Showing posts sorted by date for query mobile drives revenue. Sort by relevance Show all posts
Showing posts sorted by date for query mobile drives revenue. Sort by relevance Show all posts

Monday, December 29, 2025

Electricity Business Can Learn from Telecom Evolution

Oddly enough, local electricity generation by businesses and homeowners exposes a key problem for electricity supplier economics. Traditional pricing assumes energy consumption is equal to grid usage. 


But distributed generation breaks that assumption. Essentially, customers remove themselves, at least partially, from the system, but retain the optionality of using the grid for reliability, backup, and peak load balancing. 


But fixed costs stay embedded in the price of per-kiloWatt hour charges, so rates will rise as sales fall. At the same time, new demand driven by high-performance computing and associated data centers increases the need for new investments in transmission infrastructure as well as generation, increasing the fixed costs. 


The basic problem is a combination of high fixed costs; low marginal costs per additional kWh and the impact on ability to cover fixed costs when demand is reduced by local generation. 


Since fixed costs do not decline proportionally with local generation, all remaining sales must cover more fixed cost per kWh consumed. 


This pushes per-kWh rates upward for customers who still rely heavily on the grid. 


But the network still must be designed for peak load, sized to serve customers when solar output drops (night, winter, clouds). So self-generation reduces energy delivered, not the need for the grid.


Share of Customers with On-Site Generation

Utility Retail Sales (as % of original)

Fixed Cost Recovery per kWh

Average Retail Rate Impact for Non-Solar Customers

0% (baseline)

100%

$0.10/kWh

Baseline

10%

93%

$0.108/kWh

+8%

25%

82%

$0.122/kWh

+22%

40%

68%

$0.147/kWh

+47%

60%

52%

$0.192/kWh

+92%


What’s really happening is a decoupling of value from volume, something that also happens in other infrastructure contexts. 


The grid’s value is optionality and insurance, but it’s priced like a commodity pipeline. Distributed generation exposes that mismatch.


So what might be done to fix this problem? Fixed monthly connection charges are one way of “socializing” grid costs. Time-of-use pricing and demand charges also can help. But as with mobile and fixed telecom networks, “access” to the network might be more important than usage charges. 


So reframing the product might be conceptually necessary. The “product” electrical utilities sell is reliability, capacity, and load balancing, not just energy. 


Energy is a commodity that is part of the service, but grid access becomes the actual “product.” 


Beyond all that, perhaps more explicitly cross subsidies are needed, as once was the case for communications services, where business user profits subsidized consumer usage. Perhaps business customers and self-generators must subsidize customers unable (for financial or physical reasons) to participate in self generation. 


Until pricing reflects capacity and availability, not just kWh, rising self-generation will continue to raise rates for those most dependent on the grid.


This reminds me very much of how economics of the “telecom” business changed with competition. 


Both electrical grids and telecom networks have the same core traits:

  • Extremely high fixed costs

  • Very low marginal cost per additional unit of usage

  • Peak demand, not average usage, drives capital investment

  • Universal-service expectations layered on top of commercial economics


Historically, both industries solved this with implicit cross-subsidies. But widespread technology changes and deregulation changed the telecom business model. 


Traditionally, high prices for business customers (especially long distance calling) provided the profits that allowed affordable service for consumers. 


This worked as long as high-margin users couldn’t easily bypass the network and suppliers had pricing power. 


Self generation in the electricity business has the same dynamics. When high-value customers (commercial, industrial, affluent residential) can self-generate, electricity providers lose the profits that allow them to serve mass-market customers reliant on the grid with affordable rates.


The cross-subsidy that once flowed invisibly is exposed. The analogy with telecom after deregulation, mobile substitution for fixed voice, embrace of internet protocol and reliance on internet access as a core service for the fixed network illustrate the issues. 


Dimension

Traditional Telecom Access

Electric Grid (Emerging)

Core asset

Nationwide access network

Transmission & distribution grid

Cost structure

High fixed / low marginal

High fixed / low marginal

What drives capex

Peak simultaneous usage

Peak demand & reliability

Primary pricing unit

Minutes / lines

kWh

Implicit subsidy source

Business & long-distance margins

High-usage / high-income customers

Subsidy recipient

Residential & rural users

Low-income & non-solar customers

Bypass mechanism

VoIP, wireless, OTT apps

Rooftop solar, storage, microgrids

Resulting problem

Access prices no longer cover costs

Volumetric rates no longer recover fixed costs

Regulatory response

Access charges, USF fees

Grid access charges, demand charges (emerging)

Political constraint

Universal service obligation

Universal service + decarbonization goals


The problems are similar. Neither industry can simultaneously have volume-based pricing; high fixed costs; widespread abandonment of the core network and stable rates for mass-market customers. 


The telecom industry adapted by shifting its revenue model. Today,  customers do not primarily pay for minutes or megabytes anymore. They pay for “access to the network.” Think of it like Wi-Fi access. One pays to be connected, not for usage (bytes consumed or time connected or bandwidth provided). 


