A few things happened. Relational databases enabled more new development, larger databases, interoperability, all of which needed programmers.

With more flexibility in the database companies could collect, store, and use more data. And that data had to get into the DBMS somehow: more code.

Setting up and managing databases required DBAs, a role often filled by programmers in smaller companies, and still filled by programmers today. And only larger companies had business analysts. In smaller companies programmers or maybe technically-proficient managers did that work.

Anyone who had devoted their career solely to building bespoke database systems had to pivot or walk in the late ‘80s, but very few programmers only did that — it was part of the larger application development. If you were good enough to write robust database code pre-Oracle you had plenty of options.

In the ‘80s when RDBMSs started to take over I worked in enterprise logistics. Oracle created jobs at the places I worked, and no programmers had to leave because we got a better tool.

I’ve worked in large and small organizations for 40+ years. I’ve never worked with a manager or analyst who could write SQL more complex than “SELECT * FROM orders WHERE total > 10000.” I’m sure they exist, but not in career-threatening numbers.

AI impact will be the same - simplify here and there, but expand the scope and total amount of work because we will be doing so many more things.

In my department I recruited DBAs as recent as last month and this is a permanent position filled by a team of several people that do just DBA work. I saw developers ("programmers") try to do this work in some small companies or in areas where the databases were small (hundreds of MB, a few GB), but I did not see that when the databases exceed 100 GB or when there are dozens of production SQL servers that need to run 24x7. Solutions are implemented based on needs.

Big companies have DBAs. Small companies don’t, the programmers do it or they outsource to someone like me.

Maybe ChatGPT will get good at designing relational database schemas, who knows?

The industry grew because the desire for data analysis grew, which is because the technology's ability to meet the desire grew. This can repeat itself a couple more times.

Cotton gins and the Jevons effect.

Give what even pessimistic AI alignment people are saying, I think you're correct that there are a few more repeats possible before AGI.

(Whether the pessimists are correct that all of them will happen before the 30s, I cannot say).

You're considering that every company that needs SQL today would hire enough developers to essentially write most of it from scratch. While some might, most companies that use SQL would not exist, because the cost of developing their product would be prohibitive.

Maybe. Before Oracle (mid-80s) every company did write their own database code.

I think a lot of smaller companies would struggle if that was still a requirement, but if relational/SQL had not come along we’d have something else like it.

That's exactly my point, though. In the mid 80s there were a lot fewer companies producing software. Nowadays we have many more.

Indeed SQL was not the only local maxima we could have gone for, but the point is that having an easy to use database with a powerful query language did not reduce the number of jobs, but instead increased it. Instead of a few companies hiring a lot of developers, we have a lot of companies hiring a few. The latter will usually mean more jobs.

If hardware was cheaper, but writing software required an army of developers to do it, the costs would still be too great. If you read carefully, my point isn't that SQL was the cause of the tech boom, but rather that SQL and other technologies that make developers more productive didn't really take jobs away, because the market for tech would be smaller if you needed too many developers to do anything.

Imagine if every little web startup writing a custom CRUD for their business needs needed to write a new bespoke database. It simply would not be feasible or they'd need some crazy funding.

>Before Oracle (mid-80s) every company did write their own database code.

Not really. There were a ton of ISVs competing with Oracle and pretty much every mainframe and minicomputer maker also had their own database products, many of them non-SQL.

Oracle was the first commercial RDBMS (1979), with an early version of SQL. At that time every mainframe and minicomputer company offered one or more “databases,” often little more than file managers with a library of utilities. ISAM was a popular model but that describes a lot of data management systems, not a product or standard.

All commercial databases pre-Oracle were non-SQL. Watching that history get rediscovered and pushed as an improvement by people mostly too young to know better — so-called NoSQL databases — brings to mind Alan Kay’s quip about the software industry constantly reinventing the flat tire.

The hash-based Pick OS/database came out before Oracle but only ran on a few computers from Microdata and later Pr1me and Honeywell. Pick-based systems remained popular into the early 2000s in some sectors. A friend of mine still works on one, for a state government agency.

You could construct a database management system from the ISAM-based libraries for COBOL or Fortran, but I wouldn’t call those database management systems comparable to Oracle. Mostly they didn’t have a query language per se — you had to write code to get anything in or out. And they ran as part of the application process, not as a separate server dedicated to running the database engine.

I was thinking Db2 might have been a little earlier but you're right. And it's fair that the earlier databases definitely lacked elements of what we'd consider a database management system today even if they handled a lot of the low-level database heavy lifting for companies.

Or they just did less. Productivity gains make people expect more, not just make what they expect now easier.