A shockingly inefficient trillion-dollar project — large language models scale quadratically, not logarithmically, driving a global memory shortage and price spikes across computing.
Share of the world's high-end computer memory supply AI companies may be purchasing, per The Wall Street Journal.
Price rise for hard drives over two years, now out of stock, per the article's own reporting.
Laptop price increases reported by Business Insider, hitting low-cost computers hardest.
Planned multiplication of total U.S. data-center capacity over the next few years, per CleanView.
Some forecasts suggest affordable entry-level computers may disappear by 2028, and the shortage is expected to last years. Demand for electricity at AI data centers is so great that some companies are repurposing jet engines to power them.
Efficient software scales logarithmically — added input costs proportionally less over time. Large language models instead scale quadratically: the bigger the input, the disproportionately more time and memory each additional word costs. Model sizes have grown from 175 billion parameters in 2020 to more than 1 trillion today, chasing diminishing returns under the industry's belief in scaling laws.
Chatbot companies claim vague "compute multiplier" efficiency gains with no public evidence that quadratic scaling has been solved. Meanwhile, Microsoft researcher Alexia Jolicoeur-Martineau won a $50,000 prize for a tiny recursive model that solves logic problems without massive-scale training.
LLMs were integrated into Windows and macOS in 2024–2025, and added to Adobe Photoshop and Microsoft Word — raising the computing power needed for basic tasks even as Moore's Law slows due to molecular-scale limits on shrinking chip components.
The article argues a near-religious conviction that something mindlike could arise from LLMs persists despite their inability to recall basic facts, lack of common sense, and dissimilarity to a biological brain.
Reid Hoffman and Chris Yeh's Blitzscaling framework — prioritize speed of growth over operational efficiency, on the assumption that efficiency can be retrofitted after scale is captured — maps onto frontier labs' brute-force scaling bet in ways that clarify what's actually happening, and one important way it doesn't.
Sutskever's framing of brute-force scaling as "low-risk" mirrors blitzscaling's core logic: prioritize speed-to-capability over efficient engineering, with scaling laws serving as ideological cover for unresolved inefficiency.
Classic blitzscaling externalizes cost onto competitors or investors. Frontier labs are externalizing theirs onto a shared physical input market — the memory shortage and hardware price spikes documented above.
Blitzscaling works when scale reduces unit cost through network effects. Quadratic LLM scaling inverts that: each added parameter costs more, not less — the structural break this article documents.
Hoffman describes blitzscaling as "assembling a plane while falling." Vague compute-multiplier claims mean the trillion-dollar bet can't retrofit efficiency later if capital access tightens.
Undisclosed sizes, as with ChatGPT and Claude, make efficiency claims hard to verify.
Efficient systems scale logarithmically; unscalable systems like current LLMs scale quadratically.
Absent public verification, assume the underlying quadratic-scaling problem is unresolved.
Rising memory, storage, and laptop prices are a real-world proxy for AI's resource inefficiency.
Check whether a small, efficient model can perform the same task before assuming a massive LLM is necessary.
Chip improvements have slowed, so AI's exponential resource curve is increasingly unsustainable to meet with hardware gains alone.
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