---
title: "Slides: Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind"
category: "slides"
video_id: "SS-A8sE7hkw"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind

## Source Video
[Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind](https://www.youtube.com/watch?v=SS-A8sE7hkw)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/SS-A8sE7hkw/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-005.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/border-trim/opencv-adaptive`.
- OCR decision: ready — Chart slide with small labels and numeric bars; OCR is likely more reliable than manual transcription.

Slide text:

> Gemma4
> Arena Elo Score
> AIE
> 31B26B754B 61008397B:6858
> Engineering the future ofAl
> : AlEngg

![[assets/slides/SS-A8sE7hkw/slide-006.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-006.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: agent_vision.
- OCR decision: ready — Product/UI screenshot with dense small interface text; OCR is likely more reliable than manual transcription.

Slide text:

> ai.dev

![[assets/slides/SS-A8sE7hkw/slide-007.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-007.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Efficiency & "Intelligence-per-Parameter"
> Gemma 4 family:
> E2B, E4B, 26B A4B, 31B.
> Extreme parameter efficiency:
> max("intelligence-per-parameter")
> Performance equivalent to models up to 10x their size on targeted logic tasks.

![[assets/slides/SS-A8sE7hkw/slide-008.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-008.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> The Agentic "Thinking Tax"
> Autonomous loops, tool calls,
> and self-correction dominate
> current application demand.
> Iterative agentic workflows
> consume 5–9x more tokens than
> standard chat.
> Relying purely on pay-per-token
> "Leased Intelligence" creates
> heavy scaling operational liabilities.

![[assets/slides/SS-A8sE7hkw/slide-009.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-009.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Personal & Edge
> Personal (NPU/Mobile): Shifting execution to
> local "sunk-cost" hardware to maintain local
> data by design.
> Edge (Desktop/Single-GPU): Establishing
> fixed-cost reasoning on a 24GB-80GB
> footprint.
> Battery Priority: On mobile, power utilization is
> part of the cost and is arguably
> more critical than raw token generation cost.

![[assets/slides/SS-A8sE7hkw/slide-010.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-010.html)
- AI slide classifier: `content_slide` confidence `0.9`
- Text source: none.
- OCR decision: ready — Dense product/UI screenshot with small text; OCR is likely more efficient than manual transcription.
- Slide text: not surfaced (`illegible` by AI classifier).
![[assets/slides/SS-A8sE7hkw/slide-011.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/SS-A8sE7hkw/slide-011.html)
- AI slide classifier: `content_slide` confidence `0.91`
- Text source: agent_vision.
- OCR decision: ready — Dense slide-editor screenshot with thumbnail sidebar and UI chrome; OCR is likely useful for the embedded slide content.

Slide text:

> Gemma 4 demos


### Hidden Non-Slide Evidence
- [`slide-001.jpg`](/assets/slides/SS-A8sE7hkw/slide-001.jpg) — `speaker_stage` confidence `0.98`; Stage photo with audience and podium; not a readable presentation slide.
- [`slide-002.jpg`](/assets/slides/SS-A8sE7hkw/slide-002.jpg) — `title_card` confidence `0.97`; Speaker intro card with headshots and names; not a content slide.
- [`slide-003.jpg`](/assets/slides/SS-A8sE7hkw/slide-003.jpg) — `title_card` confidence `0.95`; Title/logo slide with minimal content; not a substantive presentation slide.
- [`slide-004.jpg`](/assets/slides/SS-A8sE7hkw/slide-004.jpg) — `title_card` confidence `0.95`; Title/logo slide with minimal content; not a substantive presentation slide.
- [`slide-012.jpg`](/assets/slides/SS-A8sE7hkw/slide-012.jpg) — `title_card` confidence `0.99`; End card/logo slate with only branding and URL; no substantive presentation content.

Classification audit: `raw/sources/slide-ai-classification/slides/SS-A8sE7hkw/audit.json`

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.