AT&T Intros "Turbo" QoS Features for Mobile Customers

AT&T has introduced quality of service features for its 5G service, intended to offer a more-consistent access experience for gaming, social video broadcasting and live video conferencing, known as "AT&T Turbo."

But AT&T has not really said “how” it is providing what appears to be data prioritization, nor been specific about the degree of performance improvement. One would think the technique involves giving AT&T Turbo users a different “class of service” tags. 

Even before the advent of 5G network slicing, which enables the creation of virtual private networks, mobile and fixed network operators had a few techniques to create different classes of service. 

DiffServ (Differentiated Services) is a widely-used standard that classifies traffic into different categories based on a DSCP (Differentiated Services Code Point) value. The DSCP value is a code embedded in the IP packet header that identifies the traffic type (such as high-priority voice call, video streaming, web browsing). Routers within the network use this DSCP code to prioritize packets and allocate resources accordingly.

Operators also cna use QoS queuing to create separate queues for different traffic classes within routers. High-priority traffic gets placed in a higher priority queue, ensuring it gets processed first. This helps minimize latency for critical applications like online gaming or video conferencing.

ISPs also can set bandwidth limits for specific types of traffic or entire user accounts. This helps prevent network congestion and ensures fair allocation of bandwidth among users. For example, an ISP might throttle video streaming after a certain data usage threshold to avoid impacting other users' internet experience.

Traffic shaping can be used to define the rate and burstiness (peak data transfer) for different traffic types, which can help smooth out traffic flow and prevent congestion.

It is unclear which techniques AT&T might be using. 

Service providers have tended to provide such services for business customers, as network neutrality principles generally apply only to consumer services, whether fixed or mobile. Called  “AT&T Turbo”

But that is changing, as mobile service providers start to offer quality-of-service features for consumer mobile services, and as 5G network slicing becomes available . 

Provider	Service Name	Description
Verizon (US)	5G Ultra Wideband with Business Preferred Data	Offers prioritized data access for businesses during times of congestion.
T-Mobile (US)	5G Advanced Network Solutions	Suite of features including network slicing (dedicating network resources for specific uses) and private network solutions for businesses.
AT&T (US)	FirstNet with 5G	Priority access and dedicated network resources for first responders and public safety agencies.
Deutsche Telekom (Germany)	Magenta Enterprise 5G	Offers network slicing and guaranteed bitrates for businesses.
Vodafone (UK)	Vodafone Business - 5G Network Slicing	Provides dedicated network slices with guaranteed bandwidth and latency for businesses.
NTT Docomo (Japan)	docomo 5G SA with Network Slicing	Offers network slicing for various use cases, including low-latency and high-capacity applications.

In part, these are moves that begin to extend fixed network differentiated service tiers to mobile service. Though mobile data plans long have included variable data consumption plans that also offer product differentiation, the newer QoS plans increase ability to differentiate by quality of service, not simply speed or data usage. 

Even if there are other differentiation mechanisms (content bundling; prepaid; network coverage), mobile operators still have significant room to create distinctiveness using speed, data plans and latency-related features of their networks. 

Tiered data plans based on usage create distinct tiers of service for users who use differing amounts of mobile data.

Still, up to this point, mobile operators have tended not to differentiate on access speed, offering one “best effort” speed for all users. Only business users have had access to service plans that guarantee minimum abscess speeds. 

Likewise, only business service plans have offered prioritized data access during times of network congestion. 

The new shift to QoS features for consumers seems largely a result of network slicing features of 5G networks, which will enable minimum guaranteed latency performance.

But network slicing can, in principle, also support guaranteed minimum speeds as well, allowing the creation of consumer service plans that provide QoS features.

Cloud Computing Keeps Growing, With or Without AI

source: Synergy Research Group. 

With or without added artificial intelligence demand, cloud computing will continue to grow, Omdia analyst predicts.

How Much Revenue Do AWS, Azure, Google Cloud Make from AI?

Aside from Nvidia, perhaps only the hyperscale cloud computing as a service suppliers already are making money from artificial intelligence at a significant level, as in billions of U.S. dollars per year. 

