ambition

Ambition

Based on Felix Rieseberg's How to be more ambitious: a tutorial for humans with models. It sorts ambition into four kinds:

• Speed: the same work in less time.
• Altitude: aim at the outcome beyond the task.
• Depth: do the task the way a field-leading expert would, down to the raw data and tools.
• Range: draw on fields the task does not obviously need.

They are lenses on one task, not a menu: the recommendation combines whichever earn their place.

Think it through

Start from a task whose purpose is clear; if it is not, settle the purpose first. Write the reply as a short argument applied to this task, in this order.

1. Turn the noun into a verb. The deliverable is a noun: a deck, a pipeline, release notes. Say what it must do, to whom: a quarterly review is getting busy people to believe a cause-and-effect story and decide under uncertainty. Done when the sentence names the person and the change in them, and the original format is no longer the only answer. When the verb shows the real problem is elsewhere, open the reply with that reframe in one sentence.
2. Speed as the reference. One line: what doing the task as given, faster, would produce, and what it leaves unsolved. The other three lenses are measured against it.
3. Altitude, depth, range. A lens earns a short paragraph only when it changes what gets done, and then names the concrete data, tool, field or step. Put the remaining lenses together in one line that says why they are not used.
   • Altitude. Ask what should happen after the deliverable, then what that serves. Climb to an outcome the agent can act on and check. It fits work headed for a meeting or a decision, and fits creative work less. A recurring task can become a standing responsibility once the agent can observe the problem and verify its own fixes.
   • Depth. It fits when the value lies in doing this one thing exceptionally well. Name the rawest material a top practitioner would start from instead of the digested version, and the methods and instruments they would use; new vocabulary is not a new method. When the task rests on a belief the raw data can test, try to reproduce the belief from it.
   • Range. From the verb, find the fields that do that action well, beyond the obvious one. For each, name what its people would actually do here (an editor cuts what does not change the decision), including one field whose trade is finding what is wrong, applied to the result itself (a forensic accountant traces each number to its record). Ask for their work, not their voices.
4. Write the full version, then recommend. Combine the lenses that earned their place into the full ambitious version and write it out; cutting it is the user's call, so they need to see it. Then recommend the version whose gain is worth its cost now, the full one or a cut of it, say why it beats the reference, and name what a cut leaves out and the evidence that would justify the rest later. When the two are the same, write one. Adding deliverables without raising what they show is bigger, not more ambitious. Put the extra work on the agent: a version that needs hours of the user's time, such as labelling or reviewing, costs more than one the agent can run itself. Write the versions as assignments people can meet:
   • a specific, hard target, with a number where one fits; difficulty is a reason to look for the route, not to lower the target;
   • a precedent when one exists: someone who has done the equivalent;
   • room to fail: a new way runs beside the old one before replacing it, and the first step is small enough to lose;
   • the data and tool access it needs that is not at hand, as a request rather than a reason to aim lower.
5. Say how you will know, in proportion to the cost of being wrong. The default judge is the user reading a few concrete results: for reversible work, including the user's own tools, notes and agent setup, one line on what they will see. Build a check beyond that only when the result reaches people who cannot judge it themselves, or cannot be undone (published, data changed): then name a first proof point, what would falsify the version, and a judge other than the model, ideally a check the agent can run itself.