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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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  • 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
  • Constraints like shortages can lead to interesting technology solutions

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

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

    60% confidence
What we know · the intelligence behind this page
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What we're seeing
AI Chip Boom Lifts Semiconductors as Export-Control Gaps Persist
AI infrastructure demand is fueling a broad semiconductor rally — Broadcom's AI chip revenue and Q4 guidance, Amazon's custom silicon crossing a $25B annual run rate, and bullish analyst calls on Micron and Sandisk tied to a memory chip boom underestimated even by bulls — with ASML rallying on sympathy. That momentum runs alongside unresolved US-China tech tensions: Belgium's arrest of a suspect for stealing chip technology for China and a blacklisted Chinese firm still acquiring Nvidia's top AI chips show export-control enforcement lagging the pace of AI chip demand.
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Signals we're tracking
EPKINLY Regulatory-Clinical Success Cascade
High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
Patterns we're watching ›
Where sources disagree
JPMorgan Chase & Co.
Both facts record the same attribute (net_income) for JPMorgan Chase & Co. in the identical fiscal period (Q1 2026) and observation date (2026-03-31), but report values that differ by approximately 1 billion times: $16,494,000,000 vs $16.49. These cannot both be true simultaneously. The discrepancy suggests either a unit mismatch (e.g., one is total net income, the other earnings per share mislabeled as net_income), a decimal point error, or data entry corruption. For the same entity, attribute, and time period, only one value can be correct.
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