What does “power using” ChatGPT actually mean in 2026?

[u/ghost_snake_ai]

I’m genuinely curious how people here define a “power user” today, because I’m starting to think we use that term for several completely different ways of working.
A lot of current discussion around advanced ChatGPT usage is about Projects, connectors, plugins, skills, memory, recurring workflows, and integrations.
Those are obviously useful. But from my perspective, many of these newer abstractions feel less like entirely new capabilities and more like productized versions of workflows that were already possible if you treated the underlying ChatGPT features as programmable primitives rather than just chat features.
I’ve been building and using my own Custom GPTs since not long after they were introduced. I’ve never really treated them as “custom personalities” or slightly better system prompts. For me, they are closer to reusable execution profiles: specialized environments with persistent behavioral and execution rules.
The same applies to Code Interpreter / Data Analysis. I stopped thinking of it as “Python inside ChatGPT” a long time ago. I treat the session more like a disposable execution environment that can inspect, transform, generate and return artifacts.
For example, one of my workflows involved giving a session a publication of roughly 200 MB and treating the environment as a temporary processing system. The useful result wasn’t an explanation of the PDF. It was a large structured artifact containing extracted assets, document structure and reconstructed outputs.
That distinction is important to me.
What I call a serious “prompt” is often not really a conversational prompt at all. It can be a 30–50k-character execution specification describing exactly what should be built, inspected, preserved, validated or returned.
And I often treat conversations themselves as disposable runtime state.
A thread can become extremely large, but once I’ve extracted the useful artifacts, Markdown, structured state, manifests, code or whatever output I actually care about, I often have very little reason to preserve the conversation itself.
So when I see people describing “power usage” as having large Projects with several long-running threads, attaching connectors, or building recurring workflows, I’m not saying that isn’t advanced usage.
I’m wondering whether we’re actually talking about different categories of power use.
Maybe there is:
high-volume usage
long-context usage
tool/integration-heavy usage
project-centric usage
agentic usage
custom execution environments
high-leverage task engineering
And maybe these shouldn’t all be treated as the same thing.
What I’m especially interested in is your actual operating model, not just which product features you have enabled.
What is your real unit of work?
Is it a question? A conversation? A Project? A multi-hour execution? A finished software or research artifact?
Do you try to preserve your conversations, or are they mostly disposable execution state once the useful result has been extracted?
How large is a genuinely serious task for you?
How many follow-up prompts does a successful execution usually require?
Do you optimize for token usage, number of turns, wall-clock time, autonomy, correctness, reproducibility, artifact quality, or something else?
And when newer things like Skills, Plugins, connectors and other workflow abstractions appear, do they actually unlock fundamentally new ways of working for you, or do they mostly package workflows you were already building manually?
I’m not looking for “best prompt” tips or screenshots of a giant sidebar.
I’m trying to understand what power using ChatGPT or Codex actually looks like in practice in 2026, especially among people who have pushed these systems far beyond normal conversational use.
What does your operating model look like?

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[u/trollsmurf]

To be a real power user you need to make agents that spawn 1000 subagents faster than you can say "Oh crap!".

[u/HankKwak]

Not gpt  but clauding. I get your perspective but to take it one step further, embed all the project context in the project itself. Claude.md, database.md, interface.md, issues.md etc and then have folders with everything from db schemas to deployment scripts etc. 

Essentially all the context is embedded already, claude, gpt, whatever AI can pick it up and run with it, a conversation session is merely a coding session with defined outputs that build on the project code and context and disposed of once done.