AI

Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms

Posted on by Alan Weissberger

Disclaimer: Perplexity.ai was used for research and analysis in this article.

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Executive Summary:

This past Monday, six giant Wall Street asset managers, private-equity firms and banks announced an effort to raise $500 billion to keep fueling the A.I. boom by financing more data centers, power plants and chips. The proposed platform would direct capital to Nvidia customers—including AI startups and data-center operators—at precisely the point when many have struggled to obtain funding through ordinary credit channels.

We take that as a huge warning sign for the proposed AI investments.  Here’s why: If the underlying projects offered clearly proven cash flows, predictable utilization and collateral with durable value, lenders would not need a specially assembled consortium, headline-scale commitments and Nvidia’s direct involvement to make the loans happen. The initiative appears designed to overcome a financing bottleneck created by the extraordinary gap between AI infrastructure spending and demonstrated AI revenue.

This proposed $500 billion AI-financing initiative is less a validation of durable AI economics than an admission that the sector’s spending plans have outgrown its customers’ ability—or willingness—to finance them conventionally. Rather than demonstrating independently sustainable demand, the arrangement risks extending an investment cycle increasingly dependent on vendor-enabled credit, opaque commitments and financial engineering.

The firms—Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR—said they were working together to come up with that huge sum to lend to Nvidia’s customers, including the start-ups that use the company’s chips in data centers to develop and operate A.I. software. These customers, Nvidia said, have been struggling to secure financing for chips and data centers.

Nvidia will connect its customers with one of the six lenders, which will provide financing that could range from loans to credit. The financing will be “at attractive rates,” Nvidia said in a blog post.

In practical terms, this is a vendor-financing mechanism, even if the capital technically comes from third parties. Nvidia is helping its customers obtain the money required to buy Nvidia-dependent infrastructure. The more readily startups and data-center operators can borrow, the more equipment they can order; the more equipment they order, the more Nvidia can sell. That does not mean the demand is fictitious, but it does make it harder to distinguish independent end-user demand from demand supported by an ecosystem that is financing itself.

Circularity is the principal concern. When suppliers, investors, cloud providers, AI labs and lenders all have financial incentives to keep capital circulating within the same small group of counterparties, reported growth can look more robust than the ultimate economics justify. The risk is not simply that projects fail individually. It is that a shortfall in AI-service revenues, utilization or pricing could spread simultaneously through hardware vendors, AI developers, cloud operators, private-credit vehicles and the securities backed by their cash flows. Circular financing can blur the line between real external demand and investment-funded purchases.

Executives from the lenders joined Jensen Huang, Nvidia’s CEO & founder, for an unusual, extended interview on CNBC, where they talked up their new, seemingly insatiable desire to finance infrastructure for A.I. Huang said on the air. He added that “A.I. labs” and “A.I. start-ups” would have access to the financing. He did not name those companies or whether Nvidia would receive any money as part of the effort.  David M. Solomon, Goldman’s chief executive, said the consortium was Mr. Huang’s idea.

“We need to raise this money as fast as possible,” said Larry Fink, BlackRock’s chief executive. He also stated: “There’s quite a bit of negativity around A.I. and data centers right now, but let’s be clear: This is going to be creating a huge amount of jobs.”

Skeptical Analysis:

The urgency in those statements deserves more scrutiny. Speed is not a substitute for underwriting, and job-creation claims do not establish that investments will meet their cost of capital. The industry is attempting to finance assets whose useful economic life may be much shorter and more uncertain than that of traditional infrastructure. A GPU fleet can lose competitiveness rapidly when a new architecture, memory standard or systems design emerges. Its resale value can fall sharply if capacity demand weakens. Treating such hardware as collateral comparable to a toll road, utility asset or long-lived building is a major assumption—not an established fact.

The announcement punctuates a head rush on Wall Street and in Silicon Valley into anything that even vaguely resembles A.I. The stocks of tech giants and chipmakers have soared for most of this year, and a pair of the biggest names in the space, Anthropic and OpenAI, are expected to file for initial public offerings that could value them at $1 trillion apiece.

But soaring equity valuations and enormous projected IPO valuations do not answer the basic return-on-investment question: who will pay enough, for long enough, to justify the total cost of the data centers, power plants, networking, GPUs and debt now being assembled? AI vendors may generate impressive revenue growth while still failing to earn enough to cover the depreciation, energy, financing and replacement costs of the infrastructure required to produce it.

Nvidia’s financing narrative appears to conflate broad interest in AI-enabled services with broad, independent demand for capital-intensive AI infrastructure. Governments, enterprises and startups may all seek access to AI capabilities, but the current demand for hyperscale GPU clusters, dedicated power capacity and purpose-built “AI factories” remains concentrated among a relatively small group of frontier-model developers and cloud platforms.

That distinction matters because broad adoption of AI applications does not automatically translate into economically sustainable demand for vast new data-center capacity. Enterprises can consume AI through APIs, hosted platforms, smaller models and open-source software without owning—or financing—dedicated high-density compute infrastructure. Nvidia itself describes demand as spanning enterprises, startups, governments, nations and AI labs, but the critical question for investors is how much of that demand converts into durable, contracted infrastructure revenue rather than experimentation, pilots or subsidized consumption.

As Bloomberg reported last week, OpenAI accounted for somewhere between 50% and 70% of Microsoft’s AI revenue in the 12 months to 30 June.

That degree of concentration is significant. Microsoft disclosed $24.1 billion in sales from OpenAI during the year ended in June; outside estimates place that at more than half, and perhaps about 70%, of Microsoft’s AI-related revenue. Much of the revenue reflects OpenAI’s spending on Microsoft cloud and model-development services, meaning a substantial portion of the apparent AI revenue base may be generated within a tightly linked commercial relationship rather than by a diversified population of external enterprise customers.

That could be problematic if the picture is similar at other hyperscalers and at other frontier AI companies like Anthropic, for example. A market in which a handful of loss-making model developers drive an outsized share of cloud and infrastructure revenue is inherently more fragile than one supported by a broad base of profitable end users. It exposes infrastructure providers and lenders to customer concentration, correlated capital spending and the possibility that a reduction in financing at one frontier lab quickly reduces demand across the supply chain.

Indeed, as has been reported by multiple correspondents – most notably staunch AI critic Ed Zitron – LLM makers like OpenAI are losing money hand over fist and can only charge so much for tokens before customers either curb their usage or switch to open source models.  Zitron wrote:

“It’s estimated that 70% or more of the AI revenues of Microsoft, Google, and Amazon were from either OpenAI or Anthropic. UBS estimated that next year, Anthropic and OpenAI’s compute spend would be 48% of all Google Cloud revenues — which means that they likely account for even more than 70% of its AI revenues.”

The commercial challenge is not whether frontier models have value- they do. It is whether they can deliver that value at prices that exceed the combined costs of training, inference, electricity, networking, cloud capacity, depreciation and ongoing model development. If token pricing rises too far, customers may reduce usage, shift workloads to lower-cost models, or use open-source alternatives. If pricing remains low, frontier-model providers may struggle to cover their infrastructure bills. Open-source fine-tuning can materially reduce costs for specialized enterprise workloads, reinforcing the competitive pressure on premium proprietary-model pricing.

The most plausible downside is that the technology becomes broadly useful but insufficiently profitable to support today’s extreme capital intensity. That outcome would leave the sector with too much high-cost capacity, thin margins, declining GPU collateral values and lenders dependent on assumptions about utilization and cash flows that have not yet been tested through a downturn. The foundational financial risk is straightforward: AI-service revenues may prove insufficient to service the fixed obligations incurred to construct the infrastructure.  Notably, Nvidia stock dipped modestly on Monday after The Financial Times reported that the company was nearing the mammoth financing deal.

While the contours of the arrangements were announced Monday afternoon, details remained scarce. A joint news release referred only to “memorandums of understanding” to “create dedicated pools of capital at significant scale.”

That language is important. Memorandums of Understanding (MoU’s) are not equivalent to binding, fully funded commitments. Until investors know the actual terms—capital committed, leverage permitted, collateral requirements, Nvidia’s role in losses, loan maturities, customer concentration limits and underwriting standards—the $500 billion figure is better understood as an ambitious financing target than as validated capital deployment. Reporting has described the initiative as a multiyear target rather than cash already committed, while questions remain about the scale of any Nvidia backstop.

During their television interview, the lenders’ executives alluded vaguely to “yield-based products” and even securitization, or the creation of bonds that would divvy up the revenue from A.I. labs into risky and less risky categories. There were several references to A.I. as a new asset class and to allowing smaller investors an opportunity to invest in debt backed by the data centers.

This is where the proposal moves from aggressive investment to potential systemic-risk creation. Securitization can distribute risk, but it does not eliminate it; it can instead diffuse difficult-to-value exposure across a wider investor base. Packaging AI-data-center debt into yield products may make financing more available, but it also risks obscuring the quality of the underlying cash flows, the degree of correlated exposure among borrowers and the vulnerability of rapidly depreciating hardware collateral. The more complex the capital stack becomes, the greater the danger that investors mistake engineered liquidity for genuine economic value.

Executive Quotes:

“NVIDIA has reached an important milestone. We began by building chips; today, we are helping create a new class of productive, investable infrastructure: AI factories,” said Jensen Huang, founder and CEO of NVIDIA. “In AI, compute is revenue. NVIDIA compute is uniquely suited for this role. It is broadly adopted, flexible across models and workloads, fungible and transferable across customers and operators, and continuously improved through CUDA software — extending its useful life and improving its economics over time. It is supported by a deep global ecosystem of developers, customers and offtakers. That is why we are bringing the world’s leading long-term capital providers together to independently underwrite AI infrastructure. These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country in the age of AI.”

