of years. It means that every ten years, the cost of the processing that can be done by a computer will decline by a factor of one hundred. But the implications of this process stretch far beyond our personal laptop use—and far beyond the interests of any one laptop manufacturer.
In general, if an organization needs to do something that uses computation, and that task is too expensive today, it probably won’t be too expensive in a couple of years. For companies, this realization has deep significance. Firms that figured out that the effective price of computation was declining, even if the notional price of what they were buying was staying the same (or even rising), could plan, practice, and experiment with the near future in mind. Even if those futuristic activities were expensive now, they would become affordable soon enough. Organizations that understood this deflation, and planned for it, became well-positioned to take advantage of the Exponential Age.
If Amazon’s early recognition of this trend helped transform it into one of the most valuable companies in history, they were not alone. Many of the new digital giants—from Uber to Alibaba, Spotify to TikTok—took a similar path. And following in their footsteps were firms who understand how these processes apply in other sectors. The bosses at Tesla understood that the prices of electric vehicles might decline on an exponential curve, and launched the electric vehicle revolution. The founders of Impossible Foods understood how the expensive process of precision fermentation (which involves genetically modified microorganisms) would get cheaper and cheaper. Executives at space companies like Spire and Planet Labs understood this process would drive down the cost of putting satellites in orbit. Companies that didn’t adapt to exponential technology shifts, like much of the newspaper publishing industry, didn’t stand a chance.
We can visualize the gap by returning to our now-familiar exponential curve. As we’ve seen, individual technologies develop according to an S-curve, which begins by roughly following an exponential trajectory. And as we’ve seen, it starts off looking a bit humdrum. In those early days, exponential change is distinctly boring, and most people and organizations ignore it. At this point in the curve, the industry producing an exponential technology looks exciting to those in it, but like a backwater to everyone else. But at some point, the line of exponential change crosses that of linear change. Soon it reaches an inflection point. That shift in gear, which is both sudden and subtle, is hard to fathom.
Because, for all the visibility of exponential change, most of the institutions that make up our society follow a linear trajectory. Codified laws and unspoken social norms; legacy companies and NGOs; political systems and intergovernmental bodies—all have only ever known how to adapt incrementally. Stability is an important force within institutions. In fact, it’s built into them.
The gap between our institutions’ capacity to change and our new technologies’ accelerating speed is the defining consequence of our shift into the Exponential Age. On the one side, you have the new behaviors, relationships, and structures that are enabled by exponentially improving technologies, and the products and services built from them. On the other, you have the norms that have evolved or been designed to suit the needs of earlier configurations of technology. The gap leads to extreme tension. In the Exponential Age, this divergence is ongoing—and it is everywhere.
