Which Edge Will Dominate AI Processing?

Edge computing advantages generally are said to revolve around use cases requiring low-latency response, and the same is generally true for artificial intelligence processing as well. 

Some use cases requiring low-latency response will be best executed “on the device” rather than at a remote data center, and often on the device rather than at an “edge” data center. 

That might especially be true as some estimate consumer apps will represent as much as 70 percent of total generative artificial intelligence compute requirements. 

So does that mean we see graphics processor units on most smartphones? Probably not, even if GPU prices fall over time. We’ll likely see lots of accelerator chips, though, including more use of tensor processing units or neural processing units and application specific integrated circuits, for reasons of cost.  

The general principle is always that the cost of computing facilities increases, while efficiency decreases, as computing moves to the network edge. In other words, centralized computing tends to be the most efficient while computing at the edge--which necessarily involves huge numbers of processors--is necessarily more capital intensive. 

For most physical networks, as much as 80 percent of cost is at the network edges. 

Beyond content delivery, many have struggled to define the business model for edge computing, however. Either from an end user experience perspective or an edge computing supplier perspective. 

Sheer infrastructure cost remains an issue, as do compelling use cases. Beyond those issues, there arguably are standardization and interoperability issues similar to multi-cloud, complexity concerns and fragmented or sub-scale revenue opportunities. 

In many cases, “edge” use cases also make more sense for “on the device” processing, something we already see with image processing, speech-to-text and real-time language translation. 

To be sure, battery drain, processors and memory (and therefore cost) will be issues, initially. 

On-Device Use Case	Benefits	Considerations
Image Processing (Basic)	Privacy: Processes images locally without sending data to servers.  Offline Functionality: Works even without internet connection. - Low Latency: Real-time effects and filters.	Limited Model Complexity: Simpler tasks like noise reduction or basic filters work well on-device. - Battery Drain: Complex processing can drain battery life.
Voice Interface (Simple Commands)	Privacy: Voice data stays on device for sensitive commands. - Low Latency: Faster response for basic commands (e.g., smart home controls).	Limited Vocabulary and Understanding: On-device models may not handle complex requests. - Limited Customization: Pre-trained models offer less user personalization.
Language Translation (Simple Phrases)	Offline Functionality: Translates basic phrases even without internet. - Privacy: Sensitive conversations remain on device.	Limited Languages and Accuracy: Fewer languages and potentially lower accuracy compared to cloud-based models.  Storage Requirements: Larger models for complex languages might not fit on all devices.
Message Autocomplete	Privacy: Keeps message content on device.  Offline Functionality: Auto-completes even without internet.	Limited Context Understanding: Relying solely on local message history might limit accuracy. - Personalized Experience: On-device models may not adapt to individual writing styles as well.
Music Playlist Generation (Offline)	Offline Functionality: Creates playlists based on downloaded music library. - Privacy: No need to send music preferences to the cloud.	Limited Music Library Size: On-device storage limits playlist diversity. - Static Recommendations: Playlists may not adapt to changing user tastes as effectively.
Maps Features (Limited Functionality)	Offline Functionality: Access basic maps and navigation even without internet. - Privacy: No user location data sent to servers for basic features.	Limited Features: Offline functionality may lack real-time traffic updates or detailed points of interest. - Outdated Maps: Requires periodic updates downloaded to the device.

Remote processing (edge or remote) will tend to favor use cases including augmented reality; advanced image processing; personalized content recommendations or predictive maintenance. 

Latency requirements for these and other apps will tend to drive the need for edge processing.

From AI Chatbots to AI Agents

The evolution of generative artificial intelligence chatbots to AI agents might be likened to the difference between a customer service representative who follows a script and a knowledgeable personal assistant who understands your needs, accesses information, and completes tasks on your behalf. 

Chatbots are sort of “manual” operators: they respond to user questions, but do nothing unless they are asked, responding using pre-defined knowledge bases and scripts. 

AI agents, in principle, are designed to operate more automatically, performing tasks on a user’s behalf.

May access and manipulate data to complete tasks (booking a flight, recommending products). Agents are able to handle more-complex interactions and conduct more-complex tasks. 