“Modern compute has emerged as a scarce, mission-critical asset class with compelling investment characteristics that is positioned to drive significant long-term economic growth and productivity gains,” said Apollo President Jim Zelter. “The combination of NVIDIA’s proprietary technology ecosystem and Apollo’s flexible, long-term capital base provides a strong foundation to support the next stage of the AI buildout as part of the broader Global Industrial Renaissance.”

“The AI buildout will require unprecedented investment and a skilled workforce to turn that investment into the infrastructure that will help power future growth,” said Larry Fink, Chairman and CEO of BlackRock. “This partnership deepens our relationship with NVIDIA, including through the AI Infrastructure Partnership, and brings together NVIDIA’s leadership in accelerated computing with BlackRock’s ability to connect long-term capital to essential infrastructure. Together, we can help deliver the compute capacity that companies need to grow and create more jobs, supporting the continued growth of the U.S. and global economies, while creating attractive, long-term investment opportunities for our clients.”

“NVIDIA has created extraordinary demand for its compute through an intense focus on customer value and versatile technology,” said Jon Gray, President and COO of Blackstone. “We continue to be enormous investors globally across the NVIDIA ecosystem, and this announcement further underscores our confidence in their platform and the future of AI infrastructure.”

“As our strategic partner, NVIDIA is enabling us to scale AI factories. We are excited about further collaboration to build and fund the backbone of AI globally,” said Bruce Flatt, CEO of Brookfield. “With demand for large-scale AI compute growing significantly as adoption scales across industries, compute is fast becoming the essential layer of infrastructure and a core pillar of the Brookfield AI infrastructure strategy.”

“We’re in a pivotal moment of a historic AI investment cycle. NVIDIA’s full-stack platform is in high demand and uniquely positioned at the center of that global buildout,” said David Solomon, Chairman and CEO of Goldman Sachs. “Our investment and distribution roles reflect our confidence in NVIDIA’s leadership, and we’re excited for the new opportunity to create a market for credit backed by NVIDIA compute.”

“Compute has become a critical infrastructure asset. As we’ve scaled our approach to digital infrastructure, we’ve learned that delivery, not ambition, is the hard part. That’s why we’re excited to build on our strategic partnership with NVIDIA, a founding investor in Helix Digital Infrastructure, to bring together NVIDIA’s accelerated computing platform with KKR’s long-duration capital, infrastructure expertise and capital markets capabilities to turn growing demand into real capacity at extraordinary scale,” said Joe Bae and Scott Nuttall, Co-Chief Executive Officers of KKR.

“This is the very beginning — like what it was when I started in the mortgage-backed securities market in the 1970s,” Mr. Fink said. “I look upon this as a next future for financial engineering.”

That analogy should be treated as cautionary, not reassuring. Financial engineering can expand access to capital and spread risk efficiently when assets have transparent valuations, stable cash flows and conservative underwriting. It becomes dangerous when it is used to finance unproven revenue models, rapidly obsolescing assets and demand forecasts that must remain exceptionally optimistic simply to justify the initial investment.

Huge Risks Explained:

If AI demand stalls, Nvidia faces a sharp reversal in hardware demand and a potentially damaging credit overhang, while lenders could be left financing underutilized data centers secured by equipment whose value can decline far faster than conventional infrastructure. The common vulnerability is that the same uncertain AI revenue streams would be expected to support chip purchases, data-center leases, project debt and securitized investment products.   Nvidia is especially exposed to risks:

  • Order cancellations and lower pricing power. Cloud providers, AI labs and startups would likely slow GPU orders, defer deployments or renegotiate capacity commitments. Nvidia could face weaker revenue growth, inventory risk and pressure on the high margins that have supported its valuation.

  • Vendor-financing and counterparty risk. If Nvidia is arranging or backstopping financing for customers buying its systems, a demand slowdown could turn what appeared to be hardware sales into indirect credit exposure. Customers unable to earn adequate returns from AI services may struggle to repay loans used to buy Nvidia equipment. The risk is magnified where the company’s commercial success depends on borrowers gaining access to financing in the first place.247wallst+1

  • Collateral impairment. GPUs are not durable, slow-depreciating infrastructure assets. A new chip generation, a shift toward more efficient models, or weak utilization can materially reduce the resale value of installed systems. If lenders rely on those systems as collateral, a default could leave them holding equipment worth substantially less than the loan balance—and Nvidia could face lower demand for both new and prior-generation products. Moody’s identifies rapid capacity expansion and fast-changing chips, cooling and computing architectures as sources of overbuilding and technology-obsolescence risk.moodys

  • Feedback-loop risk. A slowdown could create a negative cycle: AI customers reduce spending; Nvidia’s sales weaken; lenders become more cautious; financing availability tightens; customers cut orders further. Where vendors, customers and capital providers are financially intertwined, the decline can be more abrupt than a normal inventory correction.

  • Equity-valuation risk. Nvidia’s market value reflects unusually strong assumptions about the longevity of AI spending, margins and growth. A reassessment of those assumptions could compress the valuation sharply even if Nvidia remains profitable. BIS warns that disappointment in AI returns could trigger a sudden financing pullback and turn the capex boom into a prolonged investment bust.bis

Lenders’ exposure:

Risk How a demand stall transmits losses
Default risk AI labs, cloud operators and data-center developers may fail to generate enough revenue to cover interest, principal, energy and operating costs.
Underutilized capacity Empty or lightly used data halls produce far less cash flow than underwriting models assume, impairing debt-service coverage.
Collateral-value risk Specialized GPU, networking and cooling systems may have weak resale value in a downturn, especially if many borrowers liquidate comparable equipment simultaneously.
Refinancing risk Projects commonly require follow-on funding after construction. If markets reprice AI risk, borrowers may be unable to refinance maturing debt except at much higher rates—or at all.
Concentration risk Multiple loans, funds and securitizations may rely on a small number of AI labs, hyperscalers, equipment suppliers and power projects. A weakness in one tenant or customer can affect many nominally separate investments.
Structured-finance risk Securitizing data-center revenues can spread exposure across private-credit funds, insurers, pensions and bondholders. It diversifies ownership of the risk, but does not improve the underlying cash flow.

The most immediate lender risk is a mismatch between long-lived debt obligations and unstable, technology-dependent revenue. A data center might be financed over many years, but its GPU fleet may need continual upgrades to remain competitive—requiring additional capital expenditure before the original debt is repaid. Moody’s notes that this combination of increasing capital intensity, uncertain compute requirements and structured finance can pressure developers, landlords and investors through execution, renewal and refinancing risk.moodys

Construction and power risks:

A demand slowdown could arrive before projects enter service. That creates a particularly difficult situation for lenders because construction interest, cost overruns and power-reservation charges may accumulate before revenue begins.

Permitting delays, local opposition, water constraints and electricity-grid limitations further compound this exposure. Reuters reported that lenders increasingly treat project readiness—including approvals, permits and local community support—as a credit factor because delays can increase costs, jeopardize covenants and prevent projects from reaching revenue-generating operation.

System-wide scenario:

The more serious scenario is a correlated unwind:

  1. AI applications fail to deliver enough monetizable demand.

  2. AI labs and cloud providers reduce compute commitments.

  3. Data-center utilization and expected rental income fall.

  4. Borrowers cannot service or refinance project debt.

  5. GPUs and related infrastructure lose collateral value.

  6. Losses reach private-credit funds, banks, securitized vehicles, insurers and institutional investors.

  7. New financing becomes unavailable, causing further cuts in infrastructure orders and Nvidia sales.

This would not necessarily resemble the 2008 banking crisis: some first-loss exposure sits outside regulated banks. But Chicago Booth research estimates that a severe re-rating of AI-related debt could still produce roughly $60 billion to $140 billion in realized credit losses, alongside large equity-market effects.

What matters most:

The decisive question is not whether AI is useful or whether data centers remain necessary. It is whether cash-paying end users will generate sufficient, durable revenue to justify the full cost of the hardware, power, real estate, construction and financing now being committed.  If that answer is no, the sector may discover that it financed capacity—not returns.

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References:

https://nvidianews.nvidia.com/news/nvidia-partners-with-apollo-blackrock-blackstone-brookfield-goldman-sachs-and-kkr-to-establish-ai-compute-infrastructure-financing-platforms-to-mobilize-over-500-billion-of-third-party-capital

https://www.nytimes.com/2026/08/10/business/ai-nvidia-lenders-500-billion.html

https://www.bloomberg.com/news/articles/2026-08-05/microsoft-s-ai-sales-mostly-come-from-openai-disclosures-show

https://www.wheresyoured.at/dont-look-up/

Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)

Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies

AI infrastructure spending boom: a path towards AGI or speculative bubble?

Expose: AI is more than a bubble; it’s a data center debt bomb

Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?

Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”

Big Tech AI spending binge results in massive job cuts!

AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!

FT: Scale of AI private company valuations dwarfs dot-com boom

Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)

AI Data Center Boom Carries Huge Default and Demand Risks

Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?

Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments

Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?

