Part 4/10:
Baker highlighted the transition in understanding “scaling laws,” which previously relied primarily on pre-training models. The emergence of new scaling laws related to reinforcement learning and test-time computing offers AI developers deeper insight into how they can enhance model efficiency and performance. The implications are significant: models can now be trained more cost-effectively while maintaining high levels of intelligence.
The financial ramifications are profound as well. Baker illuminated that the cost of inference—the process of using a trained model—has decreased significantly, making AI technologies more accessible. With the reductions in costs associated with model usage, businesses can expect a much higher return on investment (ROI) from AI deployments.