Showing posts sorted by date for query broadband definition. Sort by relevance Show all posts
Showing posts sorted by date for query broadband definition. Sort by relevance Show all posts

Monday, February 19, 2024

"Fair Share" Costs Will be Borne by Businesses and Consumers

So-called “fair share” payments by a few hyperscale app providers to internet access providers in Europe means a formal end to network neutrality; higher revenues for connectivity providers but higher costs for the affected few hyperscale app providers.


As always, business partners of the affected hyperscale firms will wind up paying for the higher app provider costs. Such new fees are simply a cost of doing business, and will be incorporated into the costs the affected hyperscalers must cover to sustain their businesses.


It remains possible that other ramifications will be seen, even if unexpected. The app providers may have new incentives to reshape their apps to reduce the amount of bandwidth they consume on the local access networks.


They might have new incentives to create their own networks, especially if some connectivity partners are able to forego charging the fees, thus offering a lower-cost access alternative.


To be sure, such actions would be complicated and run counter to the business interests of having apps available to as many people as possible. But higher fees for business partners would be relatively simple as a remedy.


If profitability becomes more challenging in EU countries, compared to others, the affected hyperscalers might decide to focus growth and investment priorities elsewhere, to some extent.


And that might be among the benefits legislators see. Since governments are obviously interested in promoting domestic competition to the hyperscalers, and since such smaller potential domestic competitors would not have to pay the fees, domestic broadband networks should get a boost at the same time that domestic competitors to the hyperscalers get a small cost advantage.


But some of us might also note that the additional costs are likely to resemble other regulatory costs faced by hyperscalers and other leaders in any industry who face regulatory costs. The additional costs might be unwelcome, but rarely, if ever, are life threatening.


The costs will be recovered from business partners or customers and users, in some way. A decade from now, nobody will pay much attention, as the marginal increased costs are simply built into routine cost structures.


Such payments by a few hyperscalers also effectively end network neutrality. Recall that the fundamental net neutrality principle is “non-discrimination.” 


At its core, net neutrality means ISPs must treat all internet traffic equally, regardless of the source, destination, content, platform, or application. That has, in practice, meant no blocking, throttling, or prioritizing specific content.


By definition, taxing traffic from a few hyperscale app providers--delivered at the request of the ISP’s own customers--is unequal treatment by source, content, platform and application. 


Granted, the flow of revenue within a value chain can take many forms. But the core principle in the communications business has been that customers pay for their own consumption. The possible new European Union rules on “fair share” shift the revenue flow, allowing ISPs to charge both their own customers as well as a few firms whose products their customers use extensively. 


At a high level, digital infrastructure value flows toward end users and retail customers, while revenue flows from end users back to infra suppliers. 


Content value is more complicated, as are revenue mechanisms. Some professional content creators create value that flows to distributors, and then to consumers. Revenue can flow from end users, advertisers and other business partners towards content creators and distributors. 


E-commerce providers create value that flows up from product creators and suppliers towards consumers. Revenue flows from buyers to sellers and distributors. 


Providers of social media and search functions create value that flows to end users. Revenue flows from advertisers and business partners towards the social media and search providers. 


source: Kearney 


The change in connectivity value might remain relatively unchanged if the EU imposes “fair share” requirements. End users might still be able to access their favored “fair share” apps. But changes always are possible. In the EU, surcharges or fees for some features could develop, as the affected app providers move to maintain their profit margins. 


The affected firms might optimize their platforms to minimize data consumption, which could affect user experience. They could shift resources and investments away from the EU, focusing on markets with less stringent regulations.


The affected companies might raise advertising costs for their partners. 


The affected firms might explore new data monetization methods. Subscription models for specific features or content could develop.


Overall, the affected firms would likely accelerate an exploration of alternative and additional new revenue streams to compensate for the new costs of doing business in the EU. 


Advertising on EU-consumed content might increase. For-fee elements of service could increase. 


