Generative AI Watch: Will Generative AI Drive Additional Demand for MEC Services (and 5G)?

J. Marcus

Summary Bullets:

• There are many potential use cases in the enterprise for generative AI, but many will be enabled by existing cloud solutions.

• Some use cases requiring real-time responses may emerge, generating modest demand for MEC and/or 5G services.

Expectations of demand for 5G and multi-access edge computing (MEC) services from the enterprise segment are established – in part – on enabling artificial intelligence (AI) to be used in real-time applications. AI requires considerable computing power, usually achieved in the cloud where its demanding requirements can be scaled, but where such resources are too distant (due to network latency) to be relied upon for use cases where seconds or milliseconds in application response time can determine success or failure. There are other reasons why MEC makes sense in this scenario, including both the security benefits and cost savings achieved by not sending massive amounts of data to and from the cloud. With the recent hype around generative AI and the potential impact on various professions, industries, and organizations, it is worth considering whether its uptake will mean even more demand for MEC and/or 5G.

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Telecom Wholesalers will Require an Underlying Strategy for AI

R. Muru

Summary Bullets:

• The telecom wholesale segment is behind the AI implementation curve, and companies need to do more by embracing innovation – i.e., exploiting opportunities for generative AI.

• Success for telecom wholesalers will entail developing an underlying AI strategy across the portfolio and company in a connected manner.

Telecom Wholesale Trends in 2023
GlobalData’s discussions over the last year with global leading telecom wholesale providers highlights a commonality in strategy among telecom providers selling wholesale connectivity, both in terms of strategic vision and in the products and services they offer. Where companies differ is influenced by the nature of the core networks that support products and services, global geographical reach, and strength of product/service brand (e.g., antifraud solutions, mobile roaming), and lastly partnerships.

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The Enterprise Customer Segmentation Matrix

R. Pritchard

Summary Bullets:

• Since customer databases are available for mass business markets alongside providers’ existing major enterprise knowledge, service providers have traditionally segmented target markets by number of employees.

• Service providers are realizing they need to be more sophisticated and are trying to identify factors like digital maturity and proportion of knowledge workers.

More often than not, enterprise telecoms service providers segment the market in terms of employee numbers. Typically, they divide the market into SOHO/micro (0-5 employees: owner-managers don’t count as employees), SME/SMB (from 6-250 employees), and corporate/enterprise (250+ employees). Of course, these definitions vary from one service provider to the next, and often, specialist markets such as the MNC segment and public sector are addressed outside of the employee count model. The main drivers behind this are: (1) ‘this is how we’ve always done it,’ (2) ‘we can get databases of the target market by employee numbers,’ (3) and ‘any other approach is too difficult.’

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‘AIoT’ Mashup Works Better as a Concept Than a Buzzword

J. Marcus

Summary Bullets:

• Tech buzzwords work when they successfully communicate innovation in a catchy phrase.

• The emerging ‘AIoT’ construction is awkward, but it may help IoT providers communicate their value to knowledgeable tech audiences.

The concept of combining AI and IoT has been around for a few years. More recently, some tech market players have begun using the phraseology ‘AIoT’ to capture it. A good technology buzzword helps communicate instantly to tech and non-tech audiences what the innovation is all about, or at least provides a sizable hint. Both ‘artificial intelligence’ and ‘Internet of Things’ have been pretty good at this, but the mashup term AIoT (or ‘artificial intelligence of things’) is awkward, not self-explanatory, and ultimately, unhelpful.

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Italy’s Privacy Watchdog Temporarily Bans ChatGPT: Who Will be Next?

B. Valle

Summary Bullets:

• The Italian National Authority for Data Protection has temporarily banned ChatGPT after a leak exposed the personal data of users of the paid-for version of the service.

• OpenAI, the company behind the popular chatbot, has 20 days to respond to the privacy watchdog or risks a fine equivalent to 4% of its annual turnover.