Analogy	Chatbot	Agent
Restaurant Server	Takes your order based on a fixed menu	Recommends dishes based on your preferences and dietary restrictions
Customer Service Representative	Follows a script to answer basic questions	Resolves complex issues by accessing customer history and internal databases
Travel Booking Agent	Books flights based on simple criteria (carrier, date, time, fare, direct or connecting flights).	Creates a personalized travel itinerary based on your interests and budget
Library Assistant	Keyword-based search	Researches topics across different resources and summarizes key points
Movie Recommendation System	Suggests popular movies based on genre	Recommends movies based on your past viewing history

Many Winners and Losers from Generative AI

Perhaps there is no contradiction between low historical total factor annual productivity gains and high expected generative artificial intelligence revenue impact; productivity impact or profit impact for some firms in some industries. 

Keep in mind that total productivity changes include effects from all sources, not just information technology. So unless you believe IT was solely responsible for total productivity change since 1970, the actual impact of IT arguably is rather slight, perhaps on the order of 0.5 percent up to about 1.5 percent per year, maximum. 

And those years would include the impact of personal computers, the internet and cloud computing, to name a few important information technology advances. 

Industry Sector (NAICS Code)	Description	Percent Change in Labor Productivity (2022)
Total Nonfarm Business (All)	Covers all industries except agriculture, government, and private households	1.0%
GoodsProducing Industries (1133)	Includes mining, construction, and manufacturing	0.4%
Manufacturing (3133)	Factory production of goods	0.5%
Construction (23)	Building, renovation, and maintenance of structures	1.2%
Mining (21)	Extraction of minerals and natural resources	2.3%
ServiceProviding Industries (4892)	Covers a wide range of service businesses	1.4%
Wholesale Trade (42)	Selling goods to businesses in bulk	1.2%
Retail Trade (4445)	Selling goods directly to consumers	0.4%
Transportation and Warehousing (4849)	Moving people and goods	2.1%
Information (51)	Publishing, broadcasting, and telecommunications	2.5%
Financial Activities (52)	Banking, insurance, and real estate	1.2%
Professional and Business Services (5456)	Legal, accounting, consulting, and scientific services	2.0%
Education and Health Services (6162)	Schools, hospitals, and other social services	1.3%
Leisure and Hospitality (7172)	Accommodation, food services, and entertainment	3.9%
Other Services (8189)	Repair shops, personal care services, and religious organizations	0.8%

On the other hand, some forecasts of higher impact for some firms in some industries are not necessarily incompatible with the “all industries” trends for productivity improvements. The best firms in the industries most able to use GenAI might well wring more benefit from the technology. 

In fact, that might tend to be the case for the best and worst firms in almost any industry. 

Also, cumulative productivity gains over a period of years will of course be higher than single year gains. 

Revenue gains in excess of 10 percent for some companies in some industries over a multiyear period are conceivable, even if single-year gains are in single digits or less. 

Industry	Potential Impact	Source, Forecast
Manufacturing	Increased product design efficiency and innovation  Improved production line optimization  Reduced waste and defects	McKinsey & Company: 20% to 40% productivity gains by 2030 PwC: Up to $3.7 trillion global GDP impact in manufacturing by 2030
Retail and  Ecommerce	Personalized marketing and promotions  Enhanced customer experience (chatbots, product recommendations)  Optimized pricing and inventory management	J.P. Morgan: Up to 10% revenue growth for retailers by 2030 Accenture: Up to $1 trillion in annual revenue growth for retailers by 2035
Financial Services	Fraud detection and risk management  Algorithmic trading and portfolio management  Personalized financial advice and wealth management	Goldman Sachs: Up to $1.2 trillion in annual cost savings for financial institutions McKinsey: Up to $200 billion in annual revenue growth for wealth management by 2030
Healthcare	Drug discovery and development  Personalized medicine and treatment plans  Improved medical imaging analysis	PWC: Up to $150 billion in annual savings in the US healthcare system McKinsey: Up to $6 trillion in global healthcare productivity gains by 2030
Media & Entertainment	Content creation (music, scripts, video)  Personalized content recommendations  Streamlined content production workflows	Bain & Company: Up to 10% productivity gains in media content creation by 2030

The point, though, is that big numbers predicted for applied GenAI have to be understood in context. Total-economy gains will be far smaller than many expect, even if some firms, in some industries, will show higher revenue growth; profit rates or productivity gains. 

