It’s time to reconsider your inputs
The workspace pings a collab request. He accepts. Their team lead pops in at the edge of his peripheral vision, her silhouette sharpening as the call syncs their work views.
- Hey, quick check-in. You’re formally past the rollout window.
- Nah. I’m just casually late.
He leans back from the console, arms crossed, waiting to be told what he already suspects.
- Your queue is stacked with twelve pre-generated product drafts. If they sit there, the dashboard reads like we’re shipping and then stalling, and ops will see our draft-to-approval rate drop on the weekly update tomorrow. What’s the holdup?
- Well, I keep starting and it comes out wrong.
- You realise our clients can tell the difference between yesterday and tomorrow? They see the data-cutoff stamp and the model version.
- I know, I know. I’m sorry. I just feel like I can’t tell which one is right because I never got clear on what right would feel like. I’ve kept tuning texture syncs, swapping sources and nudging meta feeds, but it’s not working.
He trails off, waiting for her to fill the silence. She doesn’t.
- Sounds like a clarity problem. You have progress, but you don’t trust it enough to ship. It’s time to reconsider your inputs.
- What exactly are you recommending?
- You can’t aim for what you can’t imagine.
He almost argues. Then he swipes through the drafts and scraps and decides not to.
- Stop gathering and start rendering. You’re drowning in raw material because you’re mistaking stockpiling for processing, like running a supply chain with no factory.
He freezes, squinting at her. That line sounded… pre-written.
- Wait, are you subprompting me right now?
He glances for the little assisted feed badge. Anything flagged gets logged. Nothing.
- Because that sounded like motivational botspeak.
- I’m not. You’re not lazy. You’re stuck in the same viewpoint, and you can’t see it from inside. Just adding more raw material won’t do anything. Turn material into constraints. Turn constraints into options you can actually compare by risk, effort, or impact.
He opens his mouth, but closes it as she continues.
- You’ve been feeding yourself the same angles for so long that you can only work with variations of what you already know. The drafts feel wrong because you can’t see what right would look like. Not yet.
One long pause and a deep sigh later, he finally responds.
- So what shall I do now?
His team lead relaxes a little.
- There it is. Now we can work with this. Pick one of those twelve drafts.
He stretches, accepting the challenge. His fingers move across the console and stop on one of the drafts.
- Okay. Now go to your saved-for-later pile of ideas you keep hoarding and never touch.
The view flips to his idea vault, where notes cluster by links and similarity. He selects a few filters and picks a heavily interconnected one.
- Got it.
- Now select Map Logic. It forces the draft to relate to the note.
- That will burn my remaining priority compute, and it feels rather random.
- Good. Random gets you unstuck. You’re not looking for logic. You’re looking for a new perspective. It’s worth it.
Once the system starts processing his input, three variant overlays snap onto the draft, like transparent skins you can toggle with a swipe to check the fit score and trace the source.
- Okay. I selected the Antarctic logistics proposal, and it’s translating staged acclimatisation into no sudden exposure.
- And?
- Look here. Draft seven, here. Wrong checkpoints, no thresholds. Now I can see the gap.
- So now you can revise.
- Oh no. Now I can choose. Thanks for digging me out of this hole.
- That’s what I’m here for. Get me a sign-off by the end of the day, and we’re even. See you around.
She waves and fades out, leaving him alone with his focus rising and new ideas already materialising.
Memories to build from this future:
1. Think back to that decision you finally made after realising you’d been researching for months without getting any closer to clarity.
What was the moment you recognised that more information wasn’t helping?
Which random constraint did you introduce that finally forced a choice?
How did the decision quality compare to what you feared during all that preparation?
What signals do you now watch for to catch yourself stockpiling instead of processing?
2. Try to recall when your organisation introduced compute budgets for idea exploration and processing.
How did visible resource costs change decisions about what directions to pursue?
What wasteful habits disappeared when exploration had a price?
How did teams learn to distinguish between valuable research and comfort-seeking information hoarding?
What unexpected quality improvements emerged from forcing earlier commitment to directions?
3. Think back to that project retrospective where you traced a shipping delay to the team’s inability to define what “right” would look like before generating options.
At what point did you realise the problem wasn’t the AI’s output quality but missing evaluation criteria?
How did you structure clear success criteria for AI-assisted work?
What questions do you now ask to test whether a team can actually recognise a good result when they see it?
How did making implicit quality standards explicit change your draft-to-approval metrics?
Each memory from the future you build sharpens your strategic instincts for the decisions ahead.
Build enough memories.
Shape better futures.
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