Anthropic's Enterprise Surge, Energy Infrastructure Urgency, and the Ongoing AI Reckoning for Consulting

Anthropic's Rapid Ascent

Claude Code reached $1 billion in annualized run-rate revenue just six months after becoming generally available, a velocity that even ChatGPT did not match during its own breakout period. By January 2026, that figure had accelerated further, and internal estimates suggest that the tool is now closer to $2 billion ARR than $1 billion. Sacra's latest projections place Anthropic's total annualized revenue at $14 billion in February 2026, up from $9 billion at the end of 2025 and just $1 billion at the close of 2024. Enterprise and startup API calls continue to drive the majority of this revenue through pay-per-token pricing.

Perscient's semantic signature tracking the density of language asserting that Anthropic's competitive leadership stands at an Index Value of 315, among the highest readings in the dataset, and rose by 78 points over the past week. According to Menlo Ventures data published by Evident Insights, 40% of enterprise workloads now flow to Anthropic models, compared to 27% for OpenAI and 21% for Google. At Davos, Anthropic CEO Dario Amodei noted that 80% of his company's business now comes from enterprise customers, compared to OpenAI's 40%.

Andreessen Horowitz's latest analysis shows that Anthropic posted the largest share increase of any frontier lab since May 2025, growing by 25% in enterprise penetration. Today, 44% of enterprises are using Anthropic in production, rising to over 63% when including testing environments. Wallet share tells a similar story: while OpenAI still commands approximately 56%, Anthropic and Gemini are steadily gaining at its expense.

Anthropic generates approximately $211 per monthly user, while OpenAI generates roughly $25 per weekly user. This 8x difference reflects the value of enterprise-focused positioning. As one analyst observed on X, "Anthropic optimizes for workflows. Fewer users, but those users run Claude for hours at a time building software, processing documents, automating operations."

Claude Opus 4.6 represents the first Opus-class model to feature a 1 million token context window in beta. On GDPval-AA, a benchmark measuring economically valuable knowledge work across finance, legal, and other domains, the model outperforms OpenAI's GPT-5.2 by approximately 144 Elo points. NVIDIA CEO Jensen Huang recently stated, "Claude is incredible. Anthropic made a huge leap in coding and reasoning. Nvidia uses it all over. Every software company needs to use it."

Meanwhile, our semantic signature tracking language asserting that OpenAI's competitive leadership declined by 6 points this week to a Current Value of -14. The signature tracking language asserting that Grok or X AI is winning the AI race rose by 59 points to a Current Value of 57, indicating that media attention is increasingly focused on a multi-player competitive narrative. Noah Smith's recent analysis posed the question directly: "What if AI succeeds but OpenAI fails?"

The Wall Street Journal reported on the phenomenon of getting "Claude-pilled," describing the moment software engineers, executives, and investors turn their work over to Claude and witness capabilities that surprise even in an age awash in powerful AI tools. Claude's contribution has led to a splintering of the generative AI market and loss of market share from both ChatGPT and Gemini. Yet despite this rapid ascent, 81% of Americans have never heard of Anthropic.

AI Capex Drives Energy Infrastructure Into Focus Once Again

The competitive dynamics reshaping the AI market depend on infrastructure, however, that is increasingly strained. While the initial AI infrastructure race centered on chip supply and model architecture, the binding constraint has now moved to electricity. Perscient's semantic signature tracking the density of language asserting that energy infrastructure will determine the AI winner stands at a Current Value of 284, among the highest readings in the dataset, and rose by 7 points over the past week.

Big tech firms are projected to spend approximately $600 billion on AI infrastructure in 2026 alone. Yahoo Finance reported from Davos that global power usage by data centers is expected to grow from around 55 gigawatts currently to 84 gigawatts within the next two years. The International Energy Agency's World Energy Outlook 2025 projects that electricity demand will rise by at least 40% by 2035, with AI workloads accounting for a growing share.

Our semantic signature tracking the density of language asserting that AI capital expenditure is large and will keep growing registered a Current Value of 88, while the signature tracking language predicting continued expansion of hyperscale builds stands at 87. As one X user summarized former Google CEO Eric Schmidt's recent comments, "Hyperscalers want up to 10 GW each. The industry needs 80 GW in the next 3-5 years, equivalent to 50+ nuclear plants."

