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GANESH P
Co-founder building ... • 1d
We’re entering a phase where AI systems don’t just assist decisions — they execute them. Payments. Subscriptions. Vendor payouts. Automated expenses. The intelligence powering these systems is improving rapidly. The controls around them are not. Today, many teams experimenting with AI-driven automation rely on best-effort checks: hard-coded limits, manual approvals, or internal scripts built under time pressure. These approaches work — until they don’t. The moment an AI system touches real money, the failure modes change. A hallucination, loop, or misinterpreted instruction can result in real financial loss. More importantly, it creates trust and accountability issues for the teams deploying these systems. What’s missing is a deterministic control layer between AI intent and payment execution. A layer that enforces: Hard, predictable rules Explicit approval flows Clear state transitions Fail-closed behavior Complete auditability This isn’t about slowing down innovation. It’s about making autonomous financial systems safe enough to deploy with confidence. I’m currently exploring and building infrastructure focused on this exact problem: enforcing control before AI-initiated payments are executed. If you’re experimenting with AI systems that can trigger real financial actions — or thinking about it — I’d love to learn how you’re approaching safety, limits, and oversight. Not selling. Just learning.
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