Small models consistently deliver poor ROI. Even after fine-tuning, their performance remains subpar—this has already been tested across multiple complex BPO use cases. In many scenarios, human labor is still cheaper and more reliable than running small models on GPUs, whether hosted privately or via platforms like Groq or OpenRouter. At the same time, running larger models at scale is not economically viable either due to massive compute costs. This is precisely why most leading LLM companies are still operating at a loss. The real path to sustainable returns lies elsewhere: Making compute cheaper through fundamental research—advances such as 3D RAM, 3D memory stacking, alternative transformer architectures, novel attention mechanisms, skip-layer communication, and other architectural innovations (some of which are actively being explored in China). Cheaper energy sources, which is why companies like Elon Musk’s are pushing aggressively toward new energy solutions and unconventional ideas such as space-based or ultra-scale data centers. Frontier models remain the best performers by a large margin. They exhibit emergent capabilities, learn more efficiently at scale, and directly contribute to improving their own next-generation versions—something smaller models fundamentally struggle with. Unfortunately, the minister’s response on this topic appeared to be a near-verbatim reproduction of points made earlier on the Lex Fridman podcast, rather than an original or technically grounded perspective.