The AI Adoption S-Curve

Why Artificial Intelligence Will Spread Faster Than Electricity and the Internet

By Adam Gower Ph.D.
March 2026

In my earlier article, Why AI Is the Third Great Utility, I outline what I call The Utility Thesis - the argument that artificial intelligence should be understood not as a product but as a foundational economic utility similar to electricity or the internet.

s-curve

Key Takeaways: AI as the Third Great Utility

  • Transformative technologies tend to follow an S-curve adoption pattern: slow early experimentation followed by rapid acceleration and eventual saturation.

 

  • Electricity required roughly 50 years to reach full economic impact. The internet compressed a comparable transformation into approximately 30 years.

 

  • Artificial intelligence is spreading faster still, reaching more than one billion users globally in under three years.

 

  • Corporate investment is signaling the same acceleration. Hyperscaler spending on AI infrastructure is projected to exceed $1 trillion between 2025 and 2027.

 

  • For professional industries including real estate, finance, and law, the central question is not whether AI will be adopted but where we are on the adoption curve today.

 

  • Current evidence suggests the global economy is still in the early phase of the AI adoption curve, with the steepest productivity gains still ahead.

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What Is the Technology Adoption S-Curve?

Major technological revolutions rarely unfold in a straight line. Instead, they follow a pattern that economists and innovation researchers describe as the S-curve of adoption.

 

The curve begins slowly. A technology is introduced, early adopters experiment with it, and its capabilities are explored. During this phase, adoption grows incrementally. Costs are high, applications are limited, and the surrounding ecosystem has not yet developed.

 

Then something changes. Infrastructure improves. Complementary technologies emerge. Businesses begin to reorganize around the new capability rather than simply experimenting with it. Adoption accelerates dramatically.

 

This is the steep middle of the S-curve, where the economic impact of a technology becomes visible across industries and societies.

 

Eventually, the curve flattens again. Once a technology reaches widespread adoption, growth slows as the market approaches saturation.

 

This pattern has appeared repeatedly across history, from railroads to electricity to the internet. The early stages often appear underwhelming, leading observers to underestimate the eventual impact. Yet once the curve steepens, transformation can occur rapidly.

 

This dynamic is part of what I describe as the Utility Thesis - the idea that artificial intelligence represents the third great general-purpose utility of the modern economy, following electricity and the internet.

 

And artificial intelligence now appears to be entering this same trajectory.

 

Electricity: The Original Industrial S-Curve

The electrification of industry provides the clearest historical example of the S-curve in action.

 

Thomas Edison opened the Pearl Street power station in New York in 1882. The technology worked. Electric light bulbs illuminated buildings across lower Manhattan. Yet the broader economy did not immediately transform.

 

For decades, factories continued to rely primarily on steam power. Electric motors existed, but most industrial facilities simply replaced the steam engine with an electric motor while maintaining the same system of belts and shafts used to distribute mechanical power throughout the factory.

 

The productivity impact of electrification did not appear until manufacturers realized that electricity allowed an entirely different organization of production. Instead of powering an entire factory from a single engine, electricity could power individual machines directly.

 

Factories redesigned their layouts. Production lines were reconfigured. Electric lighting extended working hours and improved safety. Entirely new industrial processes became possible.

 

This transition took decades. Yet once electrification spread across industry in the early twentieth century, productivity surged. Economists studying the period have consistently found that electrified factories achieved double-digit productivity improvements compared with their non-electrified counterparts.

 

The lesson is straightforward. The invention of a technology and the transformation of the economy rarely occur at the same time. The impact arrives only when organizations redesign their operations around the new capability.

 

The Internet: A Faster Adoption Curve

The internet followed the same pattern but at a significantly faster pace.

 

The first widely accessible web browser, Mosaic, appeared in 1993. Within a decade, half of American adults were online. By the early 2020s, internet penetration in the United States exceeded ninety percent.

 

Yet the economic transformation occurred not when the internet first appeared, but when businesses began reorganizing around it.

 

E-commerce platforms reshaped retail. Digital marketplaces transformed travel booking, media distribution, and advertising. Entire industries emerged that had no analog in the pre-internet world.

 

The internet compressed the adoption timeline seen with electricity. What took electrification half a century occurred within roughly three decades online.

 

More importantly, the steep middle portion of the curve – the period of explosive growth – arrived even more quickly.

 

The same dynamics now appear to be unfolding with artificial intelligence.

Artificial Intelligence: The Steepest Curve Yet

By nearly every available measure, artificial intelligence is spreading faster than any prior general-purpose technology.

