I think it's important to reinforce that SQL isn't an COBOL like attempt at building a querying language out of natural expressions (which you could see if you squint really hard). Instead SQL is a refinement of various querying languages (hence being the standard one) that co-evolved with relational algebra. If you have a chance to learn more within an academic environment courses on relational algebra and the abstract theory of set operations can be invaluable to building a basis for more naturally understanding the intent and tools available in SQL.
> They can describe what they want in natural language ("show me all active users who registerd this year"), but translating that into correct, optimized sql requires at least familiarity, and sometimes expertise
They can describe what they want in natural language only if they have sufficient familiarity and expertise.
If you think that being fluent in a language means you can ask clear and coherent questions in that language, I'd like to invite you to a couple of MS Teams calls this week.
SQL is a formal language, not a natural one. It's precise, rigid, and requires a specialized understanding of schema, joins, and logic. text-to-sql systems don't exist because people are too lazy to type; they exist because most people can't fluently express analytical intent in sql syntax. They can describe what they want in natural language ("show me all active users who registerd this year"), but translating that into correct, optimized sql requires at least familiarity, and sometimes expertise
So the governance challenges discussed in the article aren't about "oh SQL is too hard to type"...they're about trust, validation, and control when you introduce an AI intermediary that converts natural lang into a query that might affect sensitive data