Ever since Generative (Gen) AI burst into the mainstream through public-facing platforms (e.g. ChatGPT) late last year, its promising capabilities have caught the attention of many. Not surprisingly, telecom industry execs are among the curious observers wanting to try Gen AI even as it continues to evolve at a rapid pace.
MTN Consulting says the telecom industry’s bond with AI is not new though. Many telcos have deployed conventional AI tools and applications in the past several years, but Gen AI presents opportunities for telcos to deliver significant incremental value over existing AI. A few large telcos have kickstarted their quest for Gen AI by focusing on “localization.” Through localization of processes using Gen AI, telcos vow to eliminate language barriers and improve customer engagement in their respective operating markets, especially where English as a spoken language is not dominant.
Telcos can harness the power of Gen AI across a wide range of different functions, but the two vital telco domains likely to witness transformative potential of Gen AI are networks and customer service. Both these domains are crucial: network demands are rising at an unprecedented pace with increased complexity, and delivering differentiated customer experiences remains an unrealized ambition for telcos.
Several Gen AI use cases are emerging within these two telco domains to address these challenges. In the network domain, these include topology optimization, network capacity planning, and predictive maintenance, for example. In the customer support domain, they include localized virtual assistants, personalized support, and contact center documentation.
Most of the use cases leveraging Gen AI applications involve dealing with sensitive data, be it network-related or customer-related. This will have major implications from the regulatory point of view, and regulatory concerns will constrain telcos’ Gen AI adoption and deployment strategies. The big challenge is the mosaic of complex and strict regulations prevalent in different markets that telcos will have to understand and adhere to when implementing Gen AI use cases in such markets. This is an area where third-party vendors will try to cash in by offering Gen AI solutions that are compliant with regulations in the respective markets.
Vendors will also play a key role for small- and medium-sized telcos in Gen AI implementation, by eliminating constraints due to the lack of technical expertise and HW/SW resources, skilled manpower, along with opex costs burden. Key vendors to watch out for in the Gen AI space are webscale providers who possess the ideal combination of providing cloud computing resources required to train large language models (LLM) coupled with their Gen AI expertise offered through pre-trained models.
Other key points from MTN Consulting on Gen AI in the telecom industry:
- Network operations and customer support will be key transformative areas.
- Telco workforce will become leaner but smarter in the Gen AI era.
- Strict regulations will be a major barrier for telcos.
- Vendors key to Gen AI integration; webscale providers set for more telco gains.
- Lock-in risks and rising software costs are key considerations in choosing vendors.
Separately, MTN Consulting’s latest forecast called for $320B of telco capex in 2023, down only slightly from the $328B recorded in 2022. Early 3Q23 revenue reports from vendors selling into the telco market call this forecast into question. The dip in the Americas is worse than expected, and Asia’s expected 2023 growth has not materialized.
Key vendors are reporting significant YoY drops in revenue, pointing to inventory corrections, macroeconomic uncertainty (interest rates, in particular), and weaker telco spending. Network infrastructure sales to telcos (Telco NI) for key vendors Ericsson and Nokia dropped 11% and 16% YoY in 3Q23, respectively, measured in US dollars. By the same metric, NEC, Fujitsu and Samsung saw +1%, -52%, and -41% YoY growth; Adtran, Casa, and Juniper declined 29%, 7%, and 20%; fiber-centric vendors Clearfield, Corning, CommScope, and Prysmian all saw double digit declines.
MTN Consulting will update its operator forecast formally next month. In advance, this comment flags a weaker spending outlook than expected. Telco capex for 2023 is likely to come in around $300-$310B.
MTN Consulting’s Network Operator Forecast Through 2027: “Telecom is essentially a zero-growth industry”
MTN Consulting: Top Telco Network Infrastructure (equipment) vendors + revenue growth changes favor cloud service providers
Proposed solutions to high energy consumption of Generative AI LLMs: optimized hardware, new algorithms, green data centers
Cloud Service Providers struggle with Generative AI; Users face vendor lock-in; “The hype is here, the revenue is not”
MTN Consulting: Satellite network operators to focus on Direct-to-device (D2D), Internet of Things (IoT), and cloud-based services
Juniper Networks today announced that its customer and partner, Granite Telecommunications, a $1.8 billion provider of communications and technology solutions, has expanded its service offerings to include Juniper Networks’ full-stack of campus and branch services, including Wired Access, Wireless Access and SD-WAN, all driven by Mist AI™. This move will enhance Granite’s ability to support its customers’ unique verticals, such as healthcare, retail, education, manufacturing, hospitality and financial.
Granite has been working closely with Juniper for several years, and with this expanded AI-driven enterprise portfolio they now offer Juniper’s full suite of campus and branch networking solutions. By leveraging Mist AI and a single cloud across the wired, wireless and SD-WAN domains, Granite saves time and money with client-to-cloud automation and assurance, while accelerating deployments with Zero Touch Provisioning and automated configurations. In addition, Granite delivers more value to its customers with a broadened service portfolio that offers new highly differentiated services.
“Granite stands as Juniper’s largest AI-Driven SD-WAN partner in Managed Services within the Americas, underscoring the strength of our relationship and confidence in Juniper’s cutting-edge networking technology,” said Rob Hale, President and CEO at Granite. “As we expand our partnership, we are poised to elevate the customer experience to new heights by offering a full suite of Juniper solutions, imbued with the defining qualities of reliability, performance and security that characterize Juniper.”
