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Technological progress accelerates because each breakthrough enables multiple others. AI intensifies this pattern; for the first time, we have technology that can meaningfully improve itself and accelerate other technologies. The business implications are clear: the gap between companies that deploy AI now and those that wait isn't measured in percentage points. It's measured in multiples.
Why does it seem like the pace of change in AI grows faster and faster? Because it absolutely does. What we're experiencing with AI is an expression of the "technology acceleration curve" and how the pace of technological progress itself is increasing over time, creating a compound effect where innovation breeds faster innovation. This isn't just true of AI. Instead, it is a pattern we see consistently with every major technological advance - from the printing press to the steam engine to the internet to AI. This isn't just about technology getting better. It's about the rate of improvement accelerating. The amount of change that used to take centuries began to come in years, then months.
In fact, with AI, for the first time in history, we have a technology that can meaningfully participate in its own improvement and in the improvement of other technologies. Machine learning models design new materials, discover drug compounds, write code, optimize chip architectures, and accelerate scientific research—creating a feedback loop where AI improves AI, which then accelerates everything else. This self-reinforcing cycle means AI is compounding the rate at which all technologies advance.
Most people intuitively think about progress as linear. Build one thing, then build the next thing, then the next. One step at a time, steady improvement. But that's not how technology actually works.
Technology builds on itself in ways that create multiplication, not addition. When you invent the printing press, you don't just get faster book production. You get widespread literacy, which creates more readers, which creates demand for more books, which justifies more investment in printing technology, which makes books cheaper, which creates even more readers. Each advancement unlocks multiple downstream effects.
This is exponential growth. Unlike linear progress, where you add a constant amount each period, exponential growth multiplies. The steam engine didn't just power factories—it enabled railways, which enabled long-distance trade, which enabled urbanization, which concentrated talent, which accelerated further innovation. One breakthrough enables five more. Those five enable twenty-five. The math compounds.
Not only is progress exponential, but the rate of exponential growth itself is accelerating. The curve is getting steeper.
Look at the data. The Agricultural Revolution took 10,000 years. The Industrial Revolution took roughly 200 years to mature fully. The Information Revolution happened in about 50 years. The mobile revolution took about 15 years. AI went from research curiosity to ubiquitous tool in under 5 years.
This acceleration applies across industries and fields. Machine learning models trained on previous research accelerate new research. Code repositories let developers build on millions of existing solutions. Cloud infrastructure means you can deploy at scale on day one. Every innovation becomes raw material for the next wave of innovation. That compound effect intensifies with each cycle.
In every industry, AI is driving advances the compound, enabling further advances. Accelerating returns are a fantastic byproduct of that. Every time a company kicks AI down the road or moves slowly to define, develop, and adopt AI-powered advances, it loses ground against competitors that embrace change. Further, the amount of ground they lose grows exponentially. In AI, days and weeks matter a great deal. That’s why AI experts advise that companies make AI projects and investment a top priority.
Technology acceleration hasn't been constant. Each era has seen progressively faster innovation cycles.
Five major forces are driving the technology acceleration curve and making advancement in AI come even faster:
Software improvements can be deployed instantly to billions of users. No manufacturing, no distribution delays, no physical constraints. A single update can transform how millions of people work overnight.
Existing technologies combine in unexpected ways to create new capabilities. The smartphone wasn't just a phone—it combined GPS, cameras, internet, and sensors into something entirely new. Each technology becomes a building block that can combine with others in ways their original creators never imagined.
Cloud infrastructure, open source, and digital distribution lower the cost of innovation. You no longer need millions in capital to start a tech company. A small team can access the same computing power and tools that would have required an entire data center just a decade ago.
Distributed teams can work on problems 24/7 across time zones. Knowledge spreads instantly. A breakthrough in one country becomes available everywhere within hours. The collective intelligence of the global developer community accelerates at a pace that would have been impossible in earlier eras.
Machine learning is now automating parts of the research and development process. AI can design proteins, discover drugs, and write code—tasks that used to require years of human expertise. We're reaching a point where AI doesn't just accelerate human innovation; it participates in the innovation process directly.
The acceleration is visible across every major technology arena. In computing power, the transition from mainframes to personal computers in the 1940s through 1980s took 40 years. The shift from PCs to internet connectivity in the 1980s through 2000s took 20 years. Then the desktop to mobile computing shift in the 2000s through 2010s took just 10 years. Each major computing paradigm shift happens in half the time of the previous one.
