Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah-Hill Smith
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How did Artificial Analysis start and what problem were the founders trying to solve?
This is kind of a full circle moment for us in a way. Um 'cause The like first time artificial analysis got mentioned on a podcast was you and Alicia on Lame of Space. Amazing. Which was generally
Don't even really fall. I I don't even remember doing that. But yeah, it was it was very influential to me. Um yeah, I'm looking at AI News for Gen 17 or Gen 16, 2024. Uh I said this gem of a models and host comparison site was just launched and uh and then I put in a few screenshots. And I said, um it's an independent third party. It clearly outlines the quality versus throughput. trade off and it breaks out by model and hosting provider. Yeah. Uh I did give you shit for missing fireworks. And uh how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and uh I think we just started chatting there. Welcome, George and Micah, uh to Lane Space. Uh you've I've been following your progress.
Congrats on uh an amazing year. You guys have really come together to be the presumptive new gardener of AI. Okay, how do I pay you? Let's get our internet. How do you make
money?
Um
Well, very happy to talk about that. So it's been uh like big journey the last couple of years. Artificial analysis is gonna be two years old in January 2026, which is pretty soon now. We First, run like the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that, intend to keep doing that. We have along the way built a business that is Working out pretty sustainably. We've got just over twenty people now. And Two main customer groups. So we wanna be who enterprise look to for data and insights on AI. So we want to help them with their decisions about models and technologies for building stuff.
And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So No one pays to be on the website. We've been very clear about that from the very start, because there's no use doing what we do unless it's independent AI benchmarking.
Yeah.
Um, but Turns out a bunch of that stuff can be pretty useful to companies building AI stuff.
And is it like I'm a Fortune five hundred, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop. What what what kind of engagement is that?
So we have a benchmark and insight subscription which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so for instance, one of the report is a model deployment report. How to think about choosing between serverless inference, managed deployment solutions, or leasing chips and running inference yourself. is is is an example kind of decision that big enterprises uh face and it's hard to hard to reason through. Like this AI stuff is is really new to to everybody. And so we try and help with our reports and insight subscription Companies navigate that. We also do custom private benchmarking.
And so that's very different from the public benchmarking that we publicize. And there's no commercial model around that. For but for private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things and we help them understand what they've built with private benchmarks. Benchmarking, um, you know, through the expertise mainly that we've developed through trying to support everybody uh publicly uh with our public benchmarks.
Yeah. Uh a little talk about tech stack uh behind that. But okay, I'm gonna rewind uh all the way to when you guys started this project. Uh you were all all the way in Sydney? Yeah, Syd well, Sydney, Australia for me. George
was an
SF,
but he's Australian but a moved here already.
Yeah. And um I remember I had that Zoom call with you.
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Chapters
8 chapters
1
How did Artificial Analysis start and what problem were the founders trying to solve?
0:06–9:16
2
Why do the founders run their own independent evaluations instead of using lab‑reported numbers?
9:16–19:32
3
What is the “Mystery Shopper” policy and how does it keep benchmark results unbiased?
19:32–29:34
4
How does the Intelligence Index V3 combine multiple evals into a single confidence‑scored metric?
29:34–39:29
5
What is the Omissions Index and why does it matter for model hallucination rates?
39:29–49:33
6
How does the GDP‑Val AA benchmark test real‑world agentic workflows?
49:33–58:35
7
Why is the cost of GPT‑4‑level intelligence dropping while frontier model spend is rising?
58:35–1:10:44
8
What new metrics (e.g., Openness Index, sparsity trends) are planned for Intelligence Index V4?
1:10:44–1:18:03
Speakers
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