Is AI the 4GL We’ve Been Waiting For? A Chat Between John and Lila
John: Hey everyone, welcome back to the blog! Today, we’re diving into a fascinating question: Is AI the fourth-generation language (4GL) we’ve all been waiting for? I’ve been pondering this after reading a recent InfoWorld article, and it’s stirring up some great discussions in the tech world. Lila, as our resident curious beginner, what sparked your interest in this?
Lila: Hi John! I’ve heard about programming languages evolving over generations, but AI seems like it’s changing everything. Can you break down what a 4GL even is, and why people are comparing it to AI?
John: Absolutely, Lila. Let’s start with the basics. Fourth-generation languages, or 4GLs, emerged in the 1970s and 1980s as a way to make programming more accessible. Unlike earlier languages that required detailed, step-by-step code, 4GLs let users focus on what they want to achieve rather than how to do it—like telling a computer “find all records where the name is Smith” instead of writing loops and conditions from scratch. Sources like DBpedia and academic definitions describe them as high-level tools for database queries, report generation, and business apps, aiming for efficiency and less coding hassle.
John: If you’re comparing automation tools that embody this kind of simplicity, our deep-dive on Make.com covers features, pricing, and use cases in plain English—worth a look to see how it streamlines workflows without deep coding: Make.com (formerly Integromat) — Features, Pricing, Reviews, Use Cases.
What Makes 4GL Special?
Lila: Okay, that makes sense. So, 4GLs are about abstraction—hiding the nitty-gritty details. How does AI fit into this picture? Is it really the next step?
John: Spot on, Lila. The InfoWorld piece from a couple of weeks ago argues that AI, especially generative AI like large language models (LLMs), is echoing the promise of 4GLs but on steroids. Back in the day, 4GLs like those used in SAP or early database tools allowed non-programmers to build apps quickly. AI takes this further by letting us describe tasks in natural language—think prompting ChatGPT to “write a script that analyzes sales data”—and it generates the code. It’s not just querying databases; it’s creating entire programs or automating complex processes.
Lila: That sounds powerful. Are there real examples of this in action today?
John: Definitely. According to trends from Artificial Intelligence News in August 2025, enterprises are adopting AI for dependable generative tasks, like scaling data analysis without manual coding. It’s similar to how 4GLs revolutionized business software in the ’80s.
Current Trends and Discussions in 2025
Lila: With it being September 2025, what’s buzzing in the AI world right now? Any fresh takes on this AI-as-4GL idea?
John: Great question. From recent web trends, there’s a lot of chatter about AI’s evolution. A Frontiers journal article from June 2025 outlines AI generations, placing us in AI 2.0 (Agentic AI) moving toward AI 3.0, where systems act more autonomously—like advanced 4GLs that think for themselves. On X (formerly Twitter), verified accounts from tech influencers are discussing how LLMs are redefining coding, with posts highlighting tools that turn English descriptions into functional apps.
John: Analytics Insight’s December 2024 preview (still relevant in 2025 discussions) notes large language models ready to redefine AI, making development faster and more intuitive. And in a Learn AI Tools piece from two weeks ago, September 2025 trends emphasize breakthroughs in AI for everyday applications, much like 4GLs democratized programming.
Lila: So, it’s not just theory—people are using this now. What are some key features that make AI feel like a 4GL?
John: Let’s list them out for clarity:
- Natural Language Processing: You describe what you want in plain English, and AI handles the “how.”
- Code Generation: Tools like GitHub Copilot or similar turn prompts into working code, echoing 4GL’s non-procedural style.
- Automation and Integration: AI agents can connect apps and data sources automatically, similar to 4GL database tools.
- Scalability: As per 2025 trends from Complete AI Training, synthetic data and code copilots are making AI reliable for enterprise use.
- Accessibility: Beginners can build without deep expertise, fulfilling the 4GL dream.
Lila: That’s a helpful list! But is AI fully there yet, or are there gaps?
Challenges and Realities
John: Fair point—it’s not perfect. The InfoWorld article points out that while AI has more buzz than 4GLs ever did, it faces issues like reliability. Hallucinations in LLMs can lead to incorrect code, unlike the more predictable 4GLs. Plus, a Medium post from AnalytixLabs in February 2025 warns about ethical concerns, data privacy, and the need for human oversight in generative AI.
Lila: Yeah, I’ve read about AI making mistakes. How do we address that?
John: Trends suggest focusing on dependable AI, as per Artificial Intelligence News. Enterprises are piloting with evaluations, human reviews, and cost tracking. It’s evolving, much like how 4GLs matured over time.
Future Potential: Where AI Could Take Us
Lila: Looking ahead, could AI truly become the 4GL we’ve waited for? What might that look like in 2026 or beyond?
John: Exciting stuff! Complete AI Training’s piece from two days ago predicts trends like music generation, science surrogates, and video/3D pipelines—all building on AI’s 4GL-like abstraction. FTI Consulting’s January 2025 analysis talks about the “fourth AI inflection,” reshaping business with AI-driven transformation.
John: If creating documents or slides feels overwhelming, this step-by-step guide to Gamma shows how you can generate presentations, documents, and even websites in just minutes: Gamma — Create Presentations, Documents & Websites in Minutes.
Lila: That sounds practical. Any policy angles? I saw something about AI in education.
John: Yes, the Data Center Frontier report from three weeks ago covers the Ai4 2025 conference, grappling with rapid changes in AI policy and education. It’s all about balancing innovation with regulations.
FAQs: Wrapping Up Common Questions
Lila: Before we close, let’s tackle some FAQs. What’s the main difference between 3GL and 4GL?
John: 3GLs like C or Java are procedural—you specify steps. 4GLs are declarative—you say what you want, per definitions from My Learning Mania and Akhtar Bari’s blog.
Lila: And is AI officially a 4GL?
John: Not formally, but the parallels are strong, as discussed in en-academic and SAP Community posts.
John: If you’re inspired to try automation, check out that Make.com guide again—it’s a great starting point for no-code adventures.
John’s Reflection: Reflecting on this, AI feels like the evolution 4GLs promised, making tech creation as natural as conversation. But it’s crucial we build it responsibly to avoid pitfalls. What a time to be in tech!
Lila’s Takeaway: Wow, I get it now—AI could make programming accessible to everyone. Thanks, John; I’m excited to experiment!
This article was created based on publicly available, verified sources. References:
- Is AI the 4GL we’ve been waiting for? | InfoWorld
- Generative AI trends 2025: LLMs, data scaling & enterprise adoption
- About: Fourth-generation programming language
- Frontiers | AI generations: from AI 1.0 to AI 4.0
- 5 Generative AI Trends to Watch in 2026
- Tech Trends to Watch: Large Language Models Ready to Redefine AI in 2025