Over the past 18 months, I've been collaborating with AI to build technical systems and conduct analytical work far outside my formal training. No CS degree, no background in the domains I'm working in, no institutional affiliation.

The work is rigorous. Someone with serious credentials has engaged and asked substantive questions. The systems function as designed. But I can't point to the traditional markers that would establish legitimacy—degrees, publications, years of experience in the field.

This isn't about whether AI "did the work." I made every decision, evaluated every output, iterated through hundreds of refinements. The AI was a tool that compressed what would have taken years of formal education into months of intensive, directed learning and execution.

Here's what interests me: We're entering a period where traditional signals of competence—credentials, institutional validation, experience markers—no longer reliably predict capability. Someone can now build sophisticated systems, conduct rigorous analysis, and produce novel insights without any of the credentials that historically signaled those abilities. The gap between "can do" and "should be trusted to do" is widening rapidly.

The old gatekeeping mechanisms are breaking down faster than new ones are forming. When credentials stop being reliable indicators of competence, what replaces them? How do we collectively establish legitimacy for knowledge and capability?

This isn't just theoretical—it's happening right now, at scale. Every day, more people are building things and doing work they have no formal qualification to do. And some of that work is genuinely good.

What frameworks should we use to evaluate competence when the traditional signals are becoming obsolete? How do we establish new language around expertise when terms like "expert," "rigorous," and "qualified" have been so diluted they've lost discriminatory power?