diff --git a/contents/core/sustainable_ai/sustainable_ai.qmd b/contents/core/sustainable_ai/sustainable_ai.qmd
index b5961b6f..c449646f 100644
--- a/contents/core/sustainable_ai/sustainable_ai.qmd
+++ b/contents/core/sustainable_ai/sustainable_ai.qmd
@@ -101,7 +101,7 @@ Developing and training AI models requires immense data, computing power, and en
 
 This concept is reflected in the demand for training and inference hardware in data centers and on the edge. Inference refers to using a trained model to make predictions or decisions on real-world data. According to a [recent McKinsey analysis](https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Artificial%20intelligence%20hardware%20New%20opportunities%20for%20semiconductor%20companies/Artificial-intelligence-hardware.ashx), the need for advanced systems to train ever-larger models is rapidly growing. 
 
-However, inference computations already make up a dominant and increasing portion of total AI workloads, as shown in @fig-mckinsey. Running real-time inference with trained models--whether for image classification, speech recognition, or predictive analytics--invariably demands computing hardware like servers and chips. However, even a model handling thousands of facial recognition requests or natural language queries daily is dwarfed by massive platforms like Meta. Where inference on millions of photos and videos shared on social media, the infrastructure energy requirements continue to scale!
+However, inference computations already make up a dominant and increasing portion of total AI workloads, as shown in @fig-mckinsey. Running real-time inference with trained models--whether for image classification, speech recognition, or predictive analytics--invariably demands computing hardware like servers and chips. However, even a model handling thousands of facial recognition requests or natural language queries daily is dwarfed by massive platforms like Meta. Where inference on millions of photos and videos shared on social media, the infrastructure energy requirements continue to scale.
 
 ![Market size for inference and training hardware. Source: [McKinsey.](https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Artificial%20intelligence%20hardware%20New%20opportunities%20for%20semiconductor%20companies/Artificial-intelligence-hardware.ashx)](images/png/mckinsey_analysis.png){#fig-mckinsey}