Ray Kurzweil

Technology Feels Like It’s Accelerating — Because It Actually Is

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“Technology goes beyond mere tool making; it is a process of creating ever more powerful technology using the tools from the previous round of innovation.” –Ray Kurzweil

A decade ago, smartphones (as we know them by today’s standards) didn’t exist. Three decades earlier, no one even owned a computer. Think about that—the first personal computers arrived about 40 years ago. Today, it seems nearly everyone is gazing at a glowing, handheld computer. (In fact, two-thirds of Americans own one, according to a Pew Report.)

Intuitively, it feels like technology is progressing faster than ever. But is it really? According to Ray Kurzweil—yes, it absolutely is. In his book The Singularity Is Near, Kurzweil shows technology’s quickening pace and explains the force behind it all.

This article will explore Kurzweil’s explanation of this driving force, which he dubbed the law of accelerating returns, and the surprising implications of technology’s acceleration.

Moore’s Law is famous—but it isn’t special

Computer chips have become increasingly powerful while costing less. That’s because over the last five decades the number of transistors—or the tiny electrical components that perform basic operations—on a single chip have been doubling regularly.

This exponential doubling, known as Moore’s Law, is the reason a modern smartphone affordably packs so much dizzying capability into such a small package.

The technological progress in computer chips is well known—but surprisingly, it isn’t a special case. A range of other technologies demonstrate similar exponential growth, whether bits of data stored or DNA base pairs recorded. The outcome is the same: capabilities have increased by thousands, millions, and billions for less cost in just decades.

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The above charts show a few examples of accelerating technologies, but more examples are plentiful. These do not directly depend on the doubling of transistor counts—and yet each one moves along its own exponential curve just as computer chips do.

So, what’s going on?

According to the law of accelerating returns, the pace of technological progress—especially information technology—speeds up exponentially over time because there is a common force driving it forward. Being exponential, as it turns out, is all about evolution.

Technology is an evolutionary process

Let’s begin with biology, a familiar evolutionary process.

Biology hones natural “technologies,” so to speak. Recorded within the DNA of living things are blueprints of useful tools known as genes. Due to selective pressure—or “survival of the fittest”—advantageous innovations are passed along to offspring.

As this process plays out generation after generation over geological timescales, chaotically yet incrementally, incredible growth takes place. By building on genetic progress rather than starting over, organisms have increased in complexity and capability over time. This innovative power is evident nearly everywhere we look on Earth today.

“Evolution applies positive feedback,” Kurzweil writes. “The more capable methods resulting from one stage of evolutionary progress are used to create the next stage.”

Biology’s many innovations include cells, bones, eyes, thumbs, brains—and from thumbs and brains, technology. According to Kurzweil, technology is also an evolutionary process, like biology, only it moves from one invention to the next much faster.

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Civilizations advance by “repurposing” the ideas and breakthroughs of their predecessors. Similarly, each generation of technology builds on the advances of previous generations, and this creates a positive feedback loop of improvements.

Kurzweil’s big idea is that each new generation of technology stands on the shoulders of its predecessors—in this way, improvements in technology enable the next generation of even better technology.

Technological evolution speeds up exponentially

Because each generation of technology improves over the last, the rate of progress from version to version speeds up.

To see this, imagine making a chair with hand tools, power tools, and finally assembly lines. Production gets faster after each step. Now imagine each generation of these tools is also used to design and build better tools. Kurzweil suggests such a process is at play in the design of ever-faster computer chips with the software and computers used by engineers.

“The first computers were designed on paper and assembled by hand. Today, they are designed on computer workstations with the computers themselves working out many details of the next generation’s design, and are then produced in fully automated factories with only limited human intervention.” – Ray Kurzweil, The Singularity Is Near

This acceleration can be measured in the “returns” of the technology—such as speed, efficiency, price-performance, and overall “power”—which improve exponentially too.

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The acceleration of acceleration: It’s a bit like climbing a mountain and receiving a jetpack.

