The New Growth Epoch: Avoid at Your Peril

As with any investment, your capital is at risk. 

Over the past 20 years, investors have grown accustomed to dramatic change. The rise of ecommerce, social media and mobile computing overturned business models that had stood for decades. Yet I believe the period we are entering will dwarf those shifts. The next two decades will be marked by a speed and intensity of transformation that make the last 20 look steady by comparison.

Artificial intelligence is at the heart of this. It is not just another technology trend but a new foundation for the global economy. The challenge for investors is to recognise that standing still is not an option when the ground beneath you is moving.

Scale, Inevitability and Asymmetry

Equity markets are not a level playing field. History shows that wealth does not compound evenly across thousands of listed companies. Instead, it gathers disproportionately around a small number of extraordinary businesses. What is different today is that the outliers of the past decade are not just incrementally ahead but operating at a scale that makes them virtually unassailable.

Consider the investment required to compete in AI infrastructure. In 2025 alone, Alphabet, Amazon, Microsoft and Meta are expected to spend more than $400bn on AI datacentres, chips and cloud infrastructure. That’s equivalent to two-thirds of the UK’s total annual investment spending. In China, firms like ByteDance and Alibaba are building a parallel infrastructure within the constraints of export restrictions. These are not simply large corporations but corporate equivalents of nation-states.

For investors, this is not about chasing AI market hype. It is about recognising inevitability. When a business is both vast and still compounding at double-digit rates, the arithmetic of future market returns is tilted heavily in its direction. That is why the idea of ‘mean reversion’ feels increasingly outdated. The companies shaping the future are not drifting back to average. They are pulling further away from it.

The growth investor’s task then is not to diversify endlessly in search of safety, but to accept that the path to long-term wealth will be defined by asymmetry. A small number of companies will account for the bulk of value creation. The job is to own and hold them through the volatility that inevitably accompanies leadership.

But with the launch of ChatGPT three years ago, we crossed into a new era. To borrow from the philosopher of science Thomas Kuhn, this is a paradigm shift. In his account of scientific revolutions, Kuhn described how knowledge progresses not in a smooth line but in leaps between frameworks. Newtonian mechanics framed our thinking about what was possible until the theory of relativity supplanted it and forced us to reconsider those limits. In the same way, the internet-mobile-cloud paradigm is giving way to an intelligence paradigm. AI unlocks new capabilities that legacy systems cannot deliver or compete with.

AI is not an incremental enhancement but a new foundation altogether. The frameworks of the past two decades, such as search, ecommerce and social networks, were powerful because they connected people at scale. The new era is powerful because it connects intelligence at scale. It allows machines to reason and to collaborate to accomplish tasks that were not previously possible.

Paradigms matter because they determine where value accrues. In the internet-mobile era, the greatest rewards flowed to the application layer. To businesses like Adyen, TikTok and Instagram that built services on top of the new infrastructure. AI is following the same pattern. The infrastructure layer is being secured by a handful of cloud giants with the resources to build datacentres, buy chips and train models. However, the applications that sit on top of this infrastructure will create vast new companies of their own.

Time for platforms to reach 50 million users

ChatGPT reached 50 million monthly active users in just two months after its launch in November 2022:

Source: ©Zipline

These services exemplify what economists call Jevons’ Paradox: when technology increases efficiency, consumption rises rather than falls. Faster delivery doesn’t mean a more efficient system of the same scale – it means much greater use of delivery. More efficient coding doesn’t mean costs fall. It means vastly more software is written. We should expect to see the same phenomenon in every domain where AI will be applied. Efficiency creates new markets rather than satiating old ones.

Much of the progress in financial services can be attributed to the limited deployment of AI over recent years. Stripe’s Radar product, for example, has reduced fraud rates for merchants by learning from billions of global transactions, while Adyen applies AI to optimise authorisation rates, directly boosting client revenues. Affirm has built its buy-now-pay-later underwriting around an AI system that ingests transaction histories and behavioural signals allowing it to extend credit responsibly to a broader set of consumers.