Granite has been expanding its nationwide support to address the changing and growing needs of its customers. The company is committed to delivering specialized services for the unique requirements of its customers’ verticals. The addition of Juniper’s software-defined branch and wireless services is expected to be a significant benefit to many of its customer sectors. These services are designed to improve the performance and security of networks in various industries and make it easier for businesses to manage their network infrastructure.
“We are very excited to take our relationship with Granite Telecommunications to the next level,” said Sujai Hajela, Executive Vice President, AI-Driven Enterprise at Juniper Networks. “They have proven to be an exceptional partner and leader in the communications industry, who is especially adept at leveraging AI and the cloud to deliver high value managed services to their customers. With the full AI-driven enterprise portfolio, Granite can truly differentiate from their competition with exceptional client-to-cloud user experiences.”
With this expansion, Granite continues to demonstrate its commitment to providing customers with the best possible network experience. The addition of Juniper’s full-stack solutions will enable Granite to enhance its capabilities and better serve its customers, while also providing the company with a competitive edge in the market.
About Juniper Networks:
Juniper Networks is dedicated to dramatically simplifying network operations and driving superior experiences for end users. Our solutions deliver industry-leading insight, automation, security and AI to drive real business results. We believe that powering connections will bring us closer together while empowering us all to solve the world’s greatest challenges of well-being, sustainability and equality. Additional information can be found at Juniper Networks (www.juniper.net) or connect with Juniper on Twitter, LinkedIn and Facebook.
Communications service providers (CSPs) face a host of barriers, such as accessing high-quality data, that impede thesir ability to effectively deploy AI which could improve network and service operations, according to new research commissioned by Nokia and conducted by Analysys Mason.
“CSPs are unable to access high-quality data sets (which will enable them to make more accurate decisions) because they are using legacy systems with proprietary interfaces. This will restrict how quickly they can integrate AI into their networks,” according to the research, which is based on responses from 84 CSPs surveyed globally.
Almost 50 percent of Tier-1 CSPs ranked data collection as the most challenging stage of the telco AI use case development cycle.
Further, the research found that only six percent of CSPs surveyed believe they are at the most-advanced level of automation, or zero-touch automation, which relies on AI and machine learning (ML) algorithms to manage and improve network operations. The high-quality data issue is also impacting CSPs’ ability to retain AI talent.
Still, 87 percent of CSPs have started to implement AI into their network operations, either as proof of concepts or into production; with 57 percent saying they have deployed telco AI use cases to the point of production.
CSP respondents said they believe AI will help improve network service quality, top-line growth, customer experience, and energy optimisation to meet their sustainability goals.
The research said CSPs should evaluate their telco AI implementation strategies and develop a clear roadmap for AI implementation to overcome their data challenge and other impediments, such as an inability to scale AI use case deployments. The report can be found here.
Adaora Okeleke, Principal Analyst, at Analysys Mason said: “CSPs must transition to more-autonomous operations if they are to manage networks more efficiently and deliver on their main business priorities. But as this research demonstrates, accessing high-quality data remains a critical obstacle to deploying telco AI within their networks. They need to really examine their AI implementation strategies to work around this data quality issue.”
Andrew Burrell, Head of Business Applications Marketing, Cloud and Network Services at Nokia, said: “AI has a crucial role in driving step changes in network performance, including cutting carbon footprints. CSPs are aware of the challenges of more deeply embedding AI into their operations and, as this research points out, the steps they can take to positively alter that situation, including building the right ecosystem of vendor partners with the right skillsets that can better cater to their network needs.”
Resources and additional information:
Australia telco TPG Telecom and Ericsson today announced a new multi-year agreement to deliver an Australian-first cloud-native and AI-powered analytics tool to pinpoint and improve mobile network performance for customers. Based on Ericsson Expert Analytics and EXFO Adaptive Service Assurance, the solution gives TPG Telecom’s Technology, Network and Care teams an in-depth, end-to-end understanding of subscriber’s experience at an individual level. Through the new agreement, TPG Telecom will gain insights from its 4G and 5G Mobile, Fixed Wireless Access and IoT subscribers using smart data collection with embedded intelligence to predict, prioritise, and resolve performance issues as they arise in real-time.
These insights will enable TPG Telecom to react quicker to network issues, improve performance and reduce the need for infrastructure-based diagnoses, allowing the telco to enhance its service experience for customers.
The solution integrates Ericsson Expert Analytics, EXFO adaptive service assurance and Ericsson software probes, provided as part of Ericsson’s dual-mode 5G Core, to deliver end-to-end network visibility and reduced total costs.
TPG Telecom is the first in Australia and one of the first communication service providers (CSP) globally to deploy Ericsson Expert Analytics in a commercial network using cloud-native technologies. As a cloud-native software, its embedded scalability, agility and resilience means it is designed to flexibly handle TPG Telecom’s requirements as its network and use-cases evolve, adapting to any unexpected challenges.
TPG Telecom General Manager Cloud/Infrastructure NW Services, Chris Tsigros, said: “TPG Telecom is committed to investing in cutting edge technology designed to provide superior network experience to every single one of our customers. The analytics and troubleshooting solution we’re implementing with Ericsson will help ensure we deliver a great experience for our customers. This new technology will change the way in which the TPG Telecom customer care team interacts with our customers, leading to greater effectiveness and increased customer satisfaction. It’s just another way we’re putting our customers first.”
Emilio Romeo, Head of Ericsson, Australia and New Zealand, says: “Embarking on this multi-year deployment of advanced analytics and troubleshooting capabilities with TPG Telecom further demonstrates our commitment to bringing the best mobile telecommunications experience to all Australians. It is the latest in a long history of working side by side with TPG Telecom to bring groundbreaking technology to Australia and a new level of service experience to the Australian people. With the Ericsson Expert Analytics solution implementation, and the real-time access to the data from the dual-mode 5G Core thanks to its built-in software probes, TPG Telecom can gain greater network visibility at a lower cost, passing on the benefits to its customers as they enjoy its services across the country.”