NERC's latest assessment warns that the North American electric grid faces intensifying reliability risks over the next decade because demand growth driven by data centers and AI outpaces new resource additions. Bloomberg reported that as many as 151 million Americans are at high risk of power shortfalls or outright blackouts over the next five years. Most of the grid was built between the 1950s and 1970s, and approximately 70% is approaching the end of its life cycle.

One industry analyst noted that 48 GW of proposed data centers, roughly 33% of all planned capacity, now plan to skip the grid entirely by building their own power plants. This represents a dramatic change from December 2024, when less than 2 GW of planned capacity intended to bypass utility connections. Transformer lead times have stretched from 40 weeks pre-pandemic to 2.5-3 years now.

Siemens Energy's record-breaking performance in Q1 FY 2026 illustrates the opportunity. Order intake reached nearly €18 billion, and the company explicitly linked this growth to "enormous" demand from data centers, which accounted for orders in the "high triple-digit-million-euro volume" in the US. Electric utility balance sheets face their own pressures, and an expected doubling of power demand by 2030 accounts for about 55% of the forecast growth in peak load.

Our semantic signature tracking the density of language characterizing AI infrastructure spending as a dangerous gamble declined by 11 points this week to an Index Value of 62. The signature tracking language comparing AI spending to telecom overbuilding in the 1990s fell by 22 points to 12% above its long-term average. The prevailing narrative has moved from questioning whether the build-out is justified to grappling with how to execute it.

AI's Impact on Consulting Jobs Remains the Leading Workforce Narrative

The infrastructure investments driving AI capability are now translating into workforce disruption, with the consulting sector remaining the focal point of intensifying concern about AI's impact on professional employment. Perscient's semantic signature tracking the density of language predicting that AI will eliminate consulting industry positions registered the largest one-week change in the dataset, rising by 116 points to an Index Value of 273.

According to preliminary findings from Mercer's Global Talent Trends 2026 report, which surveyed 12,000 people worldwide, employee concerns about job loss due to AI have risen from 28% in 2024 to 40% in 2026. Mercer's research shows that 62% of employees feel that leaders underestimate AI's emotional and psychological impact. Deutsche Bank analysts wrote that "Anxiety about AI will go from a low hum to a loud roar this year."

IMF Managing Director Kristalina Georgieva described AI at Davos as "a major factor for economic growth," noting its "potential to up of 0.8% boost to growth over the next years, but it is hitting the labor market like a tsunami, and most countries and most businesses are not prepared for it." She emphasized that tasks being eliminated are usually what entry-level jobs present, leaving young people finding it harder to secure good placements.

According to McKinsey Global Institute analysis, approximately 45% of activities performed by consultants could be automated using existing technology. Anthropic CEO Dario Amodei warned that "50% of entry-level white-collar jobs could be disrupted within the next 1-5 years. From law to finance to consulting, he warns that AI is capable of handling a wide range of knowledge work." As one industry observer noted, "Most consulting work isn't $2M strategic transformations. It's $50K projects where junior analysts spend three weeks pulling data, building models, and packaging insights into slides. That's the vulnerable layer."

Our semantic signature tracking the density of language predicting that AI will eliminate financial analyst roles rose by 89 points to 63, while the signature tracking language predicting that AI will eliminate legal profession employment rose by 36 points to 42. The signature tracking language asserting that AI will increase mental health problems stands at an Index Value of 119.

The major consulting firms are racing to automate their own junior staff before clients realize that they can bypass intermediaries entirely. McKinsey has built "Lilli," and Bain and Deloitte are constructing their own internal AI systems. Mark Zuckerberg, on Meta's Q4 earnings call, stated that with AI, one talented person can now run projects that previously required large teams.

Yet the narrative contains countervailing elements. Our semantic signature tracking the density of language asserting that AI competency is required for employment stands at a Index Value of 40, while the signature tracking language predicting that AI will cause widespread joblessness rose by 9 points to 13. Research indicates that AI skills helped offset conventional disadvantages in hiring: older applicants and candidates without advanced degrees saw their prospects improve substantially when AI skills appeared on their résumés, and effects strengthened when supported by recognized certificates.

According to Future Market Insights, the AI consulting services market is expected to rise from $11.1 billion in 2026 to nearly $91 billion by 2035, showing a compound annual growth rate of about 26.2%. As one commentator observed, "The low hanging fruit of advisory work is now gone. For any expert that wants to bring real value to the table, knowledge is simply no longer enough. They must bring their experience."