Every decade, a technology platform shift is accompanied by a major disruption in tech leadership. As these shifts play out, some businesses can sustain their leadership, but they are few and far between. The strongest companies today can be disrupted if they fail to capture the next platform or lose market share in their core business. (Exhibit 1)
Exhibit 1: Evolution in top 10 publicly traded tech firms; market cap (USD bn)
Source: Bloomberg, FactSet, J.P. Morgan Asset Management; data as of June 30, 2023. Market capitalization in USD billions. The companies/securities above are shown for illustrative purposes only. Their inclusion should not be interpreted as a recommendation to buy or sell. J.P. Morgan Asset Management may or may not hold positions on behalf of its clients in any or all of the aforementioned securities. Past performance is not necessarily a reliable indicator for current and future performance.
As we consider the various ways that AI might transform the economy over the coming decade, many experts believe that we are at a critical inflection point: AI will be a catalyst for significant investment and associated growth across many industries fuelling innovation.
In this article, we discuss a few areas of focus: the power of ChatGPT and large language models (LLMs), how technology infrastructure will change, and why AI’s impact will be broad. We also examine how investors might weigh their potential.
ChatGPT: “The iPhone moment”
Innovation tends to progress in a series of S-curves where advancement can happen very quickly. Today, generative AI is experiencing its own sharp rise. ChatGPT and LLMs are providing answers to questions so accurately that their responses are often indistinguishable from experts. Nvidia cofounder Jensen Huang captured it best as an “iPhone moment”1—a point where technology becomes so immediately useful that its adoption curve accelerates for both consumers and businesses.
The next three years of infrastructure
As AI accelerates the distribution of technology across the broader economy, many envision a new computing infrastructure that can enable consumer and enterprise adoption. This will not only support demand for existing hyperscaler (leading cloud service providers) AI applications, but also encourage new application development by a community of AI-focused entrepreneurs.
Already, capital expenditure spending for new AI computing data centres is rising at a rapid pace (Exhibit 2). Accelerated spending by hyperscalers should grow the opportunity set by about 10x and equate to annual capex spending that could be as high as USD 1 trillion within the next 10 years.
Exhibit 2: Reported U.S. capex, 2004–2022
Source: Bloomberg; data as of June 30, 2023. Amazon includes retail capex; Meta data starts in 2009; Oracle introduced Oracle Cloud Infrastructure in 2016.
Who will lead the coming era of innovation?
Given the importance of proprietary data, existing customer reach and the high costs of training, some argue that the most well-resourced companies are best equipped to profit from generative AI. But that framing misses a key element: AI usage trends are set to explode as more efficient forms of data consumption are introduced.
What’s more, improvements in semiconductor efficiency and optimized software naturally democratize building and running LLM models, driving AI to the mass enterprise market and growing the roster of its potential use cases. As this happens, deployment methods can also change, with smaller or more local clouds emerging that are dedicated entirely to AI workloads.
Profound change in the offing
In an environment of rapid change and meaningful unknowns, humility and iterative thinking are essential. Given these unknowns, we have to appreciate that as technology advances and clarifies existing imaginations, new imaginations, now too obscured to fully understand, will take flight. We must be agile even if we believe generative AI can spur an aggressive investment cycle.
When Apple had its iPhone moment in 2007, it set off a series of incremental yet meaningful iterations in the mobile phone market. Those iterations made mobile phones more useful and entire new industries were created. The same process may now be underway in the wake of ChatGPT. When we look back in 10 years, we can expect to see profound change.
This article is based on J.P. Morgan Asset Management research. You can check out the complete article here: https://am.jpmorgan.com/us/en/asset-management/adv/insights/portfolio-insights/equity/artificial-intelligence-powering-the-next-wave-of-technological-innovation/