Further, as a technology becomes more effective, it attracts more attention. The result is a flood of new resources—such as increased R&D budgets, recruiting top talent, etc.—which are directed to further improving the technology.

This wave of new resources triggers a “second level” of exponential growth, where the rate of exponential growth (the exponent) also begins accelerating.

However, specific paradigms (e.g., integrated circuits) won’t grow exponentially forever. They grow until they’ve exhausted their potential, at which point a new paradigm replaces the old one.

The surprising implications of the law of accelerating returns

Kurzweil wrote in 2001 that every decade our overall rate of progress was doubling, “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).” 

This suggests that the horizons for amazingly powerful technologies may be closer than we realize. Some of Ray Kurzweil’s predictions from the last 25 years may have seemed a stretch at the time—but many were right.

Like in 1990 when he predicted that a computer would beat a pro chess player by 1998, which came true in 1997 when Garry Kasparov lost to IBM’s Deep Blue. (Now, in 2016, a computer has mastered the even more complex game Go—an accomplishment not expected by some experts for another decade.)

We’re only 15 years into the 21st century and the progress has been pretty stunning—the global adoption of the Internet, smartphones, ever-more agile robots, AI that learns. We sequenced the first human genome in 2004 at a cost of hundreds of millions of dollars. Now, machines can sequence 18,000 annually for $1,000 a genome.

These are just a few examples of the law of accelerating returns driving progress forward. Because the future is approaching much faster than we realize, it’s critical to think exponentially about where we’re headed and how we’ll get there

How to Think Exponentially and Better Predict the Future

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“The future is widely misunderstood. Our forebears expected it to be pretty much like their present, which had been pretty much like their past.” –Ray Kurzweil, The Singularity Is Near

We humans aren’t great predictors of the future. For most of history, our experience has been “local and linear.” Not much change occurred generation to generation: We used the same tools, ate the same meals, lived in the same general place.

Even as progress gets faster and faster, our caveman brains tend to think linearly.
Though the pace of technology is progressing exponentially, the default mode of our caveman brains is to think linearly.

As a result, we’ve developed an intuitive outlook of the future akin to how we approach a staircase—having climbed a number of steps, our prediction of what’s to come is simply steps followed by more steps, with each day expected to be roughly like the last.

But, as Ray Kurzweil describes in The Singularity Is Near, the rapid growth of technology is actually accelerating progress across a host of domains. This has led to unexpected degrees of technological and social change occurring not only between generations, but within them.

Against our intuition, today the future is unfolding not linearly but exponentially, making it challenging to predict just what will happen next and when. This is why the pace of technological progress tends to surprise us, and we find ourselves in situations like this:

linear-vs-exponential-41How do we prepare for a future tracking to exponential trends, if we aren’t accustomed to thinking this way? Let’s start with the basics of exponential growth.

What is exponential growth?

Unlike linear growth, which results from repeatedly adding a constant, exponential growth is the repeated multiplication of a constant. This is why linear growth produces a stable straight line over time, but exponential growth skyrockets.

Here’s another way to think about it: imagine you are going to walk down a road taking steps a meter in length. You take 6 steps, and you’ve progressed six meters (1, 2, 3, 4, 5, 6). After 24 more steps, you’re 30 meters from where you began. It’s easy to predict where 30 more steps will get you—that’s the simplicity of linear growth.

However, setting anatomy aside, imagine you could double the length of your stride. Now when you take six steps, you’ve actually progressed 32 meters (1, 2, 4, 8, 16, 32), which is significantly more than the 6 meters you’d move with equal steps. Amazingly, by step number 30, doubling your stride will put you a billion meters from where you started, a distance equal to twenty-six trips around the world.

That’s the surprising, unintuitive power of exponential growth.

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Exponential growth is deceptive, then explosive

What’s interesting about exponential growth is that when you double your stride, you progress the same distance with each step as all the previous steps combined. Before you hit a billion miles at step 30, you’re at 500 million miles at step 29. That means that any of your previous steps look minuscule compared with the last few steps of explosive growth, and most of the growth happens over a relatively short period of time.

