Operations | Monitoring | ITSM | DevOps | Cloud

More and more often, infotainment systems are being developed and delivered like software, yet often they are still tested and validated using hardware-centric processes. This is far from ideal: access to devices is limited, environments are difficult to reproduce, and iteration slows down as soon as multiple teams need to work in parallel. These challenges become even more visible as cockpit systems move toward wide displays and high resolutions.

Over the last year, I’ve talked to dozens of SRE teams about AI. The excitement is real, but conversations hit a wall when we get to production reality. How does an agent manage complex context without losing the plot? How does it avoid hallucinating relationships between signals? Who owns the orchestration logic that ties it all together? We realized the bottleneck wasn’t model intelligence. It was the lack of a reliable logic layer between the data and the model.

Alerts are only useful when you can quickly find and act on the right signal. That's why, over the past two years, we rebuilt Grafana Alerting’s UI to make it more reliable and efficient, especially at scale. The result: a faster, paginated alert rules page that handles tens of thousands of rules, with a powerful filter dropdown and saved searches so you can quickly get back to the views you care about most.

Jobs in the technological sector have been shrinking. The Chief Economist at Glassdoor states that in the first half of 2025, tech employment shrank by an average of 1,583 jobs each month. Looking at tech employment cumulatively, it has declined by 1.9% since peaking in 2022. Despite this downturn, opportunities still exist for skilled professionals who can adapt to evolving industry demands. Companies continue to invest in high-impact positions that drive innovation, efficiency, and growth.

AI is creating an overwhelming surge of digital artifacts and software components. The key to success is learning how to ride, secure, govern, and manage that wave – rather than being overwhelmed by it. This weekend, I asked my team to watch Chasing Mavericks. Jay Moriarity (not J-Frog, but stay with me) was one of the most driven and determined surfers imaginable. His courage and spirit were extraordinary. But those virtues were shaped and refined by his mentor, Frosty Hesson.

Alert fatigue rarely begins as a single mistake. It grows as systems scale, teams grow, and “just in case” monitoring becomes the default. A few extra alerts, another threshold, and soon the on-call channel becomes overwhelmed. Engineers get interrupted for noise or stop trusting pages; either way, real signals get missed. Reliability drops, and productivity quietly declines. Most teams respond tactically: tune thresholds, change notifications, suppress noise.

Your team deployed the conversational AI. Implemented sentiment analysis. Built real-time dashboards that show exactly when customers get frustrated. You can see Customer is about to churn over a billing error. You know their satisfaction score dropped from 8 to 3. And yet nothing happens. The billing error persists. The customer leaves anyway. Your NPS hasn't moved in 18 months.‍ This isn't a technology problem. It's an execution problem. And it's costing you customers.