Public Opposition to New Data Centers Disrupts but Doesn’t Derail US Facilities’ Expansion

Amy Larsen DeCarlo – Principal Analyst, Security and Data Center Services

Summary Bullets:

  • As rapid advances in AI application development drive demand for more processing capacity, US-based cloud providers are investing heavily in building out facilities to support these deployments.
  • But not everyone is on board with expansion plans, with public criticism stalling some development efforts, forcing hyperscalers to pivot to new locations, often in more remote areas.

AI is changing the cloud landscape, creating the near-term need for a vast increase in processing power and storage space. Hyperscalers are responding with substantial facility construction plans. Just this year alone, Amazon Web Services, Google, and Microsoft Azure are pouring a total of $500 to $700 billion into extensions of their data center footprints. Given AI’s dominance in enterprise technology investment plans, this is a logical track. However, not everyone is on board with these aggressive development plans -and AI plays a role in that resistance.

Fifty-two percent of adults are more worried than excited about AI, according to results from a Pew Research Center survey of 3,488 adults fielded earlier this year. Those queried had trepidations related to AI about everything from job displacement and interference with human creativity to unreliable or even malicious output.

AI anxiety is translating into opposition to in-region data center expansion. A Gallup poll of 1,000 US adults conducted earlier this year, found that 71% of those surveyed are totally opposed to the building of new data center facilities used to support AI applications in their region. By comparison, 53% object to construction of a new nuclear energy plant – a perennially unpopular build in the US for decades.

Participants in the telephone survey cited several concerns related to the new data center expansion, primarily focused on resource consumption, cost, and quality-of-life impacts. Fifty percent said excessive resource requirements associated with these builds in areas like water and energy consumption along with secondary effects such as loss of farmland, wildlife, and deforestation are behind their resistance to facilities’ expansion in their areas. Twenty-two cited concerns about property values and increased traffic. Another 20 percent noted that new data centers might bring higher utility costs and cost of living expenses.

Localities are hearing and responding to this resistance to data center expansion. Due to regional complaints, more than $100 billion in facility buildouts was stopped or disrupted in just one quarter. Over 550 local governments have suspended new facility construction or stopped issuing new permits.

Industry observers warn that impeding expansion could have unintended harmful consequences, including hindering the establishment of effective cyber defenses against hostile adversaries and creating barriers to the development and deployment of technological innovations. But cloud providers have been adept at circumventing obstacles to expansion, finding locations in more remote areas that are more hospitable to new facility construction.

Hyperscalers and other cloud providers are targeting more rural areas in the South and Midwest, and more remote locales in states like Oklahoma, Maine, and Virginia. Nearly half of all new data center builds are in the south, with states like Texas being hot spots.

Google’s New Tools Support ‘Value Maxxing’ to Address Organizations’ Growing Concern Over AI ROI and Tokenomics

R. Bhattacharyya

Summary Bullets:

• After a period of ‘token maxxing,’ organizations are looking to reign in and better control inference costs.

• Instead, enterprises are now embracing ‘value-maxxing,’ which focuses on outcomes.
Last week, Google announced several enhancements to Gemini Enterprise designed to help enterprises obtain greater and faster ROI on their AI projects. The improvements address one of the biggest frustrations expressed by organizations today, namely that the benefits promised by AI are taking too long to realize. Companies are clamoring for domain specific solutions in order to speed the deployment, reduce the integration complexity, and increase the value obtained from AI projects. Additionally, business leaders are eager for better tools to help them manage AI costs. They are looking for improved visibility on token use and costs, more proactive spending controls, and more flexible payment options.

On Tuesday, August 25th, Google announced Gemini Enterprise for Financial Services and Gemini Enterprise for Legal. The industry-specific solutions include out-of-the box AI capabilities such as specialized agents that provide shortcuts for directing workflows, data connectors, and sector-optimized models. Reusable packages of instructions teach AI agents to perform specialized tasks that are customized to meet company-specific requirements; connectors link agents to internal systems and data while maintaining access controls; pre-built agents are available to deploy out of the box; and software provider partnerships facilitate industry-specific customization and integration, while avoiding vendor lock-in. Though initially rolled out for the financial services and legal industries, Google plans to offer similar solutions for other industries, including healthcare, life sciences, and professional services.