The initial deployment phase focused on acquiring profound insights and troubleshooting capabilities through the software probes built into Ericsson’s cloud-native dual-mode 5G Core. The state-of-the-art solution delivered to TPG adopts an innovative approach that combines probing and event-based monitoring, ensuring rapid and effective issue resolution.
One of the standout features of this deployed solution is its capacity to provide comprehensive troubleshooting across the entire network, all within a unified application. TPG benefits from a range of advanced functionalities, including On-Demand Troubleshooting and robust filtering capabilities, significantly expediting the identification and resolution of network challenges.
With this implementation, TPG Telecom gains the ability to trace and monitor subscriber sessions handled by the core network, which forms the foundation of TPG Telecom’s mobile network. Currently, the solution successfully monitors approximately 5 million subscribers, and its coverage continues to expand
The full solution will continue to be rolled out in phases of further enhancements, and will give TPG Telecom the ability to automatically detect issues from captured network and subscriber insights. TPG Telecom will also benefit from AI-powered recommendations for correction of any network or customer issues found.
Ericsson Expert Analytics is an open system designed for ease of integration in multi-vendor hybrid environments. The software solution is powered by anomaly detection techniques based on machine learning and artificial intelligence, transforming data at scale into actionable insights for better business outcomes. It is designed and used to analyze the real-time network data of more than 250 million subscribers of mobile telecommunications operators all over the world.
The solution follows the 2021 completion of the virtualisation of TPG Telecom’s core network and a new partnership to deploy Ericsson’s dual-mode 5G Core for standalone 5G networks.
About TPG TELECOM:
TPG Telecom Limited, formerly named Vodafone Hutchison Australia Limited, was listed on the Australian Securities Exchange on 30 June 2020. On 13 July 2020, this newly listed company merged with TPG Corporation Limited, formerly named TPG Telecom, to bring together the resources of two of Australia’s largest telecommunications companies, creating the leading challenger full-service telecommunications provider. TPG Telecom is home to some of Australia’s most-loved brands including Vodafone, TPG, iiNet, AAPT, Internode, Lebara and felix. https://www.tpgtelecom.com.au/
Cloud Service Providers struggle with Generative AI; Users face vendor lock-in; “The hype is here, the revenue is not”
Park Place Technologies on network monitoring, predictive fault diagnosis and repair; Entuity acquisition adds analytics
Everyone agrees that Generative AI has great promise and potential. Martin Casado of Andreessen Horowitz recently wrote in the Wall Street Journal that the technology has “finally become transformative:”
“Generative AI can bring real economic benefits to large industries with established and expensive workloads. Large language models could save costs by performing tasks such as summarizing discovery documents without replacing attorneys, to take one example. And there are plenty of similar jobs spread across fields like medicine, computer programming, design and entertainment….. This all means opportunity for the new class of generative AI startups to evolve along with users, while incumbents focus on applying the technology to their existing cash-cow business lines.”
A new investment wave caused by generative AI is starting to loom among cloud service providers, raising questions about whether Big Tech’s spending cutbacks and layoffs will prove to be short lived. Pressed to say when they would see a revenue lift from AI, the big U.S. cloud companies (Microsoft, Alphabet/Google, Meta/FB and Amazon) all referred to existing services that rely heavily on investments made in the past. These range from the AWS’s machine learning services for cloud customers to AI-enhanced tools that Google and Meta offer to their advertising customers.
Microsoft offered only a cautious prediction of when AI would result in higher revenue. Amy Hood, chief financial officer, told investors during an earnings call last week that the revenue impact would be “gradual,” as the features are launched and start to catch on with customers. The caution failed to match high expectations ahead of the company’s earnings, wiping 7% off its stock price (MSFT ticker symbol) over the following week.
When it comes to the newer generative AI wave, predictions were few and far between. Amazon CEO Andy Jassy said on Thursday that the technology was in its “very early stages” and that the industry was only “a few steps into a marathon”. Many customers of Amazon’s cloud arm, AWS, see the technology as transformative, Jassy noted that “most companies are still figuring out how they want to approach it, they are figuring out how to train models.” He insisted that every part of Amazon’s business was working on generative AI initiatives and the technology was “going to be at the heart of what we do.”
There are a number of large language models that power generative AI, and many of the AI companies that make them have forged partnerships with big cloud service providers. As business technology leaders make their picks among them, they are weighing the risks and benefits of using one cloud provider’s AI ecosystem. They say it is an important decision that could have long-term consequences, including how much they spend and whether they are willing to sink deeper into one cloud provider’s set of software, tools, and services.
To date, AI large language model makers like OpenAI, Anthropic, and Cohere have led the charge in developing proprietary large language models that companies are using to boost efficiency in areas like accounting and writing code, or adding to their own products with tools like custom chatbots. Partnerships between model makers and major cloud companies include OpenAI and Microsoft Azure, Anthropic and Cohere with Google Cloud, and the machine-learning startup Hugging Face with Amazon Web Services. Databricks, a data storage and management company, agreed to buy the generative AI startup MosaicML in June.
