Iris Coleman
Dec 17, 2025 06:09
Dan Fu from collectively.ai argues that synthetic common intelligence (AGI) is achievable by optimizing software-hardware co-design, enhancing present chip utilization, and overcoming perceived {hardware} limitations.
The controversy surrounding the potential for attaining synthetic common intelligence (AGI) is intensifying, with Dan Fu, Vice President of Kernels at collectively.ai, offering an optimistic outlook. In accordance with collectively.ai, Fu challenges the notion that developments in AI are being stymied by {hardware} limitations. As a substitute, he posits that present chips are considerably underutilized and {that a} strategic method to software-hardware co-design might unlock substantial efficiency enhancements.
Present Limitations and Future Potential
Because the AI panorama evolves, issues about reaching the boundaries of digital computation have gotten extra prevalent. Some consultants counsel that {hardware} constraints, significantly in GPUs, may impede progress in the direction of growing usually helpful AI. In distinction, Fu presents a extra hopeful perspective in his publication, “Sure, AGI Can Occur – A Computational Perspective,” which argues that the ceiling has not but been reached for AI capabilities.
Underutilization of Present {Hardware}
Fu highlights that state-of-the-art AI coaching runs, comparable to DeepSeek-V3 or Llama-4, typically obtain solely about 20% Imply FLOP Utilization (MFU), with inference utilization generally within the single digits. These figures counsel a major alternative to boost effectivity by higher integration of software program and {hardware}, in addition to improvements like FP4 coaching.
Developments in Computational Fashions
Present AI fashions are primarily based on older {hardware}, and the potential of newer computational assets has not been totally realized. Fu emphasizes that large clusters of the newest technology GPUs, numbering over 100,000, have but to be totally built-in into AI growth processes, indicating a promising horizon for future developments.
Current-Day Utility and Future Implications
Regardless of the perceived limitations, current AI fashions are already revolutionizing complicated workflows, comparable to writing high-performance GPU kernels with human help. This transformation underscores the speedy utility of AI applied sciences and hints on the huge potential for future functions.
For these within the intersection of techniques engineering, {hardware} effectivity, and AI scaling, Fu’s evaluation gives beneficial insights. The total evaluation could be accessed on the collectively.ai web site.
Picture supply: Shutterstock
