According to Jeff Dean, exponential trends in core compute and storage—such as transistors per mm2 and bits per mm2—continue to compound, enabling larger AI models and cheaper inference at scale; as reported by his tweet, similar curves in solar pricing and genomic sequencing costs illustrate cross-industry cost declines that translate into lower AI training TCO and expanded edge deployment options. According to the National Human Genome Research Institute, the cost to sequence a human genome fell from roughly $100 million in 2001 to around $600 by 2023, a trend that feeds AI bioinformatics by boosting dataset volume and model fine-tuning opportunities. As reported by the International Technology Roadmap for Semiconductors and historical data aggregated by Our World in Data, sustained gains in transistor density have supported higher FLOPs per watt, directly impacting AI training throughput and inference latency. According to IDC and vendor disclosures cited by Our World in Data, areal density improvements in hard drives and flash have reduced cost per bit, enabling larger vector databases and longer context windows without prohibitive storage costs. For AI businesses, these compounding curves create opportunities in multimodal model training, genomics-driven ML services, and cost-optimized RAG platforms, with near-term ROI coming from workload placement that exploits cheaper storage tiers and higher-density accelerators, as reported by industry analyses from Our World in Data and NHGRI.