I’ve been deep in conversational AI for a while and honestly the public discourse around it drives me a little crazy. Everyone’s still arguing about whether the voice sounds robotic. That’s basically solved. The genuinely hard problems are a layer deeper and barely anyone’s talking about them. Old conversational systems were just pipelines. You speak, it transcribes, it matches an intent, it reads a script, a human saves the day if things get weird. Simple. Brittle. Boring. What’s being built now is different in a way nobody frames clearly enough: the conversation itself becomes the thing that controls the workflow. Not just responds to it. Actually drives it. That shift sounds subtle. It isn’t. The latency problem alone is nastier than it looks. Natural conversation collapses above roughly 300-500ms delay. That’s not a lot of runway when you’re running a model capable of real reasoning. So you end up doing streaming inference, handling partial utterances before the person even finishes talking, managing interruptions gracefully. Most demos skip all of this. They take turns politely like a Victorian dinner party. Real conversation is nothing like that. Then there’s context drift, which I’d argue is the most underrated unsolved problem in the space. Keeping coherent state across a genuinely messy human conversation, where someone changes their mind mid-sentence, contradicts themselves, goes on a tangent, circles back three turns later, is hard. Benchmarks don’t capture this because benchmarks don’t ramble. Real users do nothing but. And the moment these systems stop talking and start doing things, scheduling, updating records, triggering transactions, a hallucination stops being a text quality problem and becomes a system failure. Suddenly you need validation layers, rollback logic, constraint-based execution. Boring stuff nobody puts in a demo. Also the difference between a toy and something you can actually trust. The reason enterprises actually care about this isn’t because voice interfaces are cool. It’s because conversation is where most operational workflows begin. If AI can drive that conversation to a structured outcome and execute the next step autonomously, you’re not automating a chatbot. You’re compressing an entire operational queue into a dialogue loop. Very different thing. The part that genuinely gives me pause is governance. Once these systems can hold context, reason adaptively, execute actions, and self-optimize from outcomes, they’re not interfaces anymore. They’re autonomous operators. And we have basically no framework for that. Financial systems have audit trails and rate limits. Conversational AI deployments at scale largely have vibes. None of this is AGI. It’s narrow autonomy aimed exactly at the surface where most business execution starts. That combination historically moves fast. So genuinely curious: what do you think the real binding constraint is right now? Latency, reasoning, safety architecture, or just enterprise integration being a nightmare as usual? And do you think governance keeps pace with deployment, or are we waiting for a few spectacular failures before anyone gets serious? submitted by /u/Accomplished_Mix2318
Originally posted by u/Accomplished_Mix2318 on r/ArtificialInteligence