The point is that no value chain participant, facing higher costs from one of its suppliers, is going to sit still. At the very least, new efforts will be made to offset the higher costs. 


In principle, the proposed new payments are a tax on doing business in the EU. And, like all taxes, they are simply a cost of business whose costs must be recovered. They will be recovered. And the payment burden will ultimately fall on consumers and business partners.


Tuesday, December 5, 2023

Hard to Visualize Novel AI Use Cases and Firms

Thinking back to 1995, it seems obvious that people had a hard time imagining what would be possible and how business models, behavior and products could change as the internet took hold. 


Until 1994, for example, no visual web browser had become popular. Internet Explorer was not released until 1995. Prior to about 1993, the internet was text-based. Only with the commercialization of web browsers could sites develop multimedia capabilities. 


But multimedia experiences were limited by internet access bandwidth as well. Generally speaking, home broadband speeds did not become widespread until about 2001, using the traditional definition of 1.544 Mbps as the minimum for “broadband” connections. 


Year

Typical Speed (kbps)

1995

28.8

1996

33.6

1997

56

1998

128

1999

256

2000

512

2001

1024

2002

2048

2003

4096

2004

6144

2005

8192


Keep in mind that Amazon, for example, was founded in 1994, and only sold books. Netflix was founded in 1997, but only shipped compact disks. Google was not founded until 1998. Facebook did not emerge until 2004. 


You get the point: early on in any new era, it is hard to envision what will emerge. 


Company

Founding Date

Amazon

1994

Alphabet (Google)

1998

JD.com

1998

Meta (Facebook)

2004

Alibaba

1999

Tencent

1998

ByteDance

2012

Netflix

1997

Meituan

2010

PayPal

1998

Salesforce.com


1999



We can reasonably assume some new forms of infrastructure will be necessary, as internet service providers and cloud computing “as a service” were necessary for the broadband-supported and multimedia  internet. 


Harder to determine early on are the emergence of new applications and use cases, as was the case for search firms, e-commerce giants, social media, video and audio streaming services, ridesharing, software and computing “as a service,” fintech, the gig economy, cybersecurity and many forms of analytics are some of the new industries created as a result of the commercialization of the web, multimedia web and broadband-supported web


Obviously, artificial intelligence likewise will create some new industries and functions, many related to the “infrastructure” part of AI, as was the case for internet service providers, cloud computing “as a service” firms and mobile apps. 


Other trends might not create specific companies or industries but underpin all of them. At a broader level, the multimedia and “easy to use” nature of the internet was enabled by the World Wide Web, which was an enabler, not a “company.” The web, and the internet, in turn, was built on part on appliances, including mobile phones and personal computing, which did create specific companies. 


The point is that architectures often do not create companies, but infrastructure does. And devices and apps virtually always lead to company creation. All of which might seem pedestrian, but are quite relevant as we try and envision the changes AI will bring. 


Most firms and processes will change because of AI, but that does not necessarily create “new” industries or functions, except in infrastructure areas. All firms use computers and the internet and mobility, for example. 


But some new industries have arisen with personal computing, the internet and mobility, and likely will also happen with the emergence of AI. The trick is imagining what new industries and functions could arise, aside from AI reshaping all existing industries and functions.


Sunday, September 24, 2023

5G is to Edge Computing as WANS are for Cloud Computing

One way to look at 5G is to examine its role in supporting computing operations, rather than the role of enabling communications, much as global data transport networks can be looked at as essential parts of the cloud computing infrastructure, or Wi-Fi can be viewed as a key part of the internet access function. 


Simply stated, in the internet era most “computing” is inseparable from “internet access.” In other words, data processing, storage and app consumption depend on communications. 


And some use cases demand low latency, which drives demand for  edge computing. In other cases, edge computing adds value by reducing the amount of wide area network investment that has to be supported. 


