From Tokenmaxxing to Trusted Throughput
Mingsheng Hong
- When
- Wednesday, July 12:25 PM – 2:45 PM · 20 min
- Where
- Leadership 1San Francisco, CA · imported from ai.engineer's public schedule feed
About this session
AI adoption is accelerating, but for many engineering organizations, token consumption is now significant enough to demand real economic discipline. Drawing on Ironclad’s experience scaling AI across engineering, Mingsheng Hong will introduce the concept of trusted throughput: the rate at which teams convert AI usage into reviewed, validated, maintainable, and safely deployed customer value. He will share a practical framework for measuring AI cost and return, identifying bottlenecks in code review, CI, and merge workflows, and improving ROI through better guardrails, engineering practices, build-versus-buy decisions, and token optimization. Attendees will leave with a clearer way to evaluate AI efficiency—not by minimizing usage or rewarding tokenmaxxing, but by maximizing trusted customer value per dollar of AI spend and unit of human attention.
Speaker
VP of AI at Ironclad, Ironclad
Mingsheng Hong is a tech entrepreneur and executive specializing in AI and data infrastructure and products, with a Ph.D. in Computer Science from Cornell. He is the VP of AI at Ironclad, where he focuses on building AI-native products and features for legal contracting. Previously he worked in senior engineering leadership roles at Google and Microsoft. He also co-founded Bluesky Data, pioneering AI-driven workload optimization for modern data platforms and exited it through acquisition by Microsoft.
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