Hey Siri, Cortana, Google, Assistant, Alexa, BlueGenie, Hound, Galaxy, Ivee, Samantha, Jarvis, or any other voice-recognition assistant out there.

Now that Google and Apple have announced that they’ll be following Amazon into the home far-field voice assistant business, I’m wondering how many things in my home will always be on, listening for voice wakeup phrases. In addition, how will they work together (if at all). Let’s look at some possible alternatives:

Co-existence. We’re heading down a path where we as consumers will have multiple devices on and listening in our homes and each device will respond to its name when spoken to. This works well with my family; we just talk to each other, and if we need to, we use each other’s names to differentiate. I can have friends and family over or even a big party, and it doesn’t become problematic calling different people by different names.

The issue for household computer assistants all being on simultaneously is that false fires will grow in direct proportion to the number of devices on and listening. With Amazon’s Echo, I get a false fire about every other day, and Alexa does a great job of listening to what I say after the false fire and ignoring if it doesn’t seem to be an intended command. It’s actually the best performing system I’ve used and the fact that its starts playing music or talking every other week is a testament to what a good job they have done. However, interrupting my family every other week is not good enough. And if I have five always-listening devices interrupting us 10 times a month, that becomes unacceptable. And if they don’t do as good a job as Alexa, and interrupt more frequently, it becomes quite problematic.

Functional winners. Maybe each device could own a functional category. For example, all my music systems could use Alexa, my TV’s use Hi Galaxy, and all appliances are Bosch. Then I’d have less “names” to call out to and there would be some big benefits: 1) The devices using the same trigger phrase could communicate and compare what they heard to improve performance; 2) More relevant data could be collected on the specific usage models, thus further improving performance; and 3) With less names to call out, I’d have fewer false fires. Of course, this would force me as a consumer to decide on certain brands to stick to in certain categories.

Winner take all. Amazon is adopting a multi-pronged strategy of developing its own products (Echo, Dot, Tap, etc.) and also letting its products control other products. In addition, Amazon is offering the backend Alexa voice service to independent product developers. It’s unclear whether competitors will follow suit, but one thing is clear—the big guys want to own the home, not share it.

Amazon has a nice lead as it gets other products to be controlled by Echo. The company even launched an investment fund to spur more startups writing to Alexa. Consumers might choose an assistant we like (and we think performs well) and just stick with that across the household. The more we share with that assistant, the better it knows us, and the better it serves us. This knowledge base could carry across products and make our lives easier.

Just Talk. In the “co-existence” case previously mentioned, there six people in my household, so it can be a busy place. But when I speak to someone, I don’t always start with their name. In fact, I usually don’t. If there’s just one other person in the room, it’s obvious who I’m speaking to. If there are multiple people in the room, I tend to look at or gesture toward the person I’m addressing. This is more natural than speaking their name.

An “always listening” device should have other sensors to know things like how many people are in the room, where they’re standing and looking at, how they’re gesturing, and so on. These are the subconscious cues humans use to know who is talking to us, and our devices would be smarter and more capable if they could do it.

Todd Mozer is the CEO of Sensory. He holds over a dozen patents in speech technology and has been involved in previous startups that reached IPO or were acquired by public companies. Todd holds an MBA from Stanford University, and has technical experience in machine learning, semiconductors, speech recognition, computer vision, and embedded software.