How people use ChatGPT: NBER study reveals usage patterns

[ChatGPT usage breakdown 🤔] a.k.a. How people actually use Chat Assistants in real-life The National Bureau of Economic Research published some research classifying usage patterns within a representative sample of ChatGPT conversations. My hunch is the data pattern is imperfect, still, it's useful to reflect on how people use those apps (accross personal and professional use cases) ⤵️

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Samuel Onyeji

Senior Software Developer | Specializing in React ,React Native & RESTful APIs • Proven success in high-traffic environments • I create smooth buttery user interfaces that drive business value

3d

Computer Programming being 4.2% is surprising.

Fascinating insights on ChatGPT usage patterns! At Jaiinfoway (www.jaiinfoway.com), our AI services like natural language processing and predictive analytics help businesses optimize chat assistant performance for personal and professional use cases. Excited to see how these trends evolve and drive innovation!

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Jean-Jacques Borie

AI Consultant & PH.D. Candidate - Leadership in Software Engineering, Data, AI, Cybersecurity & Technology Solutions

3d

Intéressant merci pur le partage.

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Abhilash Jaiswal

AI Ready Future CTO | Patent holder | Transforming Organizations Through GenAI, AIAgents, Copilots & Intelligent Automation | Cloud Architecture & Digital Innovation Leader| Expert Vibe-coder

2d

Thanks for sharing this. For me, it’s valuable to get a fresh perspective on my key focus areas for AI development—even when I’m familiar with most of them, seeing things from a 30k ft height will help me reassess and refine my strategy!

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Subhash Nair - MBA (Finance,Strategy), B.Eng.(Computer), Lean 6 σ

Data Science | Life Science | Supply Chain | Manufacturing | AI/ML | B2B B2C | Payment Platforms | Business Process Improvement

3d

Public-facing LLMs will likely become the “idea machines” best suited for creativity, conversation and exploration. In these cases, non-deterministic outputs aren’t a flaw, they’re a feature (classic transformer architecture). I think brainstorming, tutoring, roleplay, or writing feel more valuable when the answers aren’t identical every time. On the other side, smaller or specialized language models (SLMs), often connected to deterministic computational engines, will dominate in areas where precision matters i.e. programming, math, compliance, data analysis, or industry-specific use cases. Here, correctness can’t be optional. The future will probably be hybrid - LLMs handling natural language fluency, with deterministic systems running in the background to ensure accuracy where it’s non-negotiable.

Andrew Przybilla, CISSP

AI/ML Solutions Engineer | M.S. Cybersecurity

3d

This is very insightful. I’m curious from your perspective in your position, are there any surprises that jump out to you here?

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Funny how coding shows up so low on the chart... we in tech obsess over it in the newest models, but it barely matters to the mass crowd 😂

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