Saturday, September 23, 2023

When "Growth" Capex Becomes "Maintenance" Capex

In a fundamental sense, much capital investment previously described as "growth" capex (infra investments supporting capacity, speed, new products such as SD-WAN or


One almost-perverse reality in the connectivity business is that legacy products often are more profitable than the newer products intended to replace the legacy offerings, even as demand for the legacy products dwindles.


Almost perversely, the strategic rationale for many investments, such as next-generation mobile networks or fiber-to-home, is driven less by expectations of highly-profitable new services but by the necessity of doing so to remain in business.


To be blunt, "you get to keep your business" is the investment rationale, more so than "you will boost revenues significantly." In fact, even when new products or services are created, profit margins will be lower than for legacy products.


Cable TV companies and telcos face precisely that problem with video streaming services, compared to linear video, for example. Telcos faced the same problem with VoIP and messaging. Home broadband and mobile internet access seem to be faring the best, though each of those services is precisely a "dumb pipe" offer: access but not apps; bandwidth but not "services;" flat fees for usage but not participation in app and service revenue models dependent on internet access.


There are several reasons why products with declining demand are more profitable than newer services, starting with the strategy of “harvesting” the products. Since the investments to create legacy products often have already been amortized, there is relatively little additional capital investment or operating cost required to run the business supporting those products, compared to building infrastructure and demand for new products.


Also, revenue upside from more-advanced products can actually be lower, per unit or per user, than the legacy products. Cloud computing, for example, lowers the cost of creating new software products. Open source and multimedia, general purpose networks do too. 


Legacy public switched network voice was generally more expensive to create as it used proprietary technology to create a single-purpose network. Voice over IP is generally less costly as it uses a multi-purpose network, more open platforms and more-generic hardware, with resources that can be more centralized (so less investment is required). 


Factor

PSTN Voice Services

VoIP Services

Upfront costs

High

Low

Ongoing costs

Medium

Low

Scalability

Medium

High

Flexibility

Medium

High

Complexity

High

Medium


In a classic example, service provider profits and gross revenue from VoIP can be lower than what was the case for legacy voice products. 


Product

Type

Revenue Expectation

Profit Expectation

Voice over IP (VoIP)

Newer

Low

Low

Mobile data

Newer

High

Medium

Cloud computing

Newer

High

Medium

Traditional landline service

Legacy

Medium

High

Cable TV

Legacy

Medium

High

DSL internet

Legacy

Medium

High


Almost perversely, profit margins from selling digital subscriber line home internet access, though an “inferior” product, might actually be higher than selling home broadband using fiber-to-home or other platforms. 


Service providers used to make high profits from text messaging, but they have almost no ability to monetize the multimedia messaging alternatives such as WhatsApp. In principle, one might ask why development of new products makes sense, if the financial returns are low. 


The issue, strategically, is that harvesting does not solve the “what is my business of tomorrow” problem. As with any industry, connectivity services have product lifecycles. Each product eventually faces declining demand. So a company that only harvests will eventually go out of business.


So new products, even when they feature lower-profit margins, must be created. 


One is left with the almost-inescapable conclusion that often, when new products are envisioned as substitutes for legacy products, they become “features.” 


Voice communication once was the primary value provided by a mobile network. These days, though, voice is more often a feature of a mobile service. 


A mobile service unable to handle voice calls or text messaging features would not be a competitive product, but service providers earn less revenue from voice over time. 


Voice and text/multimedia messaging might be an essential feature of the service. But spending lots of effort and money to create a new voice and messaging experience might not produce satisfying financial returns. 


In other cases, such as upgrading networks from DSL to FTTH, the strategic rationale is quite clear: a telco has no future without that upgrade. But the actual revenue and profit impact might vary quite a lot in the near term. 


In other cases, such as the transition from linear to streaming versions of entertainment video, the outcome for service providers remains unclear. In the transition from dial-up internet to DSL and cable modems, a whole class of internet service providers was forced out of business, as success shifted to ownership of access facilities. 


It’s an open question right now whether connectivity service providers will retain a role, and what sort of role, in a future where most video entertainment has shifted to streaming delivery. Can network service distributors be disintermediated, and to what extent? 


Will distribution shift to the streaming video providers who go direct to consumer? And will that shift the connectivity provider role to that of mere sales agent? It remains unclear. 


The broad point to be made is that new substitutes for legacy products are not uniformly as revenue producing or profitable as the legacy products, often because demand has shifted away, because new forms of competition limit pricing power or because the new products require high levels of investment. 


“Doing nothing” is a strategic death sentence for any copper-access-based fixed network provider. But upgrading to FTTH is not a panacea, either. FTTH creates a sustainable platform for competing, but is not an automatic net generator of higher revenue and profits. 


In a nutshell, that is the problem with much technology innovation. Some strategic imperatives that allow a firm to remain in business are not automatically going to solve the business problem of replacing legacy products. 


VoIP is not a strategic answer if the product itself faces declining demand (people shift to mobile phones for calling, for example). And there are cases where changing demand actually destroys a business opportunity. Dial-up ISPs went away when home broadband emerged, for example. 


Nor is it clear what roles might be left for connectivity providers when a fuller shift to video streaming has happened.


The point is that investments in new platforms, networks and services, often without huge expectations of higher revenues and profit margins, is a necessity in the connectivity business. In a business sense, almost all infra capital investment is for "maintenance" rather than "growth," in a fundamental sense.


5G has to replace 4G, often less because revenue will be higher but because additional capacity must be added to remain competitive and meet customer desires for data consumption and app experience. The same holds for FTTH.