If a company chooses a single AI ecosystem, it could risk “vendor lock-in” within that provider’s platform and set of services, said Ram Chakravarti, chief technology officer of Houston-based BMC Software. This paradigm is a recurring one, where a business’s IT system, software and data all sit within one digital platform, and it could become more pronounced as companies look for help in using generative AI. Companies say the problem with vendor lock-in, especially among cloud providers, is that they have difficulty moving their data to other platforms, lose negotiating power with other vendors, and must rely on one provider to keep its services online and secure.
Cloud providers, partly in response to complaints of lock-in, now offer tools to help customers move data between their own and competitors’ platforms. Businesses have increasingly signed up with more than one cloud provider to reduce their reliance on any single vendor. That is the strategy companies could end up taking with generative AI, where by using a “multiple generative AI approach,” they can avoid getting too entrenched in a particular platform. To be sure, many chief information officers have said they willingly accept such risks for the convenience, and potentially lower cost, of working with a single technology vendor or cloud provider.
A significant challenge in incorporating generative AI is that the technology is changing so quickly, analysts have said, forcing CIOs to not only keep up with the pace of innovation, but also sift through potential data privacy and cybersecurity risks.
A company using its cloud provider’s premade tools and services, plus guardrails for protecting company data and reducing inaccurate outputs, can more quickly implement generative AI off-the-shelf, said Adnan Masood, chief AI architect at digital technology and IT services firm UST. “It has privacy, it has security, it has all the compliance elements in there. At that point, people don’t really have to worry so much about the logistics of things, but rather are focused on utilizing the model.”
For other companies, it is a conservative approach to use generative AI with a large cloud platform they already trust to hold sensitive company data, said Jon Turow, a partner at Madrona Venture Group. “It’s a very natural start to a conversation to say, ‘Hey, would you also like to apply AI inside my four walls?’”
“Right now, the evidence is a little bit scarce about what the effect on revenue will be across the tech industry,” said James Tierney of Alliance Bernstein.
Brent Thill, an analyst at Jefferies, summed up the mood among investors: “The hype is here, the revenue is not. Behind the scenes, the whole industry is scrambling to figure out the business model [for generative AI]: how are we going to price it? How are we going to sell it?”
- Four major global telcos joined forces to launch the Global Telco AI Alliance to accelerate AI transformation of the existing telco business and create new business opportunities with AI services.
- They signed a Multilateral MOU for cooperation in the AI business, which includes the co-development of the Telco AI Platform.
Deutsche Telekom, e&, Singtel and SK Telecom have established a new industry group that aims to progress generative AI. Called the Global Telco AI Alliance, it represents a coordinated effort by these four operators to accelerate the AI-fuelled transformation of their businesses, and to develop new, AI-powered business models.
The Telco AI Platform will serve as the foundation both for new services – like chatbots and apps – as well as enhancements to existing telco services. The alliance members plan to establish a working group whose task will be to hammer out co-investment opportunities and the co-development of said platform.
Members will also support one another in operating AI services and apps in their respective markets, and cooperate to foster the growth of a telco AI-based ecosystem.
As of today, all the operators have done is sign a memorandum of understanding (MoU), under which they pledge to carry out all this work. A signing ceremony took place in Seoul, Korea, and was attended – either in person or virtually – by the CEOs of e&, Singtel and SK Telecom, and Deutsche Telekom’s board member for technology and innovation, Claudia Nemat. The Global Telco AI Alliance will also have to ensure that any AI-based services they develop are capable of accounting for cultural differences. They won’t get very far if their virtual assistants make culturally insensitive recommendations, for example.
The seniority of these signatories represents a strong statement of intent though, and the group said it will discuss appointing C-level representatives from each member to the Alliance.
“In order to make the most of the possibilities of generative AI for our customers and our industry, we want to develop industry-specific applications in the Telco AI Alliance. I am particularly pleased that this alliance also stands for bridging the gap between Europe and Asia and that we are jointly pursuing an open-vendor approach. Depending on the application, we can use the best technology. The founding of this alliance is an important milestone for our industry,” said Claudia Nemat, Board Member Technology and Innovation at Deutsche Telekom.
“We recognize AI’s immense potential in reshaping the telecommunications landscape and beyond and are excited to embark on this transformative journey with the formation of the Global Telco AI Alliance. The alliance signifies a strategic commitment to driving innovation and fostering collaborative efforts. Our shared goal is to redefine industry paradigms, establish new growth drivers through AI-powered business models, and pave the way for a new era of strategic cooperation, guiding our industry towards an exciting and prosperous future,” said Khalifa Al Shamsi, CEO of e& life.
“This alliance will enable us and our ecosystem of partners to significantly expedite the development of new and innovative AI services that can bring tremendous benefits to both businesses and consumers. With our advanced 5G network, we are well-placed to leverage AI to ideate and co-create and are already using it to enhance our own customer service and employee experience, increase productivity and drive learning,” said Yuen Kuan Moon, Group Chief Executive Officer of Singtel.
It is not clear at this stage of proceedings whether the operators plan to develop their own in-house AI assets, or license them from the likes of OpenAI’s ChatGPT, or Google Bard. On the one hand, going with a third party that has done most of the legwork offers efficiencies, but on the other hand, the Global Telco AI Alliance might prefer an AI that specialises in telecoms, rather than a generalist.
Japanese vendor NEC showed earlier this month – with the launch of its own large language model (LLM) for enterprises in its home market – that generative AI isn’t necessarily the preserve of Silicon Valley big tech. It also highlighted the desire to develop localised AI for different languages.
The announcement also doesn’t attempt to grapple with any potential ethical pitfalls that might befall the Alliance. While it’s a fairly safe bet that responsible AI development will be an important consideration, it’s always better when companies make that clear.
