The smartphone supply chain has given rise to powerful, cheap computing components that can augment our bodies, our homes, and our cities, gathering new kinds of data and providing hyper-contextual intelligence. We talk about these things as IoT devices or wearables, but when backed by machine learning in the cloud, and sewn up with other devices, they create totally new kinds of inputs and outputs for media.

The smartphone has become the universal product of the 21st century — one day soon, everyone in the world will have access to the real-time information and communication a smartphone affords. But just as importantly, everyone will also have a high quality networked camera, a highly tuned microphone, and a precise record of where they are in the world. These sensors will increasingly be used not just to capture media, but as their own full-fledged sources of data.

Text has always been the lingua franca of computing: it’s how we create software, and how software has received our inputs about the world – until now. Over the past few years, machine learning has progressed to the point of rendering images and videos as transparent to computers as text has always been: Google can find all your photos that include your pets, or that volcano you toured in Hawaii, and can translate street signs in real time when you point your phone at them. And the dual-camera system which first shipped in the iPhone 7 Plus can process depth, making it suitable for capturing scenes in 3D space.

And while we’ve been talking to our phones, in the form of Siri and Google, for awhile now, Amazon’s placement of Alexa outside the context of the phone – as an assistant that’s tied to a place, rather than a person – has supercharged the use of voice interfaces. Taken together, the camera and microphone as discrete inputs will provide additional context, and greatly increase our virtual assistant’s ability to respond as a human would. It’s time to stop thinking about cameras and microphones as digital updates to 20th century devices, and start thinking about them as the eyes and ears of our software and services.

“Is this the right office for my 1pm meeting?” “Looks like you’re on the right block, but let me see the building. Yes, you can enter at the red door on the right.”

We’ve already seen the first company founded on this principle: when they rebranded the company from Snapchat to Snap, CEO Evan Spiegel stressed the point that they were not a social network, but a camera company. And while the declaration was made alongside the announcement of Snap’s first hardware camera, Spectacles, the implications go far deeper. Snap is the first company founded on the notion that our modern, networked cameras are fundamentally different than their analog ancestors. We’ve written extensively about how Snapchat is preparing consumers for the augmented reality future, but along with that will come incredible amounts of data, which Snap will use to power its advertising platform. Spectacles won’t have to ask to see where you are or what you’re looking at, because they’ll already know — World Lenses look like a toy today, but are already providing Snap with contextual data about where their users are and what they’re interested in.

Along with these new types of inputs come entirely new types of media. Virtual reality made waves last year as we saw the launch of major new consumer platforms, while Pokemon Go and Snapchat are preparing consumers for the coming onslaught of augmented reality. While these markets are nascent, they represent two sides of the same coin: virtual reality is the purest media experience we’ve ever created, fully immersing us in the content. And augmented reality will be the inverse: pushing media out to every corner of our physical world. Both are still nascent technologies, but together represent the future of media, and are fertile playgrounds for how to speak to the consumer of the future.

As brands looking to capitalize on these new interfaces, we must start from the data – what information about your consumers would allow you to deliver a more personalized, tailored experience with your product? That customization will extend beyond the product itself, but it’s important that it begin from a position of improving the experience for the consumer. Once that data strategy is identified, we can partner with the relevant platforms to create experiences access that data. In some cases, it might make sense for a brand to create its own Advanced Interface, if you can authentically create value. But that authenticity is key – you must offer a compelling use case, and you must carefully protect the data you do capture. An inauthentic experience will be a waste of resources, and badly secured user data will set back your trust by years. Improve both the experience and your customers’ trust, and you’ll generate a wealth of proprietary data, building a moat around their brand loyalty.

Last year, we focused on Appified TV, and the seismic shift in media creation, distribution, and monetization that was coming to television, and the brand advertising which supports it. But this is just one piece of the puzzle, as all media — not just video content — shifts to delivery by software rather than speciality analog networks. In 2017, we’re shifting focus to the globalization of all culture, enabled by real-time communication, and content designed to cross the borders of regions and platforms. It’s the app-ification of all media which is what allows it to quickly adapt to these new Advanced Interfaces, and these twin engines of platform and content are what will drive the next major shift in media and attention.

Legacy media is often bound by licensing terms from a pre-Internet era, assuming that consumers had no way to obtain access to content from another country, if they were even aware that content existed in the first place. In contrast, app stores are global by default, allowing for business models that operate on that presumption. Netflix has planted the flag in the ground as the world’s first global TV network, but Amazon, YouTube, Facebook, and Snapchat are hot on their heels. A global audience for these new mega-channels can make Super Bowl numbers look quaint, as new genres such as esports can easily usurp them.

This is only possible as these platforms have created, bought, or nurtured their own original content. And sometimes this content looks very different in form, as platforms like Snapchat, YouTube, and Twitch have developed their own aesthetics. Content on YouTube from Korea is going to share more in common with YouTube content from the US than either does to the traditional television from either country.

In contrast, Netflix, Apple, and others are experimenting with adapting traditional forms for a global market. Netflix’s Sense8 has all the trappings of prestige drama, but with a storyline that supports telling local stories from around the globe, and an international cast whom are all well-known in their home countries. While the biggest criticism of the show is that it hasn’t taken the global perspective far enough, it points the way toward how to build a fiction that can resonate in many cultures simultaneously, aggregating audiences much larger than before. Similarly, Apple created a global streaming radio station as a flagship feature of their music streaming service, with DJs anchoring the programming from a diverse set of cities around the world.

