• Tencent continues to advance its Hy AI model, focusing on cost-efficiency, performance, and specialization around productivity tasks.
• With Tencent WorkBuddy, Tencent is helping enterprises leverage AI to raise workforce productivity to benefit from the AI technology.
Tencent Cloud has been advancing its AI solutions despite not grabbing headlines internationally. The company is preparing the release of Tencent Hy4, the fourth generation of the Tencent Hy (Hunyuan) AI model. Tencent Hy4 is a 770 billion parameter, open-source, mixture-of-expert (MoE) model with 49 billion active parameters per token (i.e., optimizing for speed and compute resources by activating only a subset of the full model parameters). It is designed to improve reasoning efficiency and to support productivity tasks and visual understanding. The company has also designed Tencent Hy4 to support its AI products and applications by bringing expertise across Tencent including software engineering, gaming, finance, and security. As cost is one of the major concerns for enterprises, Tencent Cloud will also be adding Tencent Hy4 Flash, a more cost-effective model that carries Tencent Hy4’s visual understanding and agentic capabilities while reducing computational overhead. This is also responding to the broader industry trend toward a two-tier strategy, with a deep reasoning and a highly optimized, low-latency version (e.g., Gemini 3.8 Flash, GPT-4o mini, Claude 3.5 Haiku, DeepSeek-V4.1-Flash, and Qwen3.8-Flash).
• As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph, which gives an AI agent a roadmap for querying data.
• Querying a structured database is quicker and burns far fewer tokens, which helps address the AI cost concerns that are top of mind among enterprises.
The need to manage costs and recognize a positive ROI are top concerns among organizations rolling out generative and agentic AI. Even the largest enterprises are taking a more careful look at token economics as they seek to scale the use of AI tools throughout their organizations. As employees, developers and agents all gain access to generative AI and use an ever-growing number of tokens, spending is skyrocketing. During the Snowflake World Tour 2026 in New York City in October, Jeremy Burton, former CEO of Observe and currently General Manager of Snowflake’s Observability business, notes that companies let chaos reign while they experimented with AI and now need to rein in token usage. One often-overlooked lever is context. The context graph helps agents investigate operational telemetry more efficiently, which can improve answers and reduce token usage in those workflows. For this reason, the impact of Snowflake’s acquisition of Observe, which helps organizations better structure and understand how data is being used, has the potential to manage overall enterprise AI spending as well.
Snowflake’s acquisition of Observe expands its observability portfolio. As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph that maps semantics and relationships across logs and metrics, which gives an AI agent a roadmap for querying data. An agent could dig through raw data, but querying a structured database is quicker and burns far fewer tokens. As Observe puts it, the company does not have better AI; it has better data. Observe ingests data based on OpenTelemetry open standards and can store it in Apache Iceberg formats, providing greater flexibility and control. It uses object storage, elastic compute, and interoperable standards to meet customers’ requests for flexibility and cost sensitive solutions.
Observe complements Snowflake Cortex AI Gateway, announced in July 2026. The gateway governs how Snowflake and third-party agents access tools, models and data. It routes requests to approved models based on cost, quality, and latency; allocates spend to teams and workloads; sets limits; and records agent actions. Observe Agent Observability goes deeper, tracking which tools agents access and correlating agent activity with backend telemetry. It adds more detail directly from the agents themselves, such as connections to underlying application and infrastructure behavior. Observe stores the telemetry in Snowflake on low-cost object storage. At the scale of telemetry data, scan-based queries using Observe’s architecture are cheaper and faster than using index-based tools. Together, Cortex AI Gateway and Observe provide an end-to-end picture of an agent, from chat client to gateway to back end. Plenty of tools observe the agent itself; Observe provides deeper insight into the surrounding telemetry.