Even big tech has come round to that way of thinking, with the launch earlier this week of the Frontier Model Forum. Established by Google, Microsoft, OpenAI and self-styled ethical AI company Anthropic, the group aims to advance the development of responsible artificial intelligence for the benefit of humanity.
Generative Artificial Intelligence (AI) uncertainty is especially challenging for the telecommunications industry which has a history of very slow adaptation to change and thus faces lots of pressure to adopt generative AI in their services and infrastructure. Indeed, Deutsche Telekom stated that AI poses massive challenges for telecom industry in this IEEE Techblog post.
Consulting firm Bain & Co. highlighted that inertia in a recent report titled, “Telcos, Stop Debating Generative AI and Just Get Going” Three partners stated network operators need to act fast in order to jump on this opportunity. “Speedy action trumps perfect planning here,” Herbert Blum, Jeff Katzin and Velu Sinha wrote in the brief. “It’s more important for telcos to quickly launch an initial set of generative AI applications that fit the company’s strategy, and do so in a responsible way – or risk missing a window of opportunity in this fast-evolving sector.”
Generative AI use cases can be divided into phases based on ease of implementation, inherent risk, and value:
Telcos can pursue generative AI applications across business functions, starting with knowledge management:
Separately, a McKinsey & Co. report opined that AI has highlighted business leader priorities. The consulting firm cited organizations that have top executives championing an organization’s AI initiatives, including the need to fund those programs. This is counter to organizations that lack a clear directive on their AI plans, which results in wasted spending and stalled development. “Reaching this state of AI maturity is no easy task, but it is certainly within the reach of telcos,” the firm noted. “Indeed, with all the pressures they face, embracing large-scale deployment of AI and transitioning to being AI-native organizations could be key to driving growth and renewal. Telcos that are starting to recognize this is non-negotiable are scaling AI investments as the business impact generated by the technology materializes.”
Ishwar Parulkar, chief technologist for the telco industry at AWS, touted several areas that should be of generative AI interest to telecom operators. The first few were common ones tied to improving the customer experience. This includes building on machine learning (ML) to help improve that interaction and potentially reduce customer churn.
“We have worked with some leading customers and implemented this in production where they can take customer voice calls, translate that to text, do sentiment analysis on it … and then feed that into reducing customer churn,” Parulkar said. “That goes up another notch with generative AI, where you can have chat bots and more interactive types of interfaces for customers as well as for customer care agent systems in a call. So that just goes up another notch of generative AI.”
The next step is using generative AI to help operators bolster their business operations and systems. This is for things like revenue assurance and finding revenue leakage, items that Parulkar noted were in a “more established space in terms of what machine learning can do.”
However, Parulkar said the bigger opportunity is around helping operators better design and manage network operations. This is an area that remains the most immature, but one that Parulkar is “most excited about.” This can begin from the planning and installation phase, with an example of helping technicians when they are installing physical equipment.
“In installation of network equipment today, you have technicians who go through manuals and have procedures to install routers and base stations and connect links and fibers,” Parulkar said. “That all can be now made interactive [using] chat bot, natural language kind of framework. You can have a lot of this documentation, training data that can train foundational models that can create that type of an interface, improves productivity, makes it easier to target specific problems very quickly in terms of what you want to deploy.”
This can also help with network configuration by using large datasets to help automatically generate configurations. This could include the ability to help configure routers, VPNs and MPLS circuits to support network performance.
The final area of support could be in the running of those networks once they are deployed. Parulkar cited functions like troubleshooting failures that can be supported by a generative AI model.
“There are recipes that operators go through to troubleshoot and triage failure,” Parulkar said “A lot of times it’s trial-and-error method that can be significantly improved in a more interactive, natural language, prompt-based system that guides you through troubleshooting and operating the network.”
This model could be especially compelling for operators as they integrate more routers to support disaggregated 5G network models for mobile edge computing (MEC), private networks and the use of millimeter-wave (mmWave) spectrum bands.
Federal Communications Commission (FCC) Chairwoman Jessica Rosenworcel this week also hinted at the ability for AI to help manage spectrum resources.
“For decades we have licensed large slices of our airwaves and come up with unlicensed policies for joint use in others,” Rosenworcel said during a speech at this week’s FCC and National Science Foundation Joint Workshop. “But this scheme is not truly dynamic. And as demands on our airwaves grow – as we move from a world of mobile phones to billions of devices in the internet of things (IoT)– we can take newfound cognitive abilities and teach our wireless devices to manage transmissions on their own. Smarter radios using AI can work with each other without a central authority dictating the best of use of spectrum in every environment. If that sounds far off, it’s not. Consider that a large wireless provider’s network can generate several million performance measurements every minute. And consider the insights that machine learning can provide to better understand network usage and support greater spectrum efficiency.”
While generative AI does have potential, Parulkar also left open the door for what he termed “traditional AI” and which he described as “supervised and unsupervised learning.”
“Those techniques still work for a lot of the parts in the network and we see a combination of these two,” Parulkar said. “For example, you might use anomaly detection for getting some insights into the things to look at and then followed by a generative AI system that will then give an output in a very interactive format and we see that in some of the use cases as well. I think this is a big area for telcos to explore and we’re having active conversations with multiple telcos and network vendors.”
Parulkar’s comments come as AWS has been busy updating its generative AI platforms. One of the most recent was the launch of its $100 million Generative AI Innovation Center, which is targeted at helping guide businesses through the process of developing, building and deploying generative AI tools.