Another example: let’s say you want to get to a certain location and you’re going to double your stride again to get there. Progress toward your destination appears distant at one percent of the way there, but in fact, you’re only seven steps (or doublings) away—and much of all that progress happens in the last step.

The point is we often miss exponential trends in their early stages because the initial pace of exponential growth is deceptive—it begins slow and steady and is hard to differentiate from linear growth. Hence, predictions based on the expectation of an exponential pace can seem improbable.

Ray Kurzweil gives this example: “When the human genome scan got underway in 1990 critics pointed out that given the speed with which the genome could then be scanned, it would take thousands of years to finish the project. Yet the fifteen-year project was completed slightly ahead of schedule, with a first draft in 2003.”

Here’s a great visual of exponential growth’s deceptive then explosive nature in computers. See how most of the progress happens right at the end after years of doubling?

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Image courtesy of Pawel Sisiak/AI Revolution.

Will exponential growth eventually end?

In practice, exponential trends do not last forever. However, some trends can continue for long periods, driven along by successive technological paradigms.

A broad exponential trend, computing for example, is made up of a series of consecutive S-shaped technological life cycles, or S-curves.

Each curve looks like the letter ‘S’ because of the three growth stages it represents—initial slow growth, explosive growth, and leveling off as the technology matures. These S-curves overlap, and when one technology slows, a new one takes over and speeds up. With each new S-curve, the amount of time it takes to reach higher levels of performance is less.

Kurzweil lists five computing paradigms in the 20th century: electromechanical, relay, vacuum tubes, discrete transistors, and integrated circuits. When one technology exhausted its potential, the next took over making more progress than its predecessors.

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Planning for an exponential future

“[T]he future will be far more surprising than most people realize, because few observers have truly internalized the implications of the fact that the rate of change itself is accelerating.” Ray Kurzweil, The Singularity Is Near

The rule of thumb here is: expect to be surprised, then plan accordingly.

For example, what might the next five years look like? One way to forecast them would be to look at the last five and extend this pace forward. By now, the problem with this thinking should be clear: The pace itself is changing.

A better forecast would be to look at the last five and then reduce the time it will take to make a similar amount of progress in the next five. It’s more likely that what you think will happen in the next five years will actually happen in the next three.

The practice of exponential thinking isn’t really about the ins and outs of how you plan—you know how to do that—it’s about better timing your plan (whatever it may be).

In fact, Kurzweil’s law of accelerating returns arose from very practical of origins.

“As an inventor in the 1970s, I came to realize that my inventions needed to make sense in terms of the enabling technologies and market forces that would exist when the inventions were introduced, as that world would be a very different one from the one in which they were conceived,” Kurzweil wrote in the Singularity Is Near.

With a little practice, we can all make better plans by becoming consciously aware of our intuitive, linear expectations and adjusting them for an exponential future.

Why is learning to think exponentially valuable?

This isn’t just an interesting concept—our linear brains can get us into real trouble.

Thinking linearly causes businesses, governments, and individuals to get blindsided by factors that trend to exponential growth. Big firms get disrupted by new competition; governments struggle to keep policy current; all of us worry our future is out of control.

Exponential thinking reduces some of this disruptive stress and reveals new opportunities. If we can better plan for the accelerating pace, we can ease the transition from one paradigm to the next, and greet the future in stride.

Will the End of Moore’s Law Halt Computing’s Exponential Rise?

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Much of the future we envision today depends on the exponential trends commonly do.” –Ray Kurzweil, The Singularity Is Near

Much of the future we envision today depends on the exponential progress of information technology, most popularly illustrated by Moore’s Law. Thanks to shrinking processors, computers have gone from plodding, room-sized monoliths to the quick devices in our pockets or on our wrists. Looking back, this accelerating progress is hard to miss—it’s been amazingly consistent for over five decades.

But how long will it continue?

This post will explore Moore’s Law, the five paradigms of computing (as described by Ray Kurzweil), and the reason many are convinced that exponential trends in computing will not end anytime soon.

