It's like AI insurance

2026-06-04 · Source: Matthew Berman · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Fundamental Awareness, quick

Summary

Cognition is introducing an "AI insurance" model designed to compensate companies if their AI agents fail to deliver real value or productivity. This initiative addresses the significant investments companies are making in AI for tasks like code generation, exemplified by Uber's rapid expenditure of its annual budget in three months. Cognition has developed a sophisticated system to estimate an AI agent's output productivity and benchmark it against the time a human engineer would require for the same task. The goal is to shift the focus from mere token usage to tangible business value, ensuring that AI deployments genuinely contribute to a company's bottom line by offering financial recourse when performance falls short.

Key takeaway

For Directors of AI/ML evaluating new agent deployments, Cognition's "AI insurance" model changes the risk calculus. You can now consider AI solutions with a built-in financial safety net, mitigating the risk of underperforming investments. This shifts your focus from raw compute costs to guaranteed productivity, potentially accelerating adoption of high-value, outcome-driven AI applications within your organization.

Key insights

Cognition offers "AI insurance," compensating companies if their AI agents fail to deliver measurable productivity and real business value.

Principles

• AI value must be quantifiable.
• Productivity should exceed human effort.
• Shift focus from tokens to output.

Method

Cognition built a system to estimate AI agent output productivity and compare it against the time a human engineer would take to complete the same work, determining compensation eligibility.

In practice

• Evaluate AI agent ROI.
• Prioritize value over token usage.
• Seek performance-based AI contracts.

Topics

• AI Productivity
• AI Insurance
• AI Agent Performance
• Code Generation AI
• Value-Based AI
• AI Risk Management

Best for: CTO, VP of Engineering/Data, AI Product Manager, Director of AI/ML, Consultant, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.