Executive Briefing: Uber Burned Its Entire AI Budget Early. The Bill Was Trying to Tell Them Something.

2026-06-05 · Source: Nate’s Substack · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Operations & Process Management · Depth: Intermediate, quick

Summary

Uber recently faced a significant AI budget overrun, spending heavily on AI coding tools that led to increased engineer usage, higher token consumption, and more AI-driven commits. Despite this widespread adoption, Uber's president and COO, Andrew McDonald, stated the company could not directly link this AI usage to a clear increase in useful customer features. While this situation is widely interpreted as AI being too expensive or agents backfiring, the author argues this perspective is flawed. Instead, the core issue is the high demand for tokens, indicating that frontier intelligence and open models are becoming genuinely useful, and companies are struggling to manage agentic work with outdated controls.

Key takeaway

For AI/ML leaders evaluating the return on investment for AI coding tools and agentic systems, Uber's experience suggests looking beyond immediate budget overruns. Your focus should shift from merely cutting costs to understanding if high token demand signifies genuine utility and adoption. Prioritize scaling access to computational resources and evolving organizational controls to effectively manage advanced agentic workflows, rather than prematurely dismissing AI's value based on initial expenditure.

Key insights

High token demand, not just cost, signals useful AI adoption, challenging simplistic ROI metrics for agentic work.

Principles

• Frontier intelligence and open models are becoming genuinely useful.
• Managing agentic work requires updated organizational controls.
• High token demand can indicate a bottleneck in power, not just expense.

Topics

• AI Budget Management
• Token Usage
• Agentic AI
• AI ROI
• AI Coding Tools
• Organizational Controls

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Nate’s Substack.