The Italian National Authority for Data Protection became the first regulator to start an investigation into OpenAI’s ChatGPT last week. Debate turned to the inevitability of increased regulatory oversight to control the effects of the explosion in the use of generative AI, and the possibility that the measure could be followed by other Western democracies, as the conversation around AI and ethics becomes more urgent.

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A Sino-Centric Approach by Gulf Nations and Telcos Should be an Alarm Bell for Western Tech

I. Patel

Summary Bullets:

• For China, brokering political stability in the Middle East means investment opportunities. It is working at both in tandem as an overture to its long-term strategic interests in the region.

• Policy and government diktat will not compensate for the fragmented approach by Western governments and tech companies when challenging the likes of Huawei in eastern markets.

After a seven-year stand-off that involved the Saudi and Iranian Embassies gathering dust in each other’s countries, Riyadh (Saudi Arabia) announced a rapprochement with Tehran (Iran) under the auspices of Beijing (China) on March 10, 2023. This was a surprise announcement made by representatives from the two countries attending a summit in China with analysts across all sectors pouring over what it means. Technology is one sector that is worthy of analysis comment to determine the wider implications for the international technology market.

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The Pachyderm in the Room? HPE Leverages Reproducible AI for Enterprises

B. Valle

Summary Bullets:

• Hewlett Packard Enterprise (HPE) has acquired Pachyderm, a software company that uses reproducible AI to help organizations implement machine learning (ML) projects at scale.

• Pachyderm’s software capabilities will add an important differentiator to HPE’s Machine Learning Development Environment through increased reliability and transparency of the data.

Until not so long ago, Pachyderm was just another independent firm in the crowded space of San Francisco, California’s (US) AI startup landscape. However, many industry players had been aware of Pachyderm for a while.

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ChatGPT Bucks the Trend Toward More Responsible AI

R. Bhattacharyya

Summary Bullets:

• The enterprise AI community has embraced the trend toward more ‘Explainable AI,’ which enables users to understand the degree to which various factors impact a model’s output.

• ChatGPT’s inability to provide its sources of information flies against organizations’ desire to embrace ‘Responsible AI’ to promote greater adoption of the technology.

ChatGPT is impressive. The app can research, write, and even weave a narrative, performing tasks that we used to think were so uniquely human that they couldn’t be done well by a computer. However, it is doing them so well that it is often difficult, if not impossible, to determine whether the output is prepared by a human or an algorithm, or whether it’s fact or fiction. And therein lies the problem.

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The New Age of Developers—Written by ChatGPT

C. Dunlap
Research Director

Summary Bullets:

• ChatGPT makes its case for how it helps developers write better apps

• Despite its staggering AI chatbot innovation of its most recent version, ChatGPT lacks the human connection—and perspective

I took my boss’s advice and instructed ChatGPT to write a blog for me on how the prototype AI chatbot will significantly improve developers’ careers. It provided an adequate description of how the technology enhances the app development process based primarily on customer service use cases. I also learned a little more about natural language understanding (NLU).

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ThousandEyes Could be a Key Differentiator in Cisco’s Cloud Networking Quest

M. Rogers

Summary Bullets:

• Cisco’s efforts to further integrate ThousandEyes across its solution portfolio offers a powerful tool as it aims to grow as a leading cloud-networking platform provider.

• While ThousandEyes offers great visibility into the entire stack, Cisco could offer deeper integrations in areas like WAN optimization to make the most of its investment.

During the Keynote at this year’s Cisco Live 2022 APJ, in Melbourne Australia, Cisco executive VP and GM of security and collaboration, Jeetu Patel stated that providing a vendor-neutral platform for cloud-networking was core to the company’s strategy. More specifically the company hopes to abstract the security and networking layers away from the leading hyperscale platforms, and to provide a unified platform for networking services and security that allows traffic to flow securely to any cloud platform. This is a lofty goal, and Cisco has a long list of competitors in the networking, monitoring, and security space. The hyperscalers are growing their WAN offerings While Cisco remains one of the leaders in networking and is growing stronger in security, the company needs to develop as many points of differentiation as possible to achieve this goal.

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