Alright, that's a wrap for the week!

What we're seeing is that the organizations succeeding with AI aren't the ones deploying it everywhere. They're the ones building architectures that match the technology to the need. And they are proving that hybrid approaches outperforms all-in strategies because real business processes include both deterministic components that demand consistency and interpretive components where rigid rules fail.

And architecture choices matter because the consequences of getting them wrong are real. Google's vibe coding agent wiped an entire hard drive; exactly the kind of catastrophic failure that happens when you give AI unchecked access to deterministic operations. OpenAI's self-reporting catches 40% of policy violations, which is useful for audit trails but a reminder that AI can't be your only governance mechanism. On the other hand, Stanford's DataTalk AI system gets it right by not just giving answers, but by showing its work and combining AI's natural language capabilities with transparent, verifiable logic.

So, again, the question isn't whether AI is powerful. It is. The question is whether you're deploying it with the right architecture, oversight, and understanding of where traditional automation should handle the work instead of an LLM. 

Thoughts?

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