 

Hyperscaler AI Race: Soaring Capex Wipes Out Free Cash Flow; AGI and Digital Gods

Posted on by Alan Weissberger
The tsunami wave of generative AI investment is now facing intense scrutiny due to an unsustainable imbalance between massive capital expenditure (capex) and negligible return on investment (ROI). Despite unprecedented infrastructure spending (mostly for AI Data Center buildouts), the sector has yet to deliver a definitive “killer app” or high-utility enterprise software capable of generating meaningful corporate revenue.  Consequently, stakeholders are shifting from speculative funding toward rigorous evaluation of tangible monetization and operational efficiencies. This lack of clear value realization raises valid concerns about a potential market correction as the technology struggles to transition from a capital sink to a self-sustaining ecosystem.
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Google parent company Alphabet boosted its forecast for capital spending for both 2026 and 2027 last week, citing supply constraints amid surging demand for more computing power. The company said its 2026 capex would increase its potential maximum to $205 billion from $190 billion.  That $15 billion increase places Alphabet neck-and-neck with Amazon at the absolute top of the hyperscaler spending ladder. Paul Meeks, head of technology research at Freedom Capital Markets, told CNBC that Wall Street is expecting about $260 billion in capex from Google/Alphabet in 2027.  “I think people would be satisfied [with that],” he added. “The thing I worry about is if you have a drop in spending: All of a sudden it’s $205 billion for Google this year, and next year it’s, say, $100 billion – it collapses.”
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Hyperscaler Annual Capex Forecast (2024–2027):
All figures represent billions of USD ($B) and reflect current consensus updates.

Company 2024 (Actual) 2025 (Actual) 2026 (Current Guidance / Est) 2027 (Projected)
📦 Amazon $53B $112B $195B – $210B $230B – $260B
🔍 Alphabet (Google) $51B $104B $195B – $205B $240B – $280B
💻 Microsoft $56B $108B $185B – $195B $220B – $250B
♾️ Meta $38B $85B $125B – $145B $150B – $180B
🗄️ Oracle $13B $25B $45B – $50B $55B – $65B
🧮 Combined Aggregate $211B $434B $745B – $805B $895B – $1,035B
Source: Google Gemini
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The huge increase in hyperscaler capex, wipes out their free cash flow (revenues-expenses is now negative for all but Microsoft). The shift in focus by investors from earnings to free cash flow marks a turning point in market perceptions.  The correct way to describe free cash flow is the cash flow a company generates during a period of time that is available to be paid to the company’s shareholders and debtholders.Companies with negative free cash flow are only able to cover the interest and principal on their debt by additional borrowing or by issuing new equity. In other words, cash is flowing from investors to the company, not the other way around.  In a financial crisis, investors become unwilling to support companies not able to cover interest and principal, with the result being a cascade of defaults and runs on financial institutions.

A major concern with the massive AI-capex which has occurred during the last two years is that much of it is debt financed. As the real cost of generative AI-tokens is becoming clear, lower priced Chinese competitors are emerging, and AI customers are beginning to economize on their use of AI. As a result, investors are becoming increasingly alarmed about whether U.S. AI firms will be able to cover their debt obligations.  AI-capex has been the main, and perhaps only driver of U.S. economic growth. If more companies announce negative free cash flows, that increase in magnitude, the financial system and overall economy will move closer to the tipping point.

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But wait, Google/Alphabet co-founder says it’s more about winning AI market share than skyrocketing capex or ROI.  On Patrick O’Shaughnessy’s Invest Like the Best podcast, Gavin Baker, Chief Investment Officer for Atreides Management, shared an anecdote about what’s been going on within Google/Alphabet offices. According to Baker, Google co-founder Larry Page has been telling Google employees, “I am willing to go bankrupt rather than lose this race.” That shows how high the person who led Alphabet through its halcyon days thinks the stakes are in AI.

Baker went on to describe the leaders of Meta Platforms, Microsoft, and Alphabet as being in a race to create a “Digital God,” or artificial general intelligence (AGI), which is likely to be worth trillions of dollars in value if not tens of trillions or even more. He also explained that the tech giants are counting on the models to scale, or get better as they get bigger, and the tech giants are unlikely to slow down their spending on AI infrastructure until they’re proven otherwise. AGI could be more disruptive than any technology before it, including the internet, and most tech CEOs seem to think this.  OpenAI CEO Sam Altman told Time magazine last December, “I think AGI will be the most powerful technology humanity has yet invented.”
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References:

https://www.forbes.com/sites/hershshefrin/2026/07/2/market-experiences-an-ai-capex-turning-point-with-tipping-point-to-follow/

https://www.fool.com/investing/2024/08/31/thinking-of-selling-nvidia-stock-larry-page-quote/

Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs 

Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?

Autonomous customer experience required for AI-Native 6G and distributed intelligence at the network edge

Posted on by Alan Weissberger

Executive Sumary:

Communications service providers (CSPs) have historically competed on network-centric KPIs—coverage, capacity, reliability, and price—anchored in 3GPP performance and management frameworks (e.g., TS 28-series, TS 23.501 QoS models). However, these metrics alone are no longer sufficient to sustain differentiation in increasingly saturated and capital-intensive markets, according to Chantel Cary, Product Marketing Senior Manager at Oracle Communications [1],

“The battleground has shifted,” Cary told Capacity Global. “Today, customer experience is becoming the clearest point of differentiation, and in many cases, the most important driver of growth.”

This shift is unfolding alongside structural constraints: flat ARPU, rising capex associated with 5G standalone, fiber access (FTTx), and edge cloud expansion, and increasing customer acquisition and retention costs. At the same time, customer expectations—benchmarked against hyperscaler-grade digital platforms—are becoming uniformly high across mobile and fixed broadband services.

“They do not compare a telecom provider only to other providers,” she said. “They compare every experience to the best experience they have anywhere.”

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Note 1. Oracle Communications is a dedicated global business unit and product portfolio fully owned and operated by Oracle. It provides enterprise software and infrastructure designed specifically for telecommunications service providers (like AT&T or Verizon) and large enterprises. Their solutions manage everything from network routing, security, and signaling (including 5G) to back-office billing, revenue management, and customer experience operations,

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Analysys Mason reports that 97% of operators view AI-powered automation as essential for survival and growth, reinforcing alignment with TM Forum’s Autonomous Networks framework and the broader industry transition toward AI-native system design.

AI-Native Customer Experience Architecture:

Cary’s concept of “autonomous customer experience” maps directly to the emerging paradigm of AI-native networks, where intelligence is embedded across both network and service layers rather than applied as an overlay.  “It is not about removing the human element from engagement,” she explained. “It is about using AI to continuously orchestrate the customer lifecycle in ways that humans alone cannot manage at scale.”

In wireless networks, this evolution is reflected in 3GPP-defined enablers such as the Network Data Analytics Function (NWDAF, TS 23.288), which provides real-time analytics to optimize policy control, mobility, and QoS. In parallel, O-RAN Alliance architectures introduce the near-real-time and non-real-time RAN Intelligent Controllers (near-RT RIC, non-RT RIC), enabling AI-driven control loops for radio resource management and service optimization.

Image Credit: Aisera

In wireline and converged networks, similar principles are emerging through SDN-based control planes, broadband network gateways (BNG) with telemetry streaming, and ITU-T frameworks (e.g., Y.3172 for machine learning in future networks), enabling closed-loop optimization across access, aggregation, and core domains.

However, Cary notes that most OSS/BSS environments remain fragmented, limiting the ability to operationalize these capabilities at the customer experience layer. Data silos, batch-oriented processing, and loosely coupled workflows constrain real-time, cross-domain orchestration.

AI Across Commercial and Network Domains:

Cary identifies three primary domains of impact, increasingly converging with network intelligence:

  • Marketing: AI-driven personalization is evolving toward real-time, context-aware engagement informed by both customer behavior and network conditions (e.g., location, QoS state, congestion). This aligns with event-driven architectures and customer data platforms integrated with network analytics (e.g., NWDAF exposure via APIs). “Personalisation shifts from broad audiences to the individual,” she said, adding that relevance is now “a prerequisite for attention.”

  • Sales: AI enables next-best-action and dynamic offer generation, incorporating network-aware insights such as service availability, slice characteristics (in 5G SA), and fiber capacity constraints. Integration with policy control (3GPP TS 23.203) and service orchestration frameworks supports closed-loop order capture and fulfillment. “That combination of higher conversion and lower friction is valuable,” she said.

  • Service: AI-driven assurance is transitioning from reactive fault management to predictive and intent-based service assurance across both wireless and wireline domains. Telemetry from RAN, transport, and fixed access networks feeds AI models that anticipate degradation and trigger remediation before customer impact. “Human agents still play a central role,” Cary said, “but they can be augmented with real-time recommendations, contextual history and autonomous processes that improve both speed and consistency.”

Scaling Challenges in AI-Native Transformation:

Despite progress in AI models and domain-specific analytics, Cary highlights a systemic gap in operationalization.

“What it lacks, in many cases, is the ability to turn fragmented customer data into real-time decisions that can actually be executed across marketing, sales and service,” she said.

Analysys Mason data indicates that only 6% of operators achieve ROI above 25% from AI initiatives, while 60% advance just 20% of proofs of concept into production. This reflects challenges in integrating heterogeneous data sources across OSS, BSS, and network domains, as well as limitations in MLOps and real-time orchestration frameworks.

Fragmentation is compounded in converged networks, where wireless (3GPP-based) and wireline (e.g., Broadband Forum TR-369/USP, TR-383 for disaggregated BNG) ecosystems often evolve independently. Additionally, 93% of operators cite multi-vendor complexity as increasing total cost of ownership, underscoring the need for interoperable, standards-based integration across AI, network, and IT domains.

“That creates an unfortunate pattern across the industry,” she said, “promising AI initiatives that demonstrate value in isolation but fail to scale because they are not connected to the data, systems and processes where real work happens.”

