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The AI Revolution vs the Mobile Revolution ByRex Woodbury

 width=Rex Woodbury is the Founder and Managing Partner of Daybreak, an early-stage venture capital firm based in New York. He partners with Pre-Seed and Seed founders building products with the potential for viral adoption. Before founding Daybreak, Rex was a partner at Index Ventures. Rex also writes Digital Native, a weekly technology publication, for a global audience of 50,000+ readers.
In this post, Rex reflects on the impact of technological revolutions, from the Industrial Revolution of the 1770s to recent advancements, including the Mobile Revolution. How does the latest AI Revolution compare with those that came before it? What lies in store for the future?

Technology revolutions take time. Despite the hype for AI right now, we’re still early: while 58% of American adults have heard of ChatGPT, only 18% have used it. In recent months, ChatGPT monthly active users actually ticked down. I expect we’ll need more vertical-specific, userfriendly LLM applications for the technology to really break through. Many of those applications are being built or dreamt up right now.

This quest for understanding technological revolutions drove me to read Technological Revolutions and Financial Capital, an excellent book by the economist Carlota Perez (thank you to Rick Zullo for the recommendation). Perez wrote her book in 2002, shortly after the dotcom bubble burst. The book proved prescient in forecasting the 2000s and 2010s of technology, and I believe it offers some key insights for where we sit in 2023.

This piece explores how today’s AI revolution compares to past revolutions – from the Industrial Revolution of the 1770s to the Steel Revolution of the 1870s, the Internet Revolution of the late 90s to the Mobile Revolution circa 2010.

The key argument I’ll make is this: the AI Revolution isn’t comparable to the Mobile Revolution, as the latter was more a distribution revolution. Rather, AI is more comparable to the dawn of the internet. Or, more fundamentally, AI is an even larger-scale technology shift – it’s the dawn of a new discrete revolution that’s built not around computers acting like calculators, but computers acting like the human brain.

In short, we’re coming to a close of the “Information Age” that started in 1971, and we’re beginning a new era in technology.


In the moment, it can be difficult to quantify a new technology’s impact.

This leads to predictions that don’t age wellfor example, the economist Paul Krugman’s 1998 declaration, “By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.” Yikes. Poor Paul has had to live down that sentence for a quarter-century.

This is why it’s helpful to zoom out – to study history’s past cycles of innovation and to try to discern patterns. This is the focus of Carlota Perez’s book. Perez focuses on five distinct technological revolutions from the past 250 years. Each revolution, she argues, was sparked by a “big bang” breakthrough:

Our most recent technology revolution, the dawn of the so-called “Information Age,” began in 1971 with Intel developing the microprocessor. Microprocessors were manufactured with silicon, giving Silicon Valley its name; the rest is history.

Technology revolutions follow predictable boom-and-bust cycles. An exciting new technology leads to frenzied investment in that technology; frenzied investment leads to the formation of an asset bubble; that bubble eventually bursts, cooling an overheated market. What makes Perez’s book remarkable is that she wrote it in 2002, shortly after the dotcom bubble had burst. Many people at the time were declaring the end of the internet era. But Perez argued that the bubble bursting was only the middle of a predictable cycle; the dotcom crash was rather a so-called “turning point” that would usher in the internet’s Golden Age (what she calls “Synergy”).

According to Perez, technology revolutions follow 50-year cycles. “Turning points” – which often come in the form of a market crash – typically occur about halfway into the cycle. Many crashes bear the name of the revolution’s prevailing technology: canal mania (1790s); railway mania (1840s); the dotcom bubble (late 1990s).

After a technology’s turning point, the technology enters the deployment phase – this is 20 years of steady growth and broad wealth creation. The internet’s widespread adoption in the 2000s and 2010s, buoyed by the arrivals of mobile and cloud, bears this out. Looking retrospectively from 2023, Perez’s framework appears spot on.

We can observe the “shift” from one technology revolution to the next in the companies that dominate an era. In the 1930s and 1940s, for instance, oil and automobile companies replaced steel companies as the largest businesses in America.

Here are the 10 largest companies in the world in 1990:


Today, of course, tech companies dominate: Apple, Alphabet, Amazon, Microsoft, Nvidia, Meta. Tech domination is more pronounced via market cap, while the table above shows revenues, but the point remains. (Apple, for what it’s worth, brought in $394B in sales last year – with 25% profit margins to boot.) In the 1990s and 2000s, we saw technology begin to dominate; today, Big Tech represents 27% of the S&P 500.

But tech domination is also a sign of something else: maturation.


Take another look at Perez’s chart above; the final stage is when a technology begins to mature . And that’s what we’ve been seeing with Big Tech. Companies like Alphabet and Meta have a bad case of arthritis.

Maturation extends to the private sector. I had a debate with a former colleague the other day – how many companies founded since 2016, we wondered, had hit $100M in ARR? Wiz, Ramp, Deel, Rippling. There might be a few others. But the list is short. And how many new apps reliably hover near the top of the App Store – apps not funded by Bytedance (TikTok, CapCut) or Pinduoduo (Temu)?

