not a benchmark. not a research paper. a production account of what autonomous AI decision making actually looks like when the consequences are real. PayWithLocus is the company. Locus Founder is the product. YC backed this year. VC backed. the system runs entire businesses autonomously. ads across Google Facebook and Instagram, lead generation through Apollo, cold email, full CRM. Locus Checkout powers the transaction layer end to end. real money. real consequences. nine months of continuous operation. here is what surprised us. capability was not the hard problem the capabilities were mostly there faster than we expected. copy that converts, reasonable targeting decisions, storefronts that look legitimate. two years ago these felt ambitious. in production they are baseline. the hard problem was not can the AI do the task. it was does the AI know when it should not. the confidence problem in familiar conditions the system performs well and knows it. confidence is calibrated. in genuinely novel conditions the system performs badly and does not know it. executes confidently on wrong decisions that look correct until you examine downstream consequences. the confidence does not decrease to reflect the novelty. it pattern matches to the nearest familiar situation and proceeds with the same certainty it would have in actually familiar territory. not a capability failure. a metacognitive failure. the system does not know what it does not know. what we tried confidence thresholds with escalation. problem is the threshold is applied to a miscalibrated signal. distribution shift detection at input level. better. misses cases where inputs look familiar but the situation is actually novel. outcome monitoring with anomaly detection. catches problems after they occur. does not prevent confident wrong execution before it happens. no complete solution. the metacognitive gap remains. free 24 hour trial with enough credits to launch a first website. forms.gle/nW7CGN1PNBHgqrBb8 is confidence calibration in genuinely novel conditions solvable with better training and more data or does it require something architecturally different. genuinely want to hear from people who think about this from first principles. submitted by /u/IAmDreTheKid
Originally posted by u/IAmDreTheKid on r/ArtificialInteligence