The New Stack Podcast How Can We Solve Observability's Data Capture and Spending Problem?
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Nov 20, 2025 In this engaging discussion, Jacob Yackenovich, a senior observability and cloud-native expert from IBM, dives into the complexities of telemetry in modern applications. He highlights the need for high-granularity data capture to ensure no critical performance signals are missed. Jacob also addresses the challenges posed by AI integration, distinguishing between add-on and blocking AI impacts on workflows. Plus, he emphasizes the importance of predictable pricing models for observability that allow innovation without financial blind spots. A must-listen for anyone in tech!
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Observability Is Continuously Evolving
- Observability expectations change continuously as new tech and releases appear.
- Jacob Yackenovich says tools must adapt weekly to shifting production realities.
Two Patterns Of AI Failures
- AI introduces two failure patterns: add-on and blocking components with different impacts.
- Jacob Yackenovich says blocking AI failures can effectively take an application down and harm user trust.
Accuracy As A Golden Signal
- Accuracy becomes a new 'fifth golden signal' for AI-driven content generation.
- Jacob Yackenovich argues we must monitor content accuracy alongside latency, errors, and throughput.