Will AI push programming to lower level abstractions? (twitter.com)

Grok 4.8, a 2.5 trillion parameter AI model, is set to complete its training this week using a newly developed C++ software stack. Following the completion of its training, Grok 4.8 will enter a phase focused on reinforcement learning (RL). This advancement highlights a significant shift in AI tooling, as the use of lower-level programming languages like C++ may enable more efficient model optimization and performance improvements. The significance of Grok 4.8 for the AI/ML community lies in its potential to push the boundaries of model capability and efficiency. By leveraging a lower-level programming paradigm, developers are likely to gain finer control over computational processes, leading to enhancements in model training speeds and resource management. This shift may inspire further exploration of lower-level abstractions in AI development, challenging traditional reliance on higher-level frameworks and potentially fostering innovations in model architecture and deployment strategies.