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#compute-scaling

Will financing bottleneck AI compute? An Anthropic case study (15 minute read)

TLDR AI · 2026-08-14 Cached

Epoch AI analyzes whether financing will bottleneck AI compute scaling, using Anthropic's $50B infrastructure buildout funded by debt as a case study. It argues institutional investors are willing to lend against long-term payment commitments, especially with supplier backing, making financing unlikely to be the immediate limit on frontier compute growth.

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#compute-scaling

@bayeslord: https://x.com/bayeslord/status/2072056960430789032

X AI KOLs Following · 2026-06-30 Cached

A detailed speculative thread on the near future of AI, arguing that algorithmic progress will surprise many, with 4-10 orders of magnitude improvement in intelligence possible, and that we are in an early takeoff where AI accelerating AI research will lead to rapid advances.

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#compute-scaling

@Suhail: Every time I acquire more compute, I discover that I could use even more compute to do other new things.

X AI KOLs Following · 2026-06-25

A personal observation that acquiring more compute only reveals further needs for compute, reflecting the insatiable demand in AI and tech.

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#compute-scaling

The Cognitive Kardashev Scale: Quantifying the Material Envelope of Civilisational Computation

arXiv cs.AI · 2026-05-25 Cached

This paper proposes a Cognitive Kardashev Scale ranking civilizations by their sustained AI-grade computation capacity, using total power and efficiency. It places current humanity at K≈0.73 and explores future scaling trajectories.

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#compute-scaling

Unified Neural Scaling Laws

Hugging Face Daily Papers · 2026-05-25 Cached

Presents a unified neural scaling law that accurately models deep neural network scaling across multiple dimensions including parameters, dataset size, training steps, and compute, validated across diverse architectures and tasks.

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@AI_Whisper_X: Bitter Lesson Part Two: If you have enough compute, the best data filter is no filter. The biggest takeaway from reading this paper is that Rich Sutton's bitter lesson is now coming to the data side? Stanford's Hashimoto published "A Bitter Lesson for Data Filtering"...

X AI KOLs Timeline · 2026-05-24 Cached

A research paper from Stanford University proposes that with sufficient compute, the best data filtering strategy is no filtering. Experiments show that large-scale models are robust to low-quality data, and unfiltered data pools perform better at larger scales. However, this conclusion applies to standard pre-training of dense models, and filtering remains important when compute is limited.

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#compute-scaling

The "just add more compute" argument for ai reasoning is getting exhausting

Reddit r/artificial · 2026-05-18

A critical take on the scaling argument for AI reasoning, arguing that autoregressive LLMs cannot achieve correctness through more compute alone, and highlighting alternative architectures like EBMs and formal verification as superior for critical applications.

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#compute-scaling

@rohanpaul_ai: Ex Google CEO, Dr. Eric Schmidt: AI may hit a money wall before it hits a power wall. "The real limit to AI is not ener…

X AI KOLs Following · 2026-05-14

Ex-Google CEO Eric Schmidt states that the real limit to AI is financial, not energy, estimating that 10 gigawatts of compute could cost half a trillion dollars, which only a few entities like the US or China can afford.

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