Where is Telco "Core Competency" Going?

If you ask just about any telco executive or middle manager what their firm’s “core competency” is, the traditional answer almost always has something to do with “creating and operating communications networks.”

Keep in mind that a core competency is not a list of “things we do well.” Instead, it is the combination of resources and skills that give a company a strategic advantage in the marketplace. It's essentially what a company does best and what differentiates it from competitors.

In the monopoly era before 1980, that answer would have made sense, as the law prohibited all others from operating in the business. In the competitive era the traditional answer is eroding. Firms such as Google and others actually fund, build and operate their own connectivity networks. So do cable TV companies and all sorts of mobile communications companies. 

So it is hard to make the argument that “creating and operating networks” actually is the core competency anymore, especially as new suppliers continue to enter the market.  

Decade	Core Competency Claim	Supporting Studies	Limitations
1980s	Network Building & Operation	* Caves, R. E. (1982). Multinational enterprise and economic structure. North-Holland ([study of vertical integration in telecoms])	Network infrastructure was seen as a significant barrier to entry, giving telcos a strong advantage.
1990s	Network Building & Operation (contested)	* Faulkenberry, G. D., & Caves, R. E. (1995). Telecommunications infrastructure: The missing link to economic development. Brookings Institution Press. ([study on telecom infrastructure's role in economic development])	Technological advancements and regulatory changes began to challenge the dominance of network ownership.
2000s	Network Building & Operation (further challenged)	* Witt, R. (2004). How the mobile network became ubiquitous: The telco industry and the regulators. Information, Communication & Society, 7(1), 73-95. ([analysis of co-opetition and network sharing in mobile telecoms])	The rise of wholesale fiber networks and joint ventures cast doubt on network ownership as the sole core competency.
2010s - Present	Shifting Landscape	* Brynjolfsson, E., Rockaway, R., & Van Alstyne, M. W. (2017. Platform revolution: How networked markets are transforming the economy and making winner-take-all competition irrelevant. W. W. Norton & Company. ([study on platform business models in telecoms])	Regulatory expertise, innovation & service delivery, and customer experience are increasingly seen as crucial alongside network capabilities.

Precisely where core competency might eventually be identified is among the core questions for industry leaders, especially as “network operation” ceases to be a clear and indisputable core competence. 

Some might suggest the eventual core competency could be as an “orchestrator of connectivity.” In other words, some tier-one telcos could evolve as system integrators for global communications across all networks (fiber, wireless, satellite) for businesses and consumers. They might own some of the resources used, but largely function as one-stop-ship global connectivity suppliers. 

Perhaps some might try to become “platforms” and “enablers,” essentially becoming connectivity infrastructure providers supporting business partners who develop and deliver the actual end-user services.

Roles for smaller firms could be dramatically different, as smaller telcos might not have the means to support global integrator roles, and might function as suppliers of local resources in a particular geography to the larger tier-one suppliers. 

Arguably less likely are evolutions that would reposition telcos as suppliers of targeted advertising, network optimization solutions, or location-based services supported by their ability to target network users. 

And though cybersecurity is increasingly embedded into all hardware and software, it seems unlikely that most tier-one telcos and smaller firms can become cybersecurity specialists.

The core competency underlying all  these future scenarios includes ability to manage and integrate complex communication ecosystems; creating seamless and personalized communication experiences.

If I had to guess right now, I’d assume the global connectivity integrator role would make sense for a handful of tier-one providers. 

The enabler role might become more prominent for smaller service providers, in the same way that the internet separates app development and ownership  from network access; or compute infrastructure from apps. 

The big switch is from specialized app provider (voice and data connectivity for business customers) to “internet access and transport” provider; global system integration rather than regional franchise; retail point of contact rather than physical layer transport provider. 

Even the move to joint ventures, wholesale network operation and ability of third parties to enter the business all suggest that the “network operator” competence is changing to something else.

The issue is how firms discover that new core competence.