AI Tools Landscape¶
The AI tools landscape for researchers encompasses the growing ecosystem of chatbots, coding assistants, IDE integrations, and specialized tools that support academic work.
Context & Background¶
The AI tools ecosystem evolves rapidly, but several categories have stabilized:
- General-purpose chatbots: ChatGPT, Claude, Gemini — for brainstorming, writing, analysis
- Coding assistants: Claude Code, GitHub Copilot, Cursor — for writing and debugging code
- IDE integrations: VS Code extensions, JetBrains plugins — embedded AI in development environments
- Specialized research tools: NotebookLM (document analysis), Refine.ink (referee reports), Elicit (literature review)
- Infrastructure: MCP servers, API access, local model runners (Ollama)
Key Perspectives¶
Choosing the right tool depends on the task. For economics research, the key trade-offs are between capability, cost, privacy, and integration with existing workflows. Claude and ChatGPT compete on general tasks, while specialized tools offer deeper functionality in narrow domains.
Practical Implications¶
- Don't over-commit to one tool: The landscape changes rapidly; maintain flexibility
- Match tools to tasks: Use specialized tools for specialized tasks (NotebookLM for document review, Claude Code for coding)
- Consider the full stack: Think about how tools integrate with each other (e.g., MCP connecting Claude Code to databases)
- Budget realistically: Professional AI use typically costs $20-200/month depending on usage intensity
- Evaluate privacy implications: Each tool has different data handling policies