“Generative AI is one of those technological shifts that we are in the early stages of that will impact all organizations across the globe in some form of fashion,” Sri Elaprolu, senior leader of generative AI at AWS, told SDxCentral. “We have the goal of helping as many customers as we can, and as we need to, in accelerating their journey with generative AI.”
China’s tech sector has a new obsession: competing with U.S. titans like Google and Microsoft Corp. in the breakneck global artificial intelligence race. A ChatGPT-inspired global wave of AI activity is only just beginning in the next battle for supremacy in technology.
Billionaire entrepreneurs, mid-level engineers and veterans of foreign firms alike now harbor a remarkably consistent ambition: to outdo China’s geopolitical rival in a technology that may determine the global power stakes. Among them is internet mogul Wang Xiaochuan, who entered the field after OpenAI’s ChatGPT debuted to a social media firestorm in November. He joins the ranks of Chinese scientists, programmers and financiers — including former employees of ByteDance Ltd., e-commerce platform JD.com Inc. and Google — expected to propel some $15 billion of spending on AI technology this year.
For Wang, who founded the search engine Sogou that Tencent Holdings Ltd. bought out in a $3.5 billion deal less than two years ago, the opportunity came fast. By April, the computer science graduate had already set up his own startup and secured $50 million in seed capital. He reached out to former subordinates at Sogou, many of whom he convinced to come on board. By June, his firm had launched an open-source large language model and it’s already in use by researchers at China’s two most prominent universities.
“We all heard the sound of the starter pistol in the race. Tech companies, big or small, are all on the same starting line,” Wang, who named his startup Baichuan or “A Hundred Rivers,” told Bloomberg News. “China is still three years behind the US, but we may not need three years to catch up.”
The top-flight Chinese talent and financing flowing into AI mirrors a wave of activity convulsing Silicon Valley, which has deep implications for Beijing’s escalating conflict with Washington. Analysts and executives believe AI will shape the technology leaders of the future, much like the internet and smartphone created a corps of global titans. Moreover, it could propel applications from supercomputing to military prowess — potentially tilting the geopolitical balance.
China is a vastly different landscape — one reined in by US tech sanctions, regulators’ data and censorship demands, and Western distrust that limits the international expansion of its national champions. All that will make it harder to play catch-up with the US.
AI investments in the US dwarf that of China, totaling $26.6 billion in the year to mid-June versus China’s $4 billion, according to previously unreported data collated by consultancy Preqin.
Yet that gap is already gradually narrowing, at least in terms of deal flow. The number of Chinese venture deals in AI comprised more than two-thirds of the US total of about 447 in the year to mid-June, versus about 50% over the previous two years. China-based AI venture deals also outpaced consumer tech in 2022 and early 2023, according to Preqin.
All this is not lost on Beijing. Xi Jinping’s administration realizes that AI, much like semiconductors, will be critical to maintaining China’s ascendancy and is likely to mobilize the nation’s resources to drive advances. While startup investment cratered during the years Beijing went after tech giants and “reckless expansion of capital,” the feeling is the Party encourages AI exploration.
It’s a familiar challenge for Chinese tech players. During the mobile era, a generation of startups led by Tencent, Alibaba Group Holding Ltd. and TikTok-owner ByteDance built an industry that could genuinely rival Silicon Valley. It helped that Facebook, YouTube and WhatsApp were shut out of the booming market of 1.4 billion people. At one point in 2018, venture capital funding in China was even on track to surpass that of the U.S. — until the trade war exacerbated an economic downturn. That situation, where local firms thrive when U.S. rivals are absent, is likely to play out once more in an AI arena from which ChatGPT and Google’s Bard are effectively barred.
Large AI models could eventually behave much like the smartphone operating systems Android and iOS, which provided the infrastructure or platforms on which Tencent, ByteDance and Ant Group Co. broke new ground: in social media with WeChat, video with Douyin and Tiktok, and payments with Alipay. The idea is that generative AI services could speed the emergence of new platforms to host a wave of revolutionary apps for businesses and consumers.
That’s a potential gold mine for an industry just emerging from the trauma of Xi’s two-year internet crackdown, which starved tech companies of the heady growth of years past. No one today wants to miss out on what Nvidia Corp. CEO Jensen Huang called the “iPhone moment” of their generation.
“This is an AI arms race going on both in the US and China,” said Daniel Ives, a senior analyst at Wedbush Securities. “China tech is dealing with a stricter regulatory environment around AI, which puts one hand behind the back in this ‘Game of Thrones’ battle. This is an $800 billion market opportunity globally over the next decade we estimate around AI, and we are only on the very early stages.”
Joining them are some of the biggest names in the industry. Their ranks include Wang Changhu, the former director of ByteDance’s AI Lab; Zhou Bowen, ex-president of JD.com Inc.’s AI and cloud computing division; Meituan co-founder Wang Huiwen and current boss Wang Xing; and venture capitalist Kai-fu Lee, who made his name backing Baidu.
Ex-Baidu President Zhang Yaqin, now dean of Tsinghua University’s Institute for AI Industry Research and overseer of a number of budding projects, told Chinese media in March that investors sought him out almost daily that month. He estimates there’re as many as 50 firms working on large language models across the country. Wang Changhu, former lead researcher at Microsoft Research before he joined Bytedance in 2017, said dozens of investors approached him on WeChat in a single day when he was preparing to set up his generative AI startup.
“This is at least a once-in-a-decade opportunity, an opportunity for startups to create companies comparable to the behemoths,” Wang told Bloomberg News.
