---
title: "Slides: Trends Across the AI Frontier — George Cameron, ArtificialAnalysis.ai"
category: "slides"
video_id: "sRpqPgKeXNk"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Trends Across the AI Frontier — George Cameron, ArtificialAnalysis.ai

## Source Video
[Trends Across the AI Frontier — George Cameron, ArtificialAnalysis.ai](https://www.youtube.com/watch?v=sRpqPgKeXNk)

## 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/sRpqPgKeXNk/slide-001.jpg]]

OCR text:

> INNOVATIONPARTNER
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> PLATINUMSPONSORS
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> WWindsurf
> MongoDB
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![[assets/slides/sRpqPgKeXNk/slide-002.jpg]]

OCR text:

> AI Engineer
> World's Fair
> Artificial Analysis
> Trends Across the AI Frontier
> Presented by
> George Cameron, Co-founder at Artificial Analysis
> AI Engineer World's Fair
> Engineering the future of AI

![[assets/slides/sRpqPgKeXNk/slide-003.jpg]]

OCR text:

> World's Fair Artificial Analysis is a leading independent Al benchmarking company
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![[assets/slides/sRpqPgKeXNk/slide-004.jpg]]

OCR text:

> ] Frontier Intelligence: OpenAl, Google , DeepSeek and xAl lead frontier intelligence with
> their latest reasoning models, followed closely by other labs
> Leading Large Language Model (LLMs), by Al lab
> Artticial Analysis Intelligence index (incorporates MMLU-Pro, GPQA, Humanity’s Last Exam, LivoCodeBench, SciCode, AME, MATH-500)
> A) Artificial Analysis
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![[assets/slides/sRpqPgKeXNk/slide-005.jpg]]

OCR text:

> 1) Reasoning vs. Non-reasoning |
> j Reasoning models: Treating reasoning & non-reasoning models as distinct categories is
> a helpful framework for understanding today’s model landscape
> Intelligence vs. Output Tokens Used to Run Artificial Analysis Intelligence Index
> Artificial Analysis intelligence index (Version 2, released Feb 25), Output Tokens Used (~SM input tokens)
> Most attractive quadrant
> 5 vay Geman? A Artificial Anatysis
> 
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> 2 (Now 24)
> 4M Ge 20M 30M 70M 100M 3008
> Output Tokens Used In Artificial Analysis Intelligence Index (Log Scale)
> A. Artificial Anatysis

![[assets/slides/sRpqPgKeXNk/slide-006.jpg]]

OCR text:

> OL?
> j Reasoning model latency: Reasoning models are slower to provide their response,
> making them less suitable for latency sensitive tasks
> End-to-End Response Time
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![[assets/slides/sRpqPgKeXNk/slide-007.jpg]]

OCR text:

> @ Cee ee Me Lae
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> | Today's open weights frontier is led by China-based Al labs, namely DeepSeek and Alibaba
> Reasoning: Open Weights Language Models, by Country Non-reasoning: Open Weights Language Models, by Country
> Artificial Analysis Intelligence Index, leading open weights reasoning models Artificial Analysis Intelligence index, leading open weights non-reasoning models
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> Feo — er Te As Artificial Anatysis

![[assets/slides/sRpqPgKeXNk/slide-008.jpg]]

OCR text:

> J Overall cost is a function of both cost per token and tokens per query; we now see a
> >500X spread in the cost to run Artificial Analysis Intelligence Index
> Cost to Run Artificial Analysis Intelligence Index
> Cost (USD) ta run all evatuations in the Artificial Analysis intelligence Index (Version 2, released Feb 25, ~5M input tokens) Reasoning models
> Input Cost MB Reasoning Cost MM Output Cost NON-EXHAUSTIVE
> A) Artificial Analysis
> $1952
> $1485 $1335
> $228
> ia $627
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> $106 $76 $66 $65 84917 $3 $10 $7 $3
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![[assets/slides/sRpqPgKeXNk/slide-009.jpg]]

OCR text:

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![[assets/slides/sRpqPgKeXNk/slide-010.jpg]]

OCR text:

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![[assets/slides/sRpqPgKeXNk/slide-011.jpg]]

OCR text:

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> rere a Engineering the future of Al

## 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.