The limitations of scaling and emerging alternatives
Understanding the limitations of the scaling paradigm and the emerging alternatives is crucial for anyone building or implementing AI systems today. As developers and stakeholders, recognizing where diminishing returns are setting in helps inform better investment decisions, technology choices, and implementation strategies. The shift beyond scaling represents both a challenge and an opportunity—a challenge to rethink how we advance AI capabilities, and an opportunity to create more efficient, accessible, and specialized systems. By exploring these limitations and alternatives, readers will be better equipped to navigate the evolving AI landscape, make informed architecture decisions, and identify the most promising paths forward for their specific use cases.
The scaling hypothesis challenged
The current doubling time in training compute of very large models is about 8 months, outpacing established scaling laws such...
