The Evolution of Fourth Generation Languages (4GLs)

The ability to describe what you want and get it without knowing the implementation details started with Fourth Generation Languages (4GLs) like SQL. SQL emerged in the 1970s and was commercialized alongside other 4GLs in the 1980s.

For example, SELECT * FROM users WHERE age > 25 expresses what you want rather than requiring you to write loops and conditionals to iterate through records.

Of course, 4GLs still required specific syntax and were limited to specific domains. GenAI, by contrast, allows natural language and spans a much broader set of domains.

While GenAI represents a significant change, it is also a continuation of a long arc in computing towards democratization. This evolution excites me, as it promises broader participation in technology.

For instance, individuals without deep technical backgrounds can now ask for code examples or request explanations without needing to know low-level details. GenAI’s usefulness extends beyond coding, with summarization proving valuable in various contexts.

Certainly, there will still be implementation details and mistakes to address. Buggy code, security vulnerabilities, and logic errors will persist. However, I hope this technology will make technology more approachable and empowering for many.

AI-powered summaries can assist small businesses in automating routine tasks and enable humanities students to explore data confidently without requiring deep expertise in R or Python.

While challenges remain, the potential for broader participation is a future worth anticipating.