Social media discussion from September 2026 about AI capital expenditure trends, focusing on rising GPU prices (B200s up 21% monthly to $7.19/hr), strong AI infrastructure demand for inference workloads, and concerns about whether major tech companies can sustain massive capex investments without guaranteed revenue returns.
Hugo Vergnes trained a 3.8B-parameter language model scoring 0.384 on CORE using 65B tokens in 43 hours for $998, demonstrating that meaningful model training is accessible outside major labs. The project used a config-driven framework called little-lm with standard Llama-style architecture, and key improvements over earlier failed runs included better learning rate schedules, optimizer choices, and dataset selection.