Most people, when asked if they’re using AI, say yes. They mean: they’ve got ChatGPT open in a browser tab. They type a question, read the answer, maybe copy some text. Sometimes it’s useful. Sometimes it’s confidently wrong. They close the tab and get on with their day.
That’s not wrong. That’s Level 1.
What most people don’t know is that there are two more levels. Each one unlocks a meaningfully different order of magnitude of value. This isn’t about hype or future predictions. It’s a map. Knowing which level you’re operating at, and what moving up looks like, is more useful than any prompt tip you’ll find on X.
Level 1: Chat
The default mode. You open a chat interface, ask something, get a response. ChatGPT, Claude, Copilot, Gemini. The tool doesn’t much matter. Every conversation starts cold.
And it’s genuinely powerful. But “just chatting” undersells what’s actually happening.
The thing chat gives you that Google never could is follow-up. You ask a question, get an answer, and immediately ask a better question based on what you just learned. You can change direction mid-conversation, push back, ask for a different angle, say “that’s not quite what I meant” and have the model adjust. You can have an entire technical discussion in the time it used to take to read three Stack Overflow threads and two blog posts from 2017.
When I was building a Linux gaming PC, a chat bot was extremely useful in helping me research a build based on my budget. I told it I wanted to run Bazzite, and it made sure I got the right CPU and compatible motherboard. I experimented with different GPUs, and even got it to incorporate some existing components. In the end I built a powerful mid range PC that I was really happy with. It went together perfectly and has never had an issue. The people on r/BuildaPC hate it, but I don’t care. Chat helped me configure the BIOS and split my game saves between users. Using a search engine for this would have been like using a dictionary to write a novel.
The constraint isn’t the quality of the conversations. It’s that you haven’t invested in context. The AI is working from general knowledge and whatever you happen to include in the chat. It doesn’t know your specific codebase, your team’s conventions, your architectural decisions, or the constraints that make the “obvious answer” wrong for your situation. It’s brilliant and generic.
For one-off problems, that’s fine. Brilliant and generic gets you most of the way there. The ceiling appears when the work is ongoing: every session starts with the same re-explanation, answers are correct in principle but miss the specifics of your situation, the tool is useful but never truly tailored.
Level 2: Workspaces (or Harnesses, as the engineering community is starting to call them)
This is the level most people skip entirely, and it’s where the real productivity shift starts.
A workspace is a structured environment you build around the AI so it can do useful work without you re-explaining everything every time. The engineering community is starting to call these “harnesses”, a better name, because it captures the intent: you’re not just chatting, you’re building a rig that constrains, guides, and focuses the AI on your specific situation.
In practice, a harness has a few components:
- Persistent instructions: who you are, what you’re doing, how you like to work
- Reference documents: your architecture decisions, coding conventions, product context
- Custom commands: slash commands that trigger multi-step workflows you’d otherwise explain from scratch every session
- Tool integrations: MCP servers, external APIs, live data sources
- Claude Projects, custom GPTs, and Claude Code with a well-built CLAUDE.md and a commands/ folder are all implementations of this idea. You build it once, and it pays interest from there.
I’ve built workspaces/harnesses for most of my productive work.
Generating my son’s grade 6 homework, questions that are interesting for him personally and push the boundaries for a grade 6 student.
I have a learning workspace with MCPs hooked directly up to Microsoft Learn. It knows what I’ve studied, which exam I’m currently working on, and it constantly checks the material for updates. It generates practice questions and exercises based on the material, and ensures the answers are backed
I even use a workspace for building this blog. 😮 (it’s true). It researches topics, builds outlines, generates social media posts, and even image generation prompts for the blog posts. I am passionate about making sure every word is my own, but this workspace gives me a strong launch point and provides incredibly useful editorial, planning, and marketing assistance. These are all things I am terrible at!
The gap between Level 1 and Level 2 isn’t technical ability. You don’t need to write code. Custom GPTs and Claude Projects are configured in plain language. The gap is knowing these features exist.
Most AI content teaches you to write better prompts (still Level 1 thinking) and then skips straight to “build your own agent.” The middle layer gets almost no coverage. Which is a shame, because for most people it’s the highest-return move available right now.
Level 2 is entirely about setup, not technical skill.
Level 3: Engineering
This is where developers have an advantage most people don’t realise they have.
Level 3 is AI as infrastructure: multi-agent systems, MCP integrations, RAG pipelines, automated workflows that run without a human in the loop. Not AI-assisted work. AI-powered systems. The defining difference is ownership of the next step. In a chat, you decide what to do with the output. In an engineered system, the system decides.
The mental model shift at Level 3 is significant. You stop thinking about prompts and start thinking about systems. Inputs, outputs, failure modes, verification, observability. Which is exactly how experienced software developers already think about every external dependency they touch.
A well-designed service wraps a third-party API behind an interface, handles failure gracefully, and validates output before passing it downstream. The AI model in a Level 3 system is just another external dependency. You don’t trust it unconditionally. You design a harness that can catch when it’s wrong.
That is not a new skill for a developer. That’s Tuesday.
The teams winning at Level 3 are the ones who’ve stopped treating AI output as a deliverable and started treating it as an unverified input to a system that catches errors. If that sentence sounds obvious to you, that’s the developer advantage. It isn’t obvious to most people.
This Is a Stack, Not a Ladder
These aren’t rungs you climb and leave behind. They’re layers, and you can, and probably should, operate at all three simultaneously.
I use casual chat constantly for quick lookups, throwaway drafts, and checking my mental model of an API against reality. I run a structured workspace for every content and planning session. And I build and maintain agentic systems for anything complex enough to warrant the engineering investment.
The value is knowing what level you’re using for what problem, and whether it’s actually the right fit.
Most people are at Level 1 for everything, not because the other levels are hard, but because nobody told them they exist.
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