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Nvidia’s third-quarter corporate earnings results show the buildout of artificial intelligence (AI) data centers is powering ahead as tech firms race to build the infrastructure AI needs. We think AI could eventually radically reshape economies and markets. Yet uncertainty over how AI evolves from here raises big questions. We use our three-phase framework – buildout, adoption, transformation – to track the AI revolution. Taking an active approach helps us identify and capture investment opportunities.
In the buildout phase underway, tech giants are pouring record amounts of capital into AI. Chips are the single largest cost, and Nvidia is one of the big winners of that demand. Some of the most powerful chips can cost $40 billion per gigawatt versus $10-$20 billion for traditional chips, according to Thunder Said Energy.
Advanced chips are one reason AI data centers – the backbone of the buildout – are costlier than traditional ones (see the chart below). Spending on traditional and AI data centers combined could top US$700 billion annually by 2030, industry estimates show. All this spending could add to inflation, including via higher near-term energy costs given AI’s huge power needs. Eventually, AI could boost energy efficiency, offsetting some of the initial spike in energy demand. Yet those savings can come only after mass AI adoption, a process that will take time.
We think investment in AI could rival the amount seen in the industrial revolution – especially once including the spending on energy infrastructure as part of both the data center buildout and the low-carbon transition. Investment of this magnitude demands significant financing, creating a key funding role for capital markets and private markets. Yet private markets are complex and not suitable for all investors. We see big cloud providers and chip producers as the buildout’s main beneficiaries – particularly mega-cap tech companies, whose unmatched resources and tech expertise give them a competitive advantage.
Questions around AI overinvestment are valid. Yet we think this should be assessed in aggregate, given AI’s potential to unlock new revenue streams across the whole economy. Mega-cap tech does not look overextended for now. Comparisons to the dot-com era fall short, according to BlackRock’s Systematic Active Equity team: Analysis of hundreds of metrics on valuations, earnings, and other features reveals few similarities between now and then. Beyond tech, other likely beneficiaries of the buildout include companies in the utilities, energy, industrials, materials, and real estate sectors providing key inputs.
What comes after the buildout raises more big questions.
Part of AI’s promise hinges on its ability to drive a productivity boom. Near term, we expect moderate productivity gains as AI reshapes specific tasks. Longer term, AI could accelerate the process of generating new ideas and discoveries, with far-reaching implications for innovation and growth.
Much depends on how rapidly AI is adopted across industries. Broad adoption could alter the makeup of the economy by shifting labor and resources, creating new jobs and industries. Sectors like finance and IT could benefit as early adopters. If adoption happens too quickly, it could drive inflation as demand grows faster than resources can be reallocated and workers reskilled.
Yet it is difficult now to imagine all of the future AI use cases. Navigating this uncertainty calls for an active investment approach, in our view. Private markets can provide an opportunity to invest in potential winners before they are publicly listed.
Investment opportunities in the AI buildout expand beyond tech into sectors providing key energy, infrastructure, and data center inputs. Uncertainty beyond the buildout calls for an active approach to identify future beneficiaries.
Wei Li, Managing Director, is the Global Chief Investment Strategist at BlackRock Investment Institute at BlackRock Inc.
Jean Boivin is Managing Director, Head of the BlackRock Investment Institute at BlackRock Inc.
Raffaele Savi, Global Head of Systematic – BlackRock, and Nicholas Fawcett, Senior Economist – BlackRock Investment Institute, contributed to this article.
Disclaimer
This material is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date indicated and may change as subsequent conditions vary. The information and opinions contained in this post are derived from proprietary and nonproprietary sources deemed by BlackRock to be reliable, are not necessarily all-inclusive and are not guaranteed as to accuracy. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by BlackRock, its officers, employees or agents. This post may contain “forward-looking” information that is not purely historical in nature. Such information may include, among other things, projections and forecasts. There is no guarantee that any of these views will come to pass. Reliance upon information in this post is at the sole discretion of the reader.
© 2024 BlackRock Inc. All rights reserved. iSHARES and BLACKROCK are registered trademarks of BlackRock, Inc., or its subsidiaries in the United States and elsewhere. This article first appeared November 11, 2024, on the BlackRock website. Used with permission.
Image: iStock.com/NanoStockk
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