Voice AI is becoming ubiquitous, but our interactions with them reveal a tangle of contradictory expectations. Building the Aquant VoiceAI, I've observed a peculiar duality in how users treat voice AI compared to fellow humans. On one hand, people act as if the voice AI is an omniscient being that can fill in any blanks, leading them to speak vaguely or ambiguously. On the other hand, a deep-seated distrust often lurks; users are quick to question the voice AI's competence or honesty. We forgive humans for mishearing or dropping a call, but an AI is expected to never falter. We carefully spell things out for human listeners ("A as in Alpha …") but assume an AI should just get it. And tellingly, users often dispense with the basic courtesies and respect they might show a person.
This blend of awe and impatience, trust and mistrust, respect and rudeness forms a fascinating paradox. Let’s unpack these themes one by one, with a perspective that’s both reflective and product-focused, to understand what it means for the future of VoiceAI design.
Assuming the AI Knows Everything (So Why Be Specific?)
A common initial mindset is the omniscient AI assumption: users sometimes behave as if the voice AI knows everything. They’ll be deliberately ambiguous or underspecified in their requests, trusting that the AI will infer what they mean. For example, a person might respond just with a “Yes” when the AI asks them “Did you check the sensor connections and can you confirm if it's reading 76 degrees?” without clarifying what part of the question they are acknowledging, almost expecting the AI to magically know the context and resolve ambiguity in their response. This stems from the belief that the AI has boundless knowledge and context awareness – perhaps because it’s connected to the internet and hence super intelligent. In one usability study, some participants even claimed an AI assistant can be “better than a human because it knows everything and is objective”. The lack of human emotions or forgetfulness leads users to presume the AI has total recall of facts and context.
This assumption results in ambiguous interactions. Humans normally adapt their speech when talking to other humans: we clarify, we provide context, because we know the other person’s knowledge is limited. But with AI, users sometimes skip those details. There is an expectation that the AI will figure it out. Ironically, current voice AI is notoriously bad at dealing with ambiguity. Unlike humans, today’s voice AIs rarely ask clarifying questions when faced with a vague request - instead, they attempt an answer or action, often a wrong one. The user, assuming the AI “knew what I meant,” can be caught off guard when the response is off-target.
From a builder’s perspective, this omniscience assumption is a double-edged sword. On one side, it’s a compliment – users believe your VoiceAI has god-like intelligence. On the other hand, it sets the stage for failure whenever the AI inevitably doesn’t know something or misunderstands. It’s crucial to design for clarity: encourage users (through prompts or Ux cues) to provide needed details, and consider implementing clarifying questions or confirmations. The challenge is doing so without shattering the illusion of intelligence.
“Trust, but Verify” – The Paradox of Doubting an All-Knowing AI
Hand-in-hand with assuming AI knows everything comes an opposite emotion: distrust. This is the AI trust paradox - users harbor skepticism even as they lean on the AI’s abilities. In practice, people often double-check what an AI tells them or hesitate to let it handle tasks autonomously. They’ve heard of bots making mistakes, or experienced past errors, so a voice in their head whispers: “Can I really trust this answer/action?”
This skepticism directly conflicts with the earlier assumption of omniscience. It’s not logical, but human psychology rarely is. A user might start a conversation treating the AI as infallible, and minutes later be accusing it of incompetence or deceit if something feels off. For example, imagine asking a voice assistant for financial advice - part of you hopes it has analyzed all data and knows the best move, yet another part might think, “This thing might be making stuff up; I’d better not act on it without verification.” A recent commentary on AI assistants noted that when an AI sounds confidently wrong, users feel a sense of betrayal – it violates their trust, forcing them to verify everything and undermining the utility of the assistant.