Toward Fully AI-Native Operations:

Cary emphasizes that the target state is not incremental automation but fully AI-native operations, where intelligence is embedded into both network control loops and customer engagement workflows.

“This is why the future of customer experience in communications is not about layering AI onto the edge of the enterprise,” Cary said. “It is about making AI operational at the core of engagement.”

This vision aligns with emerging 6G research directions, where AI is treated as a native design primitive across RAN, core, and service layers, as well as with TM Forum’s Open Digital Architecture (ODA), which promotes composable, API-driven integration between OSS, BSS, and AI components.

Oracle’s approach reflects this convergence by unifying customer data, embedding AI into engagement and orchestration layers, and integrating these capabilities with telecom operational systems across both wireless and wireline domains.

Implementation Suggestions:

Cary said that network providers do not need to transform everything at once. She recommends starting by unifying customer data across touchpoints to establish a trusted, real-time view, before activating high-value AI use cases across marketing, sales and service. From there, providers can embed AI into workflows so insight translates into action rather than sitting in dashboards, eventually connecting those capabilities into end-to-end orchestration.

Indeed, Cary advocates a phased approach consistent with AI-native transformation:

  • Establish a unified, real-time data fabric spanning customer, service, and network domains.

  • Deploy high-value AI use cases (e.g., next-best-action, churn prediction, predictive assurance) leveraging both IT and network telemetry.

  • Embed AI into execution workflows to enable closed-loop, intent-driven orchestration across the customer lifecycle.

This progression reflects the broader industry trajectory toward converged, AI-native networks, where customer experience is no longer an overlay on connectivity, but a direct outcome of tightly coupled intelligence across wireless and wireline infrastructures.

“The communications providers that lead in the years ahead will not be the ones that simply adopt more AI tools. They will be the ones that use AI to rethink how customer engagement works across the enterprise. AI is not just enhancing customer experience,” she added. “It is redefining how customer experience is delivered, and in communications, that shift is likely to separate the providers that keep pace from the ones that set the pace.”

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Editorial Analysis:

Cary’s vision aligns with IMT 2030/6G’s shift from AI as an overlay to AI as an architectural primitive, spanning the air interface, semantic service handling, and distributed edge intelligence across wireless and wireline domains.  The cleanest way to map her suggestions into 6G is to treat “autonomous customer experience” as the service-layer expression of an AI-native network stack: AI decisions would no longer sit only in OSS/BSS, but would be distributed across RAN, transport, core, and edge applications, with closed-loop control spanning wireless and wireline domains. That maps well to current AI-native 6G proposals that emphasize model interdependencies, distributed intelligence, and AI embedded directly in the architecture rather than layered on top.

For the AI native air interface, the link is to AI-assisted radio control, where the network uses learned models to optimize scheduling, mobility, beam management, and QoS-aware policy decisions in real time. In a 6G framing, that extends beyond today’s AI for RAN optimization and toward an AI-native air interface in which the radio stack itself is designed for machine-driven adaptation, including distributed control loops between UE, RAN, and core. For your article, this supports language that customer experience is increasingly shaped by network intelligence at the point of access, not just by back-office engagement systems.ieeexplore.ieee+2

Semantic communications maps to the idea that the network should optimize for meaning or task relevance, not simply bit delivery. In practice, that means a 6G service layer could prioritize the semantic value of an interaction—such as whether a customer is trying to resolve an outage, confirm a move order, or change a plan—and allocate resources accordingly across wireless and wireline paths. The relevance to Cary’s argument is that customer experience becomes more contextual and intent-aware when the network itself can distinguish between low-value traffic and high-importance service interactions.

Distributed intelligence at the network edge is the most direct bridge between CSP operations and 6G design. In a converged wireless-wireline environment, edge AI can fuse RAN telemetry, fixed access metrics, subscriber context, and service history to trigger local decisions such as proactive care, dynamic QoS adjustment, or preemptive fault mitigation. That makes the experience layer more autonomous because the decision point moves closer to where the event occurs, reducing dependence on centralized, slower, batch-oriented processing.

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References:

https://capacityglobal.com/news/why-customer-experience-is-becoming-telecoms-clearest-differentiator/

What is AI Native?

https://www.nokia.com/6g/unlocking-the-full-potential-of-ai-native-6g-through-standards/

Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)

SHIELD-6G with AI-native cyber threat intelligence platform to enhance cybersecurity for Europe’s future 6G networks

AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC

Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026

NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU

AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC

 

 

Ookla: AI workloads will force changes in 5G mobile network infrastructure

Posted on by Alan Weissberger

Introduction:

Ookla’s latest research study examines how AI use cases will stress 5G mobile networks, relative to standard internet traffic. The report, based on Speedtest Intelligence® data across 22 markets, evaluates metrics like upload capacity, latency under load, and cloud infrastructure pathways (see graphs below).  Using Speedtest 5G data from 2025 across 22 markets and 86 operators in North America, Europe, Asia Pacific, the Middle East, and Latin America, it measures upload capacity, latency under load, and the quality of the path to the cloud. It also shows where current 5G falls short of what AI actually demands.

Analysis:

Ookla’s report argues that 5G network evaluation is entering a new phase: raw download speed is no longer enough to describe user experience or network capability in an AI-driven era. The more relevant indicators are upload performance, latency, consistency, and resilience, because AI-heavy applications tend to be interactive, symmetric, and sensitive to delay.  The report’s timing is important because it reframes 5G from a consumer mobile broadband service into an infrastructure question for AI workloads. That shift matters for network operators, because uplink and latency have historically received less attention than headline download rates in market rankings and public messaging.

Here’s the lead-in (emphasis added):

“AI has changed what a good mobile network looks like, and the metric the industry has marketed for two decades — peak download speed — no longer predicts it. The networks that top the download charts are often not the ones best prepared for AI traffic. Whether an AI application feels instant or breaks depends in large part on how much a network can upload, how it holds up under load, and how consistently it reaches the cloud, and on those measures, different networks come out on top. This report rebuilds the industry’s download-led scorecard around what AI actually asks of a network, and shows where today’s 5G mobile networks are ready and where they fall short. AI traffic is not one thing. Text chat, conversational voice, multimodal and AR vision, generated video, and agentic activity each load the network differently, and most of them lean on parts of the network that download speed never tested. The change AI brings is less about raw capacity, which operators have expanded for years, than about the shape of the traffic — heavier on upload, always on, and bursty, rather than download-led and session-based.”

A few high-level takeaways for the U.S. market include:

  • Although the United States ranks among the strongest on overall network performance, it sits at 5.1% for the proportion of network capacity allocated to the uplink, which is the lowest in the dataset.
  • The U.S. upload share has contracted, declining from 8.0% to 5.1% between 2023 and 2025.
  • The U.S. market top network operators fall short of the 20 Mbps upload target required for AR and multimodal AI.
  • For baseline network responsiveness, the U.S. records a multi-server latency of 50.5 ms, missing the target of less than 50 ms for text-based large language models (LLMs).

Technical Implications:

Ookla’s framing implicitly favors 5G SA, 5G Advanced, and edge-assisted architectures, since these are the network generations most likely to improve latency determinism and support more efficient uplink behavior. It also suggests that future benchmarking should include workload-aware tests, not just conventional speed tests, because AI applications stress networks differently from video streaming or web browsing.  The report has immediate relevance for markets where 5G download speeds look strong but uplink and latency remain weaker, because those networks may appear healthy under older metrics while still underperforming for AI use cases. That is a useful lens for comparing operators, especially where regulators and carriers are beginning to discuss AI readiness as part of national digital infrastructure strategy.

Conclusions:

With the rise of AI workloads, mobile network measurement is becoming application-specific. The central question is no longer just “How fast is 5G?” but “How well does the network support AI-era traffic patterns, especially interactive and uplink-heavy traffic?”  In this new context, metrics such as upload capacity, latency consistency, and service resilience are becoming just as important as peak downlink speed. For operators, this implies that competitive advantage will increasingly depend on how well the network supports real-time, bidirectional, and latency-sensitive applications, rather than how well it performs on legacy consumer benchmarks.

Traditional speed tests still matter, but they are increasingly insufficient as a proxy for user experience in an AI-native environment. In practice, the networks that win will be those that can deliver symmetry, resilience, and predictable latency across real workloads, not merely impressive headline throughput.

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Ookla Charts:

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References:

https://www.ookla.com/articles/benchmarking-5g-ai-workloads-2026

https://www.ookla.com/s/media/2026/07/Ookla_Research_AI_network_readiness_07262.pdf

Ookla: AI workloads strain 5G infrastructure

Ookla: AI platform reliability decreases as outages surge

Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure

AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap

Ericsson’s June 2026 Mobility Report Highlights + AI impact on network traffic

Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035

Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic

Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?

Ookla on the Global D2D Market

Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area

Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

Dell’Oro: Mobile Core Networks +15% in 2025; Ookla: Global Reality Check on 5G SA and 5G Advanced in 2026

Ookla: FWA Speed Test Results for big 3 U.S. Carriers & Wireless Connectivity Performance at Busy Airports

AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era

Posted on by Alan Weissberger

Disclaimer:  Perplexity.ai was used for research in this article.

Executive Summary:

A cluster of announcements in early-to-mid June 2026 signals a real shift from AI research to commercial AI-driven network automation.  Telcos are transitioning from isolated AI pilots to production-grade AI operations deployed across live networks.

