The AI revolution is under way and it’s not just about ChatGPT writing emails faster or image generators producing ad campaigns overnight. Artificial intelligence is on track to become the most powerful deflationary force we’ve seen since the industrial revolution. AI is moving rapidly from new potential to infrastructure. As it progresses and becomes more integrated, it will drive down the cost of producing almost everything. Not just goods and services, but knowledge and even decision making itself. That will have massive implications for the economy and business, as well as investors in the future.

AI’s influence will be a persistent tailwind for productivity, removing inefficiencies from business models and supply chains in every sector. Design, engineering and testing can now happen in days instead of months. Manufacturing processes can optimise themselves in real time with minimal human oversight. Customer service can operate around the clock without the cost or fatigue of human staff. This is not a theory for the future. It is already happening in parts of the economy today. The longer-term effect is lower costs across the board, bringing prices down.

Historically, the deflationary pull of new technology has been masked by population growth, rising demand and loose monetary policy. But AI is arriving at a moment when many of the world’s major economies are entering demographic decline too. Japan has been living with it for decades. China’s population has already peaked and is set to shrink by hundreds of millions over the next 25 years. South Korea and much of Europe are heading the same way. What was once a demographic tailwind for growth is becoming a headwind. More than that, when falling demand in areas with declining populations meets rapidly falling costs, the deflationary impact will be magnified, creating the potential for a significant economic shock.

AI will affect all industries and skill levels, from blue collar manufacturing roles to white collar professional services, through to creative work. Its reach is so broad and so fast that its impact on prices will be more profound and more global than past innovations. In industries where AI commoditises operations, margins will be compressed as competition intensifies. But there will be areas where it not only reduces costs but opens entirely new markets, and the winners will achieve extraordinary growth. These will be the businesses that use AI to create products or services that were not previously possible, or that own unique data sets that AI models depend on.

This is where it starts to get tricky because reducing costs will translate into many job losses. We are at a point where almost everyone you talk to is starting to feel some concern about job security in the future because of AI. Either their own job or someone’s in their family. As exciting as the advancements in AI technology are for future productivity, it won’t be long before the psychological shift around job security is felt in the economy. This is not good news for an already weakening economy and job market. If fear around job security starts to become embedded in the economy, then we risk a self-fulfilling spiral downward to much higher unemployment as people slow their spending and businesses suffer. This will compound the actual impact of job losses as AI starts to scale up.

But deflation on its own is not inherently bad. For investors and business leaders, it will create a bifurcated world where incumbents with legacy cost structures are under constant pressure, while more agile operators with lower fixed costs thrive. The challenge will be working out the sectors where AI driven deflation destroys profitability and those where it fuels entirely new growth. Major technological shifts usually create more wealth over time than they destroy, but the distribution is uneven. The opportunity is in identifying where value will emerge as costs fall, whether that’s in platforms, data ownership, or in the service companies that evolve from and around them.

AI’s deflationary power will reshape the global economy in ways that are both exciting and uncomfortable. We’re entering an era where capital will matter more than labour, and adaptability more than scale. But its impact won’t unfold in isolation. In the decade ahead, while AI drives costs lower, we will also see demand growth slow as populations age and shrink. That convergence will be disruptive, redistributing wealth and changing the rules of competition. For investors, the winners will be those who understand that the world will be shaped almost as much by shifting demographics as by AI’s technological progress.

General Disclaimer: This information is of a general nature only and may not be relevant to your particular circumstances. The circumstances of each investor are different, and you should seek advice from an investment adviser who can consider if the strategies and products are right for you. Historical performance is often not a reliable indicator of future performance. You should not rely solely on historical performance to make investment decisions.

We’ve all heard of first mover advantage, the prime position earned by the company that innovated before the others. Whether it’s launching a product, claiming market share, or pioneering a new technology, being early is often equated with being better. It's what everyone aims for from company founders through to investors. But history tells a more complex story. There are plenty of examples where being first has effectively been a disadvantage. Understanding this is important for companies, but even more so for investors as they allocate capital for the long term.

What people often forget is that many new technologies, especially those that transform or create new industries, require huge amounts of capital simply to build the underlying infrastructure for the technology to scale and reach the mass market. It means that along the way many of the most exciting companies building towards the vision of mass adoption of a technology fall by the wayside, make too many mistakes or run out of money along the way.

One of the best examples of this in recent times is the rise of the internet. I remember in early 2000, as the massive hype was building, it became clear that this technology was going to be transformative. It was, but the amount of capital needed to get the industry to where it needed to go in those early days was massive. Of course, the dot-com boom helped to ensure that the capital and total investment in aggregate needed to build out the foundations of the industry were raised.

In many of the most transformative industries, from airlines to automobiles, there is a similar pattern. Where the biggest rewards often go to the later entrants. Being first means more risk, more uncertainty, and more cost. It means making many mistakes with no guarantee of a path forward. Meanwhile, fast followers are sitting back, watching, learning, and preparing to strike with better timing, better economics, and fewer mistakes.

This isn’t to say that the first movers never win. They do. But surprisingly, the success stories are the exception, not the rule. Amazon, for example, was an early mover in online retail. By the time traditional retailers caught on, Amazon had already established dominance in infrastructure, logistics, and customer trust. Similarly, Netflix made the leap from DVD rentals to streaming before anyone else was even thinking about it seriously. These companies gained scale, users, and built moats that others struggled to create.

However, there is a much longer list of first movers who never made it. Friendster came before Facebook. AltaVista came before Google. Netscape came before Chrome. Myspace came before Instagram, and Palm Pilots and Blackberry came before the iPhone. First movers have to spend more on R&D and infrastructure, educate the market at their own cost, and make the big mistakes others can learn from as part of building towards mass adoption. Conversely, fast followers can analyse what worked, avoid what didn’t, and capitalise on a more informed and receptive market.

This dynamic is even more pronounced in industries with large capital requirements and slow adoption curves. When the Wright brothers took flight at the turn of the century, they changed the course of history. But it wasn’t until decades later that air travel became a commercial business. Hundreds of airline startups burned through capital before a few major carriers found sustainable models. The same was true with automobiles. Dozens of early manufacturers came and went before Ford revolutionised production with the Model T.

Technologies like the internet, mobile networks, and AI are no different. Being first to market often means bearing the costs of infrastructure, educating consumers, navigating regulatory grey zones, and building products that may not yet have viable markets. Fast followers will have more data, more capital, and the benefit of watching early failures. In many cases, the third or fourth wave of players win by building for a world that’s finally ready.

For investors, the key takeaway is that you don’t have to find the next big thing first. You don't have to rush. You have time. Take that time to understand the industry and where it will be best to invest in the long term. While there will always be companies that garner hype and headlines as new technology emerges, you don't need to rush.

There will always be opportunities throughout the adoption cycle of a new technology. Early-stage companies may deliver great returns when they win, but they also carry immense risk. The middle of the cycle, where demand is more certain and adoption is accelerating, can be just as lucrative with less downside.

AI is a great current example. Dozens of companies are rushing to launch models, tools, and applications. Some are burning through cash just to claim a spot in the conversation. But many of the future winners may not yet exist or will emerge as the business case is clearer and the infrastructure is more robust. Investors need to be patient and remember that being early is not the same as being right.

Deflation’s Perfect Storm

First Mover Disadvantage