Locus Founder runs entire businesses autonomously. Storefront, product sourcing, copy, ongoing ad management across Google Facebook and Instagram. Continuous operation without a human in the loop. We got into YCombinator earlier this year.
Here's what eight months in production actually taught us.
Capability is no longer the bottleneck. The AI can write copy that converts, generate storefronts that look legitimate, make reasonable targeting decisions. Those questions are mostly answered in ways that would have seemed ambitious two years ago.
The bottleneck now is judgment.
Specifically the gap between performing well inside expected conditions and recognizing when you're outside them. The most dangerous failure mode we've encountered isn't the AI doing something obviously wrong. It's the AI doing something confidently wrong in a way that looks right until you examine the downstream consequences. Locally optimal decisions that are globally wrong. Copy that converts short term and damages brand trust long term. The system doesn't know what it doesn't know.
That's the problem we haven't solved and we think it's the most interesting unsolved problem in autonomous AI systems right now.
Build layer is solid. Operations layer works well in normal conditions. Edge cases are still the edge cases.
Opening 100 free beta spots this week. Free to use you keep everything you make.
Beta form: https://forms.gle/nW7CGN1PNBHgqrBb8
Is the confident wrong call problem a fundamental limitation of current architectures or an engineering problem that gets solved with better uncertainty quantification. Genuine question, want to hear what people who think about this seriously actually think
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