Use Case

Value from Low Latency

Value from Reduced Bandwidth

Self-driving cars

Critical

Significant

Augmented reality/virtual reality

Critical

Significant

Industrial process control

Critical

Significant

Smart cities

Moderate

Moderate

Content delivery networks (CDNs)

Significant

Critical

Internet of Things (IoT) devices

Moderate

Significant


Edge computing value also varies among use cases. It is hard to imagine successful widespread use of self-driving vehicles without very-low-latency data processing “on the device,” accessible without wires. 


On-device is the place to put real-time language translation activities, such as translation during a voice call or during video or audio playback of content. Untethered access is essential when the devices are smartphones. 


5G also enables new cloud-based gaming services. These services allow gamers to play high-end games on their mobile devices without the need to download or install any software. These use cases typically require both low latency. 


5G is being used to automate industrial processes by connecting untethered devices to local servers for real-time process control. 


Proposed virtual reality use cases typically require both on-device and remote computing support, but with low latency crucial for realism. 


But many edge computing scenarios actually benefit from a mix of low latency and bandwidth-reduction value. 


5G is used to stream high-definition and ultra-high-definition video to mobile devices from edge content servers. Live streaming of sporting events and concerts to mobile devices also requires untethered access. But there the value mostly is bandwidth reduction, not so much latency. 


Likewise, 5G is being developed to support smart city applications, such as traffic management, public safety, and utility monitoring, though most of these apps will initially use remote computing rather than on-device computing. 


In some cases, as when delivering a self-contained on-device app, perhaps the greatest need is simply downloading the app and updating the app, ad delivery and uploading of usage and behavior profiles. 


In many other use cases, virtually every keystroke for a document, every frame of a video, every note of a song, every pixel of an image has to be transported to a remote server location. 


Our computing architecture includes processing on-device, on the premises, metro or regional data center and remote data center venues. 


Traditionally we’d describe the various key parts of the connectivity network as involving the inside home network (local area network using Ethernet or Wi-Fi); access network (home broadband); middle mile (connection between local network and nearest internet point of presence) and wide area network (long distances between points of presence). 


In all these cases, connectivity to nearby or remote resources is required. 


Edge computing, in general, is driven by the need for processing with low latency, and sometimes by the added advantage of reducing network bandwidth demand. 


Language translation “on the fly” is an example of the former; video content delivery an example of the latter. 


Edge Use Case

Description

Object detection and recognition

AI can be used to detect and recognize objects in images and videos. This can be used for a variety of applications, such as security, surveillance, and quality control. For example, AI-powered cameras can be used to detect intruders in a secure facility, or to identify defects in products on a production line.

Natural language processing (NLP)

NLP can be used to understand and respond to human language. This can be used for a variety of applications, such as customer service, chatbots, and voice assistants. For example, NLP-powered chatbots can be used to answer customer questions about products and services, or to help customers book appointments.

Machine learning (ML)

ML can be used to train AI models to learn from data and make predictions. This can be used for a variety of applications, such as fraud detection, predictive maintenance, and medical diagnosis. For example, ML-powered models can be used to detect fraudulent transactions, or to predict when a machine is likely to fail.

Computer vision

Computer vision is a field of AI that deals with the extraction of meaningful information from digital images and videos. This can be used for a variety of applications, such as self-driving cars, facial recognition, and medical imaging. For example, computer vision-powered systems can be used to identify pedestrians and other objects on the road, or to detect cancer cells in medical images.

Self-driving autos

AI at the edge is used to power the self-driving features in cars, such as lane keeping assist and adaptive cruise control, which, by definition, must use untethered mobile access. 

Smart homes

AI is used to power smart home devices, such as thermostats and security systems, which use untethered access and mobile networks for connectivity. 

Smart cities

AI is used to collect and analyze data from IoT devices, such as sensors and actuators, which use mobile networks for network connectivity.

VR/AR

Uses a mix of edge and remote computing


The point is that 5G can be viewed through the lens of “compute platform” rather than “communications,” just as cloud computing, data centers, edge computing and devices can be assessed as computing venues.


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