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The demand for artificial intelligence (AI) applications is exceeding supply, a trend that has been reinforced by evidence of productivity gains for companies adopting the technology. Research by Morgan Stanley found that companies using AI saw an average productivity increase of 11.5%. Separately, IDC estimated that each dollar spent on AI solutions could generate $4.90 in economic value.
At the same time, AI software providers are seeing strong demand that outpaces their ability to deliver. Palantir Technologies, for example, has reported significant growth in its revenue backlog. That imbalance has contributed to aggressive spending by hyperscalers to expand data center capacity, though expansion is constrained by shortages of semiconductors and memory chips.
The shortage of AI-enabling hardware is creating investment opportunities across the AI supply chain. Memory companies are benefiting from large price increases that are supporting revenue and earnings growth. The broader market dynamic is also influencing expectations for companies positioned to supply key infrastructure for AI workloads.
While Nvidia and Broadcom have been leading names in AI chips, Advanced Micro Devices (AMD) has been expanding its presence. The company signed major deals last year with OpenAI and Meta Platforms to provide a combined 12 gigawatts (GW) of graphics processing units (GPUs) for AI data centers.
AMD’s data center opportunity is also supported by share gains in server CPUs. According to Mercury Research, AMD’s unit share of server CPUs increased by 3.1 percentage points year over year in the fourth quarter of 2025 to 28.8%. Its revenue share rose even more, reaching 41.3%, indicating premium pricing.
Pricing power may be reinforced by supply constraints. Demand for server CPUs exceeds supply, and Omdia expects server CPU prices to rise by 11% to 15%.
AMD estimates its annual data center revenue could reach $100 billion within five years. The article also cites a scenario in which earnings grow at 15% per year in 2029 and 2030, lifting earnings to $19.55 per share after five years. If the stock trades at 31 times earnings—aligned with the Nasdaq-100 earnings multiple at the time—its price could reach $667, implying a potential 2.4x move from current levels and a path toward a $1 trillion market capitalization from roughly $450 billion.
Oracle’s role in AI infrastructure is tied to its capacity expansion. Although the stock has fallen 40% over the past six months, the article argues investors are underestimating Oracle’s contribution to addressing a shortage of AI data center computing capacity.
Oracle added 400 megawatts (MW) of new data center capacity in the third quarter of fiscal 2026, which ended on Feb. 28. On its earnings call, the company also stated it has secured more than 10 gigawatts of power and data capacity coming online over the next three years.
Oracle further noted that more than 90% of the capacity coming online in the next three years will be fully funded by its partners. The article suggests this partner-funded model could help address investor concerns about Oracle’s debt and may support expectations for faster earnings growth.
Oracle estimates adjusted earnings could reach $21 per share in fiscal 2030. The article points to remaining performance obligations of $553 billion at the end of the previous quarter, with the metric rising 325% year over year. It also cites a valuation scenario in which, after five years, Oracle trades at 31 times earnings (matching the Nasdaq-100 average earnings multiple), implying a stock price of $665—about 3.8 times the current level. With a market cap of $500 billion as of the article’s writing, the piece concludes that Oracle could reach the trillion-dollar mark by the end of the decade.
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