The pressure of juggling research, a new corporate role, and personal projects is real, but the anxiety you are feeling is often self-inflicted by the very tools designed to help. When I read about your workflow, I see a common trap: the assumption that more AI subscriptions equal more progress. You are asking which model to use for which part of the flow, but the real question is which parts of the flow shouldn't involve a model at all. The urge to optimize every brainstorming session and every line of math through a chat window is exactly what leads to the jumbled, anxious feeling you described. It's not about finding the perfect tool; it's about building a system that respects your cognitive load.
Your instinct to treat a GPT subscription as mandatory is worth scrutinizing. For ideation and formulation, a capable model is genuinely useful, but you do not need the most expensive tier to get value from it. The marginal gain from the latest Pro model over a standard version is rarely in the brainstorming itself; it's in the speed of response, which matters less when you are thinking deeply. What you actually need is a clear separation between divergent thinking, like exploring research problem statements, and convergent tasks, like debugging code or maintaining a codebase. For the former, use a model with strong conversational memory. For the latter, a tool that integrates with your editor and can read your repository context is far more valuable than any chat interface. The fact that you are considering multiple subscriptions suggests you are optimizing for capability, but you should be optimizing for workflow fit.
Here is the honest take: you are trying to accelerate a process that is inherently slow. Research publication, especially for an undergrad, is a marathon. Your corporate work demands reliability over novelty. Your hobby projects should be fun, not another source of stress. If you set up a stack today, I would tell you to start with one solid assistant for research and writing, a second tool for code completion and repository analysis, and then stop. Do not add a third subscription until you have hit a specific, measurable bottleneck that you cannot solve with a prompt. The Meta’s Muse AI Agent Gains Ground in Conversational Performance is a reminder that the frontier of AI is moving toward agents that can handle entire tasks, but for your current situation, you do not need an agent. You need a better prompt. The Verify Your AI's Understanding: A Simple Check for Tax Season piece highlights a practical truth: the value of AI is in verification, not generation. Use it to check your math, not to design your experiments.
The most practical advice I can give you is to time-box your AI usage. Give yourself a hard limit for brainstorming, say 30 minutes, and then move to execution. The anxiety you are feeling is not from a lack of tools; it is from the constant switching between contexts. Your brain needs to be in research mode, work mode, and hobby mode, and AI should not blur those lines. When you sit down to write code, do not ask the model to brainstorm architecture. Ask it to implement a specific function. When you are working on your paper, do not ask it to generate related work. Ask it to critique your proof. The Explore the Future: When AI Designs Its Own Hardware shows where this is heading, but you are living in the present. A specific takeaway you can quote: "Use AI to verify your work, not to think for you." That is the separation that will reduce your strain.