  • Ericsson launched its AI in RAN commercial software subscription on June 11th, claiming up to 20% higher downlink throughput and up to 10% better spectral efficiency across more than 15 live deployments using existing baseband silicon.

  • Nokia and Indosat Ooredoo Hutchison (Indonesia) announced a GPU-accelerated AI-RAN partnership in Indonesia on June 8, expanding the Nokia–NVIDIA architecture already adopted by T-Mobile US, SoftBank, and Vodafone.

  • Verizon disclosed that its 60,000-site vRAN is now applying agentic AI to planned configuration changes, service assurance, and network optimization, while publicly calling for industry-wide interoperability standards for agentic systems.

  • Nokia launched an agentic AI framework for IP network operations within its Network Services Platform (NSP), marking its third agentic product announcement in a four-week period.

A growing number of network operators are transitioning from traditional connectivity providers into AI infrastructure operators. SK Telecom (South Korea) announced a gigawatt-scale AI Cloud built on NVIDIA DGX SuperPOD architecture; Deutsche Telekom (Germany) secured the German federal government’s sovereign AI cloud contract; and MTN Group (South Africa) detailed a plan to convert 18,000 African tower locations into a distributed AI inference grid.

Over a six-week window, six major network operators—SK Telecom, Deutsche Telekom, MTN Group, Verizon, SoftBank, and Indosat Ooredoo Hutchison—have converged on a single strategic premise: existing telecommunications infrastructure, including connectivity, physical real estate, and data center capacity, constitutes the foundational footprint for a commercial AI compute business.

Source: https://www.vamsitalkstech.com/agentic-ai/agentic-ai-in-ran-optimization-building-towards-autonomous-networks/

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Government’s Buys Into AI Compute:

Government involvement in AI compute is intensifying, with direct implications for telecom strategy. China’s $295 billion program defines the upper bound of state-backed AI compute investment. Beijing announced plans to invest 2 trillion yuan ($295 billion) over five years in AI datacenter infrastructure. China Mobile and China Telecom are designated as the primary operators of a national AI compute network, while Huawei will supply the majority of AI chips—explicitly bypassing NVIDIA. The plan accelerates China’s original 2030 national computing network target to 2028, funded through sovereign debt.

  • China’s National Data Administration reported 140 trillion daily AI token flows by March 2026—up 1,400-fold from the start of 2024. China Mobile, China Telecom, and China Unicom launched commercial AI token packages in May, with per-token costs that centralized operations can reduce by approximately 30 percent. China Mobile separately unveiled AI-eSIM, which embeds an autonomous decision layer directly into the SIM.
  • Chinese network operators are advancing through the full AI lifecycle—token economy, infrastructure mandate, and sovereign chip supply—at a pace and scale unmatched by any other single market.

Technical Implications for Network Architecture:

The convergence of AI-RAN, agentic AI, and AI infrastructure provision demands architectural evolution across several dimensions:

Dimension Technical Shift
Compute Location GPU acceleration at the RAN edge for real-time optimization; centralized AI clouds for token economy and sovereign compute
Latency Requirements Sub-millisecond for AI-RAN beamforming; millisecond-scale for agentic configuration changes; seconds-to-minutes for token inference
Network Slicing AI compute traffic requires dedicated slices with guaranteed QoS, separate from traditional user data
Interoperability Agentic AI systems must interoperate across vendor domains—Verizon’s call for standards reflects a critical gap
Security AI-eSIM and autonomous decision layers introduce new attack surfaces requiring zero-trust architectures

Standards and Interoperability Gap:

Verizon’s public call for industry-wide interoperability standards for agentic systems highlights a critical bottleneck. Agentic AI frameworks from Ericsson, Nokia, and other vendors must interoperate across multi-vendor networks, yet no standardized protocol exists for agentic command, control, and assurance. This gap mirrors the early RAN interoperability challenges that Open RAN later addressed.

The TM Forum’s Autonomous Networks L4/5 roadmap and the 3GPP 6G standardization process will need to incorporate agentic AI interoperability as a core requirement. Without standards, telcos risk vendor lock-in for AI automation capabilities, undermining the multi-vendor flexibility that has been a telco industry priority for decades.

What This Means for 5G and 6G Roadmaps:

AI-driven automation is becoming a prerequisite for 6G L4/5 autonomous networks. The June 2026 announcements suggest that:

  1. 5G Advanced deployments will increasingly incorporate AI-RAN as a standard feature, not an optional enhancement.

  2. 6G specifications (expected by end-2028 per Ericsson) will likely embed agentic AI and autonomous decision layers as core architectural elements.

  3. Network economics will shift from bandwidth-centric to compute-centric revenue models, with AI token services and inference grids becoming significant revenue streams.

For network architects, the implication is clear: AI infrastructure must be designed as a first-order network capability, not a second-order application layer. GPU acceleration, agentic orchestration, and token-economy support need to be part of the baseline network architecture from the outset.

Conclusions — The Automation Tipping Point:

June 2026 marks a tipping point where AI-driven network automation transitions from pilot to production. The combination of commercial AI-RAN subscriptions, agentic AI deployments at tens-of-thousands-of-site scale, and telco-led AI infrastructure provision signals that AI is no longer an experimental capability but a core network function.

The critical question for telcos is not whether to adopt AI automation, but how to avoid vendor lock-in while achieving the interoperability required for multi-vendor, multi-domain autonomous networks. Standards bodies, operator consortia, and vendor alliances must address this gap before agentic AI becomes a strategic constraint rather than a competitive advantage.

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References:

https://mtnconsulting.substack.com/p/the-unmanned-network-17-june-2026

Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure

Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure

STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation

Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth

Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks

Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)

Ericsson’s June 2026 Mobility Report Highlights + AI impact on network traffic

Ericsson launches AI in RAN as commercial software subscription:   

https://techblog.comsoc.org/2026/06/16/ericssons-june-2026-mobility-report-agentic-ai-impact-on-network-traffic/#comment-25498

Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN

Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN

RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN

RAN Silicon Rethink- Part II; vRAN and General-Purpose Compute

Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact

Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy

Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined

Indosat Ooredoo Hutchison, Nokia and Nvidia AI-RAN research center in Indonesia amongst telco skepticism

 

Ookla: AI platform reliability decreases as outages surge

Posted on by Alan Weissberger

So you thought “AI Hallucinations” were the only big problem with AI performance?  Think again!  In a new Ookla reliability report, data from its Downdetector reveals that AI platform outages surged from 6 high-disruption days in Q1 2025 to 51 in Q1 2026 , as AI tools transitioned from novelties to critical business infrastructure. These disruptions stem from rapid scale-up volatility, cloud provider failures, and complex, agentic workflows.  Analysing 471 days of US Downdetector data from 1 January 2025 to 16 April 2026 across ChatGPT, Claude, Gemini, Microsoft Copilot, AWS and Microsoft Azure, Ookla recorded 3.7 million user-reported problems.

High-signal disruption days, defined as when a service recorded more than 10 times its own median daily report volume, rose from six across four major AI apps in Q1 2025 to 51 in Q1 2026, according to the report by Ookla analyst Luke Kehoe.

Anthropic’s Claude model accounted for 39 of those 51 disruption days. Gemini accounted for seven, Copilot three and ChatGPT two.  Here’s a summary:

  • Claude: Anthropic’s platform was the clearest example of scale-up volatility, accounting for 39 of the 51 high-signal disruption days in early 2026 due to rapid adoption and scaling.  
  • ChatGPT: While it generated some of the largest raw disruption spikes—often linked to model updates or demand surges—its median daily report trend improved compared to the prior year.  
  • Microsoft Copilot: Outage reports heavily clustered on weekdays, reflecting its core integration into enterprise business workflows rather than consumer use. 
  • Gemini: Incidents rose to seven alongside expanding user adoption.
  • Cloud Infrastructure: A significant portion of AI downtime wasn’t the AI model itself, but outages at the cloud level that caused cascading failures.  AWS’s 20 October 2025 DynamoDB DNS event generated more than 315,000 US disruption reports, while Microsoft’s Azure Front Door incident on 29 October produced nearly 96,000, illustrating how failures in cloud control planes can cascade into AI platform disruptions.

Claude’s growth over the past 12 months was accompanied by significant disruption. Ookla describes it as “the clearest example of scale-up volatility,” with disruptions to its offering starting to move the needle in July last year as adoption rose. There’s a hint that the upward trajectory will continue – Ookla notes that at 2,830 daily reports on average, Claude’s report volume in March was three times that it recorded in February.

AI reliability now spans multiple failure layers:

AI platforms are not single systems from the user’s point of view, even when they present a single interface. A ChatGPT, Claude, Gemini, or Copilot failure can sit in the product layer, the provider orchestration layer, the hyperscaler layer, or the edge and access layer. The product layer is what users actually see. The provider orchestration layer includes login, routing, model selection, rate limits, feature flags, inference scheduling, retry behavior, and capacity allocation. The hyperscaler layer includes compute, databases, storage, networking, and regional control planes. The edge and access layer includes DNS, web gateways, bot protection, content delivery, and authentication flows.

Ookla’s Kehoe wrote, “As AI systems move from short chat sessions into longer-running agentic tasks, a failed prompt, login loop, stalled code task, unavailable file, or broken connector can interrupt work that now sits inside real business processes.” This is a very serious concern!

Those layers are not always owned by different companies, and they are not the full physical internet stack. Network operators, subsea cables, data centers, and user access networks still matter. The focus here is narrower: the service and dependency layers that are most visible in Downdetector data and public incident records.

