YouTube. Claude. Zerodha. Just a few months into the year, and the number of outages already seems to have outnumbered long weekends. Rob Newell , Senior Vice President and Managing Director, APJ at New Relic , spoke to The Hans India about why even exceptional engineers are stuck in a reactive loop, the steep price businesses pay every time they react instead of prevent, and how intelligent observability can change that equation.
YouTube's outage has become an unlikely mirror for businesses everywhere - one that doesn't just reflect their vulnerabilities but forces them to look inward. YouTube runs on one of the most sophisticated engineering foundations ever built. The teams behind it aren't cutting corners. They are, by any reasonable measure, among the best in the business.
Yet the outage still happened.
When a platform like YouTube goes dark, its impact is felt strongly by the creative economy, hitting every creator, advertiser, and business that lives and thrives within its ecosystem. New Relic's Observability Forecast Report shows that outages can cost organisations between $1-3 million per hour in lost revenue, not to mention long-term damage to customer trust and brand reputation. While a platform of YouTube's scale can recover from the impact, the same may not be true for other businesses.
What the mirror reveals
Recent outages reveal a simple theory: engineering teams today are operating at the edge of what human visibility alone can handle. Modern systems span microservices, cloud environments, APIs, and third-party integrations. Traditional monitoring tools were never designed for this level of complexity. Problems often simmered quietly until they cascaded and became outages.
This is why businesses need intelligent observability with agentic AI capabilities to rethink how systems are monitored, understood, and acted upon. Instead of simply collecting data, they can assist teams by automatically surfacing signals, correlating system behaviour, and narrowing down the problem space, helping engineers pinpoint root causes before the cascade begins.
In the YouTube scenario, intelligent observability could have identified the anomaly within the vast pool of system data, pinpointed what caused it, and recommended or even automatically initiated remedial measures before it became a customer-facing outage. This kind of internal remediation changes the story for many enterprises that are competing hard to be seen as reliable digital service providers by their customers. And with just this small shift, it may well be the difference between a blip and a headline.