Recent discussions among AI developers highlight significant advancements in model efficiency and cost reduction. One developer noted that integrating GPT into a platform called Godinabox resulted in a tenfold decrease in inference costs within weeks. This raises the possibility that training smaller models on compute-intensive tasks could lead to faster generation times on less powerful machines. However, there are concerns regarding data movement bottlenecks, which may limit the scaling of AI training runs to approximately 100 times beyond current models, with a theoretical maximum of around 1,000 times due to latency issues. Another developer shared their experience training a 200MB TinyGPT model on a coffee recipe database, demonstrating that smaller models can achieve capabilities comparable to larger ones, such as 1 billion parameter models, indicating a trend toward embedding intelligence directly into applications.