This distinction is important because the same user-facing symptom can have different operational meanings. A failed prompt, login loop, missing chat history, rate-limit error, unavailable file, or stalled agent task may not share the same root cause. For enterprise buyers and risk teams, resilience is about understanding more than whether an AI platform was simply available. They need to know where the issue occurred, which workflows were affected, and whether it reflected a problem with a single provider or a broader dependency across the AI stack.

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References:

https://www.ookla.com/articles/ai-platform-reliability

https://www.mobileworldlive.com/ai-cloud/ookla-finds-ai-platform-outages-surge-as-adoption-grows

https://www.telecoms.com/ai/ai-app-disruption-is-on-the-up

Will 2026 be the “Year of the AI Ontology” for telecoms?

Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact

Posted on by Alan Weissberger

Executive Summary:

According to Light Reading, Nvidia is working on a GPU combo chip that would sit directly in the 6G radio unit [1.], extending its AI-RAN push from baseband/server  into the radio itself.  It’s reported to be a more hardware-integrated, sub-100W embedded design rather than just GPU acceleration in centralized RAN compute.

Note 1.  6G/IMT 2030 Radio Interface Technologies (RITs) have yet to be defined, let alone specified by 3GPP or ITU-R WP5D.  They won’t be solidified until the end of 2030 so any specific silicon design won’t be completed until then or 2031!

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Light Reading’s headline frames it as a “radical new AI-RAN plan and they wrote that “the move was confirmed by knowledgeable sources, with Nvidia saying GPUs in more advanced radios will become “essential” in future. It marks a dramatic new development in the GPU giant’s “AI-RAN” strategy.”

If accurate, this would be a notable shift for Nvidia, because it would let them influence the whole RAN stack, not just centralized compute. That could matter for performance, power efficiency, and AI-native functions such as sensing, spectrum optimization, and real-time signal processing. Nvidia’s broader 6G messaging already emphasizes AI-native wireless, integrated sensing and communications, and spectrum agility as core themes.

The unconfirmed report fits Nvidia’s existing telecom roadmap rather than appearing out of nowhere. Nvidia has already announced an AI-native wireless stack for 6G with partners including Cisco, MITRE, Booz Allen, ODC, and T-Mobile, and it has promoted AI-RAN as a way to combine connectivity, computing, and sensing on one platform.  It also aligns with the company’s recent partnership with Nokia, where Nvidia introduced the ARC-Pro 6G-ready accelerated computing platform and described it as a software-upgradable path from 5G-Advanced to 6G. That makes the rumored radio-chip move look like a vertical extension of the same strategy.

For wireless network operators, a radio-unit chip from Nvidia would be significant only if it improves cost, power, or flexibility versus incumbent RU silicon. The practical test will be whether it can deliver enough RF, baseband, and AI function integration to justify another architecture layer at the edge. It would also intensify competition in the radio-access supply chain and reinforce the trend toward AI-native, software-defined RANs. It also suggests Nvidia wants to shape not only the compute layer but the physical radio layer of 6G networks.

Possible AI Silicon Features and Functions:

Nvidia would most likely add AI-for-RAN features into radio silicon first, because those map directly to signal processing and link adaptation rather than to generic “AI at the edge.” Nvidia’s own AI-RAN materials emphasize embedding AI/ML into the radio signal-processing layer to improve spectral efficiency, coverage, capacity, and performance.  Here are a few likely AI features/functions for the rumored 6G AI Nvidia super chip:

  • Neural channel estimation and equalization, to infer cleaner channel state from noisy RF observations and improve link reliability. Nvidia’s open-source Aerial release specifically calls out advanced neural models for channel estimation.

  • Real-time beam management, including beam selection, beam tracking, and beam refinement for massive MIMO and mmWave/upper-midband deployments. These are natural AI-RAN use cases because they depend on fast adaptation to changing propagation conditions.

  • Spectrum agility and interference mitigation, such as identifying jammed or congested resource blocks and dynamically avoiding them. NVIDIA and partners have already described spectrum agility applications that freeze only affected frequencies while keeping the rest of the system online.

  • Dynamic resource scheduling, using learned traffic and channel patterns to allocate PRBs, power, and compute more efficiently in real time. Nvidia describes AI-RAN as improving spectral efficiency and dynamic traffic handling through AI.

  • Integrated sensing and communications support, where the radio helps detect objects, motion, or environmental context in parallel with communication. Nvidia has already highlighted ISAC-style applications with camera/RF fusion and object tracking.

  • Edge inference hooks, letting the RU expose real-time PHY data to AI applications or a dApp-style framework. Nvidia’s open-source Aerial stack says third-party apps can access physical-layer data through secure APIs and modify RAN behavior in real time.

  • Self-optimization and closed-loop control, where the radio silicon learns local conditions and continuously retunes thresholds, coding, MCS selection, and precoding policies. That fits Nvidia’s broader framing of AI-native networks as software-defined and continuously adaptable.

The most plausible first wave is not a fully autonomous “AI radio,” but a hybrid RU chip that accelerates selected PHY functions and exposes telemetry/data paths to the rest of the AI-RAN stack. Nvidia’s current messaging emphasizes software-defined infrastructure, deterministic performance, and layered AI-RAN capabilities rather than replacing the entire RAN with a black-box model.

The real differentiator would be whether Nvidia can combine RF signal processing with its GPU/CUDA ecosystem, so the same platform handles channel learning, inference, and orchestration across RU/DU/CU tiers. That would let operators optimize for spectral efficiency and OPEX while still keeping a software-upgrade path to 6G.  Radio electronics is constrained by power, latency, determinism, and certification, so Nvidia would need to prove these AI features help without destabilizing PHY timing. That is why the likely starting point is assistive AI inside the signal chain, not a fully learned end-to-end radio.

Image Credit: Nvidia

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Competitive Analysis:

Nvidia’s reported move into a 6G radio-unit chip is most threatening to Marvell and Qualcomm at the silicon layer, while it is more of a strategic architecture challenge to Nokia and Ericsson at the system level. The immediate effect is less about a single chip and more about Nvidia trying to pull compute, connectivity, and AI deeper into the RAN value chain

Qualcomm is the closest direct competitor if Nvidia is trying to put silicon into the radio or near-radio layer. Qualcomm already has a Layer 1 strategy that combines silicon and software in SmartNIC/server-adjacent form factors, so Nvidia would be moving into a space where Qualcomm has both telecom credibility and established IP.

The risk for Qualcomm is that Nvidia can use its AI brand, CUDA ecosystem, and hyperscale relationships to redefine what “performance” means in RAN silicon, especially if AI-native functions become a buying criterion. The counterpoint is that Qualcomm still has a strong edge in wireless-specific silicon integration and standards heritage, which matters if the 6G radio path remains RF- and modem-centric.

Nokia looks less exposed in the short term because it is already partnering with Nvidia rather than treating it as a pure adversary. Nvidia and Nokia have publicly framed their relationship as an AI-native 5G-Advanced/6G platform effort, and Nokia says it will add NVIDIA-powered commercial AI-RAN products to its RAN portfolio.

Nonetheless, a Nvidia radio-chip push could still compress Nokia’s differentiation over time if more of the RAN stack becomes software-defined and GPU-centric. The strategic question is whether Nokia remains the integrator and operator-facing systems vendor, or whether Nvidia gradually becomes the architectural center of gravity.

Ericsson is the most structurally interesting case because it sits at the high end of global RAN share and has been more cautious about Nvidia as a Layer 1 option. Light Reading notes Ericsson is currently dismissive of Nvidia as a Layer 1 choice, even while the broader ecosystem explores AI-RAN collaboration.

For Ericsson, the threat is not immediate revenue loss from a single chip; it is erosion of the traditional assumption that RAN leadership comes from proprietary radio and baseband stacks. If Nvidia can make AI-native RAN a default design paradigm, Ericsson may be forced to defend its software and systems value rather than simply its box-selling model.

Samsung Electronics contacted Light Reading after their story was published to point out that it also works with AMD as a chip partner. “Samsung supports full Layer 1 (L1) processing using Intel’s telco CPUs (e.g., Xeon 6 Granite Rapids) and lookaside accelerator approach and in addition has successfully demonstrated full L1 processing on AMD’s CPUs without relying on dedicated L1 accelerators,”  a Samsung spokesperson said via email.

Marvell is the most exposed chip supplier in this story because its telecom position is more concentrated in custom Layer 1 silicon. Light Reading specifically points out that Marvell is a critical supplier to Nokia in Layer 1, which makes a Nvidia radio-chip effort a direct substitution threat in portions of the stack.

If Nvidia succeeds, Marvell faces a two-sided squeeze: loss of design wins in telecom silicon and a narrative shift toward AI-native programmable platforms that favor Nvidia’s broader ecosystem. Marvell’s defense is that telecom operators still care about power, latency, and deterministic functionality, areas where custom silicon can remain more efficient than a generalized AI-compute approach.

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Summary Table:

Company Impact level Why
Qualcomm High Direct silicon adjacency and overlapping Layer 1 ambitions.
Marvell High Telecom custom-silicon exposure, especially Layer 1.
Ericsson Medium Strategic and architectural threat more than immediate chip displacement.
Nokia Medium to low near term Partnered with Nvidia, so risk is more about future dependence and stack control.

Source: Perplexity.ai

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Conclusions:

It’s unknown whether Nvidia’s rumored radio chip becomes a product, a reference design, or just an extension of its AI-RAN platform. If it ships, watch for operator trials, power-envelope disclosures, and whether it targets RU integration, DU acceleration, or a hybrid AI-RAN endpoint. If it stays at the partnership/reference-design level, the market impact will be more narrative than revenue-relevant.

