Key takeaways

• The post-Moore era is not a slowdown of computational capability. It is a shift from transistor scaling to systems-level scaling.
• AI training compute and algorithmic efficiency have compounded faster than classical Moore’s Law, changing the economics of scientific discovery.
• The next bottlenecks are power, data, capital intensity, and interconnect latency rather than lithography alone.
• Compute-intensive AI has already produced domain-level results in structural biology, materials discovery, weather forecasting, and formal mathematical reasoning.
• Executive AI strategy needs infrastructure realism: compute access, energy exposure, vendor concentration, data strategy, and operating model design.