AI Summary
5 min readAndrew Sheets, Global Head of Fixed Income Research at Morgan Stanley, examines why investments in AI infrastructure—covering chips, power, and data centers—exhibit price inelasticity, meaning demand persists despite sharp cost increases.
Price Elasticity Basics
Sheets starts with the economic concept of elasticity, which measures sensitivity to price changes. Everyday items like pizza show elastic demand: a price hike might shift buyers to burgers. In contrast, essentials like electricity or highly desired goods like concert tickets for a favorite artist are inelastic—consumers complain but pay up anyway. This framework applies directly to the AI build-out, where demand for key components remains strong even as prices surge.
Scale of the AI Investment Wave
The AI push drives massive spending by large U.S. technology companies, estimated at $800 billion this year—almost double last year's outlay and triple 2024 levels. Morgan Stanley colleagues forecast this rising to $1.1 trillion in 2027, with projections consistently revised higher. This scale underpins U.S. growth amid slowing job creation and bolsters stock market earnings despite geopolitical tensions like those over Iran. Multiple companies pursuing large-scale AI infrastructure simultaneously intensifies competition for resources.
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What you'll learn
- 1 (00:00) **Intro to Price Elasticity** - Host Andrew Sheets introduces economics concept of elasticity and its relevance to AI
- 2 (00:16) **Defining Elastic vs Inelastic Demand** - Explains how demand for non-essentials drops with price hikes, but holds for essentials
- 3 (00:59) **AI Build-Out as Market Driver** - AI investments in chips/power/data centers fuel US growth and earnings amid uncertainties
- 4 (01:25) **Scale of AI Capex Surge** - US tech firms to spend ~$800B this year, double last year and triple 2024 levels
- 5 (01:42) **Component Price Spikes** - Copper up 40%, gas turbines 50%, memory 150-300% in past year despite rising costs
- 6 (02:07) **Demand Acceleration and Forecasts** - AI spending forecasts revised higher; $800B this year to $1.1T in 2027
- 7 (02:30) **Financing Insensitivity** - Debt costs up but issuance at record pace to fund AI
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Show Notes
Our Global Head of Fixed Income Research Andrew Sheets explains the economic theory behind the unwavering spending on AI infrastructure.
Read more insights from Morgan Stanley.
----- Transcript -----
Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley.
Today, a uniquely price insensitive development.
It's Monday, May 11th at 2pm in London.
Elasticity is one of the first concepts that they teach in economics, and for good reason.
It's the idea that our sensitivity to the price of something differs from item to item. If the price of pizza goes up, for example, you may decide to go out for burgers. But if the price for something essential, like electricity, or deeply desired, like tickets to see your favorite artist perform; well, if those go up a lot, you're probably going to complain, but also end up paying anyway.
This latter category is what we would call inelastic. The demand for these items holds up even as the price increases, and maybe if the price increases quite a bit. And that is becoming very relevant as we all debate the AI build-out.
It's not an exaggeration that the investment in AI, chips, power, and datacenters is at the center of many market conversations. It's supporting U.S. growth despite a sharp slowdown in job creation. It's supporting stock market earnings, even as uncertainty over the Iran conflict continues to percolate.
Part of this importance is just the sheer size of this build-out. We estimate about $800 billion of investment by large U.S. technology companies this year, almost double their spending last year and triple their spending in 2024. But it's not just the size, it's the idea that this investment may happen almost whatever the cost.
Specifically, we're looking at a desire by multiple large companies to build out large AI infrastructure all at the same time, and that's increased the price of these components. The copper needed to wire together that data center? Well, it's up about 40 percent in the last year. A gas turbine to power it? Up 50 percent. The memory to run it? It's up 150 to 300 percent over the last year alone. And yet, despite these extremely large price increases, the demand to build in AI has been accelerating.
Our forecasts for 2026 spending have been consistently revised higher. And that $800 billion that we think is spent this year is set to be dwarfed by $1.1 trillion of estimated spending in 2027, based on the view of my Morgan Stanley colleagues.
This idea of inelasticity or price insensitivity extends even to the costs of financing the spending. Debt costs for these companies have increased this year, and yet they continue to issue at a record pace.
A quick aside as to wh
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