Many of the fledgling firms are squarely aimed at the home crowd, given growing concern in the West about Chinese technology. Even so, there’s an open field in a consumer market ringfenced to themselves, which also happens to be the world’s largest internet arena. In the works are AI-fueled applications, from a chatbot to help manufacturers track consumption trends, to an intelligent operating system offering companionship to counter depression, and smart enterprise tools to transcribe and analyze meetings.
Still, Chinese demos so far make it clear that most have a long way to go. The skeptical point out true innovation requires the free-wheeling exploration and experimentation that the US cultivates but is restrained in China. Pervasive censorship in turn means the datasets that China’s aspirants are using are inherently flawed and artificially constrained, they argue.
“Investors are chasing the concept,” said Grant Pan, chief financial officer of Noah Holdings, whose subsidiary Gopher invests in over 100 funds including Sequoia China (now HongShan) and ZhenFund in China. “However, the commercial use and impact to industry chains are not clear yet.”
Then there are Beijing’s regulations on generative AI, with its top internet overseer signaling that the onus for training algorithms and implementing censorship will fall on platform providers.
“Beijing’s censorship regime will put China’s ChatGPT-like applications at a serious disadvantage vis-à-vis their US peers,” said Xiaomeng Lu, director of the Eurasia Group’s geotechnology practice.
Last but not least, powerful chipsets from the likes of Nvidia and Advanced Micro Devices Inc. are crucial in training large AI models — but Washington bars the most capable from the country. The Biden administration is now considering tightening restrictions as soon as in coming months, essentially eliminating less-capable chips that Nvidia has devised for Chinese customers, the Wall Street Journal reported, citing anonymous sources.
But these hurdles haven’t stopped the ambitious in China, from Baidu and iFlytek Co. to the slew of new startups, from setting their sights on matching and surpassing the US on AI.
Executives, including from Tencent, argue models can tack on more chipsets to make up for lesser performance. Baichuan’s Wang said it got by with Nvidia’s A800 chips, and will obtain more capable H800s in June.
Others like Lan Zhenzhong, a veteran of Google’s AI Research Institute who founded Hangzhou-based Westlake Xinchen in 2021, employ a costly hybrid approach. The Baidu Ventures-backed company uses fewer than 1,000 GPUs for model training, then deploys domestic cloud services for inference, or sustaining the program. Lan said it cost about 7 to 8 yuan per hour to rent an A100 chip from cloud services: “Very expensive.”
Billionaire Baidu founder Robin Li, who in March unfurled China’s first answer to ChatGPT, has said the US and China both account for roughly a third of the world’s computing power. But that alone won’t make the difference because “innovation is not something you can buy.”
“Why aren’t people willing to invest in the longer-term and dream big?” asked Wayne Shiong, a partner at China Growth Capital. “Now that we’ve been handed this assignment by the other side, China will be able to play catch-up.”
Read more about the US-China AI war:
- Xi Remade China’s Tech Industry in His Own Image With Crackdown
- Baidu Leads China AI Rally After Chat Bot Scores Strong Reviews
- AI Unicorns Are Everywhere and Their Founders Are Getting Rich
- How China Aims to Counter US Efforts at ‘Containment’: QuickTake
Cristiano Amon, President & CEO, Qualcomm discusses Qualcomm’s role in the AI evolution and how AI will impact our workplaces and homes in the near future with Bloomberg’s Ed Ludlow at the Bloomberg Technology Summit.
“If AI becomes pervasive (which we believe it will), it’s going to happen at the edge. That’s how you should think about Qualcomm. If AI is going to get scale, you’re going to see it running on Qualcomm Snapdragon (SoC) devices, whether it’s on your phone, in your car, in your PC or in other machines.”
“For generative AI to become truly mainstream, much of the inferencing will need to be executed on edge devices,” said Ziad Asghar, senior vice president of product management, Qualcomm Technologies, Inc. “Our best-in-class AI hardware and software empowers developers to make full use of our powerful AI capabilities, delivering incredible new user experiences on laptops, phones and other devices powered by Snapdragon.”
Qualcomm’s Sascha Segan explains on-device generative AI and Stable Diffusion in this video.
“Generative artificial intelligence” is set to add up to $4.4 trillion of value to the global economy annually, according to a report from McKinsey Global Institute, in what is one of the rosier predictions about the economic effects of the rapidly evolving technology.
Generative A.I., which includes chatbots such as ChatGPT that can generate text in response to prompts, can potentially boost productivity by saving 60 to 70 percent of workers’ time through automation of their work, according to the 68-page report, which was published early Wednesday. Half of all work will be automated between 2030 and 2060, the report said.
McKinsey had previously predicted that A.I. would automate half of all work between 2035 and 2075, but the power of generative A.I. tools — which exploded onto the tech scene late last year — accelerated the company’s forecast.
“Generative A.I. has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities,” the report said.
McKinsey’s report is one of the few so far to quantify the long-term impact of generative A.I. on the economy. The report arrives as Silicon Valley has been gripped by a fervor over generative A.I. tools like ChatGPT and Google’s Bard, with tech companies and venture capitalists investing billions of dollars in the technology.
Despite mounting pressure on venture capital in a difficult economic environment, money is still flowing into generative Artificial Intelligence (AI) startups. Indeed, AI startups have emerged as a bright spot for VC investments this year amid a wider slowdown in funding caused by rising interest rates, a slowing economy and high inflation.
VCs have already poured $10.7 billion into Generative AI [1.] start-ups within the first three months of this year, a thirteen-fold increase from a year earlier, according to PitchBook, which tracks start-ups.
Note 1. Generative AI is a type of artificial intelligence that can create new content, such as text, synthetic data, images, and audio. The recent buzz around Generative AI has been driven by the simplicity of new user interfaces for creating high-quality content in a matter of seconds.