At the frontier, we are witnessing possibilities that border on the surreal. Colossal Biosciences has already achieved the first ‘de-extinction’ and plans to bring back the dodo and woolly mammoth in the coming years. It sounds fantastical, but it is enabled by AI’s ability to process and manipulate genomic data at a scale that would be impossible otherwise. Whether or not one agrees with the ethics, the commercial and scientific implications are vast.

These examples demonstrate the same underlying truth: AI is not a single product or service. It is a general-purpose technology that will ripple through every corner of the economy. From the obvious (coding, transport, logistics) to the unexpected (cosmetics, conservation and beyond), it lays the foundation for new categories, business models and winners.

The Illusion of Safety

One of the most dangerous assumptions in investing is that stability can be found in companies deemed ‘safe’. Time and again, real capital destruction has come not from volatile growth stocks, but from incumbents whose earnings and valuations were built on the illusion of permanence.

The supposed safe havens can be the most exposed when technological change accelerates. They suffer a double blow: earnings erode as their models are disrupted, and valuations compress as the market recognises they were never as dependable as assumed. Some of my worst investment mistakes have come from backing companies that looked steady, only to discover that steadiness was a mirage.

This is why I believe the framework of mean reversion is increasingly unhelpful. Much market commentary still rests on the idea that sectors and valuations revert to long-run averages. But in a world of exponential change, ‘the long run’ itself is shifting. The average of yesterday may bear little resemblance to the future.

The cost of a technology falls predictably as cumulative production rises. In semiconductors, in solar, in batteries, this pattern has held true. The companies deploying those technologies have displaced the incumbents tied to the inflating costs of alternative approaches. AI is no different. Since 2010, the compute used to train leading models has doubled roughly every six months. Costs per unit of intelligence are falling so fast that new models have limited shelf lives. Incumbents built on yesterday’s cost curves will not revert to the mean, because they cannot compete with the phenomenal increases in productivity in this vibrant new industry.

At the frontier, the competition for talent already illustrates the point. The very best AI engineers now command packages in the hundreds of millions. Meta reportedly offered up to $1bn pay packages to lure researchers from rivals, an almost unimaginable price for individual talent. At the same time, entire categories of routine work are at risk of being automated away. Within companies, this creates vast disparities in pay. Between companies, those who can harness AI surge ahead, while others fall irretrievably behind. Between countries, those with affordable energy and computing infrastructure become hubs of value creation, while others are relegated to consumers of imported intelligence.

With its high energy costs, the UK falls into the latter camp. We will ‘pay for it’ rather than ‘build it’. That is not a position of strength.

Inequality at these scales does not just show up in income data; it reshapes politics. As the benefits of AI accrue narrowly, populism finds fertile ground. We have already seen democratic systems strain under far less pressure. The following decades will test them further.

And then there are the more disquieting edges: malign uses of generative media, the potential for autonomous weapons, the societal shock of de-extinction and synthetic biology. These are not distant hypotheticals. They are already happening on a small scale. The world is about to get ‘weird’ more quickly than many of us can comfortably imagine.

Yet I remain, on balance, an optimist. For a century, human progress has been constrained by limited access to intelligence. If we can augment human capability with machine intelligence on an exponentially declining cost curve, the potential to enhance quality of life is immense. The challenge is not whether the technology works but whether our institutions, cultures, and policies can adapt quickly enough to channel it productively.

Investing in the Inevitable

Today's real danger lies in assuming things will revert to how they have always been. They will not. The coming decades will bring extraordinary advances and extraordinary dislocations. They will reward scale, ambition, and adaptability. They will penalise complacency, inertia, and the illusion of safety.

For investors, the prudent course is not to spread capital thinly across what looks comfortable, but to ensure meaningful exposure to what is inevitable.

In today’s market, that means owning the companies that are shaping the intelligence paradigm and creating the next wave of applications on top of it.

Avoiding growth companies – in the US and around the world – is not just a missed opportunity. It is a failure to insure portfolios against the upheavals ahead. In a world of accelerating change, it is, quite simply, perilous.

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