Tuesday, March 4, 2025

Spectrum Costs Might Not be the Burden Some Claim

As you would expect, mobile service provider executives complained at MWC25 that spectrum costs are too high and regulation too strict. The implication is that governments should release spectrum at lower costs. As the saying goes, “good luck with that.” 


Spectrum licenses are a revenue source for governments who see themselves as custodians of a public resource, and are likely to be loathe to do so. Also, there are many ways for service providers to increase capacity, with other approaches. Use of smaller cells, better radios, retail pricing policies and marketing as well as other spectrum efficiency moves can increase the effective capacity of mobile networks, as important as additional spectrum allocations are. 


With the caveat that the U.S. mobile service provider market typically is more robust than that of European providers, and that spectrum costs are not identical across markets, there still tend to be global benchmarks for spectrum. 


And there is a difference between spectrum license cost as a matter of capital expense, and its amortization as an operating expense. Service providers capitalize spectrum license costs, meaning the actual cost burden happens as an amortization (depreciation) line item. 


So  it is far from clear how much cost spectrum licenses actually represent, as a percentage of operating cost, for example. For the larger European operators (Vodafone, DT, Orange, BT, for example), spectrum might seem among the bigger cost drivers, but is not among the biggest cost contributors. 


Cost Driver

% of Total Costs

Components

Network Infrastructure

35–45%

Includes CapEx for towers, base stations, and 5G rollout; maintenance costs.

Spectrum Acquisition

10–20%

Costs of licensing fees/bidding for spectrum

Operational Expenses

20–25%

Energy, IT systems, leases, and backhaul connectivity.

Personnel Costs

10–15%

Salaries, benefits, and training for employees.

Sales & Marketing

5–10%

Advertising, customer acquisition, and retention programs.

Regulatory & Compliance

3–5%

Fees, taxes, and adherence to EU regulations (GDPR, roaming rules).

Other (e.g., R&D, Admin)

5–10%

Research, development, and administrative overhead.


And some observers might point out that the actual  cost of spectrum is less the capex amount than the amortization and depreciation impact, despite the impact on cash flow. 


Some industry estimates suggest that spectrum-related amortization might account for 20 percent to 40 percent  of the depreciation and amortization cost for a U.S. mobile operator, for example. ,Assuming a mid-range 30 percent, that might suggest that spectrum costs (amortized) represent about five percent to 6.5 percent of service provider depreciation costs:


Verizon: ~$5.3 billion (30% of $17.6B) out of $105B opex = ~5%.

AT&T: ~$5.6 billion (30% of $18.8B) out of $97B opex = ~6%.

T-Mobile: ~$4 billion (30% of $13.2B) out of $62B opex = ~6.5%.


Compared to other cost contributors, that is relatively slight. The point is that if lower costs are important, and they are, other places seem to be more likely places to find such efficiencies. Spectrum costs are, after all, a direct reflection of operator demand. 


And one value aspect is clear enough:unless an operator can acquire new spectrum to support each new mobile platform, it will eventually go out of business. So spectrum costs are an existential matter for mobile service providers, and that is unlikely to depress idemand, or willingness to pay.


Nvidia Wants to Move in Adjacencies; So Do Many in the AI Value Chain

Participants in the artificial intelligence value chain can be expected to eventually begin encroaching on other roles. Much of that already is happening. Large end users already design their own acceleration chips.


And Nvidia’s business ambitions already extend far beyond being a supplier of graphical processing units and seem primarily focused on becoming a supplier of Artificial Intelligence as a Service, high-performance computing and possibly some parts of the enterprise AI solutions market.


Nvidia is not trying to be a general-purpose cloud provider like AWS or Azure and  does not want to compete in general cloud hosting (compute, storage, databases) like AWS EC2, Azure Blob Storage, or Google Cloud Functions.


Nor does it want to become a supplier of enterprise solutions such as Microsoft’s productivity tools. 


On the other hand, HPC and AIaaS offer lots of room for expansion within the ecosystem.  


