The collapse in AI inference costs
The cost of running an AI model at GPT-3.5-equivalent quality fell from about USD 20 per million tokens in late 2022 to roughly USD 0.07 per million tokens by October 2024 — a more than 280-fold reduction in about 18 months. Capable AI has become dramatically cheaper to deploy.
Source: Stanford HAI — AI Index 2025: State of AI in 10 Charts (2025)
What it means
A 280-fold fall in the cost of capable AI in under two years is why applications that were uneconomic in 2023 — analysing every maintenance log, every sensor stream, every quality image — are now affordable to run continuously. For an operator the practical message is that the budget barrier to applying AI across operations has largely disappeared.
Context
The Stanford AI Index tracks the price of achieving a fixed quality threshold (about 64.8% on the MMLU benchmark) rather than the price of a single named model. Depending on the task, the report finds inference prices falling anywhere from 9 to 900 times per year. Because the metric holds quality fixed while hardware and models improve, it captures genuine economic gains rather than simple discounting.
How to interpret this data
About the source: This data comes from Stanford HAI. Public datasets like this are the foundation of fact-based decision-making in industry. When you see these numbers cited in vendor proposals or consultant reports, remember: the raw data is freely available, and the value is in how you interpret it for your specific plant and situation.
Where this matters: Generative AI in manufacturing, AI agents for industrial maintenance are built on insights like the data shown here. Rather than treat data in isolation, read the deeper guides to see how these trends translate into actionable levers for your plant.
Sector relevance: This dataset is especially relevant to Chemicals, Food Processing. These sectors face the trends and challenges you see in this chart daily — energy cost pressure, the push for decarbonization, adoption of AI and predictive maintenance. Use this data to benchmark your plant against the industry average and identify where you lag or lead.
How to use this data: Take the headline number but look deeper at the chart. Is it growing or shrinking? Which segments or regions drive the trend? Does your plant's data align, or are you an outlier? Outliers are often where the best opportunities hide — either an efficiency gap you can exploit, or a leading practice you can copy.
Related charts
How widely manufacturers have adopted AI
Industrial robot installations worldwide
Robot density in manufacturing
Related topics
Generative AI in Manufacturing: Practical Examples · AI Agents for Industrial Maintenance: What They Are and Where They Help · Large Language Model (LLM) · Generative AI · Machine Learning (Industrial)
Relevant to: Chemicals · Food Processing · Pharmaceuticals