Improved context sits at the center of several trends related to how data is accessed. Software is going headless, as users prefer prompts and APIs to complicated interfaces when interacting with information. Walled gardens are crumbling as APIs, CLIs, and MCP streamline access to information and enable AI agents to drive actions. Evaluations, which interrogate how an agent reached its answer in testing and production, depend on rich telemetry. The next opportunity lies in context graphs. Enterprises that structure their data first can make more efficient use of tokens. With AI bills climbing, vendors that offer solutions that reduce token usage offer a compelling value proposition.
• US investment in fiber is accelerating at an all-time high as AT&T, Zayo, Verizon, and Lumen expand their networks to support AI and data-center growth.
• Building more fiber does not automatically translate into greater profits; customer demand, network utilization and investment discipline will determine the winners.
The US fiber market is in the middle of an AI gold rush, with telecom providers appearing determined that the machines never have to wait for bandwidth. AT&T’s recent partnership with Global Infrastructure Partners (GIP) and CPP Investments adds momentum to an investment race that already includes Zayo, Lumen, and Verizon. Each company is reshaping their networks to capture future demand from AI workloads and data center expansion. The enthusiasm is understandable, but with so many carriers reaching for their shovels, a more fundamental question remains: will AI demand deliver the returns the industry needs or will some investors be left digging for profit?
AT&T’s deal points to a shift in how communication service providers (CSPs) fund the next phase of their network expansion. By partnering with investment and private equity firms, AT&T can combine external funds with its existing fiber assets, accelerating deployment while sharing the financial risk. The venture’s wholesale, open-access model also widens the potential customer base to include operators seeking to support enterprise connectivity, data center interconnections, and AI workloads. The arrangement carries a trade-off, however. External funding eases pressure on AT&T’s balance sheet and speeds up the build, but AT&T must share future revenue and profit with its investment partners.
AI has now become the industry’s preferred justification for fiber spending, and every major carrier is investing in fiber networks to support the next phase of digital infrastructure growth. The expansion of hyperscale data centers and AI computing facilities is increasing the demand for those high-bandwidth connections between data centers, cloud platforms, and enterprise locations, which makes low latency and network resilience more important. Capacity alone will not guarantee success. Carriers must identify the routes and markets with the greatest potential, secure customer commitments, and maximize utilization to generate sustainable returns. AI is accelerating the investment cycle, but commercial discipline will determine which providers turn infrastructure spending into profitable growth.
As investment in US fiber networks continues to gain pace, providers must ensure that spending is backed by real customer demand rather than expectations of future growth. With AT&T, Verizon, Zayo, and Lumen all expanding their networks, competition will also grow across key markets, putting pressure on revenues and profitability. Partnerships like these can help spread investment costs and attract additional customers, but success will come down to how effectively CSPs use their networks. Routes that connect to established data center hubs may offer more immediate opportunities, while investments in emerging markets may bring greater uncertainty. Carriers must balance opportunity to capture growth against the risk of building capacity which may take years to generate adequate revenue.
AI is creating a significant opportunity for the US fiber market, but scale and timing of the future demand is less certain. As these four expand their networks, CSPs must look beyond growth and focus on profitable returns. AT&T’s partnership highlights one way to fund network expansion, while sharing the risk, however all carriers must have the customer demand to support long-term revenue growth. With competition increasing, more fiber will not automatically translate into greater profits. That leaves the question of whether success will come to those with the largest networks, or those making the smartest investment decisions.
• AI is driving up voice traffic while also making voice more vulnerable, so voice security needs urgent attention.
• The building blocks are in place, but solutions are fragmented. Voice security needs to be integrated on the carrier network level and backed by strong verification at the business.
Voice security is of paramount importance today. Advances in AI are affecting voice in two ways. First, AI is improving the voice experience as it becomes more interactive, which is expected to increase overall voice traffic. Second, AI can be used to bypass security barriers, significantly raising the threat level. The threat landscape varies depending on the call type, recipients, and network routing (such as international borders); this blog focuses on voice security within customer engagement (particularly contact centers), predominantly in the US.
• The UK’s Competition and Market Authority released its interim findings on nexfibre’s proposed acquisition of Netomnia, in which it raised concerns that the acquisition could reduce wholesale competition.
