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A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none... A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none of us has a clean answer to: How much should you let an engineer spend on AI? One SVP at a company of similar size and stage is in calibration mode and capping engineers at $200 per month. Hit the cap, you can self-bump by $100. Hit that, you need your manager. I told the thread our number. $5,000.

A major new study from Harvard Medical School and Beth Israel Deaconess Medical Center has found that a large language model (LLM) outperformed physicians across a wide range of clinical reasoning tasks, including making emergency-room triage decisions from messy, real-world patient data. The findings, published April 30 in Science, represent one of the largest comparisons yet between AI and physicians on clinical tasks.

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.

There’s a new question teams are asking. How can we prevent AI agents from deleting production. When Cursor deleted PocketOS’s entire production database in nine seconds, the agent wasn’t malfunctioning. It had full technical capability, but it was inferring operational authority from static code rather than live environment state. That gap between capability and context is the root cause. This article breaks down exactly how that happens, and what runtime visibility does to stop it.

AI promise and AI capabilities often diverge, with developers often reporting much faster code production, but not enough change in how incidents are handled. When the rate of change is faster than ever, but the rate of recovery from incidents isn’t moving, developers wind up stuck in firefighting mode. And, when these systems fail, it’s costly. According to PagerDuty’s State of AI-First Operations, over a third of surveyed companies report losing $500K per hour of downtime.

Edwin AI extends ServiceNow Now Assist with real-time incident intelligence, acting as a context broker between observability data and ServiceNow incidents. Responders get the context they need inside the IT operations workflow they already use. Edwin AI now: The Edwin AI Agent for ServiceNow brings real-time incident intelligence into Now Assist and Workspace, giving ITOps teams root cause, impact, and recommended next steps directly inside the ServiceNow incident record.