Mobile World Congress was largely about artificial intelligence, hence largely about “AI” smartphones.
Such devices are likely to pose issues--or create opportunities--related to processing tasks and memory on devices; use of edge computing or cloud computing resources. That, in turn, might create an opening for different ways of envisioning device and service business models.
Regarding on-board resources, on-board machine learning models might require more on-device memory. to load up even before we get to running them, although the availability of compressed models surely is coming.
Processing also will be an issue. Running an ML model arguably requires more unique arithmetic logic blocks than your typical CPU, so specialized processors are likely necessary.
Smartphone processing likely will continue to be constrained by power consumption and heat generation limits, as well, so there will be some limits on on-board processing power.
Leveraging cloud or edge computing obviously is a potential solution. Processing of some tasks--such as real-time language translation; camera features and voice-to-text will continue to make sense as an “on-board processing” capability.
Other features might continue to make sense as an “edge- or cloud-supported” capability.
The issues are that this could reshape needs for end-user data plan features and higher-speed, latency-bounded networks. Roaming costs also are an issue.
So even if on-board processing is, in principle, ideal, it might not be practical for all devices (mid-range and low-end devices, for example). And heat and processor cost issues must be considered as well.
As a marketing issue, “subscription phones and service” might need a rethink. To some extent, consumers often take advantage of subsidized phone offers from their service providers. So a service plan with a two-year contract that includes the cost of the device in the recurring cost already is a form of “phone as a service.”
Subscription plans for advanced AI service (Google’s Gemini or Microsoft’s Copilot) already exist. So we might see a rethink of possible product bundles that include, on a subscription basis, the device, the AI capabilities (on-board plus cloud or edge) and the recurring service plan costs.
Creating such bundles should be easier for consumers to understand once we develop more valuable AI-enhanced apps and features usable on smartphones. People might expect AI features such as camera performance or image editing and translation services to be “bundled” with the device.
But, eventually, some compelling additional use cases could--and should--develop that require an AI service plan that relies on cloud and edge computing, faster connections and more data allowances. So think of a 5G service plan using mid-band spectrum (for speed); unlimited data usage (so the external cloud and edge processing can be used) plus “AI device” supplied on a subscription basis, with a “new” device supplied every two or three years.
Aside from all the practical details of figuring out the service provider’s cost to do so, we still need some new “killer apps” that make the purchase of an AI service plan such as Gemini or Copilot a reasonable and necessary investment by the consumer.
As a business problem, this is a “logical bundle” issue. What features (device, AI, recurring service cost, features and apps) will make sense for many customers when all of those features are a subscription, not just the mobile service plan and the AI?
Right now, it is not so clear what the new killer value requiring AI--and therefore a more-powerful device plus remote processing--is the trigger. Still, once one or a few such use cases do develop, with high customer interest, it will be easier to conceive of, and sell, new bundles of device, AI and service, for one recurring monthly price.
