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Business as usual is over. Artificial intelligence has the potential to write a new chapter in growth investing. Let me explain why.
I am wary of most explanations of moves in stock prices. In the very short term, the noise is overwhelming. Over more extended periods, there is an obsession with linking stock market activity with economic variables, particularly interest rates. Such links are tenuous at best.
Instead, the process of capitalism drives changes in stock markets. As the economist Joseph Schumpeter put it: “The fundamental impulse that sets and keeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of production or transportation, the new markets, the new forms of industrial organisation that capitalist enterprise creates.”
I would make two observations about this “fundamental impulse”. Firstly, it does not move at an even pace. We go through periods of relative stability and periods of rapid change. Secondly, it gets more potent over time, reflecting the exponentially increasing number of ways technologies can combine to produce goods and services.
Given the scale and complexity of markets, it can be hard to discern these trends in stock prices. However, narrowing our lens to look at only the largest companies in the S&P 500 helps illustrate the point.
American Telephone and Telegraph (AT&T) was the largest company in the index as we entered the 1980s, reflecting the powerful monopolies created by the network effects of fixed-line telephony. The development of first the mainframe and then the personal computer propelled IBM past AT&T in 1981, and it remained in the top spot for most of the decade.
As the PC opportunity became saturated and the value capture from software grew, IBM plateaued, and the presence of technology companies at the top of the market waned. The 1990s was a decade of rapid US consumption growth, and stock markets reflected this dynamic, with companies such as Walmart, Philip Morris, General Electric and Coca-Cola dominating the top spots in the index.
As client-server networks became increasingly important in the enterprise, Microsoft, Intel and Cisco rose to prominence in the late 1990s, with Microsoft staying near the top for most of the 2000s. By the end of that decade, the internet and mobile telephony had powered Google and Apple up into the top three.
Apple displaced Exxon as the US’s largest company in 2011. The build-out of the mobile internet and cloud computing combined with the dominance of the largest platforms meant that by the start of the 2020s, we had Apple, Microsoft, Google, Amazon and Facebook occupying the top five index slots.
Paradigm shifts
This changing pecking order in the US’s top companies reflects the shifting technology landscape. Each development didn’t necessarily spell the end for the previous period’s market giants. However, as their products lost relevance, they reached the limits of their expansion potential.
One way to understand this process is through the philosopher Thomas Kuhn’s work on paradigms in scientific development. He suggests that normal science operates within an existing paradigm, but over time, anomalies emerge that cannot be accounted for. This leads to crisis and, ultimately, a new understanding of the world – the new paradigm.
Kuhn says that most normal science doesn’t aim at novelty but seeks to prove results within a paradigm. By contrast, a crisis involves a period of extraordinary rather than normal science, a proliferation of competing articulations and the willingness to try anything.
For example, Newton’s laws of motion constituted a paradigm for explaining the movement of the world around us. Scientists were happy with them for decades until they amassed observations that these laws couldn’t explain. This eventually led to Einstein’s theory of relativity and a new way of thinking about the world.
There is a parallel in stock markets. A dominant technology paradigm can emerge and drive significant productivity gains for consumers and businesses. However, at some point, the incremental gains are insufficient to solve emerging pain points, and this fuels an appetite for different approaches.
It isn’t necessary for there to be a crisis but simply a set of unmet needs that become more pressing. For example, client-server computing worked well for people in the same building, but as users moved further away from the server, they experienced degraded performance. As this need became more pressing, it created the demand for cloud infrastructure.
The road to today’s AI
The paradigms framework gives us a perspective on the evolving stock market order of the past four decades. Fixed-line telephony was a paradigm that displaced previous methods of communication and facilitated an era of rapid economic development and wealth creation. The arrival of the personal computer sowed the seeds for a paradigm shift as we started to move digital information over our communication networks. A new path for economic development was possible, and the internet was built.
This was superseded by mobile, which helped spur the development of the cloud. And so on. The dominant incumbents thrived in periods of calm (analogous to Kuhn’s ‘normal science’). The periods of change (paradigm shifts) saw the emergence of new powers that created immense wealth using a different model.
As we entered the 2020s, we looked to be in a period of ‘normal science’. The technology stack was well established. The winners in different sectors were clear, and they were iterating their products and services with few radical changes.
Covid and the associated economic gyrations did little to change this. 2022 was a challenging year for companies as they reacted to dramatic increases in the cost of capital by cutting costs, shedding employees and scaling back their ambitions. The boom years accelerated expansion for many companies, and the bust laid bare the limits of their potential. The future no longer looked so bright.
Then, at the end of November 2022, OpenAI released ChatGPT. For many in the artificial intelligence community, this was not a particularly noteworthy event. The system’s capability was in line with the work done at other AI labs. What made ChatGPT special was finding a way to put the technology in the hands of users everywhere. The world woke up to its potential.
With the subsequent release of GPT-4, the company showed the emergent properties of these systems that come with scale, and the seed for a new technology paradigm started to go mainstream. Peers scrambled to release competing systems, and companies everywhere began experimenting with the technology.
This is the type of development that, as growth investors, we pay attention to. Not because the specific implications of this progress are clear. Far from it. Most of the critical implications are unknown. But the status quo is disrupted. We are no longer in a period of ‘normal science’.
The efficiency drives of the past 24 months have been an important capitalist process. However, they are a clearing of the decks, not the foundations of a new growth era. Instead, generative AI – the ability to create text, images, code and other content at human-like levels – and the new paradigm it represents can create the great growth opportunities of the coming decade.
A technology’s architecture can determine the strategies and business models open to a company. This dynamic is often underappreciated. The cloud and mobile internet era has created a vast landscape that has favoured a small number of dominant giants. AI dictates a new set of rules regarding what is possible.
Receptive to radical change
In navigating times like these, we prioritise finding the people we trust to guide us. Product-focused founder chief executives are highly attuned to the potential impacts of technology on their businesses and are quick to understand the implications. They can react in radical ways if they believe it is in their long-term interests. Listening to what they say and observing what they do are essential clues. Shopify pivoted from building delivery infrastructure to focusing on AI in its core products to ensure it remains competitive.
In the context of our long-term framework, we must be open to the possibility of radical change and be prepared to change our minds in response. We sold our holding in enterprise software developer Appian as machine-written code may well displace its low-code approach to systems development.
We have looked for emerging winners in existing companies and are tracking the deployment of venture capital dollars. We are looking for companies with both a compelling technical offering and an emerging business model. This is evident in the messaging services of Meta: Messenger, Instagram and WhatsApp. It can deploy AI tools to enable businesses to respond to customers through click-to-message ads.
We took a holding in Samsara – a firm that enables its clients to use sensor-gathered data to enhance their physical operations – as we believe proprietary datasets will allow companies to capture the benefits of AI in the products they can deliver to customers.
Where we find signs of traction, we must be prepared to embrace ‘growth at an unreasonable price’. This means that for the eventual winners, the opportunity will be massively underestimated, and the others will have been vastly overpriced.
The deployment of AI systems at or around current capabilities will have a meaningful impact on the global economy. There is almost unlimited demand for intelligence, and the pace of deployment will likely be limited only by the cost of supply.
We have yet to determine the limits of this technology or how quickly it will continue to improve. The applications and the impacts will get more dramatic the longer we stay on the current trajectory of progress. For the growth investor, things are just starting to get interesting.
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