Emerging technologies often follow a common evolutionary path from novelty to utility. It’s all about fun & games before settling into lasting value in everyday mundane utilities. Consider the iPhone’s arc from novelty apps like iBeer and Zippo to staples like Uber and Spotify.

The same thing happened on the web. After the early 2000’s bubble burst from an inflated atmosphere of grandiose visions, the web eventually reached those elevated valuations….but in a different form. The web’s killer apps are decidedly mundane: search, email, news and productivity.

Mundane sounds like a bad word, but it’s not. The above killer apps have one thing in common: a business case. Everyday use cases aren’t as sexy as the novelties that preceded them, but they breed sustainable business models through sheer scale. They’re things that everyone uses.

The question this all leads to is how and if AR will follow this trend. It likewise starts strong with fun (social lenses) and games (Pokémon Go), but we’ve long been bullish on all-day utilities like visual search. Another utility-driven use case already gaining traction is camera commerce.

Where is Spatial Computing in its Lifecycle?

ML Magic

With that utilitarian framework, one app that caught our eye recently is Brickit. It’s not marketed as an AR experience but it elegantly brings digital augmentation to physical objects. It does this by applying computer vision to make sense of random assortments of lego blocks.

In other words, by pointing one’s camera at a pile of lego pieces, the app can recognize them and suggest what you can build with them. As long as they’re laid out in a single layer of pieces on a flat surface, the app performs its machine-learning magic in a matter of seconds.

Suggested projects come with directional indicators for where in your pile of lego blocks the right pieces are. Speaking of instructions, the third-party app’s ML could benefit from direct Lego database integrations, which could come about through partnership (or acquisition).

Either way, this is a clever spin on AR that can help inspire projects and teach kids about computer vision and machine learning. Conceptually, it reminds us of Snap Lens’ machine-learning fueled integration with Allrecipes that suggests meals based on items in your fridge.

Altogether it has a combination of real utility and the magic that draws us to AR. In fact, this computer-vision-centric flavor of AR could overtake more-popular object/product visualization use cases. Placing superheroes in your room is fun at first but lacks utility and staying power.

Broadening AR

Beyond its clever and utilitarian angle, Brikit is notable in that it expands the boundaries of AR. Just like the evolution of and lifecycle of emerging technologies examined earlier, AR will grow into its own skin and expand into forms of augmentation that we don’t currently associate.

Those boundaries continue to expand, such as audio AR. Though it’s still developing, the thought is that intelligent audio cues can be delivered through increasingly pervasive hearables. Besides a growing installed base, it’s easier to deliver and involves fewer style crimes than AR glasses.

If you widen the aperture even further, AR’s range of applicability can include everything from Zoom backdrops to the LED walls increasingly used in film production. And AR’s most anticipated product — Apple Glass — could very well redefine what augmentation means.

In practical terms, broadening AR’s definitions means broadening its use cases. And that in turn means broadening its business cases. This could be a positive step as it means potentially more revenue for the early and unproven AR sector that’s still in the process of defining itself.

Header Image by Caleb Angel on Unsplash

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