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Diagram titled 'The meter is the business.' A single amber line charts Harvey's reported gross margin across 2026: about +50% early in the year, about -50% by June as agent usage spiked on rented frontier models, and back positive after August when Harvey Tenet, its in-house open-weight model, shipped. Beside the chart, a tile reads 'about 1T to 12-13T tokens per month, Jan to May 2026.' Three cards below cover concentration risk (enterprise about 80% of Anthropic revenue; top 1% of customers about 80% of enterprise revenue), open weights as COGS strategy (Harvey, Abridge, Decagon, Ramp, Thomson Reuters), and the hybrid stack (frontier models kept for the hardest tasks). Footer reads 'Rented intelligence has unit economics' and objectedge.com.

Harvey's Margin Swing Is a Tokenomics Story, Not Only a Legal-AI Story

Harvey's gross margin went from about +50% to about -50% and back positive in 2026 as agent usage spiked on rented frontier models, then moved onto its own open-weight model. What that swing says about metered knowledge work, revenue concentration at Anthropic and OpenAI, and open weights as a cost-of-goods strategy.
Diagram titled 'Enterprise AI Has Two Operating Models' comparing two panels: Development Work (bounded, logical, testable — code, tests, PRs, and builds evaluated by compile/run/test/perf with a fast automated feedback loop, optimizing correctness of artifacts) versus Knowledge Work (contextual, subjective, operational — decisions, workflows, and audits evaluated by 'good enough to act on' with a human, governed feedback loop, optimizing correctness of action), joined by a center badge reading 'Same Discipline' and a footer listing routing, memory, classification, orchestration, and token discipline as applied to both

Enterprise AI Has Two Operating Models: Development Work and Knowledge Work

Enterprise AI is splitting into two operating models — development work and knowledge work. Why coding-copilot playbooks stall on operational workflows, and which engineering disciplines transfer.
Diagram titled 'Unveiling AI Tokenomics: Engineering Precision in Enterprise AI' showing data sources flowing through a knowledge graph layer, entity graph, episodic memory, and context-aware retrieval into token-flow optimization and semantic structure, then into an LLM that produces structured reports, generated decisions, and accurate information

Tokenomics Is the New AI Efficiency Frontier — and Here's How We're Winning It

AI tokenomics is the discipline of managing token consumption at enterprise scale. Learn how semantic infrastructure, context-aware retrieval, and agent budgeting cut AI costs without sacrificing quality.