{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fpodcast.paiml.com%2Fepisodes%2F60-000-times-slower-python-aClDB7LD","width":444,"version":"1.0","type":"rich","title":"60,000 Times Slower Python","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/c66602cd-e6b1-4159-8e89-ae595a0d7c1b/b1e69521-4871-4413-a568-b88c49a1c684/52-weeks-aws.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"","height":200,"description":"The end of Moore's Law - where transistor counts doubled every two years - is forcing a fundamental shift in how we approach computing performance. While Python and other interpreted languages prioritized developer productivity when hardware gains were automatic, a simple matrix multiplication example shows potential 60,000x speedups through optimization, highlighting massive inefficiencies in modern software. Future gains will come from three key areas: software performance engineering to eliminate bloat, algorithmic improvements that can match hardware gains, and specialized hardware architectures like GPUs and TPUs. Unlike Moore's Law's predictable improvements, these gains will be opportunistic and domain-specific, requiring coordinated optimization across language design, algorithms, and hardware. Modern compiled languages like Rust, Go, and Zig represent this shift toward performance-first design, suggesting that in the future, it may be unacceptable to deploy code slower than C-level performance."}