Capex Fatigue, Agentic Ambitions, and the Continuing Consulting Shakeout

AI Infrastructure Skepticism Continues Rise as Capex Fatigue Meets Energy Constraints

The collision between Wall Street's patience and Silicon Valley's ambitions grew more pronounced this week as analysts and investors voiced mounting concerns about the sustainability of massive AI infrastructure spending. Perscient's semantic signature tracking language questioning massive AI infrastructure projects registered a z-score of 2.5, while our signature capturing sentiment that AI capital expenditure represents a risky bet with uncertain payoffs stood even higher at 2.7.

The numbers behind this skepticism are striking. Meta recently raised its capital expenditure forecast to between $70 billion and $72 billion, up from a prior range of $66 billion to $69 billion. Across the hyperscaler sector, roughly $400 billion flowed into AI infrastructure in 2025, yet Goldman Sachs warned this week that Big Tech may only generate half the profit needed to justify these investments. The investment bank noted that as AI capex growth begins to slow in 2026, the profit threshold required to justify existing spending will only climb higher.

Our semantic signature tracking resistance to big AI capital projects sits at a z-score of 1.8. One particularly blunt assessment circulating on social media characterized OpenAI's capex plan as something approaching "a pyramid scheme for investors," pointing to spending levels that far outpace any defensible moat. Meanwhile, a survey of investors revealed a repositioning: only 20% still favor large U.S. tech firms for AI investments, while over half now prefer energy providers, recognizing that power generation has become the binding constraint on AI progress.

Coverage from CES 2026 emphasized that the bottleneck has moved from "can we build better models?" to "can we power and cool the infrastructure to run them?" This observation found support in data showing that $98 billion in planned AI data center development was derailed in a single quarter last year by community organizing and pushback, exceeding all disruptions tracked since 2023.

The Trump administration has now entered this arena directly. President Trump announced Friday that Microsoft would make "major changes" to ensure that consumers don't see rising utility bills tied to AI infrastructure. Microsoft responded by unveiling an initiative to curb water usage and limit power consumption impacts. Our semantic signature tracking calls to regulate AI's impact on energy and water rose by 0.1 over the past week to a z-score of 1.5.

The energy constraint is forcing creative solutions. Elon Musk's xAI purchased five massive 380MW gas turbines to power over 600,000 Nvidia GB200-class GPUs, building energy infrastructure independent from public grids. Macquarie's latest memory report suggested that combined supply capacity of major memory makers can only support roughly 15GW of AI data center buildout over the next two years.

Despite these headwinds, only 7% of surveyed investors believe that AI represents a market bubble. The tension captured by elevated readings for both "AI Capex is Huge and Will Keep Growing" (z-score 1.8) and "Companies are becoming more skeptical of big AI investments" (z-score 1.1) reflects a market grappling with the gap between AI's transformative potential and the physical, financial, and operational realities of bringing that potential to fruition.

Agentic AI Emerges as the Enterprise Battleground of 2026

While infrastructure debates dominated one conversation, enterprise technology leaders focused on a different question: when will AI move from augmenting human work to performing it autonomously? Our semantic signature tracking language about improved agentic behavior as the path to LLM advancement registered a z-score of 0.5, but more striking was its trajectory, rising by 0.7 over the past week, the largest single-week increase in our dataset.

The agentic AI field is transitioning from experimental prototypes to production-ready systems. Industry analysts project that the market will surge from $7.8 billion today to over $52 billion by 2030, while Gartner predicts that 40% of enterprise applications will embed AI agents by the end of 2026, up from less than 5% in 2025. IDC forecasts that up to 40% of all Global 2000 job roles will involve working with AI agents this year.

Jason Mendel of Battery Ventures framed 2026 as the moment AI tools evolve into agents that automate full workflows rather than merely augmenting them, "delivering on the human-labor displacement value proposition in some areas." One technology executive noted on social media that enterprise AI deployment reveals capabilities "far more powerful than the chat interfaces consumers are used to," predicting that labor as a percentage of revenue will decline at many companies within five years.

The practical challenges of this transition are becoming clearer. While nearly two-thirds of organizations are experimenting with AI agents, fewer than one in four have successfully scaled them to production. Enterprises are discovering that automating existing processes designed for human workers without reimagining how work should actually be done produces disappointing results.

