{
    "action": "final",
    "content": "## Distribution Summary for Numeric Columns\n\nI've analyzed all numeric columns in the AI supercomputers dataset. Here's the distribution summary:\n\n### Computational Performance Metrics\n\n**Max OP/s (log)**\n- Range: 15.30 to 20.74 (log scale)\n- Mean: 17.69, Median: 17.60\n- Standard deviation: 0.92\n- Coverage: 460 of 482 systems (95%)\n\nThe log-scale operations per second shows relatively tight clustering around the median, with a standard deviation under 1.0, indicating most systems fall within a similar performance band when viewed logarithmically.\n\n**H100 equivalents**\n- Range: 1.0 to 275,796 equivalents\n- Mean: 3,420, Median: 200\n- Standard deviation: 18,454\n- Coverage: 460 of 482 systems (95%)\n\nHighly right-skewed distribution—the mean is 17× the median, indicating a few extremely large clusters (like the 275K H100-equivalent system) pull the average up significantly, while most systems are much smaller.\n\n### Hardware Scale Metrics\n\n**Chip quantity (primary)**\n- Range: 64 to 200,000 chips\n- Mean: 5,897, Median: 944\n- Standard deviation: 19,585\n- Coverage: 428 of 482 systems (89%)\n\nExtremely right-skewed—mean is 6× the median. Most clusters have under 1,000 chips, but outliers with tens of thousands skew the distribution.\n\n**Chip quantity (secondary)**\n- Range: 1 to 50,000 chips\n- Mean: 5,997, Median: 608\n- Standard deviation: 13,072\n- Coverage: Only 18 of 482 systems (4%)\n\nVery sparse—only 18 systems report secondary chips. Among those that do, the distribution is similarly right-skewed.\n\n**Total number of AI chips**\n- Range: 64 to 230,000 chips\n- Mean: 6,222, Median: 1,000\n- Standard deviation: 21,127\n- Coverage: 435 of 482 systems (90%)\n\nCombines primary and secondary chips. The 6:1 mean-to-median ratio confirms the extreme concentration—a handful of massive clusters dominate the upper range.\n\n### Infrastructure Metrics\n\n**Power Capacity (MW)**\n- Range: 0.11 to 352.35 MW\n- Mean: 6.29 MW, Median: 0.75 MW\n- Standard deviation: 26.44 MW\n- Coverage: 422 of 482 systems (88%)\n\nExtremely right-skewed (mean is 8× median). Most systems draw under 1 MW, but a few hyperscale facilities require hundreds of megawatts.\n\n**Energy Efficiency (log)**\n- Range: 10.08 to 12.46 (log scale)\n- Mean: 11.50, Median: 11.55\n- Standard deviation: 0.30\n- Coverage: 406 of 482 systems (84%)\n\nMost symmetric distribution in the dataset—mean and median are nearly identical, with low standard deviation. Energy efficiency improvements appear to follow a more normal distribution on the log scale.\n\n### Cost Metrics\n\n**Hardware Cost**\n- Range: $3.5M to $7.1B\n- Mean: $132.8M, Median: $19.7M\n- Standard deviation: $528.4M\n- Coverage: 384 of 482 systems (80%)\n\nExtremely right-skewed (mean is 7× median). The multi-billion-dollar outliers dramatically inflate the average, while most systems cost under $20M in hardware.\n\n### Key Patterns\n\n1. **Extreme right skew dominates**: Nearly every metric shows mean >> median, indicating a small number of hyperscale systems dwarf the typical installation\n2. **Log-scale metrics are more normal**: Max OP/s (log) and Energy Efficiency (log) show mean ≈ median with lower standard deviations\n3. **Data completeness varies**: Core metrics like H100 equivalents have 95% coverage, while secondary chip counts are sparse (4%)\n4. **Scale concentration**: The largest systems are 1,000–3,000× larger than the median across chips, power, and cost"
}