TL;DR
Observability grew in the 2010s to help engineers debug distributed, cloud-native systems, driven by tracing and a new operational philosophy. Despite heavy investment in telemetry, today's observability practices still struggle with interpretation and operational burdens, and the author warns AI-driven software growth will make effective observability even more critical.
What happened
The author traces observability from its origins as a practical response to rising system complexity in the early 2010s to its current, muddled state. Distributed tracing and a mindset shift toward observability emerged as answers to problems introduced by cloud, containers, and microservices; tracing projects and publications from 2010–2018 helped codify approaches. Over time teams reacted by piling on instrumentation, dashboards, SLOs and runbooks, turning observability into an industry of its own. Today observability is widely deployed—companies pay for managed platforms and enforce heavy instrumentation—but the core challenge has shifted. While telemetry is abundant, engineering teams still struggle to make sense of signals, keep dashboards relevant, and resolve incidents quickly. Looking ahead, the author argues that the coming surge in AI-enabled software creation will massively increase complexity and that observability must evolve from signal production toward helping engineers reason about and act on data.
Why it matters
- Modern software complexity outpaced traditional debugging methods, driving observability's rise as a discipline and toolset.
- Despite greater telemetry, teams still struggle with interpretation, causing slow incident detection and resolution.
- AI-driven growth in code and features is expected to dramatically increase operational scale, making observability more essential.
- If observability doesn’t evolve, the industry risks a widening gap between data produced and useful operational insight.
Key facts
- Distributed tracing and observability gained momentum in the 2010s as cloud, containers, and microservices increased system complexity.
- Notable tracing milestones cited: Google’s Dapper (2010), Twitter’s Zipkin (2012), Honeycomb founded (2015), OpenTracing adopted (2016), Jaeger introduced (2016), Datadog APM launched (2017), and an O’Reilly book on distributed observability published (2018).
- Observability as a named discipline was popularized in the software community by Twitter and later advocated by figures like Charity Majors and Peter Bourgon.
- By the early 2020s many teams had extensive instrumentation, dashboards, SLOs, runbooks, and incident processes—but operational pain remained.
- Common present-day problems include slow instrumentation, stale dashboards, noisy or context-free alerts, and onerous on-call duties.
- The author has recently left a previous role and decided to start a new company focused on observability (mentioned in a prior post).
- The piece argues the primary shortfall is not data volume or tooling but the industry’s limited ability to interpret telemetry and turn it into reliable outcomes.
- The author predicts AI will lower the cost of writing software, leading to a large increase in deployed applications and operational complexity.
What to watch next
- Whether new observability tools shift emphasis from generating telemetry to helping engineers interpret signals and derive actionable insights.
- How the volume of software and telemetry grows as AI reduces the cost of producing code and features.
- Progress and product direction of the author’s new observability company: not confirmed in the source.
Quick glossary
- Observability: An engineering discipline and set of practices focused on producing and using telemetry to understand system behavior and diagnose failures.
- Distributed tracing: A technique that tracks requests across services to expose latency and causal relationships in distributed systems.
- Telemetry: Operational signals from systems—such as logs, metrics, and traces—used to monitor and troubleshoot software.
- SLO (Service Level Objective): A target level of service reliability or performance used to guide monitoring, alerting, and operational priorities.
Reader FAQ
Why did observability emerge?
It arose as a response to the complexity of cloud-native, distributed systems and the limits of traditional logging and metrics in finding root causes.
Are current observability tools failing?
The author argues tools and processes have not solved the core problem: teams produce much telemetry but still struggle to interpret it and resolve incidents efficiently.
Will AI change observability?
The piece asserts AI will substantially increase software production and complexity, making effective observability more important—details on specific changes are not provided.
Is the author launching a new company?
The author previously mentioned deciding to start a company focused on observability; further details are not confirmed in the source.
Observability's Past, Present, and Future 05 Jan, 2026 In my last post, Round Two, I wrote about my career, my passion for dev tools, and my decision to start a…
Sources
- Observability's past, present, and future
- The Evolution of Observability – From Monitoring to …
- The Evolution of Observability
- Failure modeling in observability | by Michal Bojko
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