Our semantic signature tracking the proposition that AI is now a necessary skill declined by 0.6 over the past week, falling from 1.3 to 0.7. This moderation may reflect attention shifting from personal AI skills to system-level agentic deployments. Simultaneously, our signature tracking context and memory improvement as the path to LLM advancement rose from 0.5 to 0.6.

The infrastructure requirements for agentic AI are substantial. One analysis described "Agent Harnesses" as the next layer of infrastructure needed, systems that wrap around AI models to manage long-running tasks, providing prompt presets, human-in-the-loop handling, lifecycle hooks, and sub-agent management. McKinsey emphasized that while agentic AI is powerful, autonomy introduces new security and trust risks that leaders cannot ignore.

Some observers counsel caution. Agents may be following generative AI into what Gartner calls the trough of disillusionment. Our semantic signature tracking the proposition that productivity gains from AI haven't materialized stands at a z-score of 1.5, suggesting that enterprises are betting on agents to finally deliver the productivity improvements that earlier AI implementations failed to produce. As one analyst put it, enterprises have stopped asking "Can your AI summarize this?" and started asking "Can it take action without breaking things?"

Consulting Industry Continued to Bear the Brunt of Expected Disruption

The theoretical debates about AI's impact on knowledge work became considerably more concrete this week with McKinsey & Company's announcement that it plans to cut roughly 10% of its workforce. Our semantic signature tracking language about AI destroying jobs in consulting registered a z-score of 1.1, while the broader signature tracking AI's transformation of white-collar work remained near average at 0.1.

McKinsey's CEO Bob Sternfels explained that AI had led to a workforce shift, with half the business adding jobs and the other half shrinking. The firm now employs what it describes as 40,000 humans and 25,000 AI agents, up from only a few thousand agents eighteen months ago. The goal is for every employee to be "enabled by at least one or more agents" within the next 18 months, with AI initiatives now accounting for 40% of the firm's work.

Analysis of McKinsey's moves characterized this as signaling "an irreversible shift in how value is created in consulting." Traditionally, elite firms thrived on top talent from prestigious schools excelling in problem-solving and insight generation. Digital tools reduced information asymmetry between consultants and clients, but generative AI eliminates the edge in data crunching and recommendations that once justified premium fees.

The numbers on white-collar displacement are sobering. The World Economic Forum's Future of Jobs Report 2025 projects 92 million roles displaced by 2030, pointing to 22% churn in the global workforce with roles eliminated and recreated faster than workers can realistically transition. Over the next five years, nearly 300 million white-collar jobs may be impacted globally, with around 100 million at risk of becoming obsolete. Challenger, Gray & Christmas recorded nearly 55,000 AI-linked job cuts in 2025 alone, a category that barely existed before.

Our semantic signature tracking the proposition that AI will cause massive unemployment remained near average at a z-score of 0.3 and actually declined by 0.2 over the past week. Similarly, our signature tracking the idea that AI will create entirely new jobs remained near average at -0.1.

Some analysts urge caution in interpreting layoff announcements. Forrester noted that financially driven layoffs are being confused with AI-driven layoffs, and many companies announcing AI-related cuts do not have mature AI applications in place. "The complexity here is that many enterprises, despite how ready or not they are to successfully use AI solutions, will say that they are increasing their investments in AI to explain why they are cutting back spending in other areas or trimming workforces. In reality, AI will become the scapegoat for executives looking to cover for past mistakes."

McKinsey is changing how it hires, seeking candidates who can move between traditional consulting and an engineering mindset, people who can work alongside AI. One observer summarized the transformation: client-facing roles growing by 25%, non-client roles declining by 25%, with productivity up by 10% in shrinking functions.

After the rise of AI video tools in 2025, analysts warn that 2026 could bring visible job losses, especially in lower-level white-collar roles. Trades and hands-on professions remain less exposed for now. The combination of elevated readings for our consulting disruption signature alongside moderate readings for "AI is entering a trough of disillusionment" (z-score 1.4) suggests a narrative where AI's enterprise impact is becoming more tangible even as broader hype moderates.