Why AGI Will Not Happen — Tim Dettmers

kept by eddie dec 11, 25

Tim Dettmers argues AGI will never happen because computation is physical, linear progress requires exponential resources, and GPU improvements have plateaued.

Yes, AGI Can Happen - A Computational Perspective
Contra Tim Dettmers, Dan Fu argues current AI systems significantly underutilize hardware, models lag hardware buildout, and multiple paths exist to useful AGI.
also on Tim Dettmers, #scaling-laws, #ai-research, #hardware
How To Scale Your Model
This online book teaches LLM scaling through roofline analysis, TPU/GPU hardware, Transformer math, and parallelism techniques to estimate training and inference costs.
also on Transformer, #scaling-laws, #ai-research
How did ‘large’ language models get that way? The role of Transformers and Pretraining in GPT - LessWrong 2.0 viewer
The transformer architecture and pretraining enabled enormous scaling of language models, making 'large language models' a fitting description.
also on Transformer, #ai-research, #ai
The 6-Stage AI Infrastructure Journey: Navigating the Three FinOps & Hardware Crises with ACE Gateway — ACE Blog
The piece outlines a six‑stage AI infrastructure journey, maps three predictable cost/latency crises, and demonstrates how ACE Gateway’s control‑plane features cut spend and prevent hardware failures at each stage.
also on vLLM, #hardware, #ai
Inference cost at scale with napkin math
Napkin math shows serving a 32B LLM on an NVIDIA B200 GPU costs ~$9.36 per user per month when 300 users share the GPU with typical idle duty cycles.
also on vLLM, #hardware, #ai