 

ChatGPT reached one hundred million users within two months of its launch in November 2022. For comparison, Instagram required more than two years to reach the same milestone. TikTok achieved it in approximately nine months.

 

Within three years, global AI usage surpassed one billion people. This pace of adoption is unprecedented.

 

Investment trends point to the same conclusion. Technology companies are committing extraordinary capital to AI infrastructure, including data centers, specialized chips, and high-performance computing clusters. Hyperscaler spending on AI infrastructure alone is projected to exceed one trillion dollars between 2025 and 2027.

 

These investments resemble the large-scale infrastructure buildouts that accompanied earlier utility revolutions, including the expansion of electrical grids and the construction of global telecommunications networks.

 

Economic projections also suggest that the steep portion of the adoption curve may still lie ahead. Research from institutions including McKinsey and PwC estimates that artificial intelligence could contribute trillions of dollars to global economic output by the end of the decade, largely through productivity improvements and new forms of consumption.

 

If those projections prove even partially correct, the steepest part of the AI adoption curve has not yet arrived.

 

Where We Are on the AI Adoption Curve Today

Despite the rapid spread of AI tools, most industries remain in the early stages of the adoption curve.

 

Professionals across sectors have experimented with large language models, image generation tools, and other AI systems. Yet in many organizations these technologies remain peripheral rather than foundational.

 

Workflows have not yet been redesigned around AI capabilities. Processes that could be automated or augmented remain largely unchanged. Most companies are still exploring isolated use cases rather than rethinking their operating models. This mirrors why operators resist AI adoption in the early phase of every utility S-curve.

 

In this sense, the current moment resembles the early years of electrification, when factories installed electric motors but continued to operate as if steam engines still powered the system.

 

The gap between AI capability and AI implementation remains large.

 

For professionals and organizations willing to bridge that gap, the opportunity may be significant.

 

Readers interested in the broader framework that explains this transformation can explore the argument that artificial intelligence represents the third great economic utility here: Why AI is the third great utility

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Frequently Asked Questions

What is the technology adoption S-curve?

The S-curve describes the pattern by which transformative technologies spread through the economy. Adoption begins slowly, accelerates rapidly once complementary systems develop, and eventually stabilizes as the technology reaches widespread use.

 

Why do new technologies often appear slow at first?

Early adoption tends to be limited because infrastructure is immature, costs are high, and practical applications are still being discovered. The real transformation typically occurs once businesses reorganize around the technology.

 

How long did electricity take to transform the economy?

Electrification began with Edison’s power station in 1882 but required several decades before widespread industrial and household adoption produced measurable economic impact.

 

How quickly did the internet spread compared with electricity?

The internet compressed a similar transformation into roughly three decades, reaching widespread adoption far faster than electricity.

 

Why is artificial intelligence spreading so quickly?

AI benefits from existing global digital infrastructure, instantaneous software distribution, massive corporate investment, and intense competitive pressure among companies seeking productivity advantages.

 

How many people currently use AI tools?

Estimates suggest that more than one billion people worldwide now interact with AI tools in some form, making AI one of the fastest-adopted technologies in history.

 

What industries will be most affected by the AI adoption curve?

Industries built around information processing, analysis, and decision-making - including finance, law, consulting, and real estate - are likely to experience significant changes as AI capabilities mature.

 

Where are we on the AI adoption curve today?

Most evidence suggests that AI remains in the early stages of the adoption curve. The steepest phase of productivity gains and industry transformation may still lie ahead.

Conclusion

The history of technological transformation suggests that the most consequential shifts are rarely obvious while they are unfolding. Electrification looked incremental for decades before it reshaped the industrial economy.

 

The internet appeared to be a niche communication tool before it reorganized global commerce. Artificial intelligence now appears to be following the same pattern, but at a far faster pace.

 

The S-curve is beginning to steepen, infrastructure investment is accelerating, and the gap between early adopters and the rest of the market is widening. Whether one works in real estate, finance, law, or any other knowledge-intensive profession, the strategic question is no longer whether artificial intelligence will become a foundational capability of the modern economy.

 

The more pressing question is where you will stand on the adoption curve as the next phase of that transformation unfolds.

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About Dr. Adam Gower

Dr. Adam Gower is the founder of GowerCrowd and a leading authority on real estate syndication and crowdfunding. With 30+ years in real estate and $1.5B in transactions, he helps sponsors build marketing systems that attract high-net-worth investors.

30+ Years Experience | $1.5B In Transactions | 30,000+ CRE Professional Community

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