A number of people are talking about implications of AI...
This article strongly resonates and has clear angles for your perspective
Quick Take
This directly connects to Brian's world as an engineer using AI tools daily while building products. The education angle maps perfectly to how engineering teams need to adapt to AI-assisted development - when to rely on AI, when to go manual, and how to evaluate AI-generated code.
Relevant Domains
Blog Angles
"Why Code Reviews Are the New In-Class Exams"
Just like educators moving to monitored work, engineering teams need synchronous code review sessions to evaluate real developer skills vs AI assistance.
Specific example from his fintech team where they discovered a critical logic flaw that would have been caught in live review but slipped through async AI-assisted PR.
"Teaching My Junior Devs to Use AI Without Becoming Useless"
The same principles apply to mentoring developers - they need to know when to lean on AI and when to solve problems from first principles.
Story about onboarding a new dev who could ship features fast with AI but couldn't debug when things broke.
"AI-Assisted Development: The Skills That Still Matter"
Like students needing to verify AI homework, developers need core competencies to validate AI-generated solutions.
Debugging a webhook integration where ChatGPT's solution worked in testing but failed in production due to edge cases only experience would catch.
Key Quotes
AI use in homework is impossible to reliably detect
Move most grading to monitored, in-class work
Teach students to use AI well and verify/solve by hand when needed