Will TinyML Displace Multi-Access Edge Computing?

New technologies have a way of redesigning older markets out of existence or reshaping older markets. TinyML, for example, is already a possible functional replacement for mobile edge computing. 

By enabling on-device intelligence, TinyML replaces (and limits the size of) the MEC revenue opportunity for mobile operators, as it supports AI and other processing right on the device. For example, if TinyML enabled on-device processing, it could cut MEC revenue in half. 

MEC Scenario	Growth Rate Assumption	Estimated Revenue (2024-2028)
Low	Slow adoption of MEC, primarily driven by enterprise use cases with limited consumer uptake.	$10-20 billion
Moderate	Moderate adoption across both enterprise and consumer sectors, with MEC-enabled services like augmented reality and connected vehicles gaining traction.	$30-50 billion
High	Rapid adoption fuelled by widespread deployment of 5G and increased integration of MEC into essential services like healthcare and smart cities.	$50-100 billion

Tiny machine learning (TinyML) is machine learning (including hardware, algorithms and software) capable of performing on-device sensor data analytics at extremely low power, enabling a variety of always-on use-cases and targeting battery operated devices.

It therefore enables any number of AI inference operations on a device, eliminating the need to transmit data to an external processor located elsewhere. Though part of the broader “AI at the edge” possibility, it further decentralizes AI inference operations, reduces latency to the greatest extent and likely will be used to support  highly-specialized inference operations running lightweight language models. 

Some obvious use cases include:

• Wearables such as a fitness tracker that analyzes your movements in real-time, offering personalized coaching or detecting falls.

• Smart homes devices that monitor temperature, humidity, and air quality, adjusting settings automatically.

• Predictive maintenance on machinery to predict potential failures before they happen.

• Environmental monitoring

• Agricultural sensors to optimize irrigation, detect pests and diseases.

But note that such lists of use cases actually are substitutes for older categories such as “internet of things” sensors. In many cases, devices and software that support TinyML will be used by the same devices once touted as being in the  “IoT at the edge” category.

It is not a new development. In the past, we saw tablets and smartphones displace PCs.  Smartphones displaced watches, cameras, GPS sensors, home phones and pagers. Now watches have in many cases become wearable computers. 

By some estimates, at least $48 billion worth of global device and product sales are lost every year because smartphones have displaced them. Some estimates believe the substitution results in a lost $70 billion of legacy product and sales activity annually. 

Displaced Device	Annual Revenue Loss from Sales of Other Products Because Smartphone Replaces Them (USD Billion)	Assumptions
Home Phone Service	25-35	Includes landlines and traditional VoIP services. Assumes partial displacement, with landlines remaining in niche markets.
Digital Cameras (Standalone)	15-20	Considers point-and-shoot and high-end DSLR cameras. Excludes mirrorless cameras still maintaining market share.
Wristwatches (Traditional)	5-10	Accounts for lost sales of non-smart watches, with smartwatches capturing a new market segment.
GPS Devices (Standalone)	2-3	Considers dedicated car and portable GPS units. Increased smartphone navigation usage contributes to revenue loss.
MP3 Players and Portable Media Players	1-2	Includes digital audio players and video playback devices. Niche market remains for high-fidelity audio equipment.
Pagers	Negligible	Pagers are practically obsolete, with near-complete displacement by smartphones.

In the U.S. market, lost revenue likely was in the $8 billion range in 2023, for example. That arguably is a low estimate, as the loss of a single residential phone line is assumed to be only $30 a month worth of lost revenue. 

There also could be a loss of other revenue from cost recovery mechanisms and also customer churn when the phone line is part of a service bundle.

Displaced Device	Lost Revenue Estimate (USD Billion)	Notes
Home Phone Service	5.8	Based on decline in residential landline subscriptions and average monthly service fee of $30.
Camera Sales	1.9	Based on decline in point-and-shoot camera sales and estimated average camera price of ~$200.
Watch Sales	0.5	Based on decline in traditional watch sales and estimated average watch price of ~$100.

On the other hand, mobile phone service also creates new markets for mobile service providers. 

But you get the point: new technologies often can redefine older markets and in many cases can be substitutes for the legacy products and services. 

We have seen this process at work in estimates of revenue to be earned by mobile service providers using network slicing to create new types of virtual private networks. But traditional VPNs, private networks,  traffic prioritization or edge computing are substitutes for network slicing, for example.