Why Fixed Network Internet Access Features Consumer Speed Tiers, While Mobile Networks Do Not

While it might be difficult to describe the average revenue per account impact of 5G accounts, compared to 4G, it seems far easier to show revenue lift provided by fiber-to-home accounts, compared to copper-based accounts.

Company	FTTH ARPU	Copper ARPU	ARPU Difference
AT&T	$65	$45	$20
Verizon	$70	$50	$20
Lumen	$55	$40	$15
TDS	$60	$45	$15
Brightspeed	$65	$45	$20

In large part, that is likely because FTTH allows sale of higher-priced access plans, which naturally generates higher revenue. Also, unlike the case for mobile account figures, home broadband is “service to a place,” so there is no effect of “multiple users” on recurring price.

In mobile scenarios, multiple users and devices might be supported on a single account, in the form of multi-user accounts, with “lines” or “numbers” ranging from two to some higher number. 

For example, Ericsson suggests 5G boosts average revenue per user by less than five percent, even if many observers suggest ARPA drives significantly higher impact. 

Company	5G ARPA	4G ARPA
AT&T	$100	$80
Verizon	$120	$90
T-Mobile	$90	$70
NTT	$110	$80
Telefonica	$100	$80
Vodafone	$90	$70

It remains unclear whether mobile operators might eventually shift to differentiated access speed plans for their mobile customers, as do home broadband providers. 

There are clear business imperatives underlying each charging principle. The cost of supplying capacity on a fixed network is far lower than on a mobile network, traditionally, largely because fixed networks generally have more ability to add capacity (optical fiber bandwidth is almost infinite; mobile capacity is based on radio frequency resources that are inherently limited). 

Traditionally, fixed networks have huge advantages where it comes to capacity and therefore cost per gigabit of consumption. While we might argue about the precise absolute retail cost of supplying capacity, mobile bandwidth has generally been about two orders of magnitude more expensive than fixed network bandwidth. 

Year	Fixed network (USD/GB)	Mobile network (USD/GB)
2000	100	10,000
2005	10	1,000
2010	1	100
2015	0.1	10
2020	0.01	1
2025	0.001	0.1

Under such circumstances, it makes sense that mobile operators “need” to manage consumption expectations, where fixed network operators can more easily consider “tiers of service” where higher-speed access is sold at higher prices. 

Rarely will mobile operators, with their higher cost to supply capacity, have high incentives to encourage higher customer data consumption by offering higher-speed (and higher-priced) consumption tiers.

Data Warehouses and Generative AI Model Training

Snowflake, Databricks, Teradata, Amazon Redshift, Google BigQuery or Microsoft Azure Synapse Analytics, to name the obvious contenders, are data warehouses whose value for building and running AI models is foundational. After all, AI models are applications that have to be housed someplace and must be queried to produce inferences. 

But some might note that those differences are relatively inconsequential compared to the alternative of trying to build models and make inferences on a private enterprise data warehouse platform. The point many would argue is that building big generative AI models, for example, on a private data warehouse basis is arguably less reasonable than doing so using a cloud-based approach. 

The ability to customize might be among the few areas where a private data warehouse might offer some advantages. 

Feature	Private Enterprise Data Warehouse	Snowflake	Databricks	Amazon Redshift	Google BigQuery	Azure Synapse Analytics
Processing speed	Depends on the hardware and software used	Very fast	Fast	Fast	Very fast	Very fast
Cost effectiveness	Can be expensive to set up and maintain	Cost-effective	Expensive	Expensive	Cost-effective	Cost-effective
Ease of use	Can be difficult to use for non-technical users	Easy to use	Difficult to use	Difficult to use	Easy to use	Easy to use
Security	Can be complex to implement and manage	Very secure	Secure	Secure	Very secure	Secure
Scalability	Can be difficult to scale up or down	Highly scalable	Highly scalable	Scalable	Highly scalable	Highly scalable
Other key attributes	Can be customized to meet specific needs	Columnar storage	Lakehouse architecture	Shared-disk architecture	Columnar storage	Hybrid architecture

Different observers might evaluate performance and other aspects of each platform differently. Still, the basic capabilities of any data warehouse are functionally the same as required to support AI. 

In some cases, relative strengths could be an advantage for artificial intelligence processing tasks, some might argue. But, as always, platform choices can turn on subtleties, including other choices a buyer already has made. 

Feature	Snowflake	Databricks	Amazon Redshift	Google BigQuery	Azure Synapse Analytics
Processing speed	Fast	Fast	Good	Good	Good
Cost effectiveness	Good	Variable	Variable	Excellent	Variable
Ease of use	Good	Challenging	Good	Excellent	Challenging
Security	Excellent	Excellent	Excellent	Excellent	Excellent
Scalability	Excellent	Excellent	Excellent	Excellent	Excellent
Other key attributes	Columnar storage	Unified analytics platform	Fully managed	Serverless	Hybrid

Such warehouses are crucial during the initial model training. Afterwards, experts say only some of the training data has to remain in the warehouse. But new data also is expected to be added over time, to update the model. 

And of course the data warehouses must be used to house the model, once built. Data warehouses are essential for inference queries, addition of new data over time. 

Platform	Queries per second
Snowflake	12,000
Amazon Redshift	9,000
Google BigQuery	8,000
Microsoft Azure Synapse Analytics	7,000

As a rule, some would say, large global enterprises, with vastly-larger amounts of data to use as part of the training, will be more costly than building models for mid-market firms with less-voluminous training mass. Small businesses with relatively limited amounts of data to parse will face smaller charges.

Most observers might tend to agree that training arguably will cost more for any entity, of any size, when conducted using private data resources, rather than engaging a cloud computing partner. 

Building a model and training it are precisely the sorts of “one off” activities information technology professionals are advised to outsource, rather than doing themselves.

Business size	Cost of building generative AI model on-premises	Cost of building generative AI model on the cloud
Fortune 500	$10 million - $100 million	$5 million - $50 million
Mid-market	$1 million - $10 million	$500,000 - $5 million
Small business	$100,000 - $1 million	$50,000 - $100,000

Small entity costs likely will fall over time as suppliers increasingly supply generic models, already trained, to the requirements of smaller entities. As always with any software, computing or application products, versions intended for small entities will not have the same robust features as provided to the largest enterprises, but will be far more affordable.