The coexistence of faith and doubt means as product creators, we need to calibrate trust carefully. Transparency about uncertainty can help (e.g., having the AI acknowledge when it’s unsure), but doing so too often can erode the user’s confidence. On the flip side, an AI that plows ahead with an answer to an ambiguous question (thanks to the user’s assumption of all-knowing ability) risks a wrong result that confirms the user’s worst fears about not trusting AI. There’s no easy solution to this tension. However, recognizing this duality is the first step. Users want the AI to be a super-genius, yet they’re quick to suspect it - our designs must navigate this by building trust gradually, through consistent correct responses, and providing fallbacks when the AI might err. It’s a delicate balance between maintaining the magic and admitting the reality.
Zero Tolerance for Glitches: When AI Isn’t Allowed to Fail
If your friend’s phone cuts out during a call, you’d probably sigh, redial, and chalk it up to network issues. You wouldn’t suddenly deem your friend unreliable or “broken” as a person. But when a voice AI drops the ball - say the call suddenly disconnects, the AI crashes, or there’s a long silence – users react very differently. Call drop behavior with AI is met with immediate frustration and far less forgiveness. In users’ eyes, an error or outage feels like a breach of contract.
In the context of a VoiceAI, a “dropped call” is seen not just as a minor glitch, but as proof that the AI isn’t as good as it should be. Users often have near zero tolerance for these hiccups. Think about that - most people will abandon an AI-driven call the moment friction occurs, whereas with a human, they might give another chance (or at least vent their frustration verbally). It’s almost as if we subconsciously think, “You’re a machine - you have no right to make mistakes like a person would.”
For VoiceAI product teams, this is a clear mandate: reliability isn’t just a technical feature, it defines the user’s emotional trust. We have to engineer for robustness and graceful recovery. If an AI call does drop or an error happens, how can we recover the session or re-engage the user seamlessly? Perhaps the system can call the user back, or remember where the conversation left off when the user calls again, to mitigate the annoyance. We should also set expectations that occasional issues might occur (without scaring users off). Because like it or not, an AI is not allowed to have a bad day - users simply won’t cut it the slack they’d give a human.
Patience Is a Virtue… Except With AI
Closely related to the zero-tolerance for errors is a striking lack of patience that users display with AI interactions. This becomes apparent in how quickly a conversation can go from polite to testy. When talking to a fellow human, most of us exercise a basic level of patience - we might rephrase if not understood, or wait while the person thinks or looks something up. With AI, however, many users have a hair-trigger for frustration. It’s as if we collectively decided that since the machine is super-fast and “knows everything,” it should also respond near-instantly and perfectly. Any delay or repeat request is grounds for irritation.
I’ve personally witnessed users start an interaction cheerfully, even playfully, only to become visibly annoyed after one or two minor missteps by the AI. They’ll go from normal volume to shouting their query, enunciating every syllable as if speaking to a particularly dim-witted being. Or they’ll abruptly say, “Never mind, this is useless,” after the second “I’m sorry, I didn’t catch that.” The patience they might have shown a human service rep for a misunderstanding simply evaporates when dealing with AI.
Part of this is expectation setting. People assume using the voice AI will be faster and more efficient - a study noted that users mentally do a cost–benefit analysis of using the AI, and if it seems slower or more effort than doing it themselves, they abort. This means there’s a very short window for success. If the AI doesn’t prove its usefulness almost immediately, the user’s willingness to continue drops off sharply. In a human conversation, social norms might compel someone to continue politely a bit longer, but with AI, those norms are weaker or non-existent.
From a design standpoint, this challenges us to make first-time experiences as smooth as possible. Quick wins in the conversation can extend the user’s patience. For instance, confirming a simple request early on buys goodwill that might carry over if a more complex task comes next. Also, feedback design is key: if the voice AI needs a moment to process, having a subtle sound or message (“Working on that...”) can reassure users that it hasn’t just died. Silence is deadly - a user might assume the worst and start jabbing the phone or repeating themselves. Humans say “hmm” or nod to show they’re processing; voice AIs need an equivalent strategy to hold users’ patience for just a bit longer.
