Update app.py

app.py

@@ -177,9 +177,8 @@ with st.sidebar.expander("More on Energy Mix"):
     st.markdown("""
     **Why Gas?**
     The electric grid in major hubs like Texas is effectively "sold out," with wait times for connection approaching 5 years. To bypass this, AI labs are adopting "Bring Your Own Generation" (BYOG) strategies, primarily using natural gas which can be deployed in months rather than years. In fact, current projections suggest that **nearly a third of all new data center development will deploy behind-the-meter (BTM) gas generation** to circumvent these bottlenecks.$^{1,2}$
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-    * Natural gas is the backbone of the U.S. power system, accounting for approximately **43% of total utility-scale electricity generation** in 2023.$^{3,4}$
-    * "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.$^{5}$ However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.$^{5,6}$
+    Even when projects connected to the grid, natural gas is the backbone of the US power system, accounting for approximately **43% of total utility-scale electricity generation** in 2023.$^{3,4}$
+    "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.$^{5}$ However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.$^{5,6}$
 
     **What about Solar?**
     While solar prices have dropped ~88% since 2009, it faces physical limits:$^{5}$