Why have laptops and gaming systems gotten better and cheaper over time?

Consumer electronics — laptops, gaming consoles, graphics cards — have gotten faster, smaller, and cheaper per unit of performance because the underlying chips obey a compounding cost curve that has held for roughly fifty years. The economics are not magic. They are manufacturing scale, design improvements, and decades of compounding process improvements that show up directly in the price of the final product.

The semiconductor industry’s defining feature is that the cost per transistor falls with every generation of manufacturing process. A transistor is the basic switch inside every chip. When a fab moves to a smaller process node — say, from 14 nanometers to 7 nanometers — it can fit roughly twice as many transistors in the same area, or the same number of transistors in half the area, with comparable or better performance per watt. The cost of building each fab is enormous (leading-edge fabs now cost $10–20 billion), but the cost per transistor on the resulting wafers drops roughly 30% per node on average.

This is what Gordon Moore observed in 1965 (and what the BEA paper on Moore’s Law formalizes): the number of transistors per integrated circuit doubles roughly every two years, while the cost per transistor falls by a similar factor. A 2024-vintage smartphone processor has transistors that, adjusted for inflation, would have cost tens of millions of dollars to fabricate individually in 1971.

For laptops and gaming systems, the implication is direct:

• Performance per dollar keeps rising. A mid-range laptop in 2026 typically delivers 10–20x the CPU performance, 50–100x the GPU performance, and 5–10x the memory and storage of a comparable-priced laptop from a decade earlier. Sticker prices stay flat; what you get for the price rises.
• Storage and memory scale with the chip. SSDs replaced spinning hard drives almost entirely in laptops because flash memory followed the same cost curve. The 256 GB SSD that was premium in 2014 is now entry-level; 1–2 TB is standard on mid-range machines.
• Graphics performance compounds faster than CPU. GPUs benefit more than CPUs from process shrinks because they’re parallel architectures that scale with transistor count more directly. This is why a $500 gaming laptop in 2026 runs games at settings a $2,500 desktop couldn’t match in 2014.

The Federal Reserve’s PPI series for “Semiconductor and Other Electronic Component Manufacturing” (PCU33443344 on FRED) tracks this directly: the index has fallen roughly 80–90% from its 1980s baseline, with periodic sharp drops (1985–86 when Japanese DRAM flooded the market, the 2000s as foundries consolidated) and occasional reversals (2021–2022 during the chip shortage).

There are two caveats worth knowing:

• The curve doesn’t extend forever. As process nodes shrink toward atomic-scale limits, the cost per transistor has started flattening — leading-edge fabs now cost so much that only a few companies can afford them (TSMC, Samsung, Intel). The historical “free lunch” of cheaper-and-faster every 18 months is slowing. Improvements now come more from architecture (chiplets, specialized accelerators like NPUs for AI workloads) than from raw transistor count.
• Recent price hikes are supply-chain, not trend. The 2020–2022 chip shortage and post-pandemic logistics costs pushed electronics prices up temporarily. That was a disruption of the curve, not a reversal of it.

The economic takeaway for buyers: don’t pay a premium for “future-proofing” on specs you’ll use in five years, because the same nominal dollars in three years will likely buy meaningfully more performance. If you can wait six months on a non-urgent purchase, you usually get more for the same money. The corollary for sellers: the price-performance ratio you can offer keeps improving year over year without you having to cut prices — your margins rise passively just by riding the same curve your competitors are riding.

The broader economic-indicator context is useful here — semiconductors are a leading indicator of inflation and supply-chain pressure, which is one reason the Fed watches PPI releases closely. The leading indicators answer covers how this category of data fits into the broader economic signal set, and the economic-indicator overview explains why individual data points matter less than the pattern across several.