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Long Waves: How Innovation Cycles Influence Growth

Creative destruction plays a key role in entrepreneurship and economic development.

Coined by economist Joseph Schumpeter in 1942, the theory of “creative destruction” suggests that business cycles operate under long waves of innovation. Specifically, as markets are disrupted, key clusters of industries have outsized effects on the economy.

Take the railway industry, for example. At the turn of the 19th century, railways completely reshaped urban demographics and trade. Similarly, the internet disrupted entire industries—from media to retail.

The above infographic shows how innovation cycles have impacted economies since 1785, and what’s next for the future.

Innovation Cycles: The Six Waves

From the first wave of textiles and water power in the industrial revolution, to the internet in the 1990s, here are the six waves of innovation and their key breakthroughs.

Source: Edelsen Institute, Detlef Reis

During the first wave of the Industrial Revolution, water power was instrumental in manufacturing paper, textiles, and iron goods. Unlike the mills of the past, full-sized dams fed turbines through complex belt systems. Advances in textiles brought the first factory, and cities expanded around them.

With the second wave, between about 1845 and 1900, came significant rail, steam, and steel advancements. The rail industry alone affected countless industries, from iron and oil to steel and copper. In turn, great railway monopolies were formed.

The emergence of electricity powering light and telephone communication through the third wave dominated the first half of the 1900s. Henry Ford introduced the Model T, and the assembly line transformed the auto industry. Automobiles became closely linked with the expansion of the American metropolis. Later, in the fourth wave, aviation revolutionized travel.

After the internet emerged by the early 1990s, barriers to information were upended. New media changed political discourse, news cycles, and communication in the fifth wave. The internet ushered in a new frontier of globalization, a borderless landscape of digital information flows.

Market Power

To the economist Schumpeter, technological innovations boosted economic growth and improved living standards.

However, these disruptors can also have a tendency to lead to monopolies. Especially during a cycle’s upswing, the strongest players realize wide margins, establish moats, and fend off rivals. Typically, these cycles begin when the innovations become of general use.

Of course, this can be seen today—never has the world been so closely connected. Information is more centralized than it has ever been, with Big Tech dominating global search traffic, social networks, and advertising.

Like the Big Tech behemoths of today, the rail industry had the power to control prices and push out competitors during the 19th century. At the peak, listed shares of rail companies on the New York Stock Exchange made up 60% of total stock market capitalization.

Waves of Change

As cycle longevity continues to shorten, the fifth wave may have a few years left under its belt.

The sixth wave, marked by artificial intelligence and digitization across information of things (IoT), robotics, and drones, will likely paint an entirely new picture. Namely, the automation of systems, predictive analytics, and data processing could make an impact. In turn, physical goods and services will likely be digitized. The time to complete tasks could shift from hours to even seconds.

At the same time, clean tech could come to the forefront. At the heart of each technological innovation is solving complex problems, and climate concerns are becoming increasingly pressing. Lower costs in solar PV and wind are also predicating efficiency advantages.

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AI vs. Humans: Which Performs Certain Skills Better?

Progress in computation ability, data availability, and algorithm efficiency has led to rapid gains in performance for AI vs humans.

AI vs. Humans: Which Performs Certain Skills Better?

With ChatGPT’s explosive rise, AI has been making its presence felt for the masses, especially in traditional bastions of human capabilities—reading comprehension, speech recognition and image identification.

In fact, in the chart above it’s clear that AI has surpassed human performance in quite a few areas, and looks set to overtake humans elsewhere.

How Performance Gets Tested

Using data from Contextual AI, we visualize how quickly AI models have started to beat database benchmarks, as well as whether or not they’ve yet reached human levels of skill.

Each database is devised around a certain skill, like handwriting recognition, language understanding, or reading comprehension, while each percentage score contrasts with the following benchmarks:

• 0% or “maximally performing baseline” This is equal to the best-known performance by AI at the time of dataset creation.
• 100% This mark is equal to human performance on the dataset.

By creating a scale between these two points, the progress of AI models on each dataset could be tracked. Each point on a line signifies a best result and as the line trends upwards, AI models get closer and closer to matching human performance.

Below is a table of when AI started matching human performance across all eight skills:

A key observation from the chart is how much progress has been made since 2010. In fact many of these databases—like SQuAD, GLUE, and HellaSwag—didn’t exist before 2015.

In response to benchmarks being rendered obsolete, some of the newer databases are constantly being updated with new and relevant data points. This is why AI models technically haven’t matched human performance in some areas (grade school math and code generation) yet—though they are well on their way.

What’s Led to AI Outperforming Humans?

But what has led to such speedy growth in AI’s abilities in the last few years?

Thanks to revolutions in computing power, data availability, and better algorithms, AI models are faster, have bigger datasets to learn from, and are optimized for efficiency compared to even a decade ago.

This is why headlines routinely talk about AI language models matching or beating human performance on standardized tests. In fact, a key problem for AI developers is that their models keep beating benchmark databases devised to test them, but still somehow fail real world tests.

Since further computing and algorithmic gains are expected in the next few years, this rapid progress is likely to continue. However, the next potential bottleneck to AI’s progress might not be AI itself, but a lack of data for models to train on.