AI that is gold does not glitter - using intelligence in Modern Infra
summary
Claims concrete results: a deployed Polaris agent performs predictive autoscaling, anomaly detection, root‑cause research and self‑healing; a four‑level kbert model codifies sovereignty (native, control, trusted, disconnected) so teams map workloads to cost/risk tradeoffs. Examples: hybrid legacy/mainframe mixes, 25+ years of ops, 9.6 billion EUR scale, and a cited ~10% carbon reduction via workload shifts. Warnings: AI saves when traffic is spiky or complex but wastes money on static, low‑volume, deterministic or one‑time tasks.
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chapters (15)
- 0:00AI in modern infrastructure
- 2:29AI’s rise and societal fear
- 7:04Modern infrastructure management
- 9:55ATOSS: Cloud and infrastructure
- 10:17Infrastructure stages: design, build, run
- 11:41Geopolitical and regulatory pressures 2025
- 13:26Sovereign infrastructure and zero-American software
- 15:13Kbert model for sovereignty levels
- 18:27AI managing infrastructure complexity
- 19:08AI for autoscaling and anomaly detection
- 20:56AI for cost and sustainability
- 21:59AI in capacity planning and compliance
- 23:01When AI is not the right solution for infrastructure
- 24:45AI’s impact on infrastructure future
- 25:50Shaolin master’s AI lesson
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