AI Scaling Paradigm Shifts

Scaling Limitation Analysis#

1. Computational Constraints

   • Physical limits of chip manufacturing
   • Energy consumption and cooling requirements
   • Memory bandwidth limitations
   • Distributed training communication overhead

2. Algorithmic Bottlenecks

   • Optimization landscape challenges
   • Gradient vanishing/exploding problems
   • Overfitting in massive models
   • Catastrophic forgetting in continual learning

3. Data Limitations

   • High-quality training data scarcity
   • Copyright and licensing restrictions
   • Bias and representation issues
   • Privacy and security concerns

New Evaluation Paradigms#

1. Beyond Traditional Benchmarks

   • Real-world performance metrics
   • Adaptability and learning speed measures
   • Efficiency and sustainability metrics
   • Robustness and reliability assessments

2. Comprehensive Evaluation Frameworks

   • Multi-dimensional performance assessment
   • Task-agnostic capability measurement
   • Long-term learning evaluation
   • Resource efficiency metrics