• Deploying multiple overlapping networks have put smaller altnets under financial pressure, with consolidation seemingly the only way forward.
It’s been humorously said that the UK has roughly one altnet for every postcode – well over 100 to be exact – each armed with big ambitions and a digger. It’s therefore no surprise that consolidation is now firmly on the agenda, with M&A activity set to reshape the alternative network landscape. The market that emerges could look very different from the landscape we see today. With the UK’s Competition and Markets Authority (CMA) releasing its interim findings on nexfibre’s proposed acquisition of Netomnia, raising concerns that the deal could reduce wholesale competition, particularly where the two networks overlap. Yet consolidation could create a larger stronger challenger to take on BT’s Openreach. Though this raises the broader question: Does the UK need more fiber networks, or fewer but stronger networks capable of competing at scale with Openreach?
• Chinese providers are catching up with global leaders in three areas: generative AI, operationalized solutions, and agentic AI, giving enterprises broader options to diversify their AI partners.
• However, gaps in go-to-market, professional services, and model development remain large – crucial areas to drive adoption and accelerate deployments.
AI demand continues to grow rapidly. GlobalData Market Opportunity Forecasts to 2030: Artificial Intelligence (published on June 30, 2026) forecasts the global AI market to increase at 48.2% five-year-CAGR to reach $1.3 trillion in 2030. This is also reflected by 88% of the 3,069 global enterprises interviewed by GlobalData, who increased their AI budget this year (source: GlobalData ICT Customer Insights Survey 2026. Asia-Pacific shows a similar trend with a 48.6% CAGR, accounting for 36.2% of the global AI market in 2026. While growth is reported across the stack from the GPU to the application layer, cloud-based enterprise platforms remain crucial to drive adoption by providing broad and flexible capabilities to develop, govern, and scale AI deployments. GlobalData’s recent updates on Cloud-Based Enterprise AI Platforms: Competitive Landscape Assessment (September 17, 2026) shows that global hyperscalers (Google and Amazon) continue to lead the market, followed closely by Microsoft and IBM. However, Chinese players including Alibaba, Baidu, Huawei, and Tencent are closing the gap. This is important especially in markets like Asia where these players already have a strong presence. This gives enterprises broader options to diversify their AI partners, but also attracts cost-sensitive customers through their cost-leadership strategy.
• Technology vendors are investing in forward deployed engineers to adapt AI solutions to meet customer needs.
• Systems integrators are working alongside their technology partners to help enterprises accelerate their AI adoption and ensure business outcomes are met.
It is widely reported that only a small percentage of firms that have conducted AI pilots are moving them into production. Even fewer enterprises have been able to scale their AI initiatives across multiple business functions. Based on GlobalData’s observations and discussions with enterprises, there has been greater success in AI implementations among large enterprises that have engaged a systems integrator or consulting firm to work alongside their technology partners, including hyperscale cloud providers and AI model companies (e.g., Anthropic and OpenAI).
Research Director, Enterprise Technology and Services
Summary Bullets:
• While IP voice is less ubiquitous, when it matters it, it really matters
• Agent-to-agent communication adds a new paradigm to IP voice services
Calling IP voice a legacy service is a little unfair, but only a little. Innovation in this arena is still happening as can be seen by BT’s recent launch of its automated UC edge platform designed to help enterprises move numbers between UC platforms automatically. However, a recent and stark counterpoint to this is Lumen’s decision to exit the IP voice market.
• Meta will need to shed its consumer-centric image to gain traction in the business market, even if it only targets smaller organizations.
• Convincing organizations that they can trust Meta to secure personal information and corporate intellectual property will take time and effort.
On Monday September 28, 2026, Meta announced plans to enter what is already a crowded enterprise AI market. It will develop Meta Enterprise Platform, a collection of tools and services that will help business customers implement their own AI strategies. Few details were shared other than the platform will incorporate Muse agent, Meta Business Agent, Muse API, Muse Code, and other solutions.
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