New revolutions emerge when the potential of the previous revolution approaches exhaustion. And it feels like we’re at the exhaustion point. Capital flowed into the venture world over the past decade, but that capital is increasingly chasing point-solutions. Abundant capital is thirsting for a new seismic, fundamental shift.

Thankfully, we have one. Enter: AI.

The arrival of AI is near-perfect timing to Perez’s framework. For AI, the “big bang” event – to use Perez’s terminology – was probably the release of ChatGPT last year.

You could argue that the big bang was actually the publication of the seminal paper Attention Is All You Need in 2017 , which introduced the transformer model. But I think we’ll look back at ChatGPT as the true catalysing moment.

Another sign of market saturation and the dawn of a new era: top talent drains from mature, slow-moving incumbents to strike out on its own. The co-author of Attention Is All You Need , Aidan Gomez, left Google to build The Google Brain researchers behind Google’s image model also left the Big Tech giant, founding Ideogram.

When you zoom out, the past 50 years have pretty closely followed Carlota Perez’s framework – in much the same pattern that we saw with the Industrial Revolution, with the steam engine, with steel, and with oil and mass production. To oversimplify:

● 1970s and 1980s: Irruption – venture capital is born as an industry, turbocharging the nascent Information Age.

● 1990s: Frenzy – things get a little ahead of their skis.

● 2000-2015ish: Synergy – the Golden Age, with mobile and cloud acting as accelerants on the fire.

● 2015-Present: Maturation.

Exogenous shocks muddy the picture. Many blamed the dotcom crash on the September 11th attacks, for instance, but that was incorrect; 9/11 worsened the market correction, yes, but the bubble had already begun to burst in spring 2001. The Great Recession and, later, COVID and related government spending also cloud the framework. Who could have predicted a mortgage crisis and a coronavirus pathogen? But when you zoom out, the pattern is there.

We’re now entering Phase One for AI – explosive growth and innovation:

This is exciting.

It means that the comparison in the title of this piece – the Mobile Revolution vs. the AI Revolution – is something of a misnomer. AI is bigger, a more fundamental shift in technology’s evolution, than for example the mobile revolution. VR/AR, perhaps underpinned by Apple’s forthcoming Vision Pro, might be a mobile-scale revolution – a massive shift in distribution. That’s probably 5-ish years away. But AI is bigger.

The way I think about it: we’re moving from the calculator era to the brain era.

Back when computers were being created, there was a debate among experts – should the computer be designed to mimic a calculator, or to mimic the human brain? The calculator group won out (particularly because of technology’s limits) and the computer was born as we know it: literal, pragmatic, analytical.

Computers are very good at… well, computation. They’re less good at nuance, reasoning, creativity.

Now, of course, that’s changing. AI is actually quite good at these things. In fact, AI is now better than humans at many uniquely-human tasks: reading comprehension, image recognition, language understanding, and so on. One study found that not only did ChatGPT outperform doctors on medical questions, but the chatbot had better bedside manner. (It turns out, ChatGPT doesn’t get as tired, irritable, or impatient as human physicians.)

Computers used to be good at math. Now they can write and draw and paint and sing. Naturally, this new technology epoch will bring with it new opportunities for innovation.


The economist Joseph Schumpeter once wrote: “Capitalism is a process of industrial mutation that incessantly revolutionises the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” The same is true for technology, which goes hand-in-hand with capitalism; innovation is capitalism’s spark, and technology its fuel.

We’ve come a long way since the Information Age began. A terabyte hard drive in 1956 would’ve been the size of a 40-story building; today, it fits on your fingertip.

Amara’s Law says that we tend to overestimate the effect of a new technology in the short run and underestimate the effect of that technology in the long run. This means that AI might be a little frothy right now, but If Perez’s framework holds, we’ll be in for more than one correction in the years to come. But those corrections won’t detract from the long-term potential of a new paradigm-shift in technology.

There are also open questions about where value will accrue. We’re entering this new revolution with a slew of trillion-dollar tech companies at the helm; what’s more,

Big Tech has been unusually quick to respond to the threat posted by AI. Traditionally, incumbents tend to avoid radical change for fear of upsetting the apple cart – for fear of sacrificing juicy short-term profits in favour of massive self-disruption. But maybe this time is different. The question remains: will incumbents vacuum up the value, or will more agile, AI-native upstarts be able to win major segments of the market? Time will tell if Google and Meta sound as dated in 2043 as Yahoo! and AOL do in 2023.

The internet, mobile, and cloud looked like their own distinct revolutions – but rather, they may have been sub-revolutions in the broader Information Age that’s dominated the last 50 years of capitalism. We’re now seeing a brand new sea change – one that only comes around every half-century.

In other words, we’re in for a helluva ride.

This is an excerpt from an article published on Digital Native. To access the full article including where start up opportunities will crystallise in the AI revolution go here:

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