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AI Is Insatiable

View original at spectrum.ieee.org
IEEE Spectrum - Technical Title: AI Is Insatiable Date: 2026-04-06 14:22 Source: https://spectrum.ieee.org/high-bandwidth-memory-shortage <img src="https://spectrum.ieee.org/media-library/robot-hand-catching-falling-computer-chips-from-an-open-snack-bag-in-pop-art-style.png?id=65425799&width=1200&height=800&coordinates…
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  • Makers of AI processors, notably Nvidia and AMD, are demanding more and more memory for each of their chips, driven by needs of firms like Google, Microsoft, OpenAI, and Anthropic

    60% confidence
  • AI hyperscalers' ravenous appetite for memory is a major constraint on the speed at which large language models run

    60% confidence
  • Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030

    60% confidence
  • AI electricity consumption could account for up to 12 percent of all U.S. power by 2028

    60% confidence
  • Data centers might steer toward hardware that sacrifices some performance for less memory as adaptation to shortage

    60% confidence
  • If any of the big three HBM companies—Micron, Samsung, and SK Hynix—say that they are adjusting the schedule of the arrival of new production, that'd be an important signal

    60% confidence
  • AI electricity consumption could account for up to 12 percent of all U.S. power by 2028

    60% confidence
  • AI hyperscalers' ravenous appetite for memory is causing current DRAM shortage, particularly for high bandwidth memory (HBM), which is a major constraint on the speed at which large language models run

    60% confidence
  • Constraints like shortages can lead to interesting technology solutions

    60% confidence
  • Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023

    60% confidence
  • If any of the big three HBM companies—Micron, Samsung, and SK Hynix—say that they are adjusting the schedule of the arrival of new production, that'd be an important signal

    60% confidence
  • Data centers might steer toward hardware that sacrifices some performance for less memory, and startups might pivot toward creative redesigns that use less memory as constraints lead to interesting technology solutions

    60% confidence
  • Startups developing all sorts of products might pivot toward creative redesigns that use less memory

    60% confidence
  • Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023

    60% confidence
  • Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030

    60% confidence
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Ce que nous observons
The Agentic Takeover of the CFO's Office
Enterprise finance software vendors—BlackLine, OneStream, Numero AI, and Oracle—are racing to embed autonomous AI agents into core financial operations (close, consolidation, reporting), backed by consolidation M&A (Numero-Royu, BlackLine-WiseLayer), fresh leadership hires, and survey data showing nearly a quarter of CFOs plan to boost AI spending over 50%. Adoption momentum is strong even as at least one bellwether (Oracle) sees its stock lag year-to-date, suggesting the market hasn't yet fully priced in the shift from AI-as-feature to AI-as-agent in finance.
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Signaux que nous suivons
Satellite-Terrestrial Network Integration Acceleration
Increased investment and launches in hybrid satellite-cellular networks across telecom industry; competitive responses from other carriers; regulatory activity around satellite spectrum; expansion of emergency/rural connectivity use cases
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Là où les sources divergent
Morgan Stanley & Co. LLC
Two significantly different EPS values (10.21 vs 2.68 USD_per_share) are reported for Morgan Stanley on the same observation date (2025-12-31). Fact A specifies FY 2025, while Fact B's 'N/A' fiscal period is ambiguous. If both represent FY 2025 annual EPS, these values directly conflict. The magnitude of the difference (3.8x) is too large to attribute to rounding or minor calculation variations. The missing fiscal period in Fact B raises data quality concerns, but same-date observation + same attribute should reference the same period.
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