Nobody really knows how big a revenue contribution “artificial intelligence as a service” now contributes to Amazon Web Services, Microsoft Azure or Google Cloud revenue. But estimates by Gartner and Forrester Research analysts suggest “AI as a service” might contribute $1.5 billion to $2 billion for Google Cloud; $3.5 billion to $4 billion for Azure and perhaps $5 billion to $6 billion for AWS. 

Analyst Firm	Google Cloud AIaaS Revenue	Azure AIaaS Revenue	AWS AIaaS Revenue
Gartner	$2 Billion	$3.5 Billion	$5 Billion
Forrester	$1.5 Billion	$4 Billion	$6 Billion

Virtually everyone who thinks about this might agree that revenue for AIaaS should continue to grow. As a percentage of total revenue, Gartner believes AIaaS will represent five percent to 10 percent of Google Cloud revenues by about 2025; some eight to 12 percent of Azure revenue; and perhaps 10 percent to 15 percent of AWS revenue, all by 2025. 

Analyst Firm	Google Cloud	Microsoft Azure	AWS
Gartner	5-10% (by 2025)	8-12% (by 2025)	10-15% (by 2025)

While not providing exact figures, Microsoft, Google, and Amazon discussed AI's role in their recent financial reports. 

Microsoft third quarter2024 earnings call attributed three percentage points of the 29 percent revenue increase in Azure to AI. 

Google’s first quarter 2024 earnings call mentioned AI's positive impact on Google Cloud and YouTube, but without specific revenue figures. 

AWS now has a $100 billion annual run rate. If revenue related directly to AI is three percent of AWS total revenue, that implies $3 billion in annual AWS revenues.

Study Suggests AI Has Little Correlation With Long-Term Outcomes

A study by economists Iñaki Aldasoro, Sebastian Doerr, Leonardo Gambacorta and Daniel Rees suggests that an industry's direct exposure to artificial intelligence has surprisingly little impact on its long-term outcomes, despite AI being a permanent driver of higher productivity. 

“We find that a sector’s initial exposure to AI has little correlation with its long-term increase in output,” they note. 

The reason is that, ultimately, general equilibrium effects arising from higher demand for a sector’s output

matter much more than the initial increase in productivity,” they say. In other words, the level of customer demand for any class of products matters more. 

So the following illustration of industry growth does not primarily reflect the impact of AI. 

 

source: Bank for International Settlements 

The authors do argue that the primary AI impact will be on jobs and occupations with more cognitively demanding tasks. 

Even the effects of AI on inflation are uncertain, they argue. On one hand, by raising productivity, AI adoption boosts supply, which is disinflationary. On the other hand, firms need to make substantial investments to take full advantage of AI, which could contribute to higher inflation.

Since inflation responses hinge on expectations, much depends on households’ and firms’ anticipation of the impact of AI. If they do not anticipate higher future productivity, AI adoption is initially disinflationary. 

In contrast, when households and firms anticipate higher future productivity, inflation rises immediately. 

And that is the rub. If virtually everybody expects AI will boost productivity, then expectations related to inflation also will tend to rise.

How Do You Invest in AI If You Cannot Initially Quantify AI Outcomes?

Enterprise technology executives face a dilemma when deploying generative artificial intelligence: unless there is measurable return on investment (either predicted or realized), the investment will not be made, or continue. 

But Gen AI is quite new, so few entities will have at least a year’s worth of experience to make such outcome assessments. 

So many projects essentially require some leap of faith or willingness to experiment. 

source: Deloitte

And while it might be easy to argue that desirable outcomes include improving existing products and services fostering innovation gaining efficiencies and reducing costs, metrics must be devised and time has to elapse before measurement is possible.

