The Future of Generative Models: Beyond Scaling
For the past decade, the dominant paradigm in AI advancement has been scaling—increasing model sizes (parameter count), expanding training datasets, and applying more computational resources. This approach has delivered impressive gains, with each leap in model size bringing better capabilities. However, scaling alone is facing diminishing returns and growing challenges in terms of sustainability, accessibility, and addressing fundamental AI limitations. The future of generative AI lies beyond simple scaling, in more efficient architectures, specialized approaches, and hybrid systems that overcome current limitations while democratizing access to these powerful technologies.
Throughout this book, we have explored building applications using generative AI models. Our focus on agents has been central, as we’ve developed autonomous tools that can reason, plan, and execute tasks across multiple domains. For developers and...
