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.

Slackbot Spreads Its Wings but Questions Remain

G. Willsky

Summary Bullets:

• Salesforce has integrated Slackbot more deeply into its platform, providing access purportedly to the entire Salesforce ecosystem.

• Despite positives the announcement generates concerns, the most pressing regarding security.

Salesforce has greatly extended the scope of Slackbot, the AI-driven personal work agent built into Slack, claiming it now spans the entire Salesforce platform. The change will add substantial value, keep Slack – the company – competitive with rivals, and cement the starring role Slack has come to play at Salesforce.

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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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Lack of AI Agent Oversight Brings Dueling Approaches

C. Dunlap
Research Director

Summary Bullets:

• Fast-growing use of agentic AIs within organizations has triggered agentic orchestration/governance prioritization among platform providers

• Controversy remains over two distinct approaches to orchestration: control plane construct or orchestration frameworks

Enterprises deploying AI in 2026 are turning their attention from deployment of agentic AIs to the management of growing numbers of agents being released across organizations. Companies are struggling with how to manage the hundreds or thousands of individual agents built within their organizations–agents built by different teams, running on different platforms, with inconsistent security and governance. This is problematic, considering most organizations lack visibility into agent inventory, purpose, and authorization.

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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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HCLTech Hones Its Application Development Practice, Reflecting the Disruptive Impact of AI

A woman with long, wavy dark hair and a warm smile, wearing a gray blazer and a silver necklace, posed against a neutral background.
R. Bhattacharyya

Summary Bullets:

• AI has disrupted traditional developer teams and tasks, and new processes and talent will be required to responsibly implement the intelligent automation and probabilistic nature of agentic systems.

• As enterprises drive towards a mature application landscape that is built using AI and for AI-infused applications, intelligent orchestration and integration are critical.

Although AI offers the promise of greater efficiency across a myriad of enterprise workstreams, one of the use cases with the greatest benefit is application modernization. GenAI’s effectiveness in writing and refactoring code has already been highly touted in mainstream media; less known is its use in other aspects of the software development lifecycle (SDLC). It can be used for discovery, documentation, quality assurance, autonomous testing, intelligent orchestration, and other tasks as well. Furthermore, AI is doing much more than accelerating application development; it is changing how software is engineered. Intelligence and analytics are no longer add-ons that are layered onto existing applications. Today’s applications have intelligence embedded into their workflows and decision logic, essentially creating modern apps that are designed to be AI-first.

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FinOps Takes on the AI Explosion, Including Token Management

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C. Dunlap
Research Director

Summary Bullets:

  • FinOps X conference takes place in San Diego, California (US) June 9-11, 2026.
  • Key themes will include how enterprises will operationalize AI-driven FinOps across platform engineering.

FinOps X conference in San Diego will take place in one week, and not surprisingly AI will dominate keynotes and discussions among FinOps practitioners. These experts will share insights into best practices for operationalizing AI-driven FinOps across platform engineering, including CICD, Kubernetes, and other cloud-native architectures.

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Verizon DBIR: Adversaries Weaponize AI in Stealth Attacks by Targeting Points of Exposure

A close-up portrait of a woman with light brown hair, wearing a black blazer and a light-colored turtleneck sweater, smiling softly against a light blue background.
Amy Larsen DeCarlo – Principal Analyst, Security and Data Center Services

Summary Bullets:

  • Bad actors are raising their intelligence quotient with AI, tapping it to find vulnerabilities faster and to power mobile-centric phishing campaigns.
  • Supply chains are a weak link with partner network weaknesses linked to nearly half of all breaches.

An already volatile threat landscape is becoming even more dangerous as threat actors tap AI to accelerate and improve the success of their attacks on enterprises. Verizon’s 2026 Data Breach Investigations Report (DBIR) reveals how effective adversaries have become in using AI to capitalize on enterprise weaknesses. Exploiting software vulnerabilities was the initiating factor in 31% of all breaches, notable because this is the first time in almost 20 years that it has overtaken compromised credentials as the most frequent entry point for an attack.

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April Showers Heartache on Developers Using Popular Coding Tools

Close-up portrait of a woman with blonde hair and a warm smile.
C. Dunlap
Research Director

Summary Bullets:

• Anthropic backpedals price hikes following outcry

• GitHub makes controversial move from flat-rate to usage-based billing models

April has a been a controversial and even catastrophic month for developers of popular copilots and agents.

Some enterprise and independent developers felt gut-punched following unorthodox activities including significant price increases and major subscription restructuring. Anthropic removed Claude Code from its standard Pro Plan priced at $20, offering it instead as part of its Max plan for $100 per month. Confronted with serious backlash, it was forced to reverse its decision.

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