Thursday, September 21, 2023

On-Device is the Overlooked AI Compute Venue

One way to look at 5G devices such as smartphones is as a developing computing architecture supporting artificial intelligence. In other words, some AI tasks are “best” handled on the device, while others are more appropriately handled at remote data centers. 


And some AI-related operations will make more sense at a metro data center (possibly regional) while yet others might be handled at smaller “edge” data centers.


On-device

Metro

Small "edge"

Remote data center

Real-time inference: Machine learning models that need to make predictions in real time, such as object detection and image recognition.

Data aggregation and analysis: Collecting and analyzing data from multiple devices and sensors in a localized area.

Data caching: Storing data that is frequently accessed by devices in a nearby location.

High-performance computing: Tasks that require a lot of processing power, such as video encoding and scientific computing.

Personalized experiences: Tailoring the user experience to individual preferences, such as recommending products and services.

Content delivery: Serving content to users in a localized area, such as streaming video and music.

IoE device management: Managing and monitoring IoT devices in a localized area.

Data backup and storage: Storing data for long-term retention and archiving.

Privacy-sensitive applications: Applications that handle sensitive data, such as financial transactions and medical records.

Disaster recovery: Providing a backup site for critical applications and data in the event of a disaster.

Edge analytics: Performing analytics on data at the edge of the network, such as filtering and identifying anomalies.

Batch processing: Tasks that can be processed in batches, such as data mining and machine learning training.


Most of us could predict that AI will affect data centers--in large part--by significantly increasing requirements to support generative AI model building, training and then inference operations. 


In some cases, metro or regional data centers will take the place of remote date centers. There is likely more disagreement about use of small edge data centers to support those operations. 


But often overlooked are the potential roles for device-based AI operations. 


On-device

Metro

Small "edge" data center

Remote data center

Voice assistants

Real-time traffic updates

Surveillance cameras

Batch processing

Augmented reality

Smart home devices

Self-driving cars

Data storage

Virtual reality

Industrial automation

IoT devices

Machine learning training

Mobile gaming

Content delivery networks

Real-time analytics

Disaster recovery

Natural language processing

Edge computing applications

Autonomous vehicles

High-performance computing

Computing Infra Not "Ready" for AI?

A survey of 500 information technology professionals sponsored by LogicMonitor shows at least half of those respondents believe their infrastructure is not equipped to handle increased artificial intelligence. 


And information technology analysts believe the amount of new investment to support AI could be substantial. 


A study by McKinsey Global Institute estimated that businesses would need to invest $3.5 trillion in AI by 2030 to realize AI benefits.


A study by the Boston Consulting Group suggested  that businesses would need to invest $1.7 trillion in AI by 2025 to realize AI benefits.


A study by PwC suggested that businesses would need to invest $2.2 trillion in AI by 2030 to retrain and reskill workers who are displaced by AI.


Of course, IT professionals often say the existing infra is not prepared for the platforms and functions, when a new technology emerges. In many cases, preparedness is an issue precisely because the capabilities and skills needed to introduce a new platform do not yet exist.  Many past examples include:


Electronic data interchange (EDI)

Enterprise resource planning (ERP)

Customer relationship management (CRM)

Supply chain management (SCM)

Content management systems (CMS)

Virtual private networks (VPNs)

Mobile computing

Cloud gaming

Telehealth

Robotic process automation (RPA). 


And there will always seem to be some new thing that IT professionals report they are not prepared to handle with the existing infra. CIO magazine’s annual surveys have tended to show new concerns every year, for example. 


Innovation

Study

Date of Publication

Cloud computing

The State of the CIO 2011

2011

Bring your own device (BYOD)

The State of the CIO 2014

2014

Social media

The State of the CIO 2015

2015

Big data

The State of the CIO 2016

2016

The Internet of Things (IoT)

The State of the CIO 2017

2017


Among the oft-cited concerns:


  • Lack of skilled talent. AI is a complex technology that requires specialized skills and knowledge. Many organizations do not have the in-house expertise to develop and deploy AI solutions.

  • Data quality and availability. AI algorithms need to be trained on large amounts of high-quality data. However, many organizations lack the necessary data or the resources to collect and clean it.

  • Cost. AI can be a costly investment, especially for small and medium-sized businesses. The cost of developing, deploying, and maintaining AI solutions can be prohibitive for some organizations.

  • Regulatory compliance. AI raises a number of regulatory concerns, such as data privacy and bias. Organizations need to ensure that their AI solutions comply with all applicable regulations.

  • Security risks. AI systems can be vulnerable to cyberattacks. Organizations need to take steps to protect their AI systems from unauthorized access and tampering.

  • Ethical concerns. AI raises a number of ethical concerns, such as bias and discrimination. Organizations need to develop ethical guidelines for the use of AI.


IT professionals believe that they need to make the following investments to adapt their infrastructures for AI, aside from acquiring new AI skills and continuing to streamline code development processes::


  • Data infrastructure. AI algorithms need to be trained on large amounts of data. Organizations need to invest in data infrastructure that can store and process this data efficiently.

  • Compute infrastructure. AI algorithms can be computationally demanding. Organizations need to invest in compute infrastructure that can run these algorithms quickly and efficiently.

  • Networking infrastructure. AI applications often need to access and process data from multiple sources. Organizations need to invest in networking infrastructure that can support this connectivity.

  • Security infrastructure. AI systems can be vulnerable to cyberattacks. Organizations need to invest in security infrastructure that can protect their AI systems from unauthorized access and tampering.

Directv-Dish Merger Fails

Directv’’s termination of its deal to merge with EchoStar, apparently because EchoStar bondholders did not approve, means EchoStar continue...