Evaluation
Why your LLM judge disagrees with your experts
An LLM that grades your AI is not a measurement until you prove it agrees with the people it replaces. Here is why it disagrees, and how to calibrate it.
July 12, 20267 min read
Argmin AI Blog
Practical deep dives on AI economics: why pilots fail, how costs scale, and what makes LLM products financially viable.
FeaturedEvaluation
How to build LLM-as-a-Judge criteria from reviewed failures
Split one quality verdict into named failure modes, each with criteria your experts signed off on.
July 21, 20268 min read
Cost Engineering ROI
Why most AI use cases fail - and how to make them economically viable. Part 3: AI Adoption Red Flags
Your AI pilot worked. Leadership was impressed. In Part 1 , we examined why most AI pilots fail economically - the trap of negative unit economics, the hidden complexity of proprietary data, and whydemos rarely reflect production reality. In Part 2 , we tackled the scaling pro...
February 8, 20268 min read
Cost Engineering ROI
Why most AI use cases fail - and how to make them economically viable. Part 2: Scaling Costs
Your AI pilot worked. Now comes the hard part: scaling without watching costs spiral out of control. Part 2 covers cost reduction strategies that cut expenses by 30–80%, the hidden gap between prototype and production economics, and why the demo was always a lie.
January 31, 20266 min read
Cost Engineering ROI
Why most AI use cases fail - and how to make them economically viable. Part 1: Why AI Fails?
With this series of articles we want to speak about why most AI use cases fail — not because the technology doesn't work, but because the economics don't. We break down the real costs behind LLM-powered systems, show how to optimize them, and give practical frameworks for deciding which use cases to scale and which to kill. Part 1 explains why most AI use cases fail economically, not technically. It opens with hard-hitting research: 95% of AI pilots never deliver measurable ROI (MIT), only 4 of 33 POCs reach production (IDC), and 42% of companies abandoned their AI initiatives in 2025 (S&P Global). The article then breaks down unit economics — the cost per useful outcome — as the foundation of viability, and shows how data type acts as a cost multiplier, with proprietary data adding 5–20× to per-request costs. It ends with a teaser for Part 2 on cost optimization and scaling.
January 31, 20267 min read
Cost Engineering ROI
Why LLM Costs Balloon: Understanding the True Cost Drivers of AI
Large Language Models (LLMs) are often perceived as inexpensive due to low per-token pricing. However, as usage scales, many organizations experience rapid and unexpected cost growth. This is not caused by a single factor, but by the combined effect of token-based pricing, inf...
December 17, 20255 min read