A translation engine between natural language and SQL means that everyone can communicate with a SQL database now. That's huge. Soon also DB people will use it to get the response for complex questions and queries. It's just more natural and way faster.
With technology like this, there is little reason to even know SQL anymore as the average developer. Just like today, the average developer doesn't know how databases work because the cloud takes care of it. We're moving up on the abstraction ladder and tomorrow all you need to know for SQL is to ask the right question.
This somewhat already exists in the form of semantic layers, which if done well can handle many of the queries necessary in the course of business without business users needing to know any SQL. There will still be cases where you need direct SQL, and AI tools can help the development process there at a minimum.
Yes, there will be cases where you need SQL knowledge. There will also always be cases where knowing exactly how a database works under the hood is necessary. I think this is somewhat of a weak argument because you can always construct an example of how something may be helpful for a small group of people.
The relevant question is: How many people who work with databases need to have a lot of experience with SQL? My argument is that while the answer today is "most," the answer in a couple of years might be "very few."
Sure, AI will assist more and more in the cases where people must write SQL or manage a database, perhaps to the point you suggest.
But my point was actually that more people think they need to know SQL today than is actually the case. Excluding people that manage databases or cases that go direct to SQL for things like complex ETL, your average business user / marketer / etc should not be asked to write SQL or have to ask someone else to write SQL for them. Use a semantic layer instead with a UI on top and it's almost as easy as natural language.
Here is a example of one I made below, but there are others out there with more support. At my company, and the last few I've worked for, we use this approach for ~all day to day querying and a chunk of backend SQL replacement.
I have similar thoughts on this - so few members of a team actually know what the underlying data model looks like. Gets even harder when you start trying to query across your database(s) + external sources like analytics/event systems. Natural language lets the whole team peel away at the black box of data and helps build common ground on which to improve products. I already mentioned it in another part of this thread so I won't spam my project but would love to get your feedback on my natural language to SQL tool if you're interested.
Lots of average developers these days do not (or just barely) know SQL, and it shows when the ORM generates some nonsense and nobody can figure out why the app is suddenly two orders of magnitude slower.