The counter-trend to these new mega-channels is that, leveraging the scale of a global audience, many more platforms can create sustainable businesses without needing to be the next Facebook or YouTube. Twitch and Musical.ly may operate at a smaller scale, but their global purview and passionate fan base mean that they out-rank many US cable channels in both viewership and time spent. Some of these micro-channels (where “micro” is still sizeable in traditional terms) will come from brands themselves, as they strive to aggregate their own consumers to hedge against vertically-integrated competitors. Brands like Red Bull have been building a content strategy for years, and they’re increasingly able to take that content direct to consumers, leveraging a windowing strategy that prefers owned micro-channels, while leveraging the scale of partner mega-channels over time.increasingly able to take that content direct to consumers, leveraging a windowing strategy that prefers owned micro-channels, while leveraging the scale of partner mega-channels over time.

To play in the world of global culture, brands must first identify the correct approach. If you have (or can create) a viable stable of content, distributing it in an owned channel will always provide the most value, in terms of audience attention, flexible targeting, and a feedback loop of data. This will allow you to use content to establish direct, personal relationships with your consumers, providing many of the benefits of a vertically-integrated brand even if some or none of your sales are direct-to-consumer.

For brands for whom a content strategy would be prohibitive or inauthentic, seek out the platforms which are optimized for global scale, and which understand the nuances of marketing to different cultures. In either case, it’s important to eschew the notion that you can deliver a singular experience or message that will resonate with everyone around the world. While platforms allow for targeting and segmentation to assist with this – and our own industry has been making great progress with dynamic creative – recent technological breakthroughs are beginning allow us to deploy far more personalized experiences than before, and to do so at a global scale.

Both Global Culture and new Advanced Interfaces are underpinned by cloud services, and a new generation of machine learning running atop tem. This intelligence isn’t transformative in and of itself, but instead augments our existing capabilities, allowing us to track consumer behavior across platforms and channels, and to personalize the experience for them at scale. This dynamic backend will allow brands to quickly synthesize data from new interfaces and platforms, while also developing intimate connections with consumers around the world.

The first widespread use of basic artificial intelligence for brand/consumer interaction arrived last year with the rise of chatbots and conversational interfaces. These interfaces will disrupt traditional marketing channels such as paid search and social — Google and Facebook’s current ad products are predicated on showing you lists and feeds of content, so making a few of them sponsored is an easy business, and we can optimize for organic placements. Market dynamics are driving the internet’s largest ad platforms to move away from this model, instead leveraging AI to provide a single true answer. We doubtlessly will be allowed to pay for the privilege of being that answer, but it’s less clear if any amount of optimization will help us break through organically.

But the more important implication of AI for brands in the long run will be in leveraging it for our own purposes. There are now multiple turnkey services for processing natural language input, translating text between languages, understanding the content of photos, and even generating intelligent predictions from existing data sets. Google and Amazon are currently in an arms race to provide AI-as-a-service for developers, and we can expect entries from Facebook and Apple to be forthcoming. While Google’s intelligence is widely regarded as superior, the in an arms race to provide AI-as-a-service for developers, and we can expect entries from Facebook and Apple to be forthcoming. While Google’s intelligence is widely regarded as superior, the platform advantages for developers of remaining inside the Amazon Web Services ecosystem are real, and will significantly bolster Amazon’s position in the market. Meanwhile, Facebook’s intimate knowledge of individuals may be leveraged to their advantage, while Apple is likely to combine “good enough” intelligence with consumer-friendly privacy controls, which may yield access to hyper-personal data in a way that consumers are comfortable with.

Much of the processing of this data will happen in the cloud, but it will come from disparate sources including the Advanced Interfaces discussed above. We’ll see increasing amounts of contextual data pulled from new kinds of sensors and devices — not interfaces in themselves, but real-time data sources designed to provide context and augment the intelligence of other platforms. In effect, these are specialized devices we’ll deploy to supplement our AI’s knowledge of the world, and the people in it. The type of data they’ll generate can be grouped into the private, semi-private, and public realms.

Data about our customers’ private lives may be the most desirable, but because of the intimate nature of most consumer technology, it will also be the first to come online and be accessible to third parties. With devices and sensors in our homes and on our bodies, as long as value is being offered in exchange, we’ll have ready access to some truly intimate data.

In the semi-private sphere, the workplace is becoming newly accessible to brands, as it unbundles from the IT sales force towards user-driven tools like Slack, giving us access to reach consumers where they spend most of their waking time. While it’s an obvious place for B2B brands to play, it may also make sense for consumer products and others to reach consumers in a work context.

Personal-yet-public data — the kind of information that any casual observer can report from a corner from New York City -- will prove deceptively elusive to capture. In the future, our connected cities will allow us to navigate a landscape of contextually-relevant media, while maintaining our own privacy controls. In the meantime, we can look to connected retail to enable microcosms of this experience: a logged-in user, with full purchase history and preferences, navigating a physical store with the same context as she would navigate a website.

Again, any brand that is requesting contextual data to supplement their targeting and personalization must offer a clear value exchange for that data: if I give you this access, how does your product or service get better for me? It doesn’t have to be value within the same channel or moment, as traditional loyalty is distributed across platforms and retail channels. In fact, bolstering benefits in one channel in order to access data from another may be the most prudent tactic for brands looking to augment their customer intelligence, and foritfy their relationship with their customers.