---
title: "Dense Slides: Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)"
category: "slides"
video_id: "PmZDupFP3UM"
sourceLabels: ["Captured video frames", "Local OpenCV slide-region detection"]
---

# Dense Slides: Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)

## Source Video
[Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)](https://www.youtube.com/watch?v=PmZDupFP3UM)

## Method
This deck is slide-only. The existing captured video frame set supplies candidate frames, then local OpenCV rejects sponsor/title/speaker-only frames, crops visible slide surfaces, deduplicates, and saves the cropped slide images.

## Cropped Visible Slides
![[assets/dense-slides/PmZDupFP3UM/slide-001.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-001.html)
- AI slide classifier: `title_card` confidence `0.94`
- Text source: agent_vision.

Slide text:

> The AI strategy playbook for senior executives

![[assets/dense-slides/PmZDupFP3UM/slide-002.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-002.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Mixed slide with embedded article screenshot and chart; OCR will read the dense text more reliably than manual transcription.

Slide text:

> more productive. Here's how it measures that. Sundar Pichai says Al is making Google engineers 10%
> By Huh Lrdsy
> Against Expert Forecasts and Developer Self-Reports, Early-2025 Ai Slows Down Exporienced Opon-Source Dovelopers meir
> In thr Rc!, 16 detopers wth moderte A +prance complete 2ke ura in largt ind compher Drojtt on which thty hiit t imttot o S ytst of orior tit'itnce.
> Change in time when A 50% HO
> Economles tipet trtchLt Hl epet tortcrt Drsthpt taecil Drlope iLrees dunrgtiuy ooaitd
> AIE/LEAD LEADERSHIP IN AI-ASSISTED ENGINEERING
> Google DeepMind PrEsented by JUSTIN REOCK 7 Deputy CTO DX

![[assets/dense-slides/PmZDupFP3UM/slide-003.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-003.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Dense chart slide with small labels and a quantitative callout; OCR is appropriate.

Slide text:

> Al seems to deliver modest gains in quality metrics
> Average Change Confldence DXl drlver for Al Users vs. Non-Al Users
> Data from t9,?51 doveioprt 18 pt
> Avtrsgo chtrge contldence score: Al usert vs. non users gain max
> DX1 Score Gain from 2.6 Polnt Average using Al
> 15 pt loss max
> DX
> AIE/LEAD LEADERSHIP IN AI-ASSISTED ENGINEERING
> Google DeepMind PReSented BY JUSTIN REOCK / Deputy CTO DX

![[assets/dense-slides/PmZDupFP3UM/slide-004.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-004.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Dense chart slide with many per-company bars and small labels; OCR is appropriate.

Slide text:

> Industry averages hide the big picture of quality impact
> Per-company Change Failure Rate% impact for Al Users vs. Non-Al Users
> Data from 61 compsalts.
> Change Failure Rate % Impact Per-conpsty Change Falure Rate lmpsct writh Al sdoption
> DX
> G\OB/0O
> AIE/LEAD LEADERSHIP IN AI-ASSISTED ENGINEERING
> Google DeepMind Presented b? JUSTIN REOCK / Deputy CTO: DX

![[assets/dense-slides/PmZDupFP3UM/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-005.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: agent_vision.
- OCR decision: ready — Chart-heavy slide with small labels; OCR is appropriate even though the main title is readable directly.

Slide text:

> Engineering the future of AI

![[assets/dense-slides/PmZDupFP3UM/slide-006.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/PmZDupFP3UM/slide-006.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Table slide with dense small text and multiple columns; OCR is appropriate.

Slide text:

> Measuring GenAl impact
> Metric Type Good For Not Good For Challenges
> Telemetry metrics developer output Measuring impact on Understanding how toois are being used Quantitying ROl Limited, possibly Inaccurate Incomplete story Insight
> Experlence sampling Quantitying ROl Identifying best use cases Colocting targe amounts ot data at once time Difficut to set up Must be run over perlod of
> Self-reported developer satsfaction, Measuring adoption, productivity Quantifying ROl Particlpatlon rates Can onty be run periodically
> GB0の
> AIE/LEAD LEADERSHIP IN AI-ASSISTED ENGINEERING
> Google DeepMind Prtsfnred ey JUSTIN REOCK / Deputy CTO DX


Classification audit: `raw/sources/slide-ai-classification/dense/PmZDupFP3UM/audit.json`