From the printing press to AI

Looking back, it's fair to say that each technological breakthrough the world has seen has been unique. But it's also fair to say they often follow a familiar pattern that investors should keep in mind: early excitement, fear of job losses, a period of adjustment and, ultimately, a reshaping of economic and market opportunities.

The invention of the printing press by Johannes Gutenberg around the middle of the 1400s dramatically reduced the cost of producing books and sharing information. Literacy rose, education expanded and new industries emerged around publishing and distribution.

At the time, there were concerns about the spread of uncontrolled ideas and the destabilisation of established institutions. Looking back, we can see that the printing press was responsible for economic and cultural development.

The First Industrial Revolution marked one of the most significant periods of technological disruption in history. Mechanisation, steam power and textile manufacturing changed how goods were produced, while railways transformed trade and transport.

These changes brought widespread anxiety about job losses. Skilled workers feared machines would replace them, leading to protests such as the Luddite movement, where textile workers destroyed machinery, tools and equipment.

And while many jobs were displaced in the short term, entirely new industries emerged over time. Employment, therefore, simply shifted rather than disappeared.

The introduction of electricity, mass production and the telephone in the late 19th and early 20th centuries revolutionised both industry and daily life. Factories became more efficient and households gained access to the era's new appliances, such as the telephone or electrical lighting.

However, productivity gains were not automatically immediate. Businesses had to reorganise around the new technology and redesign processes around electricity before the full benefits emerged.

This slight lag between innovation and impact is a recurring theme in technological disruption, with clear parallels today.

The rise of computing transformed office work, finance, manufacturing and communication. What began with large mainframe computers eventually evolved into personal computers entering homes and workplaces.

As businesses automated processes, fears emerged that machines would eliminate large parts of the workforce across various industries. Instead, many roles evolved. New industries and careers emerged in software, IT, digital services and technology consulting.

The introduction of the internet transformed commerce and information sharing on a global scale.

In its early years, however, markets were troubled by a significant amount of volatility. In fact, the dot-com bubble emerged in the late 1990s which saw tech stocks surge, only to drop dramatically as expectations outpaced reality.

In the long run, however, the internet delivered on its promise. It created entirely new types of businesses, such as social media companies and cybersecurity firms, and fundamentally changed how businesses operate.

That being said, many early market leaders did not survive.

The dot-com bubble showed that not every company delivering innovation lives up to expectations.

Plus, numerous technologies have promised widespread change but struggled to achieve meaningful adoption. You just have to think of 3D televisions or virtual reality platforms to see that hype doesn't always cross over into real-life results.

These examples emphasise an important distinction: invention alone is not enough. For a technology to be truly disruptive, it must achieve scale, economic viability and widespread use.

Understandably, AI is often compared to previous technological revolutions. However, it varies in several fundamental ways.

For instance, current advances have taken AI beyond simple automation. While large language models have generated content, analysed data and assisted decision-making since ChatGPT's release, the next phase, often described as "agentic AI", could see systems carrying out tasks independently in the real world.

"This is where AI starts to move from generating content to actually doing things," Richard says. "Booking a holiday, managing processes, interacting with systems – that's where it becomes much more powerful."

And, looking further ahead, the concept of AGI (artificial general intelligence), where systems can learn and improve independently, introduces even greater potential.

Looking back at a technological innovation timeline shows that across every wave of disruption, a consistent pattern emerges.

Technological change tends to be overestimated in the short term and underestimated in the long term. Early excitement can drive rapid market movements, while the full economic impact unfolds more slowly.

For us as investors, this creates both opportunities and risks. Understanding where a technology sits in its development cycle (and crucially distinguishing between actual real-life progress and speculation) is key for investors seeking to navigate changing markets.

For AI specifically, "there's a sense in markets that this is not just another cycle," Henry says. "It could represent a more permanent shift in how the economy is structured.

That's because while it shares many characteristics with past innovations, its prospective breadth and speed set it apart. From productivity gains to entirely new business models, its impact could be far-reaching.

That does not mean the path will be smooth. Questions remain around regulation, infrastructure and social impact. But if history is any guide, disruption is rarely straightforward.

Because while every revolution may promise to change everything, it is only with hindsight that we truly understand how much it did.

While still theoretical, these developments are already influencing markets and investor sentiment.

One defining feature of the current AI wave is the speed at which financial markets are reacting. Technology companies, particularly hyperscalers like Amazon, Microsoft, or Google, have seen strong demand as businesses invest heavily in computing power.

There has been subsequent concern among some about an AI-induced bubble as a result.

However, Henry disagrees.

"While there was a period where share prices were rising faster than earnings," he says, "more recently, we're starting to see those earnings come through. That makes this phase feel more justified by what's actually starting to happen."

At the same time, other parts of the market are being reassessed. Businesses built on intellectual property or software-based advantages may be forced to compete more aggressively as AI lowers barriers to entry.

In contrast, companies with physical assets, such as infrastructure, are being viewed as more resilient.

"The market is effectively saying it also prizes things that can't easily be replicated by AI," Richard adds.

Plus, there are also natural constraints that may slow the pace of disruption. AI systems require significant computing power, limiting how quickly it can be deployed at scale as there isn't the infrastructure in place currently to provide that energy.

As a result, the transition could be more gradual than it otherwise would have been if the power and infrastructure needed to run AI were already in place.