How to Be Freakishly Decisive
- Estimated fee savingsweighed against possible repair costs
- Choose for nowMake it easy to change course
- Watch for a reason to changeCostly errors or excess repair
I do not need a precise answer to every question before choosing an action. I need to know whether the uncertainty left could change what I should do.
That is a different standard from certainty. If several plausible explanations lead to the same choice, distinguishing between them may add knowledge without improving the decision. But if one plausible mistake would be expensive, even a low probability can justify more investigation.
Separate the price from the cost
I evaluated two AI models, Luna 6 and Sol 6.1, both at high reasoning effort, for interpreting simple commands. The test used 20 authored examples, rather than a sample of everyday use or a separate final test set. The models’ interpretations matched exactly on 19.
At that observed workload, estimated token fees were roughly $0.13 for Luna versus $2.01 for Sol per 1,000 comparable commands. These are estimates from 20 examples, not billing results or general model prices.
Estimate from 20 authored examples
Not billing results or general model prices. Bar lengths use unrounded estimates; displayed fees are rounded.
The ratio looks large. The absolute savings per command are small. A little extra correction work could consume them. I would count attention, waiting, and repair as real costs, not treat them as free because they do not appear on a token bill.
In five selected, blinded meaning checks, I judged Luna correct in all five and Sol correct in four. Those counts are not population accuracy, and they do not establish a winner.
The disagreement was revealing. For “I lost the plot; remind me what is going on,” Sol proposed asking for clarification. Luna proposed reading the selected session briefing. I judged the briefing interpretation right.
“I lost the plot; remind me what is going on.”
Ask for clarification.
Read the selected session briefing.
The briefing interpretation was right.
Sol is not ground truth. My judgments are fallible references too.
If I treated Sol as an unquestionable teacher, I would mark Luna down for the answer I considered correct. Sol can be wrong too. My own judgments are also fallible references.
Research needs to earn its keep
I would stop investigating when the remaining uncertainty is unlikely to change the best action enough to justify the cost of learning more.
The comparison is not “How uncertain am I?” It is “How much better might my decision become?” A rare but costly error can make another check worthwhile. A large uncertainty about something that cannot change the action may not.
- Benefit exceeds cost
- Keep researching
- Benefit does not exceed cost
- Choose now
Choose, then keep learning
My selected direction was to use Luna as the default, with sampled Sol shadow comparisons and reconsideration after real use. That was a provisional policy, not a completed rollout or a demonstrated efficiency gain.
In that policy, Sol would see sampled copies without controlling the response. A disagreement would be an investigation signal. Actual corrections and repair effort would matter more than whether two models happened to match.
Sol’s verdict is evidence, not a veto.
A concrete reversal trigger would be one confirmed, costly, hard-to-undo interpretation error: pause the default and investigate. For smaller errors, I would compare excess repair costs against the fee advantage, after paying for the shadow checks. Reversibility lowers the cost of learning through use; it does not make mistakes harmless.
Before acting, I would write down three answers:
Those answers turn “I am not sure” into a reversible choice, a priced downside, and a reason to reconsider.
Optional: the statistical assumptions, uncertainty, and sources
What the statistical model can and cannot say
A weak Beta(1,1) prior on agreement becomes Beta(20,2) after 19 matches in 20, under an exchangeable-trial assumption. This describes agreement, not correctness.
Calibration separates cases where the models match from cases where they differ. A Beta(1,1) prior on shared correctness within agreements becomes Beta(5,1) after four shared-correct labels. For disagreements, a Dirichlet prior gives each of four possible correctness combinations a weight of 0.5; the one Luna-only correct label updates that category to 1.5.
Conditional on those labels and assumptions, Luna’s modeled correctness on this authored set has a broad 95% credible interval of about 49–98%. My labels are fallible; this calculation does not estimate their error rate. It does not measure actual task completion or establish accuracy on everyday traffic.
The unrounded fee estimates
Per-command token-fee estimates at the observed workload, from the 20 authored examples. These are not billing results or general model prices.
- Luna 6 high
$0.00012619775USD- Sol 6.1 high
$0.00201204USD
Sources for the decision logic
The formal frame is Bayesian decision analysis with a value-of-information stopping rule. Remaining uncertainty matters through its chance of changing the choice and the consequences of doing so. The sampled-copy arrangement is champion–challenger shadow evaluation.
Information’s value is the expected improvement from learning, relative to choosing with what you know now. Research needs to earn back its cost through that improvement.