Artificial Intelligence as a Service (AIaaS) Market

udy

Estimated Market Size (2024)

Projected Market Size

CAGR

Projection Year

Source

Allied Market Research

$11.7 billion

$178.9 billion

35.90%

2032

alliedmarketresearch.com

Grand View Research

$16.08 billion

Not specified

36.10%

2030

grandviewresearch.com

MarketsandMarkets

Approximately $14.00 billion

$72.13 billion

38.80%

2029

marketsandmarkets.com

Precedence Research

$11.96 billion

$294.83 billion

37.78%

2034

precedenceresearch.com



High-Performance Computing (HPC) Market

Study

Estimated Market Size (2024)

Projected Market Size

CAGR

Projection Year

Source

Fortune Business Insights

$54.39 billion

$109.99 billion

9.20%

2032

fortunebusinessinsights.com

MarketsandMarkets

Not specified

$49.9 billion

6.70%

2027

marketsandmarkets.com

BCC Research

Not specified

$107.8 billion

15.60%

2028

blog.bccresearch.com


“AI as a service” is probably the most pointed challenge to Nvidia’s “cloud computing as a service” customers. DGX Cloud directly competes with the cloud providers’ AI “as a service” offerings.


Nvidia provides a “full-stack” AI solution based on frameworks (CUDA, TensorRT, NeMo) optimized for AI workloads.


Production of custom chips that could compete with AWS Trainium and Inferentia;

Google Tensor Processing Unit or Microsoft Maia AI chips is another potential area of competition. 


Beyond that, Nvidia is selling turnkey AI supercomputers (DGX SuperPODs), which cloud providers can use to build AI infrastructure. In some cases, such clusters might be sold directly to enterprises.


Nvidia’s GeForce NOW is a niche business that competes with Microsoft’s xCloud and AWS Gamelift in the cloud gaming space.


Over time, many contestants in one part of a computing value chain have moved into adjacent areas. 


Company

Original Role

Expansion Move

Adjacent Market Entered

Product/Service

Year

Intel

Semiconductor (CPU) Maker

Launched discrete GPUs

Graphics Processing (Competing with Nvidia & AMD)

Intel Arc GPUs

2022

Nvidia

GPU Maker

Developed AI Cloud Services

AI as a Service (Competing with AWS, Google, Azure)

Nvidia DGX Cloud

2023

AMD

CPU & GPU Maker

Acquired Xilinx

FPGA & Custom Silicon (Data Centers, AI, Automotive)

Xilinx Adaptive SoCs & AI Engines

2022

Amazon (AWS)

Cloud Services

Designed Custom AI Chips (Inferentia & Trainium)

AI/ML Hardware (Competing with Nvidia)

AWS Trainium & Inferentia Chips

2018+

Google (GCP)

Cloud Services & Search

Developed Custom AI Chips (TPUs)

AI/ML Hardware (Competing with Nvidia)

Google Tensor Processing Unit (TPU)

2016+

Microsoft (Azure)

Cloud Services

Launched Custom AI & ARM Chips

AI & ARM Computing (Competing with AWS, Nvidia)

Azure Maia AI Chips & Cobalt CPUs

2023

Apple

Consumer Electronics

Designed Custom ARM Chips

Semiconductor (Replacing Intel CPUs)

Apple M1, M2, M3 Chips

2020+

Tesla

EV Manufacturer

Developed AI Supercomputers (Dojo)

AI & Computing (Competing with Nvidia for AI training)

Tesla Dojo AI Training Cluster

2023+

Oracle

Database Software

Expanded Cloud & AI Infrastructure

Cloud Computing (Competing with AWS, Azure)

Oracle Cloud Infrastructure (OCI)

2016+

Meta (Facebook)

Social Media

Designed Custom AI & ML Chips

AI Hardware & Compute (Competing with Nvidia)

Meta Training & Inference Accelerator (MTIA)

2023

Monday, March 3, 2025

Gemini Shopping Use Case Illustrates Changes in "Search"

It's too early to know how important such uses of artificial intelligence are going to be, but it already seems clear enough that AI language model capabilities are the next evolution of search and information seeking, for example. This might be an example of a shopping tool, but there are lots of other use cases that could span surveillance, military applications, police work and so forth. 

As was the case for atomic bomb technology, many will have qualms, even as the technology is developed because bad actors are going to do so no matter what ethicists say. 

"Lean Back" and "Lean Forward" Differences Might Always Condition VR or Metaverse Adoption

By now, it is hard to argue against the idea that the commercial adoption of “ metaverse ” and “ virtual reality ” for consumer media was in...