Another unanswered question is whether Nokia and Ericsson keep treating Nvidia as a collaborator while preserving their own Physical layer control, or whether they start to see Nvidia as a platform owner in the making. That boundary will determine whether this is a tactical ecosystem play or the beginning of a deeper industry reset.

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References:

https://www.lightreading.com/6g/nvidia-has-a-radical-new-ai-ran-plan-a-6g-radio-unit-chip

https://www.lightreading.com/6g/analyst-insight-6g-coming-into-focus

https://www.nvidia.com/en-us/industries/telecommunications/ai-ran/

RAN Silicon Rethink- Part II; vRAN and General-Purpose Compute

Orange, Nokia, Nvidia, and Intel debate: ASICs vs. GPUs vs. General-Purpose CPUs for RAN Baseband Processing

RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN

Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN

Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions

Inside Nokia’s new AI Networking Innovation Lab

Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy

Marvell shrinking share of the RAN custom silicon market & acquisition of XConn Technologies for AI data center connectivity

 

 

Network X Americas: AT&T and Comcast reveal huge AI impact on network operations

Posted on by Alan Weissberger

Echoing a recent Cisco report, telecom leaders at the Network X Americas conference (held in Irving, TX last week) noted that AI is fundamentally shifting traffic patterns while having a very positive impact on network operations.  With billions of connected sensors and devices (like autonomous vehicles generating 20GB of data per day), operators are forced to prioritize uplink capacity and low latency over traditional consumer downlink traffic.

AT&T’s network CTO, Yigal Elbaz, cited the robo-taxi as a bellwether for how AI is affecting network traffic.  Each Waymo vehicle generates about 20 gigabytes of data per day, roughly 30 times the amount a typical mobile user consumes. Most of that traffic flows from the car to the cloud.  “Every other week,” Elbaz noted, “a new flavor of a frontier AI model drops on us.”

“We already have about 700,000 changes on a daily basis in our network made by AI,” said Elbaz, noting that AT&T has built a proprietary foundation AI model because standard large language models (LLMs) don’t understand KPIs, network alarms or fiber deployment specifics. He cited a 20-25% cost reduction and 12-15% better results than general-purpose models.

In his keynote speech, Comcast EVP and Chief Network Officer Elad Nafshi described 200 edge compute centers capable of self-healing 77% of network events. He touted AI chipsets close enough to customers’ homes to pinpoint outside plant faults with 99.2% precision, and a partnership with Nvidia to push that edge platform further.

Nafshi highlighted the gap in network provider promises vs delivery with a hypothetical small-business use case example. A pizza shop operator, could materially change workflow and productivity if the service provider delivered an AI-enabled concierge—built on a task-optimized small language model—to manage order intake and customer interaction. In that scenario, the network evolves from a passive access pipe into an application-aware platform that augments business operations. The concept is credible from a technical standpoint, but remains largely theoretical until operators can effectively reach and educate SMB customers who still perceive connectivity as a fixed monthly expense.

Both AT&T and Comcast Israeli executives said this was more than modernization and discussed the changes in what a network does. The network is now a platform, not a pipe. Today’s network learns, adapts and increasingly acts on behalf of its customers. But I can’t help but wonder if the customers know… or if that network value will ever trickle down to the customers who need it most.

In a keynote panel session titled, ” Convergence in action – Competing, scaling and winning in the AI-driven connectivity market,” Josh Goodell, AT&T’s VP of Broadband and Converged Product Development, framed the company’s objective as becoming “the greatest simplifier of our customers’ lives” while instilling “connectivity confidence.” That positioning is notable for a sector that has historically under-communicated its value proposition beyond basic service metrics.

The broader industry narrative appears to be shifting. Historically, go-to-market strategies emphasized throughput benchmarks and promotional pricing. As Omdia’s Ruth Brown (panel session moderator) observed, packaging has been largely defensive, optimized around billing constructs rather than differentiated user experience. The emerging model instead centers on networks that operate contextually and autonomously—delivering value in ways that are largely invisible to the end user.

Derek Peterson, CTO of Boingo Wireless, articulated a parallel issue in venue networks, describing the “stadium problem.” Operators dimension infrastructure for peak ingress and then underutilize that capacity once users are inside the venue. The architectural question is no longer solely about capacity provisioning, but about service-layer innovation on top of that capacity. At Petco Park, Boingo leveraged existing network assets to enable pre-entry commerce, driving incremental revenue before fans pass through the gates. The infrastructure was not the constraint; the limiting factor was identifying and executing on higher-order use cases.

A similar disconnect persists in the industry’s framing of the digital divide. AT&T’s  John Stankey and others have suggested the gap is nearing closure, citing expanded fiber footprints and fixed wireless access. While coverage metrics have improved, the divide has never been purely a function of infrastructure availability. Adoption is equally constrained by affordability and, critically, by perceived value. If connectivity continues to be positioned as a commoditized utility, the most economically vulnerable segments—those with the greatest need for digital enablement—remain the least likely to engage.

This is particularly relevant in an AI-driven economy. The users and small enterprises that could benefit most from intelligent, network-delivered services are often those least exposed to the evolving capabilities of the platform. The industry risks over-indexing on measurable deployment milestones while under-communicating the functional value of next-generation networks.

The Network X keynotes underscored that the technical roadmap is largely in place. Network operators are advancing toward networks capable of real-time traffic learning, proactive cybersecurity at the edge, and highly personalized in-home connectivity experiences. These capabilities represent a more compelling value proposition than traditional service tier comparisons.

However, the central challenge remains go-to-market execution. The industry has demonstrated that it can architect and deploy these capabilities at scale. It has yet to establish a clear, effective framework for articulating that value to end users and enterprises in a way that drives adoption.

As a final observation, the broader telecom ecosystem—illustrated by developments such as autonomous vehicle platforms—already depends on AI-enabled, highly distributed network intelligence. While the underlying infrastructure is incrementally aligning with these requirements, the industry dialogue around its broader economic and societal implications remains underdeveloped.

References:

https://www.lightreading.com/ai-machine-learning/the-ai-enabled-network-is-here-the-pitch-is-stuck-in-traffic

 

Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035

Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?

Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth

Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers

Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks

STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)

Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements

Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?

T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation

Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips

CES 2025: Intel announces edge compute processors with AI inferencing capabilities

 

Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035

Posted on by Alan Weissberger

Executive Summary:

Consumer-driven AI traffic [1.] currently represents a marginal share of aggregate Internet traffic. However, accelerating adoption of agentic AI is expected to materially reshape traffic composition over the next decade. In its “AI Impact on Wide Area Networks” report, Cisco projects that AI will emerge as the dominant driver of network traffic growth. As consumer AI adoption approaches “near-universal usage,” AI and agentic AI are forecast to increase consumer-driven network traffic by approximately 6.6× by the mid-2030s (see chart below).

Cisco estimates that this AI expansion will account for roughly 63% of incremental traffic growth relative to non-AI scenarios. The study focuses specifically on WAN implications, rather than data center or GPU infrastructure, and provides guidance on network design and capacity planning. Methodologically, the report integrates real-world traffic observations (via Cisco Crosswork Assurance User Experience), third-party industry datasets, and controlled laboratory evaluations of AI agents to characterize how AI-generated traffic diverges from conventional web traffic patterns.

Token-consumption data shows nearly 10x year-over-year growth, while in some service provider measurements Cisco is seeing ~4x growth in just eight months. Sustained growth at these rates means AI traffic will become a meaningful component of overall network traffic by 2035.

Note 1. Consumer AI traffic has a few defining technical traits: it is still dominated by short text-based exchanges, but it is becoming more stateful, more upstream-heavy, and more latency-sensitive as users move from simple prompts to agentic workflows and multimodal interactions.  Today’s consumer AI traffic is still overwhelmingly text-oriented, which is one reason the aggregate bandwidth impact remains modest despite rapid adoption. Comcast’s network observation is a useful real-world proxy: 97.1% of AI traffic was text-based, while images accounted for 2.6% and video only 0.3%. The key technical implication is that current traffic volumes are often limited more by conversation frequency and session behavior than by very large payloads, though that changes quickly as users adopt image, audio, and video generation.

Although AI inference traffic is currently “negligible” relative to dominant categories such as video streaming, Cisco projects it will comprise approximately 25% of total network traffic by 2035 (see chart below). At that point, AI traffic is expected to represent a “meaningful component” of overall network load. Importantly, AI-generated traffic exhibits distinct characteristics: inference flows are approximately twice the duration of typical web transactions, demonstrate higher upstream bandwidth demand, and operate at “software speed” rather than human interaction rates.

The emergence of AI agents as “power users” further amplifies these dynamics. Cisco notes that agent-executed tasks can generate up to 450% more traffic per task compared to human-driven interactions. This shift is expected to drive operator adoption of “flow-aware network and security systems” as traffic patterns become increasingly machine-driven and less predictable.

Cisco’s broader framing is that AI traffic “isn’t just adding traffic,” but is changing the shape of traffic, with inference flows running about twice as long as typical web transactions and, in some cases, generating up to 450% more traffic per task when an agent executes the workload.  AI inference sessions tend to hold resources longer, create more sustained flows, and push operators to think in terms of flow-aware behavior rather than only peak-throughput sizing. Cisco also notes that about 9% of AI inference flows carry more upstream than downstream traffic, versus about 0.5% for typical web traffic, which is a meaningful shift for access and broadband networks.  Cisco reports that approximately 9% of AI inference flows are upstream-dominant, compared to roughly 0.5% for traditional web traffic, with this divergence expected to widen alongside increased agentic AI utilization. In parallel, latency sensitivity is anticipated to become a more critical performance parameter for AI-driven applications.

