Service Design in the Age of AI: Why Information Flow Is the New Interface
As AI becomes embedded in every touchpoint, service designers have a new frontier to navigate. Continue reading on Medium »
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Closed model providers change behavior between API versions with no real changelog. Building anything on top of them is a gamble.
This is one of the reasons I keep gravitating back to local models even when the closed API ones are technically stronger. I had a production pipeline running on a major closed API for about four months. Stable, tested, working. Then one day the outputs started drifting. Not breaking errors, just subtle behavioral changes. Format slightly different, refusals on things it used to handle fine, confidence on certain task types quietly degraded. No changelog. No notification. Support ticket response was essentially "models are updated periodically to improve quality." There is no way to pin to a specific checkpoint. You signed up for a service that reserves the right to change what the service does at any time. The thing that gets me is how normalized this is. If a database provider silently c
Why AI Security Governance is Failing in 2026
Why AI Security Governance is Failing in 2026 73% of enterprises have AI in production without proper security controls Let me be blunt: enterprise AI security is a disaster waiting to happen. After working with AI deployments at scale, I've seen the same mistakes repeated over and over. The Real Problem Everyone's rushing to deploy AI systems, but security is an afterthought. Sound familiar? It's the same pattern we've seen with cloud adoption, DevOps, and every other major technology shift. The difference? AI systems can make decisions that directly impact business operations, customer data, and regulatory compliance. When an AI model gets compromised, the blast radius is massive. What's Actually Happening In my experience building security for large-scale systems, here's what I'm seeing
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