# Life beyond tokenmaxxing: AI efficiency for the long term | LinearB

> Watch a 45-minute session on how to measure AI's real impact across the SDLC and win the executive conversation about engineering efficiency.

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# Life beyond tokenmaxxing: AI efficiency for the long term

On-Demand

![Email_Webinar_Stop_counting_tokes_88aced1c77](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Email_Webinar_Stop_counting_tokes_88aced1c77?_a=BAVMn6ID0)

Watch On-Demand

## Watch a 45-minute session on how to measure AI's real impact across the SDLC and win the executive conversation about engineering efficiency.

### Speakers

![Headshot3_d7231cbda7](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Headshot3_d7231cbda7?_a=BAVMn6ID0)

Andrew Zigler

GTM Engineer

LinearB

![ben_lloyd_pearson_04_2025_83ee0533d9](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/ben_lloyd_pearson_04_2025_83ee0533d9?_a=BAVMn6ID0)

Ben Lloyd Pearson

Senior Director AI & Product Marketing

LinearB

### About the workshop

Your CFO isn't interested in AI adoption anymore. Now they're asking what your investment in AI has produced. The metrics that got you through the last budget cycle - including adoption rates, token consumption, and seats activated - ring hollow in executive conversations now.

The problem isn't the strategy; it's the evidence. Your team's generating more code than ever before, but everything downstream runs at the same speed it always has - or slower. The system is lopsided, and the only way to make the imbalance visible is to measure it.

Join Andrew Zigler and Ben Lloyd Pearson for a 45-minute session on the operational measurement model engineering leaders are using to answer board-level ROI questions about AI. You'll see why effort metrics around token consumption and lines of code don't tell the full story, how AI shifted bottlenecks across the SDLC, and how APEX, LinearB's measurement framework for the AI era, gives you the system-level evidence executives have started demanding.

## Your next read

[![Cover image for The engineering productivity gap: How elite AI teams are pulling away from the rest](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_2026_AI_Benchmarks_fd0e41d857?_a=BAVMn6ID0)](https://linearb.io/resources/engineering-productivity-gap)

Workshop

[The engineering productivity gap: How elite AI teams are pulling away from the rest](https://linearb.io/resources/engineering-productivity-gap)

Watch a workshop on the new benchmark data behind the engineering productivity gap, and the measurement play that puts your team on the right side of it.

[![Cover image for From measurement to action: moving off Appfire Flow without starting over](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/webinar_appfire_flow_migration_7387e46670?_a=BAVMn6ID0)](https://linearb.io/resources/flow-migration-workshop)

Workshop

[From measurement to action: moving off Appfire Flow without starting over](https://linearb.io/resources/flow-migration-workshop)

Join a live workshop on making the move off Appfire Flow without losing your history, your team structure, or your momentum.

[![Cover image for The AI engineering productivity gap: how elite teams pull ahead in 2026](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_2026_AI_Benchmarks_fd0e41d857?_a=BAVMn6ID0)](https://linearb.io/resources/ai-engineering-productivity-gap)

Guide

[The AI engineering productivity gap: how elite teams pull ahead in 2026](https://linearb.io/resources/ai-engineering-productivity-gap)

Elite AI teams nearly doubled their merge rate in 2026 while developers using no AI stalled. See the engineering productivity gap in LinearB's 2.7M-PR data.

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