Tech giants have poured effort and billions of dollars into what they say is a transformative technology, even amid rising concerns about A.I.’s role in spreading misinformation, killing jobs and one day matching human intelligence. What they don’t publicize is that the results (especially from ChatGPT) may be incorrect or inconclusive.
We take a close look at Generative AI Unicorns with an emphasis on OpenAI (the creator of ChatGPT) and the competition it will face from Google DeepMind.
Generative AI Unicorns and OpenAI:
AI startups make up half of all new unicorns (startups valued at more than $1B) in 2023, says CBInsights.
At Generative AI firms, startups are reaching $1 billion valuations at lightning speed. There are currently 13 Generative AI unicorns (see chart below), according to CBInsights which said they attained their unicorn status nearly twice as fast as the average $1 billion startup.
Across the 13 Generative AI unicorns, the average time to reach unicorn status was 3.6 years but for the unicorn club as a whole the average is 7 years — almost twice as long.
OpenAI, the poster child for Generative AI with its Chat GPT app, tops the list with a valuation of almost $30 billion. Microsoft is the largest investor as it provided OpenAI with a $1 billion investment in 2019 and a $10 billion investment in 2023. Bloomberg reported that the company recently closed an investment fund, exceeding expectations with a value that surpasses $175 million.
However, OpenAI may have a formidable competitor in Google DeepMind (more details in DeepMind section below).
Anthropic is #2 with a valuation of $4.4B. It’s an AI safety and research company based in San Francisco, CA. The company says they “develop large-scale AI systems so that we can study their safety properties at the technological frontier, where new problems are most likely to arise. We use these insights to create safer, steerable, and more reliable models, and to generate systems that we deploy externally, like Claude (to be used with Slack).”
In Q1-2023, Generative AI companies accounted for three of the entrants to the unicorn club with Anthropic, Adept, and Character.AI all gaining valuations of $1B or above.
New Generative AI Unicorns in May:
Ten companies joined the Crunchbase Unicorn Board in May 2023 — double the count for April 2023. Among them were several AI startups:
- Toronto-basedCohere, a generative AI large language model developer for enterprises, raised $270 million in its Series C funding. The funding was led by Inovia Capital valuing the 4-year-old company at $2.2 billion.
- Generative video AI company Runway, based out of New York, raised a $100 million Series D led by Google. The funding valued the 5-year-old company at $1.5 billion.
- Synthesia, a UK-based artificial intelligence (AI) startup, has raised about $90 million at a valuation of $1 billion from a funding round led by venture capital firms Accel and Nvidia-owned NVentures. “While we weren’t actively looking for new investment, Accel and NVIDIA share our vision for transforming traditional video production into a digital workflow,” said Victor Riparbelli, co-founder and CEO of Synthesia.
Alphabet CEO Sundar Pichai said in a blog post, “we’ve been an AI-first company since 2016, because we see AI as the most significant way to deliver on our mission.”
In April, Alphabet Inc. created “Google DeepMind,” in order to bring together two leading research groups in the AI field: the Brain team from Google Research, and DeepMind (the AI startup Google acquired in 2014). Their collective accomplishments in AI over the last decade span AlphaGo, Transformers, word2vec, WaveNet, AlphaFold, sequence to sequence models, distillation, deep reinforcement learning, and distributed systems and software frameworks like TensorFlow and JAX for expressing, training and deploying large scale Machine Learning (ML) models.
By launching DeepMind as Google’s Generative AI solution, there could be a new battle front opening in quantum computing, machine learning perception, gaming and mobile systems, NLP and human-computer interaction and visualization.
A recent DeepMind paper says the Alphabet unit has extended AI capabilities with faster sorting algorithms to create ordered lists. Their paper says it shows “how artificial intelligence can go beyond the current state of the art,” because ultimately AlphaDev’s sorts use fewer lines of code for sorting sequences with between three elements and eight elements — for every number of elements except four. And these shorter algorithms “do indeed lead to lower latency,” the paper points out, “as the algorithm length and latency are correlated.”
Their researchers created a program based on DeepMind’s AlphaZero program, which beat the world’s best players in chess and Go. That program trained solely by playing games against itself, getting better and better using a kind of massively automated trial-and-error that eventually determines the most optimal approach.
DeepMind’s researchers modified into a new coding-oriented program called AlphaDev, calling this an important next step. “With AlphaDev, we show how this model can transfer from games to scientific challenges, and from simulations to real-world applications,” they wrote on the DeepMind blog. The newly-discovered sorting algorithms “contain new sequences of instructions that save a single instruction each time they’re applied. AlphaDev skips over a step to connect items in a way that looks like a mistake, but is actually a shortcut.”
While many luminaries, such as Henry Kissinger, Eric Schmidt and Daniel Huttenlocher, have lauded Generative AI as the greatest invention since the printing press, the technology has yet to prove itself worthy of the enormous praise. Their central thesis, that a computer program could “transform the human cognitive process” in a way tantamount to the Enlightenment, is a huge stretch.
Gary Marcus, a well-known professor and frequent critic of A.I. technology, said that OpenAI hasn’t been transparent about the data its uses to develop its systems. He expressed doubt in CEO Sam Altman’s prediction that new jobs will replace those killed off by A.I.
“We have unprecedented opportunities here but we are also facing a perfect storm of corporate irresponsibility, widespread deployment, lack of adequate regulation and inherent unreliability,” Dr. Marcus said.
The promise and potential of Generative AI will not be realized for many years. Think of it as a “research work in progress” with many twists and turns along the way.