Is AI the answer to technical debt?

This week's must-know news for engineering leaders

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This week’s bytes:

  • A new way to tackle technical debt

  • The FAANG hunt for low performers

  • Uber’s big move

Worth reading

4-minute read by Loraine Lawson

Legacy codebases are collapsing under 75,000-class systems and microservice misuse. CodeLogic maps chaos using AI-generated knowledge graphs, layered models, and impact analyses. It tracks invisible dependencies, auto-writes JIRA tickets, and even slaps devs on the wrist. The future? AI-led code correction.

3-minute read by Chantal Kapani

Microsoft gives underperformers five days to choose: 16 weeks’ severance or a brutal PIP—with a 2-year rehire ban. Following Meta and Amazon, Microsoft is purging low performers in a bid for ruthless efficiency. Severance or PIP, no transfers, and no coming back soon. Leaders claim it’s about performance culture; critics call it fear-driven attrition. Welcome to tech’s new normal.

8-minute read by Berry Zwets

Experts from Pega, Salesforce, ServiceNow, and Cloudera agree: autonomous AI agents are only as smart as your systems. Without clean data, real-time access, clear objectives, and human-aware design, agents flail. The magic happens when you treat them like coworkers. Give them outcomes, not micromanaged rules.

2-minute read by Craig Risi

Uber migrated its machine learning workloads to Ray on Kubernetes, replacing rigid, inefficient infrastructure with elastic resource sharing, GPU-aware scheduling, and automated job scaling. The result is a unified, flexible platform that improves resource utilization, accelerates training, and simplifies life for ML engineers with no more manual provisioning or wasted compute.

2-minute read by Rebecca Szkutak

ServiceNow’s second AI acquisition this year adds data cataloging and governance startup Data.World to its platform, aiming to make enterprise data “AI-ready.” The move follows its $2.85B Moveworks deal and aligns with ServiceNow’s agentic AI strategy: build, buy, or partner to power enterprise-scale automation with clean, governed data.

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