AI investment is highly concentrated. While median firms spend trivially (around $12/employee/month), the top 1% spend thousands. This intense use by a few explains why AI's impact isn't yet visible in broad productivity statistics.

Top AI providers see 80% of revenue from 1% of customers, a power law uncommon in SaaS. This distribution mirrors the revenue of top US companies, suggesting large enterprises treat AI spend as a core operational cost proportional to their total revenue, not a typical per-seat software expense.

Analysis shows a massive revenue growth gap between companies investing heavily in AI and those that don't. Over the last three years, high AI spenders grew revenue over 100%, compared to 15-20% for non-spenders. This provides strong quantitative evidence that AI spending directly drives significant top-line growth.

Data reveals an extreme power law where model labs OpenAI and Anthropic capture nearly all AI startup revenue, and their share is growing. This indicates value is accruing to the foundational layer, posing an existential threat to the long-term viability of application-focused startups.

Analysis of Shopify's internal AI usage reveals a significant trend: the top percentile of users are increasing their token consumption much faster than others. The CTO finds this skew "not ideal," fearing it could lead to extreme imbalances in resource utilization.

A small cohort of power users are achieving massive productivity gains with AI, while most companies are stuck at the most basic stages. This creates a widening competitive gap where firms that master simple access and training will dramatically outperform those mired in bureaucratic inertia.

Data from Ramp Economics Lab reveals that enterprise AI spending is highly concentrated, with 1% of companies accounting for 80% of revenue. This power law distribution is uncommon for software but mirrors the concentration of total revenue among US businesses, suggesting AI spend is treated like a marketing line item proportional to a company's overall revenue.

Unlike traditional software where businesses consolidate on single vendors, the most advanced AI adopters actively use a multi-vendor strategy. The top 1% of AI spenders use an average of eight different vendors to leverage the best model for each task and stay ahead in a rapidly innovating market.

A study by fintech company Ramp revealed a strong, recent correlation between AI spending and business performance. Customers in the top quartile for AI spend doubled their revenue, while the bottom quartile saw flat growth. This link was absent just six months prior, signaling AI's shift from experiment to growth driver.