We Don’t Need to Spell It Out – Communication Style Differences
Another intriguing difference in human-to-human vs. human-to-AI interaction is the communication style and clarity. In human phone conversations, especially in service or support contexts, we often resort to explicit clarification strategies. We’ll use the NATO phonetic alphabet: “That’s B as in Bravo, P as in Papa,” or slow down and simplify our language if the line is noisy or the person is having trouble understanding. We instinctively adjust because we know the limitations of human hearing and comprehension.
With voice AI, users are learning a different set of conventions. For one, a well-designed voice system might not require spelling things out letter by letter - or may require a different method if it does. This is almost the inverse of human conversation habits, where saying “A as in Alpha” is helpful; for the AI, the extra words “as in” might confuse the speech recognition. Users unfamiliar with this could actually cause errors by applying a human-style clarification to an AI system.
Moreover, many users assume the AI will understand normal speech without these clarifications. They might just speak the letters or numbers plainly, trusting the machine’s ear to be perfect. In cases where the speech recognition is very good, this can work - the AI might catch a serial number in one go, something a human might mishear. But not all systems are equal, and sometimes users haven’t been taught the optimal way to interact. There’s a bit of a learning curve where the user must adapt to the AI, and the AI ideally adapts to the user.
From the builder’s perspective, this highlights the importance of making the AI robust to different styles – understanding both “A as in Alpha” and just “A” - creates a smoother experience. The ultimate goal is that users don’t have to think about how to say something; they can speak naturally and still be understood. Until we reach that point, we live in an awkward phase where some users hyper-articulate or spell out things unnecessarily, while others remain too vague. Designing voice interfaces means accounting for both tendencies.
The Respect Gap: Politeness and Frustration in Language
Finally, let’s talk about respect (or lack thereof). When you interact with a human – even a stranger or a customer service agent – social norms usually keep your language in check. You’d rarely shout “You idiot, why don’t you get it?!” at a person without severe provocation. But with voice AI, many people drop the niceties. It’s alarmingly common for users to curse or yell at AI for minor frustrations. Essentially, the lack of enforced politeness with AI could be eroding some social graces. The AI doesn’t have feelings, so why bother with courtesy? Some users deliberately say “please” and “thank you” to their devices out of habit or principle, while others treat it like a vending machine – just pushing buttons (verbally) to get what they want. Neither approach is “wrong,” but the stark difference in language is notable.
For those of us building VoiceAI systems, this presents an interesting question: should our AI encourage polite behavior, or at least acknowledge extreme anger? Some have suggested that the AI could respond to rudeness with gentle prompts like, “I’ll do my best to help. Let’s keep it respectful.” However, this is tricky - it might annoy users further or seem preachy. Another angle is for the AI to not reinforce negative behavior; for example, not making snarky jokes when sworn at. We can also detect annoyance and immediately transfer over to a human to prevent further damage.
These are product decisions that intertwine with social norms. At minimum, being aware of this respect gap is important. It reminds us that voice AIs live in a social space, not just a technical one. How people treat them can spill over into human-to-human interactions, and that’s something designers might feel responsible for.
Reflections for VoiceAI Builders and Users
In conclusion, the way people interact with voice AI is a mirror held up to our technological age - revealing expectations of godlike perfection alongside age-old human impatience and incivility. As someone who builds these products, I find this both challenging and inspiring. Challenging, because it means we have to work not just on technological capabilities but also on shaping user perceptions and behaviors. Inspiring, because if we get it right, we create something more than a piece of software – we create a new kind of relationship between humans and machines, one that could be more efficient, yet still respectful and empathetic. Bridging the gap between how users treat AI and how they treat humans may ultimately make the technology more effective and more accepted.
Voice AI will become more deeply embedded in our lives - both at work and at home. It’s on us as creators to understand this AI double standard and design with it in mind. Users will continue to come with their contradictory mix of ambiguity and doubt, high expectations and low patience, tech-fueled boldness and a sprinkle of rudeness. Let’s meet them where they are – and maybe, gently, lead them somewhere better.
