• 1960s: Focus on power over efficiency (muscle cars, gas guzzlers)
• Evolution through Japanese efficiency, turbocharging, to electric vehicles
• Similar pattern now happening in computing

• Matrix multiplication example: 7 hours vs 0.5 seconds
• 60,000x performance difference through optimization
• Demonstrates massive inefficiencies in modern languages
• Industry was misled by Moore's Law into deprioritizing performance

1. Language Choice Improvements:

   • Java: 11x faster than Python
   • C: 50x faster than Python
   • Why stop at C-level performance?

2. Additional Optimization Layers:

   • Parallel loops: 366x speedup
   • Parallel divide and conquer
   • Vectorization
   • Chip-specific features

• Moore's Law's automatic performance gains are gone
• LLMs make code generation easier but not necessarily better
• Need experts who understand performance optimization
• Pushing for "faster than C" as the new standard

• Modern compiled languages gaining attention (Rust, Go, Zig)
• Example: 16KB Zig web server in Docker
• Rethinking architectures:
  • Microservices with tiny containers
  • WebAssembly over JavaScript
  • Performance-first design

• Developer time no longer prioritized over runtime
• Production code should never be slower than C
• Single-stack ownership enables optimization
• Need for coordinated improvement across:
  • Language design
  • Algorithms
  • Hardware architecture

• Shift from interpreted to modern compiled languages
• Performance engineering becoming critical skill
• Domain-specific hardware acceleration
• Integrated approach to performance optimization

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