This illustrates a big problem in econometrics, specifically inflation measurement ("price indexes"): when comparing the prices of equivalent (baskets of) goods and services, "equivalent" gets harder and harder to define over longer time and space intervals.
The classic example is light bulbs [1]. If you use naive comparisons of like goods across years, with weighting by percentage of expenditures, to measure consumer/producer price changes from e.g. 1890-1930, you'll see:
1. decreasing weight in the basket and increasing price of whale-oil lamps over time
2. increasing weight in the basket and decreasing cost of light bulbs over time
But what you won't capture is that these two goods are both providing lighting, and that the switch from one to another provides a sharp drop in the price of light that is not captured by the change in prices of either in isolation.
Here it's the opposite effect, in that looking at Li-Ion battery prices in isolation overestimates the deflationary effect on energy storage prices.
This problem has also popped up in the context of measuring US manufacturing output over time. Specifically, the correction factors used to deal with this are called "price deflators", and their inability to deal with the pricing and business model of computer hardware may have produced an incorrect impression that US manufacturing output is holding steady or increasing, when it's actually decreasing [2]
[1] "Classic" as in used to draw the attention of the field in this important 1998 paper: https://lucept.files.wordpress.com/2014/11/william-nordhaus-...
[2] https://research.upjohn.org/cgi/viewcontent.cgi?referer=&htt...