Latent Space: The AI Engineer Podcast
Latent Space: The AI Engineer Podcast

Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah Hill-Smith

January 9, 2026

AI Summary

5 min read

🎙️ The Voices & The Context

  • The Format: Casual interview-style podcast chat with live demo of benchmarking charts.
  • The Key Players:
    • Guests: George and Micah, founders of Artificial Analysis—the leading independent AI model benchmarking site, tracking intelligence, speed, cost across 100+ models/providers. Famous for their Intelligence Index, now a 20-person company after starting as a side project.
    • Host: Swyx (podcast host), early promoter via his newsletter/podcast.
  • The Vibe: Enthusiastic and educational, blending geeky technical deep dives with optimistic AI progress hype—fun "full circle" nostalgia amid rapid ecosystem evolution.

🗝️ Key Themes & Topics

The episode traces Artificial Analysis's journey from side project to AI's "presumptive new gardener," demoing benchmarks while unpacking origins, business, evals, and trends.

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What you'll learn

  • 1 (00:00) **🎙️ Introduction: George and Micah (Artificial Analysis)**
  • 2 (01:12) **Business Model**
  • 3 (04:02) **Origins as Side Project**
  • 4 (07:05) **Benchmarking Challenges Pre-AA**
  • 5 (09:14) **Technical Eval Details**
  • 6 (14:27) **Eval Saturation and Targeting Risks**
  • 7 (16:08) **AI Grant Experience**

+ Full timestamped outline available in the app

Show Notes

don’t miss George’s AIE talk: https://www.youtube.com/watch?v=sRpqPgKeXNk

—-

From launching a side project in a Sydney basement to becoming the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities—George Cameron and Micah Hill-Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is "open" really?

We discuss:

  • The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet

  • Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers

  • The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints

  • How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)

  • The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs

  • Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding \"I don't know\"), and Claude models lead with the lowest hallucination rates despite not always being the smartest

  • GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)

  • The Openness Inde

Latent Space: The AI Engineer Podcast