Expected stale exposure
For each checkpoint chunk, the model asks when the first drift begins and counts every remaining interval as exposed.
sum p(1-p)^(j-1) x remaining_intervals
Checkpoint cadence for AI-assisted creation
Stay close enough to shape the work.
Predict when immediate feedback beats a long autonomous batch. Then change the assumptions until the answer flips.
CREATOR
IN THE LOOP
A transparent planning model, not an empirical benchmark. You supply the risk and review assumptions.
Live comparison
Batch vs. feedback
Runtime clean
Which cadence costs less?
Choose a prediction to begin.
0 revisions
30-minute batch--expected cost min
5-minute loop--expected cost min
The model adds expected stale-work exposure to the review overhead for each cadence.
What the calculation means
Speed is not just output. It is the distance between intent and correction.
A long batch creates fewer interruptions, but a misunderstanding can stay hidden across more work. A short loop spends more time reviewing, but it limits how much stale work accumulates after the first drift. The winner changes when uncertainty or review cost changes.
Worked case
At 20% drift risk and one-minute reviews, the short loop spends 8 minutes reviewing but limits expected stale work to 8 minutes. Total: 16.0.
Counterexample
At 2% risk and two-minute reviews, repeated inspection dominates. The 30-minute batch wins by 10.5 expected minutes.
Three useful distinctions
Open the tradeoff.
Latency
How long wrong-direction work can remain unseen before a creator can intervene.
Overhead
The real time spent inspecting, explaining, and redirecting at every checkpoint.
Agency
The ability to shape the emerging work while choices are still cheap to change.
Apply the model elsewhere
A refactor has unstable requirements and each wrong architecture choice contaminates later work. What changes?
Choose the rule that transfers beyond this worked example.
Complete both scenarios, then make the transfer.
Transfer pending
Why feedback cadence changes expected correction cost
Read the explanation
The worked case assumes forty minutes of work, a five minute risk interval, twenty percent first-drift probability per interval, and one minute to review each checkpoint. The source splits batching into thirty and ten minute chunks, with two reviews. The interactive strategy has eight five minute chunks and eight reviews. Expected stale work is eighteen point zero four two eight eight minutes for batching and eight for interactive. Adding review overhead gives totals twenty point zero four two eight eight and sixteen. Both bars use ten pixels per expected minute. These are planning assumptions, not measured productivity. The counterexample changes two assumptions together: drift probability falls to two percent and each checkpoint review costs two minutes. Batching then has about two point three two nine three eight stale minutes plus four review minutes, totaling six point three two nine three eight. Interactive has zero point eight stale minutes plus sixteen review minutes, totaling sixteen point eight. The same ten pixel scale now makes the batching bar shorter. Neither preset shows that one strategy is universally best: the source compares drift exposure and review overhead under supplied assumptions. The model sums the probability that the first drift happens at each interval, multiplied by the remaining stale intervals. It rounds chunk interval counts upward, so a short final chunk can still count a whole risk interval. The timeline is a separate rounded visual summary, not the exact expected-cost calculation. A prediction must be selected before comparison. Wrong predictions increment revisions; correctly completing both presets unlocks transfer assessment, and the adaptive answer enables export. Export preserves the complete prediction history and the source limitations. This is a learning trace, not a live AI performance benchmark.