What Is Moore’s Law?

“In brief, Moore’s Law predicts that computing chips will shrink by half in size and cost every 18 to 24 months. For the past 50 years it has been astoundingly correct.” –Kevin Kelly, What Technology Wants

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Gordon Moore’s chart plotting the early progress of integrated circuits.

In 1965, Fairchild Semiconductor’s Gordon Moore (later cofounder of Intel) had been closely watching early integrated circuits. He realized that as components were getting smaller, the number that could be crammed on a chip was regularly rising and processing power along with it.

Based on just five data points dating back to 1959, Moore estimated the time it took to double the number of computing elements per chip was 12 months (a number he later revised to 24 months), and that this steady exponential trend would result in far more power for less cost.

Soon it became clear Moore was right, but amazingly, this doubling didn’t taper off in the mid-70s—chip manufacturing has largely kept the pace ever since. Today, affordable computer chips pack a billion or more transistors spaced nanometers apart.

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Moore’s Law has been solid as a rock for decades, but the core technology’s ascent won’t last forever. Many believe the trend is losing steam, and it’s unclear what comes next.

Experts, including Gordon Moore, have noted Moore’s Law is less a law and more a self-fulfilling prophecy, driven by businesses spending billions to match the expected exponential pace. Since 1991, the semiconductor industry has regularly produced a technology roadmap to coordinate their efforts and spot problems early.

In recent years, the chipmaking process has become increasingly complex and costly. After processor speeds leveled off in 2004 because chips were overheating, multiple-core processors took the baton. But now, as feature sizes approach near-atomic scales, quantum effects are expected to render chips too unreliable.

This year, for the first time, the semiconductor industry roadmap will no longer use Moore’s Law as a benchmark, focusing instead on other attributes, like efficiency and connectivity, demanded by smartphones, wearables, and beyond.

As the industry shifts focus, and Moore’s Law appears to be approaching a limit, is this the end of exponential progress in computing—or might it continue awhile longer?

Moore’s Law Is the Latest Example of a Larger Trend

“Moore’s Law is actually not the first paradigm in computational systems. You can see this if you plot the price-performance—measured by instructions per second per thousand constant dollars—of forty-nine famous computational systems and computers spanning the twentieth century.” –Ray Kurzweil, The Singularity Is Near

While exponential growth in recent decades has been in integrated circuits, a larger trend is at play, one identified by Ray Kurzweil in his book, The Singularity Is Near. Because the chief outcome of Moore’s Law is more powerful computers at lower cost, Kurzweil tracked computational speed per $1,000 over time.

This measure accounts for all the “levels of ‘cleverness’” baked into every chip—such as different industrial processes, materials, and designs—and allows us to compare other computing technologies from history. The result is surprising.

The exponential trend in computing began well before Moore noticed it in integrated circuits or the industry began collaborating on a roadmap. According to Kurzweil,  Moore’s Law is the fifth computing paradigm. The first four include computers using electromechanical, relay, vacuum tube, and discrete transistor computing elements.

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There May Be ‘Moore’ to Come

“When Moore’s Law reaches the end of its S-curve, now expected before 2020, the exponential growth will continue with three-dimensional molecular computing, which will constitute the sixth paradigm.” –Ray Kurzweil, The Singularity Is Near

While the death of Moore’s Law has been often predicted, it does appear that today’s integrated circuits are nearing certain physical limitations that will be challenging to overcome, and many believe silicon chips will level off in the next decade. So, will exponential progress in computing end too? Not necessarily, according to Kurzweil.

The integrated circuits described by Moore’s Law, he says, are just the latest technology in a larger, longer exponential trend in computing—one he thinks will continue. Kurzweil suggests integrated circuits will be followed by a new 3D molecular computing paradigm (the sixth) whose technologies are now being developed. (We’ll explore candidates for potential successor technologies to Moore’s Law in future posts.)