Transportation innovation follows a similar pattern. Automobile development and mass production from the 1880s to the 1950s took 70 years. Commercial jet aviation matured over 20 years from the 1950s to the 1970s. Then, autonomous vehicle prototypes went from concept to road testing in just 10 years during the 2010s through 2020s.
Genomics and biotechnology show perhaps the most dramatic acceleration. The Human Genome Project took 53 years from DNA discovery in 1950 to completion in 2003. CRISPR went from discovery to clinical trials in just 8 years, between 2012 and 2020. Then, the COVID vaccine development went from concept to deployment in merely 11 months in 2020 through 2021.
Artificial intelligence demonstrates the acceleration most vividly. Expert systems development took 40 years, from the 1950s to the 1990s. Machine learning algorithms matured over 20 years from the 1990s to the 2010s. The deep learning revolution happened in 10 years during the 2010s through 2020s. Large language models went from research to consumer products in just 3 years, between 2020 and 2023. Now multimodal AI capabilities are advancing monthly in 2023 through 2024.
The acceleration is visible in two key metrics.
Each era's breakthrough technologies become the foundation for even faster innovation in the next era, creating the characteristic steepening curve where the rate of change itself is accelerating.
AI perfectly illustrates acceleration in action. GPT models went from GPT-1 to GPT-4 in just 5 years, with each version representing orders of magnitude improvement. The jump from GPT-3 to GPT-4 brought capabilities that seemed impossible just 18 months earlier. Context windows expanded from thousands to millions of tokens. Reasoning abilities that researchers thought were years away appeared suddenly. Multimodal capabilities emerged faster than anyone predicted.
This isn't just incremental progress. This is exponential. And it's happening while we watch.
The technology acceleration curve has profound implications for everyone.
For businesses, competitive advantages expire faster than ever before. Technology leadership requires constant reinvention rather than resting on past innovations. Companies that fail to accelerate their own innovation will be left behind, not gradually but suddenly. The gap between market leaders and laggards widens into an unbridgeable chasm in months rather than years.
When a company moves ahead quickly, it gains the advantages available at that time and lays the groundwork for the next phase. With each phase, the gap widens. The company’s lead increases as successive generations of AI bring exponential increases in value.
By contrast, companies that delay or eschew AI projects grow progressively more and more obsolete. While competitors race ahead in every aspect of their operations, they gain more and more business advantage, and their potential customers have increasingly elevated expectations. It’s not simply a question of those laggards not making gains; they are literally becoming irrelevant to customers.
The technology acceleration curve isn't just an observation—it's a reality we must navigate with clear eyes and decisive action. We're in an era where the pace of change itself is accelerating. What took decades now takes years. What took years now takes months. In some domains, we're seeing meaningful advances every single week that contribute to extraordinary business growth rates. This creates both opportunity and peril on a scale humanity has never experienced.
Some business leaders have built cautious companies that wait for competitors to act before they implement change. The feeling is that moving quickly can lead to unnecessary risk and failures.
But the real danger is in inaction. In all likelihood, any company with an aggressive AI program will have some projects that deliver outstanding ROI while others will drive more modest returns. Some may even deliver little or no ROI immediately but lay the groundwork for massive gains from future projects.
The well-worn cliché of “fail fast” has some relevance in AI. But a better way of thinking about it is that, if you carefully define projects that align with your business goals, have fast development cycles, and are deployed in weeks, you make progress. Doing nothing is like running a race in the wrong direction. The gap between you and the people who plan intelligently, and act quickly grows at a rate that becomes faster and faster every week.
As AI begins to automate discovery itself, we're likely to see further acceleration. The compound effect will intensify. We may be approaching a point where the tools of innovation become so powerful that the acceleration curve steepens beyond our ability to predict.
The question isn't whether to adapt. The question is how fast you can adapt—and whether that's fast enough to capture the opportunities before they disappear into someone else's hands.
Whichever path you take to technological evolution through AI, we urge you to take that first step as soon as possible. The next era for every industry is coming quickly, thanks to the transformational power of AI. Don’t be left behind.
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If you’d like to discuss what’s happening in your industry and how the technology acceleration curve is changing ‘what works’ in your space, contact our team, and we’ll connect you with experts who can help.
