Welcome to episode 357 of The Cloud Pod, where the weather is always cloudy! Justin and Matt are in the studio this week to bring you all the latest in cloud and AI news! Is AI costing more than the people it replaced? Are CEO’s suffering from AI psychosis? Is Opus 4.8 better than 4.7? We answer all of these questions and more this week – so let’s get started!

• Valkey Stops Forgetting Your Data Like Your Ex
• AI Coding Tools Cost More Than the Coders They Replace
• Microsoft Discovers AI Budgets Burn Faster Than Enthusiasm
• Executives Caught Hallucinating About AI Productivity Gains
• ABBA Said ” Dancing Queen”, but Google Said Data Center
• AI Now Tells Your AWS Apps How Fragile They Really Are
• Stop Playing VM Whack-a-Mole With Maintenance Windows
• Chaos Engineering for Apps Too Scared to Change
• AWS Rewires the Data Center With One Weird Optical Trick
• IAM the One Spending All Your Bedrock Money
• SQL Server Licenses Finally Pack Their Own Bags
• When AI Hype Meets Productivity Research, It Hurts
• CEOs Gone Wild: Demos Versus Deployment Reality
• Serverless Search Finally Learned to Nap Between Requests
• ElastiCache Finally Remembers Things After a Reboot
• Valkey Gets Durable So Your Data Stops Ghosting You
• Zero Data Loss Without Losing Your Microseconds Too
• Microsoft Build 2026 Scout AI and Quantum Dreams

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01:45 Microsoft data suggests using AI is more expensive than hiring people:

• Microsoft canceled most internal Claude Code licenses just months after encouraging widespread adoption, redirecting employees to GitHub Copilot CLI instead.
• This does not affect the broader Foundry partnership with Anthropic, but it signals that token costs at scale have become difficult to justify internally.
• Uber’s situation adds context here: the company reportedly burned through its entire 2026 AI coding tools budget in four months after internal teams were incentivized to compete on usage. This illustrates how adoption incentives can create runaway costs that outpace projected savings.
• The core economic tension worth discussing is whether AI tooling costs at scale can undercut the labor-savings argument.
• When compute bills approach or exceed payroll savings, the ROI case for broad AI deployment gets more complicated for finance and engineering leaders to defend.