The following day, August 26th, Google revealed expanded tools for managing AI spending. It announced that Google Antigravity, its AI agent development platform, and Android Studio, for building applications, will now be included in Gemini Enterprise subscriptions. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate license and billing siloes. Furthermore, Google is providing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise. Customers can purchase a mix of per-seat subscriptions along with a new pay-as-you-go option, to help avoid hitting token caps in the middle of a job. Companies that commit to a minimum monthly spend will receive discounts on token costs. To better control spending, Google has rolled out new guardrails that enable administrators to set limits on AI spend by project, help estimate agent runtime costs, and identify anomalies in spending. Project level guardrails can pause an agent when API call limits are reached; a FinOps agent provides spending summaries in natural language.

Google’s announcements directly address concerns many organizations have over the spend on AI inference. After a period of ‘token maxxing’ wherein greater token usage was associated with greater productivity, organizations are looking to rein in and better control inference costs. Despite declining token costs, overall consumption, and therefore spend, are skyrocketing. Thus, the industry is now embracing ‘value-maxxing,’ which focuses on outcomes. It seeks to identify and quantify results, whether they be improved performance, more insightful decisions, or greater efficiency.

Regardless of the jargon of the day, organizations are taking a more analytical and practical approach to cost, latency, and performance optimization. No longer is the fastest or most expensive model considered the best choice for all tasks; organizations are now recognizing that some workflows are served well enough by less intensive reasoning, and that the same level of accuracy is not required for all tasks. At the same time, many are considering open-source strategies, attracted to the potential of lower costs, ability to fine tune models, greater transparency, local deployment options, and the option of leveraging existing infrastructure investments. At the end of the day, the development of appropriate AI strategies relies heavily on a broader understanding of the business and its operating model; professionals that can combine this knowledge with technical AI expertise are invaluable.

AI Changing the Commercial Model for Fiber Build

B. Swan

Summary Bullets:

• Zayo will build 8,000 miles of new long-haul fiber across key AI corridors, with Nvidia becoming its anchor customer.

• Nvidia’s extends beyond GPUs and compute, with partnerships spanning the optical and networking ecosystem underpinning AI infrastructure.

Until now, the AI Infrastructure race has predominately been focused on GPUs, data centers and access to reliable power, yet beneath all three sits a less visible, but increasingly critical, layer – connectivity. As AI workloads become larger, more distributed and dependent on moving large volumes of data between locations, fiber is emerging as a fundamental component of the AI Stack. Zayo’s recent announcement to build 8,000 miles of new long-haul fiber across key AI corridors, backed by Nvidia as its anchor customer, could mark the new beginning of a new investment cycle for terrestrial networks. The bigger question is whether AI-related companies could become the anchor customer needed for the next generation of fiber investment?

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AI Requires a Reinvention of the Modern Data Center

B. Valle

Summary Bullets:

• AI is drastically changing the fabric of the traditional data center, prompting fundamental changes in design and architecture.

• The biggest challenge is that AI infrastructure requires simultaneous scaling across multiple constrained layers: electricity, cooling, networking, chips, facilities, capital, and operations.

The rise of AI workloads is pushing data centers through a major architectural shift: from relatively general-purpose, virtualized compute environments toward high-density, network-intensive AI infrastructure. For example, rack density is rising sharply, because traditional data centers were not designed for the power and thermal profiles of dense AI server clusters. This means power distribution, floor loading, cable management, and thermal design are becoming central architectural considerations. Power availability has now become a core design constraint. Energy availability is starting to influence where data centers are built, with land and power constraints pushing some infrastructure development into new or remote regions.

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8×8 AI Routing Takes a Sad Song and Makes It Better

G. Willsky

Summary Bullets:

• 8×8 AI Routing identifies the right expert anywhere in an organization that can resolve a customer’s inquiry, not just the contact center.

• While 8×8 AI Routing is marginally better than legacy systems it still merits a try out.

We’ve probably all found ourselves reciting this famous opening line to a classic song when trying to connect with someone in customer support: “Help! I need somebody. Help! Not just anybody. Help! You know I need someone. Help!”

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Google Cloud Summit Sydney: Putting Agentic AI into Action

S. Soh

Summary Bullets:

  • Enterprises are deploying AI agents leveraging Google Cloud’s solutions and achieving positive business outcomes.
  • Google Cloud offers the full AI stack, and its sovereign cloud and cyber solutions are especially crucial for enterprise customers.

