Practical deep dives on AI economics: why pilots fail, how costs scale, and what makes LLM products financially viable.
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...
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.
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.
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...