Latency and symmetry:

AI traffic is also more sensitive to latency than many ordinary consumer web transactions because the user experience is often conversational and interactive, with the expectation of near-immediate turn-taking. Cisco describes AI inference as operating at “software speed” rather than human speed, which means small delays can be more noticeable and operationally important. At the same time, upstream demand becomes more significant because prompts, context, attachments, and agent-generated actions can increase return-path traffic, especially as multimodal inputs and agentic tool use expand.

Multimodal growth:

The biggest step-up in technical impact comes when consumer AI shifts from text-only prompting to multimodal generation and agent-driven workflows. In those cases, each task can involve multiple model calls, retrieval steps, tool invocations, and richer media payloads, which expands both flow count and bytes per session. Cisco’s study suggests that this is why AI traffic will increasingly require “flow-aware network and security systems,” because the traffic profile is not just larger, but structurally different from conventional browsing.

 

Infrastructure Implications:

Telecom infrastructure is becoming “increasingly intertwined with hyperscale infrastructure, not because operators are leading AI investment, but because they are becoming part of the ecosystem that supports it,” analyst firm MTN Consulting said in an April 27th research note.  “Demand for optical transport, data-center interconnect, and edge infrastructure is rising as telecom networks carry growing volumes of cloud and AI-driven traffic,” the firm said.

“AI network traffic is already reshaping infrastructure needs. What we are seeing is clear: AI isn’t just adding traffic. It’s changing the shape of traffic,” Javier Antich, principal product management engineer in the CTO office of Cisco’s provider connectivity group, and Gurudatt Shenoy, SVP, product management, provider connectivity, explained in this blog post.

These shifts are beginning to influence access network evolution. Fiber networks already provide relatively symmetric throughput and low latency, while cable operators are advancing similar capabilities through DOCSIS upgrades. Mid-split and high-split architectures increase upstream spectrum allocation, enabling more balanced capacity profiles. Concurrently, Tier 1 operators such as Comcast and Charter Communications are introducing low-latency enhancements within DOCSIS networks.

Operational data reflects early-stage impacts. Comcast Chief Network Officer Elad Nafshi noted at the Cable Next-Gen event in March that approximately 97.1% of AI traffic on Comcast’s network remains text-based, with images accounting for 2.6% and video just 0.3%, indicating that bandwidth-intensive multimodal AI traffic has yet to scale materially.

Network design impact:

For broadband and access networks, the immediate engineering issues are upstream traffic capacity, queue behavior, and latency consistency rather than raw total throughput alone. Symmetry upgrades (such as DOCSIS mid-split and high-split for MSOs), along with low-latency capabilities, are relevant because consumer AI creates more return-path pressure and more time-sensitive sessions. In other words, the challenge is not simply to carry more bytes; it is to carry more interactive sessions with predictable performance, especially as multimodal and agentic usage scales.

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References:

https://www.cisco.com/c/dam/en/us/solutions/collateral/artificial-intelligence/mass-scale-infrastructure/ai-network-traffic-report.pdf

https://www.lightreading.com/ai-machine-learning/ai-emerging-as-top-driver-of-overall-internet-traffic-growth-study

https://www.cisco.com/site/us/en/products/networking/software/provider-connectivity-assurance/user-experience/index.html

Petabits per rack: How AI traffic is reshaping networks

Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?

Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth

Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers

Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks

STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)

Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements

Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?

T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation

Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips

CES 2025: Intel announces edge compute processors with AI inferencing capabilities

Inside Nokia’s new AI Networking Innovation Lab

Posted on by Alan Weissberger
As AI workload demands continuously affect how data center networks must operate, challenges across performance, scale, and precision must be addressed to maintain the large-scale demands on network infrastructure.  To address those needs, Nokia announced today the launch of its AI Networking Innovation Lab, a new facility designed to bolster innovation between AI and cloud partners and to accelerate next-generation development of AI infrastructure.
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Located within Nokia’s Sunnyvale, California facility, the lab serves as an innovation hub where Nokia will work across advanced AI networking technologies, architectures and ecosystems with a variety of partners to help shape the future of data center networking. The lab will serve as a testing center for Nokia Validated Designs and a co-innovation hub with its global partners, assessing real-world scenarios, commercial technologies, and the latest networking solutions.
Nokia has teamed up with several prominent infrastructure and platform providers. Early lab partners include AMD, Everpure, Keysight, Lenovo, Nscale, Supermicro and Weka.
  • Silicon & Compute: Collaborating with AMD to optimize enterprise AI workloads alongside Nokia data center switches.
  • Testing & Infrastructure: Partnering with Keysight Technologies to emulate workloads across Ultra Ethernet Consortium (UEC) and RoCEv2 transports.
  • Hardware & Servers: Integrating high-performance platforms from Lenovo and Supermicro.
  • Data Storage & Cloud: Working with Weka and cloud builders like Nscale to eliminate storage bottlenecks during heavy computational training.

Nokia’s AI Networking Innovation Lab is built upon three fundamental pillars: Technology Innovation, Ecosystem Collaboration, and Validation.  Image credit: Nokia

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Technology Innovation: The lab provides a dedicated space for AI partners to experiment with next-gen solutions across the entire networking stack – driving emerging standards forward with pioneering approaches to new protocols, switching silicon, congestion control, real-time telemetry, and automation.

Ram Periakaruppan, Vice President and General Manager, Network Applications and Security business at Keysight:
“Partnering with Nokia in the AI Networking Innovation Lab has enabled us to benchmark and optimize AI networks under real-world conditions…Together, we are helping accelerate AI network adoption by giving operators and hyperscalers the validated insights needed for confident, large-scale deployment.”

Ecosystem Collaboration: True progress depends on a strong ecosystem of technology providers – silicon manufacturers, GPU developers, system, storage and test vendors, and cloud platforms – that work together to create highly-compatible AI-ready solutions. This facilitates joint testing for interoperability, improves integration, and ensures roadmaps are aligned across different hardware, software, and orchestration layers.

Travis Karr, Corporate Vice President, HPC and Sovereign AI at AMD believes customer collaboration and an open ecosystem are fundamental to accelerating AI innovation:

“By co-developing solutions with partners, such as Nokia in their AI networking innovation lab, we ensure our AMD enterprise AI solutions are tested with Nokia data center switches on real-world workloads and network demands. An open, standards-driven approach empowers customers to integrate seamlessly across heterogeneous environments, avoiding lock-in and fostering industry-wide advancement in AI.”

Validation: This positions the lab as the testing ground for Nokia Validated Designs, where customers and partners rigorously validate multi-vendor data center architectures under authentic AI training and inference workloads. By testing failure scenarios, congestion behavior, and operational automation, the lab turns NVDs into proven, deployable solutions — enabling predictable performance, faster deployment, and reduced operational complexity and risk for organizations navigating the AI era.

Arno van Huyssteen, Vice President of Global Telecommunications for Nscale:

“Nokia is a strategic networking partner for Nscale as we build towards AI Grid, and the engineering rigour behind their Validated Designs reflects the kind of innovation needed to enable next-generation AI infrastructure. The depth of hardware, software and failure testing behind those blueprints is what will give operators the confidence to deploy complex AI environments faster, with fewer integration risks and less operational disruption. We’re excited to collaborate in the AI Networking Innovation Lab to help push the boundaries of AI-native networking and validate the next generation of solutions before they reach production.”

A primary focal point inside the lab is managing data center congestion. Unlike traditional cloud traffic, back-end AI networks feature high-density data synchronization across massive GPU clusters. The lab uses advanced automation, AIOps, and lossless Ethernet solutions—such as the Nokia 7220 IXR-H6 switches—to handle these intense uplink and synchronization demands safely.

The AI Networking Innovation Lab supports Nokia’s broader strategy to accelerate the next era of AI-driven connectivity. As demand for AI infrastructure continues to grow, data center networking has become one of the most critical foundations of the global AI ecosystem. Through this investment, Nokia is strengthening its capabilities in AI and cloud infrastructure while advancing its vision of AI-native networking.

Rudy Hoebeke, Vice President of Software Product Management at Nokia:

“The launch of Nokia’s AI Networking Innovation Lab marks a major milestone in our commitment to drive the next era of AI-native connectivity. As the industry continues to evolve with solutions like scale-across and AI-Grid, this lab is poised to accelerate AI networking technology that will not only support but optimize these emerging industry offerings. This center gives our customers and partners early access to new technologies, deeper collaboration with the world’s leading AI ecosystem players, and the confidence that their networks are validated under more realistic AI conditions. By accelerating innovation and reducing deployment risks, we’re enabling the industry to deliver faster, more reliable, and more sustainable AI experiences to people and businesses everywhere.”

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References:

https://www.nokia.com/newsroom/nokia-launches-ai-networking-lab-to-drive-co-innovation-with-partners-and-accelerate-next-era-of-ai-native-data-center-networking/

Analysis: Nokia’s strong growth in Optical Networks and AI network infrastructure

Orange, Nokia, Nvidia, and Intel debate: ASICs vs. GPUs vs. General-Purpose CPUs for RAN Baseband Processing

Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic

Australia’s NBN and Nokia demonstrate multi-generation optical technologies concurrently over existing FTTP infrastructure

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

Tampnet to expand 5G offshore connectivity in the Gulf of Mexico using Nokia AirScale 5G radios

Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN

 

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