How Artificial Analysis Makes Money

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

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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 Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)

• The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)

• Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future

• Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)

• V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)

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Artificial Analysis

• Website: https://artificialanalysis.ai (https://artificialanalysis.ai (\"https://artificialanalysis.ai\"))

• George Cameron on X: https://x.com/grmcameron (https://x.com/grmcameron (\"https://x.com/grmcameron\"))

• Micah Hill-Smith on X: https://x.com/_micah_h (https://x.com/_micah_h (\"https://x.com/_micah_h\"))

Chapters

• 00:00:00 Introduction: Full Circle Moment and Artificial Analysis Origins
• 00:01:08 Business Model: Independence and Revenue Streams
• 00:04:00 The Origin Story: From Legal AI to Benchmarking
• 00:07:00 Early Challenges: Cost, Methodology, and Independence
• 00:16:13 AI Grant and Moving to San Francisco
• 00:18:58 Evolution of the Intelligence Index: V1 to V3
• 00:27:55 New Benchmarks: Hallucination Rate and Omissions Index
• 00:33:19 Critical Point and Frontier Physics Problems
• 00:35:56 GDPVAL AA: Agentic Evaluation and Stirrup Harness
• 00:51:47 The Openness Index: Measuring Model Transparency
• 00:57:57 The Smiling Curve: Cost of Intelligence Paradox
• 01:04:00 Hardware Efficiency and Sparsity Trends
• 01:07:43 Reasoning vs Non-Reasoning: Token Efficiency Matters
• 01:10:47 Multimodal Benchmarking and Community Requests
• 01:14:50 Looking Ahead: V4 Intelligence Index and Beyond