Rippling, after spending millions on AI tools internally in just months, launched AI Spend Console, a product that tracks individual and team spending on AI services. The company did not disclose the specific amount spent or timeframe in detail. Organizations can now monitor and potentially control how much their employees spend on AI tools, addressing the challenge of ungoverned AI tool adoption in the workplace.
Uber spent its entire 2026 AI budget within months after encouraging excessive token consumption through leaderboards, then adjusted course by improving prompt caching and efficiency metrics rather than restricting access. The company quadrupled AI tool adoption while reducing cost per token, with Uber's CTO declaring the "tokenmaxxing" trend over as enterprises shift focus to efficiency. However, this shift risks triggering Jevons paradox, where cost reductions lead to increased total spending as companies deploy more AI agents and workflows.
Coinbase, Shopify, and Ramp have each built internal coding agents for their developers but continue paying Anthropic for Claude and other frontier models, revealing that enterprises are taking ownership of the orchestration layer—the agent harness—rather than the underlying LLM. Shopify reports River participates in one out of every eight merged pull requests, while Coinbase reduced overall AI spending despite processing more tokens through intelligent routing, prompt caching, and model selection policies. This architectural pattern suggests the competitive advantage for enterprises is shifting from owning the model to controlling the execution environment, governance, and cost optimization layer that sits between developers and foundation models.
UK AI companies secured $12.6bn in funding in the first half of 2026, representing three-quarters of all British venture capital invested, but AI products show gross margins averaging 52% compared to 75–85% for traditional software due to per-query compute costs that grow rather than shrink with scale. The fastest-scaling AI companies reaching $100m ARR in 18 months operate at roughly 25% gross margins, with inference costs rising from 20% to 23% of spending as products mature while talent costs fall. This structural cost difference means founders and boards must now treat infrastructure decisions—whether to self-host hardware or use APIs—as a capital allocation question rather than an engineering preference, particularly in the UK where data centre capacity and power grid constraints impose multi-quarter planning requirements.
Simon Willison’s Weblog·2 months ago·
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Accenture's internal data reveals that non-engineers are driving excessive token consumption at the company, with inefficient practices like converting PDFs to images and then to markdown files being major culprits. According to leaked meeting audio, converting PDFs to markdown is identified as one of the biggest token consumers, suggesting widespread inefficient AI usage patterns. Companies may need to train employees on more efficient AI workflows to control spiraling inference costs.
Cloudflare released Kitesurf, a cloud-hosted browser optimized for AI agents rather than human users. The browser consumes less computing power than Chromium for standard automation tasks. This enables developers to build browser-based AI agents with reduced resource requirements and lower operational costs.
Cloudflare announced Kitesurf, a new browser built on Workers specifically for AI agents that runs in V8 isolates and is more efficient than Chromium for tasks like screenshots and HTML extraction. The browser uses Rust compiled to WebAssembly, Web Platform Tests for validation, and architectural isolation to handle untrusted web content safely without tabs, extensions, or pixel-perfect rendering. Agents can now access a lightweight browser optimized for token count and cost rather than human-centric features, available free in beta through Browser Run.
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