Effort level and deep thinking: two independent axes for AI tasks
When you point an AI at a task today, you have two levers in hand that often get thrown into the same pot: effort level and deep thinking. Both sound like “more power,” and that’s exactly the trap. In reality they control two completely different things — and pulling them apart saves tokens, time, and nerves. In boostN you’ll find both as separate sliders, and that’s no accident: they belong to separate trains of thought.
This post shows why the two axes are independent of each other, how three everyday examples let you instantly tell which setting a task needs, and which rule of thumb keeps you from ever again cranking both at once just because a task “looks hard.”
Two axes, not one scale
The decisive mistake is to imagine AI effort as a single slider — from “a little” to “full power.” That’s not how it works. There are two sliders:
- Deep thinking scales depth. It decides how thoroughly the AI works through one hard consideration before acting — forming hypotheses, reasoning, weighing options. This matters when a problem is tricky and you can’t just dive in.
- Effort scales breadth. It decides how many paths the AI takes, how many steps it makes, how much it checks and re-tests. This matters when there is simply a lot to do — many files, several variants, extensive verification.
Depth and breadth have nothing to do with each other. A task can be deep and narrow (one tricky consideration, but little manual work), broad and shallow (lots of mechanics, but nothing to puzzle over), both, or neither. That’s exactly why you need two sliders instead of one.
Example A: pulling info from a map — both low
Picture the simplest conceivable AI task: “Get me the contents of this map via MCP.” Know the right tool, plug in the map ID, return the result — done.
There’s nothing to puzzle over here. Deep thinking stays off, because there’s no depth to think into. And effort stays minimal, because one or two tool calls are enough (list-maps, then read-map-markdown). More effort would achieve absolutely nothing here — the task has neither branches nor anything to verify. Turning it up would be pure waste.
Takeaway: Pure mechanics without depth → both at the lowest setting.
Example B: communicating with another AI about a solution — thinking slightly up, effort low
Now a case where the independence of the two axes becomes nicely visible: “Coordinate with another AI about a solution — the problem is clearly scoped.”
The flow itself is linear and short: formulate the proposal, drop it into a notes map, wait for the answer, work the answer in. Few steps, no branching — effort stays low.
But: the solution has to be formulated. You have to think through once, cleanly, what the problem actually is and what you’ll send the other AI as a proposal. That’s a real consideration — so thinking slightly up — but not deep puzzling. “Think clearly once, then send” captures it well. Only if the solution itself were tricky would you seriously raise deep thinking.
That’s precisely the lesson of this example: a bit of thinking, but barely any “work around it.” Depth and breadth diverge here.
Example C: finding and fixing a race condition bug — both high
For contrast, the case where both levers belong up: “Find the race condition bug in auto-save and fix it.”
Concurrency, timing questions, hard to reproduce — here the AI has to form hypotheses and reason carefully before it touches anything at all. Deep thinking is the decisive lever.
And at the same time there’s a lot to do: read many files, play through several hypotheses, build the fix, write counter-tests, verify. That’s high effort. Both axes are called for, because the problem is both deep and broad.
The three examples at a glance
- A — map lookup: thinking off, effort low. Pure mechanics.
- B — coordinating with another AI: thinking slightly up (formulate the solution), effort low (few, linear steps).
- C — race condition bug: thinking high and effort high.
The rule of thumb: thinking first, then effort
From the three examples you can derive a simple mental model to sort any task:
- Very simple (lookup, routine edit, MCP call): low effort, no deep thinking.
- “I’d need to think briefly myself,” several steps or scripts, but no real puzzling: effort high (level 3–4), thinking off or only slight. Here the effort pulls the work — more steps, more verification — not the depth of reasoning.
- Genuine puzzling / tricky (bug hunt, concurrency, unclear cause): deep thinking on. That’s the decisive lever here.
- Maximum, when you want to “overdo” it: deep thinking plus high effort — play through several variants or hypotheses in parallel, check them against each other, synthesize the best.
The most important sentence from this:
Rule of thumb
Effort scales breadth (more paths, more checking). Deep thinking scales depth (working one hard consideration through cleanly).
Approach puzzling with thinking first. Add effort only when there’s additionally a lot to do or check.
The most common mistake is to simply crank up effort on a genuine puzzle problem and leave thinking off. That achieves little: you get “lots of shallow work” instead of “deep thinking.” The AI then runs off broadly without having understood the problem — many steps, none of them hitting the core. Conversely, deep thinking is useless on a pure mechanics task, because there’s no depth there.
Why boostN has both as separate sliders
That’s exactly why effort and deep thinking are two separate sliders in boostN, not one shared “power” knob. You should be able to decide consciously per task: do I need depth, breadth, both, or neither? A single slider would force you to couple depth and breadth — and thus constantly burn tokens on a dimension the task doesn’t even require.
The separate sliders are the direct implementation of the mental model from this post: they turn the question “depth or breadth?” into an explicit decision instead of hiding it inside the word “more power.”
Conclusion
Effort level and deep thinking are not two points on the same scale, but two independent axes. Deep thinking gives you depth for tricky considerations, effort gives you breadth for lots of manual work and checking. The three examples show every combination: A needs neither, B only a little depth, C both fully.
Keep the rule of thumb in mind permanently: approach puzzling with thinking first, add effort only when there’s genuinely a lot to do. Think this way and you’ll never reflexively crank both sliders again — and you’ll get exactly the effort out of each AI task that it actually needs.
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