Further, it should be noted that Kurzweil isn’t predicting that exponential growth in computing will continue forever—it will inevitably hit a ceiling. Perhaps his most audacious idea is the ceiling is much further away than we realize.

How Does This Affect Our Lives?

Computing is already a driving force in modern life, and its influence will only increase. Artificial intelligence, automation, robotics, virtual reality, unraveling the human genome—these are a few world-shaking advances computing enables.

If we’re better able to anticipate this powerful trend, we can plan for its promise and peril, and instead of being taken by surprise, we can make the most of the future.

Kevin Kelly puts it best in What Technology Wants:

“Imagine it is 1965. You’ve seen the curves Gordon Moore discovered. What if you believed the story they were trying to tell us…You would have needed no other prophecies, no other predictions, no other details to optimize the coming benefits. As a society, if we just believed that single trajectory of Moore’s, and none other, we would have educated differently, invested differently, prepared more wisely to grasp the amazing powers it would sprout.”

Ray Kurzweil Predicts Three Technologies Will Define Our Future

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Over the last several decades, the digital revolution has changed nearly every aspect of our lives.

The pace of progress in computers has been accelerating, and today, computers and networks are in nearly every industry and home across the world.

Many observers first noticed this acceleration with the advent of modern microchips, but as Ray Kurzweil wrote in his book The Singularity Is Near, we can find a number of eerily similar trends in other areas too.

According to Kurzweil’s law of accelerating returns, technological progress is moving ahead at an exponential rate, especially in information technologies.

This means today’s best tools will help us build even better tools tomorrow, fueling this acceleration.

But our brains tend to anticipate the future linearly instead of exponentially. So, the coming years will bring more powerful technologies sooner than we imagine.

As the pace continues to accelerate, what surprising and powerful changes are in store? This post will explore three technological areas Kurzweil believes are poised to  change our world the most this century.

Genetics, Nanotechnology, and Robotics

Of all the technologies riding the wave of exponential progress, Kurzweil identifies genetics, nanotechnology, and robotics as the three overlapping revolutions which will define our lives in the decades to come. In what ways are these technologies revolutionary?

  • The genetics revolution will allow us to reprogram our own biology.
  • The nanotechnology revolution will allow us to manipulate matter at the molecular and atomic scale.
  • The robotics revolution will allow us to create a greater than human non-biological intelligence.

While genetics, nanotechnology, and robotics will peak at different times over the course of decades, we’re experiencing all three of them in some capacity already. Each is powerful in its own right, but their convergence will be even more so. Kurzweil wrote about these ideas in The Singularity Is Near over a decade ago.

Let’s take a look at what’s happening in each of these domains today, and what we might expect in the future.

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The Genetics Revolution: ‘The Intersection of Information and Biology’


“By understanding the information processes underlying life, we are starting to learn to reprogram our biology to achieve the virtual elimination of disease, dramatic expansion of human potential, and radical life extension.”
– Ray Kurzweil, The Singularity Is Near

We’ve been “reprogramming” our environment for nearly as long as humans have walked the planet. Now we have accrued enough knowledge about how our bodies work that we can begin tackling disease and aging at their genetic and cellular roots.

Biotechnology Today

We’ve anticipated the power of genetic engineering for a long time. In 1975, the Asilomar Conference debated the ethics of genetic engineering, and since then, we’ve seen remarkable progress in both the lab and in practice—genetically modified crops, for example, are already widespread (though controversial). 

Since the Human Genome Project was completed in 2003, enormous strides have been made in reading, writing and hacking our own DNA.

Now, we’re reprogramming the code of life from bacteria to beagles and soon, perhaps, in humans. The ‘how,’ ‘when,’ and ‘why’ of genetic engineering are still being debated, but the pace is quickening.

Major innovations in biotech over the last decade include:

Many challenges still need to be overcome before these new technologies are widely used on humans, but the possibilities are incredible. And we can only assume the speed of progress will continue to accelerate. The surprising result? Kurzweil proposes that most diseases will be curable and the aging process will be slowed or perhaps even reversed in the coming decades.

