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Think Bigger to Climb the 4 Levels of AI

2026-05-25 · 23:09 UTC ·Matt Maher ·2 min read

Brief

Matt Maher’s 2026 presentation Think Bigger to Climb the 4 Levels of AI (presentation) maps four operational levels and argues the barrier is how we think about work, not model capability. He contrasts Level 2 workflows with emerging Level 3/4 approaches—Claude Code/Codex CLI, /goals, multi-agent orchestrators—and points to prototype patterns and tutorials to upgrade practice.

Why it matters

Matt Maher (published 2026-05-25) defines four AI "levels" and says most practitioners are at Level 2 (timestamp 06:37): handing a system a list of features that it runs and reports completion — this approach scales work but doesn't free human effort.

Key details

  • Level 3 (timestamp 10:55) is attainable but rare because it requires a "thinking upgrade," not just better models; concrete tooling examples include /goals in Claude Code and Codex CLI, multi-agent teams in Claude Code (self-orienting agents + orchestrator + shared message bus), and spinning up multiple Claude Code instances from a shell as prototypes.
  • Level 4 (timestamp 18:17) promises order-of-magnitude gains; Maher highlights experimental external harnesses (the Ralph Wiggum loop, OpenClawed) and threading/orchestration patterns and directs Level-1 users to his "one folder" tutorial (YouTube id FcWi9j0FiYk) as the path to Level 2.
Source evidence

There's a wall between what we're doing with AI and what AI can actually do for us — and the wall is not the model. It's how we think about work.

Most of us are sitting at Level 2 of working with AI: we hand a system a list of features, it runs them, it tells us when it's done. That feels like a lot, but it scales us — it doesn't free us. The next step up, Level 3, is real, and honestly nobody is there yet. Not because the tools are fully missing (some early ones exist), but because we can barely imagine the higher-level asks in the first place.

This video walks the four levels — what each one actually looks like, where the ceilings are, what to try next, and why each jump is an order-of-magnitude gain instead of a small step.

If you're at Level 1 and using AI as a chat box, start there — go watch my "one folder" video below. That's the cleanest path to Level 2. If you're at Level 2 like most of us, the second half of this video is for you.

Companion video — start here if you're still at Level 1:
The one folder process → https://youtu.be/FcWi9j0FiYk?si=7s-j1N2SF_lpkHQZ

Tools and ideas mentioned
- /goals in Claude Code and Codex CLI — describe the objective and the evidence, let the system work against its own evaluations.
- Multi-agent teams in Claude Code — self-orienting agents with an orchestrator and a shared message bus.
- Spinning up multiple full Claude Code instances from a shell process — early, opaque, but the shape of what Level 3 wants to be.
- Other workflows reaching toward this same space — the Ralph Wiggum loop and OpenClawed — both worth a look as people experiment with external harnesses around the model.

The claim, in one paragraph
We're not behind because AI isn't smart enough. We're behind because we built our minds around the size of work one human could do, and now we're trying to make a much bigger tool fit that same shape. Level 3 isn't a tooling upgrade — it's a thinking upgrade. The tools will catch up. The harder lift is learning to ask for work at a level we've never had to ask for before.

It is difficult. But it's possible.

AI #ClaudeCode #Codex #AgentTeams #Prompting #AIWorkflow #ThinkBigger

00:00 - Intro
06:37 - LEVEL 2
10:55 - LEVEL 3
13:10 - Explorations
13:21 - Goals
14:02 - Agent Teams
15:50 - Threading Systems
18:17 - LEVEL 4
20:31 - Conclusion

Channel: Matt Maher
Published: 2026-05-25
Video URL: https://www.youtube.com/watch?v=KBmwP20jgqQ