AI agents are no longer an idea. They are now being deployed by enterprises to improve internal workplace productivity and external customer experience. At Google Cloud Summit Sydney (held on June 25, 2026), more examples of agentic AI in operations were presented, moving from deterministic AI chatbots to more autonomous systems. Bunnings, a home improvement, gardening, and hardware products retailer in Australia, upgraded its Buddy AI chatbot that helped customers with product search to an AI agent that takes customers’ descriptions of their projects and fills the shopping carts with the products that they need. Bunnings indicated an uplift of conversion rates and basket sizes when customers engage with Buddy. Similarly, Woolworths supermarket has an agentic AI powered Olive assistant that is able to build shopping baskets from recipe photos and assist with proactive meal planning. These two examples demonstrate how AI agents trained with proprietary knowledge (e.g., Bunnings’s DIY catalog and Woolworths’ recipe catalog) can deliver greater customer outcomes.

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Boomi Targets Agentic AI Governance, but Orchestration Remains Its Raison d’Etre

B. Valle

Summary Bullets:

• Boomi is evolving from an iPaaS into an enterprise platform combining integration, automation, API management, data management, and AI agent governance.

• GlobalData recently attended Boomi’s World Tour London 2026, where agentic AI was discussed at length around announcements including Boomi Connect, Boomi Orchestrate, and Boomi Companion.

Although Boomi has historically been best known as an integration platform as a service, or iPaaS, the company is going to great lengths to emphasize that it has evolved into an enterprise platform which activates data and workflows for customers and combines integration, automation, API management, data management, and AI-agent governance. The Boomi platform acts as the connective and orchestration layer between an organization’s applications, data, and AI systems, but is increasingly moving towards management of AI agents to help data enhance business processes.

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AI Wars Intensify via Major LLM/Agentic Releases

C. Dunlap
Research Director

Summary Bullets:

• Cycles between advanced AI model rollouts are significantly shortened among leaders in this space

• Developers are gaining access to agentic-injected integrated development environments (IDEs); while knowledge workers gain access to agentic AI assistants.

The second quarter marks a momentous period in the industry’s ongoing AI efforts. Platform leaders shipped next-generation agentic runtimes including autonomous and other advanced capabilities, all while managing a more compressed cycle of new AI models, which are rolling out in a matter of weeks versus months.

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Telstra and Google Deepen Infrastructure Ties to Power Australia’s AI Future

Headshot of a smiling man in a suit, wearing a pink shirt and standing against a grey background.
B. Swan

Summary Bullets:

• Telstra and Google have expanded their partnership, leveraging complementary subsea and terrestrial fiber assets to strengthen regional connectivity and digital infrastructure.

• The partnership aligns with Google’s strategy to expand its infrastructure through deeper collaboration with telecom operators.

If telecom press releases were a streaming service, “Strategic Partnership” would be the show nobody gets hyped up about, but somehow it continues to be renewed for another season. So, when Telstra and Google announced yet another episode, it would be easy to save it under the industry favorites category: “Sounds important and involves cloud, platforms, and future opportunities.” The problem is that this one might actually matter. Behind the familiar language sits a partnership that reflects a bigger shift, where telecom operators are increasingly positioning themselves as digital infrastructure providers, and where hyperscalers are becoming more embedded in the infrastructure that carries the growing volumes of data, applications and digital services. As the demand for AI and cloud continues to grow, will partnerships like this become the new battleground for telecom operators?

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What Was All That Back There, Then? Orange Business Announced 14 Offers at its March Summit

A man with dark hair and a slight smile, wearing a black jacket, against a light blue background.
John Marcus – Senior Principal Analyst, Enterprise Mobility and IoT Services

Summary Bullets:

• In March 2026, Orange Business unveiled 14 innovations at its summit, a mix of new products, major upgrades, and strategic repackaging.

• The summit’s offerings position Orange to lead in secure, sovereign enterprise services, driving market differentiation and revenue growth.

Orange Business was not shy about showing its work at its customer summit in Paris this March. The event generated five separate press releases, and included references to “14 breakthrough innovations” in its launch announcement for a collection of “trusted AI, cloud and secure connectivity” offers. If you weren’t paying attention, you may be forgiven for wondering what was all that back there, then?

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