Yes, AGI Can Happen - A Computational Perspective

kept by eddie dec 16, 25

Contra Tim Dettmers, Dan Fu argues current AI systems significantly underutilize hardware, models lag hardware buildout, and multiple paths exist to useful AGI.

Why AGI Will Not Happen — Tim Dettmers
Tim Dettmers argues AGI will never happen because computation is physical, linear progress requires exponential resources, and GPU improvements have plateaued.
also on Tim Dettmers, #scaling-laws, #ai-research, #hardware
The Scaling Wall Was A Mirage
Gemini 3's performance leap and Nvidia's $0.5 trillion revenue visibility confirm AI scaling laws are accelerating, not plateauing.
also on Blackwell, #scaling-laws, #generative-ai
https://t.co/boFKQfPb3G
Gemini 3 confirms scaling laws for pretraining are intact, marking the most important AI datapoint since o1's release and implying Blackwell models will show significant improvement.
also on Blackwell, #scaling-laws, #generative-ai
The Architecture Behind Open-Source LLMs
Open-weight LLMs now uniformly use Mixture-of-Experts transformers, with key differences in attention mechanisms, expert count, post-training via RL, and permissible licenses.
also on DeepSeek-V3, #ai-research
Why Open-Source Models Haven't Killed the Big Dogs
Open-source models remain costlier to serve reliably due to infrastructure, utilization, and operational overhead, making hosted APIs from OpenAI and Anthropic more economical despite free model weights.
also on Together AI, #generative-ai, #software-engineering