The Leading Themes in Generative AI Conversations
I asked ChatGPT, Perplexity, Claude, Gemini, Copilot, and Mistral the same question: "What are your top 5 topics?" (Let’s call this group “the GenAIs” — because "AI Platypuses" was already taken.)
Turns out, these GenAIs share a common obsession with five key themes:
Technology and Innovation – Because robots need to keep up with other robots.
Health and Wellness – Even AIs care about mental bytes and physical bits.
Science and General Knowledge – Like trivia night champions with a PhD in everything.
Personal Development and Productivity – Helping you organize your life, one motivational quote at a time.
Culture and Entertainment – Because even AIs enjoy binge-watching and debating books and movies.
How Do They Pick These Topics?
The GenAIs rely on user interactions (think friendly data-stalking), vast training datasets, trend analysis, and user feedback. The result? Answers sharper than your Monday morning coffee with tobasco in it.
Diving Deeper: The Differences
The top 5 topics the GenAI’s provide answers to are:
ChatGPT prioritizes Business and Startups (#2), Learning and Creativity (#3), and Science and Research (#4).
Perplexity ranks Technology and Innovation first and Science and Environment fourth.
Claude doesn’t list personal favorites and emphasizes a broad, thoughtful engagement with any topic.
Gemini focuses on helping with tasks, conversations, and image analysis across various topics.
Copilot highlights Science and Technology (#1), Art and Literature (#2), and History and Culture (#3).
Mistral emphasizes General Knowledge (#1), followed by Technology and Innovation and Health and Wellness.
Takeaway for GenAI Users
As a 'Lessons' reader, you know I write my first draft with a fountain pen, input it into Microsoft Word or Google Doc’s when I share it before publishing, and then use Perplexity to identify sources or ChatGPT, Claude and Mistral to refine my blog posts — translating them from “DOUGLISH” into polished American English.
Based on these results, it pays to be selective with the GenAI you use.
Conclusion
GenAIs will expand their repertoire by leveraging diverse datasets, including multimodal, niche, and real-time sources, while improving their adaptability through advanced training techniques like transfer learning, self-supervised learning, and reinforcement learning from human feedback. Enhanced memory and contextual understanding will allow them to handle long-range context and nuanced discussions. Fine-tuning for domain-specific expertise, along with hybrid models, will ensure depth in specialized fields. Agentic AI systems will autonomously explore new information and continuous learning, while collaboration with human experts will further refine and expand their knowledge base.