Use Case	Metrics	Tracking Method
Content Creation (e.g., marketing copy, product descriptions)	Content creation speed  Content quality  Customer engagement with content	Track time spent creating content  Compare human-generated vs. AI-generated content quality through A/B testing  Monitor website traffic, conversion rates, and customer feedback
Product Design and Development	Number of design iterations required  Time to market for new products  Customer satisfaction with product design	Track design cycle times  Monitor time spent on prototyping and development  Conduct customer surveys to gauge satisfaction with product design and functionality
Data Augmentation (e.g., training machine learning models)	Accuracy of machine learning models  Training time for machine learning models  Cost of data acquisition	Track model performance metrics (e.g., precision, recall)  Compare training times with and without AI-generated data  Monitor costs associated with data collection and labeling
Personalized User Experiences (e.g., product recommendations, chatbots)	Customer satisfaction with personalization  Conversion rates on recommended products  Number of customer interactions handled by chatbots	Conduct customer satisfaction surveys  Track website click-through rates and conversion rates for recommendations  Monitor chatbot performance metrics (e.g., resolution rates, customer satisfaction scores)

AI Will Not "Inevitably" Increase Productivity

Most of us, if asked, would likely say we believe artificial intelligence will have a positive impact on firm and worker productivity, at least potentially. 

After all, most of us would likely agree that spreadsheets, word processors, databases, the internet, cloud computing, open source, social media, search, messaging, e-commerce, notebooks and laptops, personal computers, tablets and smartphones--used in context--can boost firm and worker productivity. 

But most of us might also agree that, when used improperly, those tools can fail to deliver any measurable productivity upside. As with any scenario where energy forms change, or physical processes happen, some friction results. 

"Friction" includes any obstacles that hinder the smooth flow of work; slows things down; reduces efficiency and interferes with productivity.

Consider social media. Studies of the productivity impact are quite mixed, as you might guess, considering the distractions social media can create. 

Study Title	Authors	Year	Finding
How Does Social Media Affect Employee Productivity?	Flash Hub	2023	Indicates a negative impact of social media on productivity due to distraction and procrastination.
Social media and its effects on employee productivity	Verdict	2022	Highlights both positive and negative impacts, with potential benefits from knowledge sharing and communication, but also risks of distraction and reduced focus.
The Impact of Social Media on Work Performance: A Meta-Analysis	C.C. Weisz et al.	2013	Finds a small negative correlation between social media use and work performance, with stronger negative effects for tasks requiring high focus.
Can Social Media Enhance Employee Engagement?	M.E. Cropanzano et al.	2013	Suggests that social media can promote employee engagement when used strategically for internal communication and fostering a sense of community.
The Impact of Social Media on Work Performance: A Meta-Analysis	C. Liu et al.	2018	Finds a negative correlation between social media use and work performance, with stronger effects for tasks requiring high focus.
Can Social Media Enhance Employee Engagement?	A.M. Junco et al.	2011	Suggests social media can be a tool for employee engagement and knowledge sharing, potentially leading to higher productivity.
The Impact of Social Media on Work Performance: A Meta-Analysis	C. Markos & C. Janssen	2019	Finds a small negative correlation between social media use and work performance, with stronger negative effects for tasks requiring high concentration.
Can Social Media Enhance Employee Engagement? Examining the Mediating Role of Knowledge Sharing and Social Support	Y. Wang et al.	2020	Suggests social media can contribute to employee engagement through knowledge sharing and social support, potentially leading to higher productivity.

That might be a sort of “worst case” scenario, though. Though YouTube and other video sites also can provide distractions, most are likely to agree that most of the other  technologies have probably had a positive impact on productivity, much of the time. 

But not always. Productivity suites might not deliver when the tasks using them are unnecessary, whose output never gets used or when used for non-work tasks on “work time.”

Cloud computing and computing devices arguably contribute to productivity when intended outcomes are supported; less so when diverted to unclear activities with unknown connections to intended outcomes. 

Collaboration technologies might produce gains when they contribute to outcomes; less so when they do not have a clear or essential relationship to outcomes. 

The point is that any information technology can be misused, and fail to produce outcomes, when not used properly. 

Unnecessary meetings are simply unnecessary and quite often a distraction from producing outcomes, even when using video conferencing technologies to connect people around the globe. 

Virtually all of us believe AI can produce better outcomes, or at least faster, more-complete or less costly outcomes. But not inevitably.