The more I use AI for research, the less I want a linear chat thread

I’ve been noticing a weird pattern in my own AI workflow: For simple tasks, chat is perfect. Ask a question, get an answer, move on. But for serious research or creative work, the chat format starts to feel like the wrong shape. Most of my real AI workflows are not linear. They branch. A typical research task looks more like this: - collect raw sources - ask one model to summarize them - ask another model to challenge the conclusion - pull out patterns - turn those patterns into a content plan - generate drafts - revise the positioning - create visual or video ideas - come back later and continue from the same context A single vertical chat thread gets messy very quickly. I either lose the important intermediate steps, or I end up copying things into a Google Doc, Notion page, screenshots, browser tabs, and three different AI chats. At that point the bottleneck is no longer “which model is smartest.” The bottleneck is continuity. I’ve been testing Flowith for this reason, and the part that clicked for me is not just “multi-model access.” A lot of tools have that now. The more interesting idea is treating AI work as a persistent canvas instead of a disposable chat thread. For example, I was looking into Reddit discussions around AI agent use cases: what people actually care about, what they distrust, and what kinds of automation they might pay for. If I asked a normal chatbot, I would usually get a generic list like: - sales automation - customer support - content creation - research automation Useful, but shallow. The better workflow was: 1. collect real examples and discussions 2. group them by pain point 3. separate “looks impressive” from “people would actually pay for this” 4. ask another model to critique the assumptions 5. turn the output into a content / product positioning map Flowith worked well here because I could keep the sources, model outputs, branches, and final drafts visible in one place. I could use one model for broad research, another for critique, another for rewriting, and keep the reasoning chain instead of burying it inside a chat history. The same pattern also applies to creative work. If you’re building something like a music concept, a content campaign, or a knowledge base around a trend, the workflow is not just “generate me an idea.” It’s more like: - collect references - extract patterns - build a mini knowledge base - branch into different creative directions - generate text / image / video assets - compare versions - continue later without rebuilding the whole context That is where canvas-based AI tools start to make more sense to me. Not because they magically make the model better. They make the work less disposable. My current take: If your AI usage is mostly one-off prompts, a normal chat app is probably enough. But if your work regularly turns into 10 tabs, 3 AI chats, a notes doc, and a bunch of half-lost context, the interface becomes part of the problem. Curious if others feel this too. Are you still comfortable doing serious AI work in a linear chat thread, or have you started moving toward canvas / workspace / multi-model setups?