The analogy is a mobile phone service plan offered at a flat fee per month that includes “unlimited” data usage; “unlimited” national calling and text message. 


The customer pays for the ability to use the network, not consumption in a strict sense. 


Today’s electrical energy service problem is that self generation reduced kWh sales while fixed costs remain. As rates rise to cover fixed costs borne by fewer customers, there is more incentive to defect. 


So an access-fee model more effectively recovers shared fixed costs. So self generation no longer erodes fixed cost recovery. And the grid stays healthy.


Sunday, December 21, 2025

Watch What They Do, Not What They Say

Whenever a hot new technology comes along, executives in the connectivity industry seem required to embrace it and explain how they will be using it. With few exceptions, this is a distraction.


Sure, artificial intelligence will be used to design and manage complex networks, as much automation has been used in the past. But, for the most part, AI does not seem to represent a huge new product or service opportunity for connectivity providers.


We'll be hearing differently, of course. But past experience suggests it is mostly talk.


Matters are different for data center services providers, given the key role AI factories represent. Obviously, compute infrastructure is a requirement for AI training and inference, so direct revenue streams are created for providers of the compute function.


Direct and substantial participation above the infrastructure level will be less common.


But some of the usefulness will come from use of embodied AI (physical robots, drones, and autonomous systems) in managing and maintaining the physical infrastructure.


Industry

Affected Business Process/Product

Embodied AI Application

Extent of Impact

Source Link

Data Centers

Facility Inspection & Monitoring (HVAC, Power, Security)

Autonomous Mobile Robots (AMRs) (e.g., quadruped robots like Spot) equipped with thermal and acoustic sensors perform routine, 24/7 inspections of servers, cooling units, and power systems.

High (Drives down operational costs, enables predictive maintenance by detecting anomalies like hot spots or leaks that humans miss, enhances worker safety.)

YMK Technology Group - Data Center Operation and Maintenance Robot

Data Centers

Hardware Maintenance & Repair

Robotic Arms / Specialized Robots used for delicate tasks like reseating or cleaning optical transceivers, or replacing fiber cables within server racks (part of the "self-maintaining system" vision).

Medium (Emerging) (Significantly reduces the Mean Time to Repair (MTTR) for network issues, lowers risk of human error in sensitive equipment, and improves system availability.)

Microsoft - The rise of datacenter robotics!

Communications, Connectivity

Infrastructure Inspection (Cell Towers, Fiber Routes)

AI-enabled Drones or UAVs for autonomous visual and thermal inspection of remote cell towers, antennas, and long-distance fiber optic lines.

High (Improves network uptime by speeding up fault detection, eliminates the need for manual, dangerous, and time-consuming physical inspections, and keeps Digital Twins updated.)

ANYbotics - Automate industrial inspection

Data Centers, Edge Computing

Physical Security and Access Control

Autonomous Security Robots that patrol the perimeter and interior, using computer vision to detect unauthorized entry, identify anomalies, and guide personnel to specific locations.

Medium (Augments human security teams, provides a continuous, data-logging security presence, and supports intelligent guidance of maintenance staff.)

NTT DATA - Smart Robotics in Action

Communications,  Connectivity

Energy Efficiency & Sustainability (Indirect)

AI-driven Cooling Infrastructure (While the AI is informational, its effect on the physical cooling systems is profound). AI adjusts the physical state of the cooling (e.g., dampers, pumps) in real-time.

Very High (Directly reduces the Power Usage Effectiveness (PUE) of the physical data center, leading to massive energy and cost savings.)

Flexential - AI Data Centers: The Future of AI Infrastructure





Industry

Embodied AI Use Case 

Affected Business Process

Extent of Impact

Retail

Autonomous Inventory Robots (e.g., in-store floor-scanning robots)

Inventory Management & Auditing: Real-time scanning of shelves to check stock levels, identify misplaced items, and confirm pricing.

Greater: Improves stock accuracy, reduces manual labor, and enables predictive ordering.

Transportation

Autonomous Freight Trucks & Robotaxis

Logistics & Delivery: Self-driving commercial vehicles for long-haul trucking and last-mile delivery.

Greater: Reduces fuel consumption (route optimization), enables 24/7 operation, and lowers labor costs.

Logistics

Autonomous Mobile Robots (AMRs) & Drones

Warehouse Operations: Automated picking, packing, sorting, and movement of goods within warehouses and fulfillment centers.

Greater: Enhances operational efficiency, increases throughput, and improves order accuracy.

Computing

AI-Driven Infrastructure/Utility Inspection Drones

Predictive Maintenance & Safety: Drones with computer vision to inspect power grids, pipelines, or infrastructure for defects/anomalies.

Lesser/Greater: Improves safety (avoiding human risk), enables proactive repairs, and extends asset lifespan.

Content (Consumer Tech)

Robotic Assistants (e.g., Smart Home Devices/Humanoids)

Personal Assistance/Service Delivery: Physical robots or integrated AI systems that provide hands-on help or concierge services.

Lesser: Enhances customer experience and allows human employees to focus on more complex tasks.

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