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The Nanotechnology Revolution: ‘The Intersection of Information and the Physical World’


“Nanotechnology has given us the tools…to play with the ultimate toy box of nature atoms and molecules. Everything is made from it…The possibilities to create new things appear endless.”
– Nobelist Horst Störmer, The Singularity Is Near

Many people date the birth of conceptual nanotech to Richard Feynman’s 1959 speech, “There’s Plenty of Room at the Bottom,” where Feynman described the “profound implications of engineering machines at the level of atoms.” But it was only when the scanning tunneling microscope was invented in 1981 that the nanotechnology industry began in earnest.

Kurzweil argues that no matter how successfully we fine tune our DNA-based biology, it will be no match for what we will be able to engineer by manipulating matter on the molecular and atomic level.

Nanotech, Kurzweil says, will allow us to redesign and rebuild “molecule by molecule, our bodies and brains and the world in which we live.”

Nanotechnology Today

While we can already see evidence of the ‘genetics revolution’ in the news and in our daily lives, for most people, nanotech might still seem like the stuff of science fiction. However, it’s likely you already use products on a daily basis that have benefitted from nanotech research. These include sunscreens, clothing, paints, cars, and more. And of course, the digital revolution has continued thanks to new methods allowing us to make chips with nanoscale features.

In addition to already having practical applications today, there is much research and testing being conducted into groundbreaking (if still experimental) nanotechnology like:

Though we continue to improve at manipulating matter on nanoscales, we’re still far from nanobots or nanoassemblers that would build and repair atom by atom.

That said, as Feynman pointed out, the principles of physics do not speak against such a future. And we need only look to our own biology to see an already working model in the intricate nano-machinery of life.

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The Robotics Revolution: ‘Building  Strong Artificial Intelligence’

“It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating.”
– Ray Kurzweil, The Singularity Is Near

The name of this revolution might be a little confusing. Kurzweil says robotics is embodied artificial intelligence—but it’s the intelligence itself that matters most. While acknowledging the risks, he argues the AI revolution is the most profound transformation human civilization will experience in all of history. 

This is because this revolution is characterized by being able to replicate human intelligence: the “most important and powerful attribute of human civilization.”

We’re already well into the era of “narrow AI,” which is a machine that has been programmed to do one or a few specific tasks, but that’s just a teaser of what’s to come.

Strong AI will be as versatile as a human when it comes to solving problems. And according to Kurzweil, even AI that can function at the level of human intelligence will already outperform humans because of several aspects unique to machines:

  • “Machines can pool resources in ways that humans cannot.”
  • “Machines have exacting memories.”
  • Machines “can consistently perform at peak levels and can combine peak skills.”

Artificial Intelligence Today

Most of us use some form of narrow AI on a regular basis — like Siri and Google Now, and increasingly, Watson. Other forms of narrow AI include programs like:

  • Speech and image recognition software
  • Pattern recognition software for autonomous weapons
  • Programs used to detect fraud in financial transactions
  • Google’s AI-based statistical learning methods used to rank links

The next step towards strong AI will be machines that learn on their own, without being programmed or fed information by humans. This is called ‘deep learning,’ a powerful new mode of machine learning, which is currently experiencing a surge in research and applications.

Why Is This Important?

Kurzweil calls genetics, nanotechnology, and robotics overlapping revolutions because we will continue to experience them simultaneously as each one of these technologies matures.

These and other technologies will likely converge with each other and impact our lives in ways difficult to predict, and Kurzweil warns each technology will have the power to do great good or harm—as is the case with all great technologies. The extent to which we’re able to harness their power to improve lives will depend on the conversations we have and the actions we take today.

“GNR will provide the means to overcome age-old problems such as illness and poverty, but it will also empower destructive ideologies,” Kurzweil writes. “We have no choice but to strengthen our defenses while we apply these quickening technologies to advance our human values, despite a lack of consensus on what those values should be.”

The more we anticipate and debate these three powerful technological revolutions, the better we can guide their development toward outcomes that do more good than harm.