From the article:

• AI has gotten cheaper more quickly than any other transformative technology in history. The cost of achieving a given level of AI performance has fallen about 47% per quarter since 2023, or 13× per year. That price drop is four times faster than DNA sequencing, six times faster than compute, 18 times faster than lithium batteries, and (in the century up to 1973) 54 times faster than electricity.

[...]

The next chart shows some examples. On January 31, 2025, OpenAI released a new iteration in its series of “reasoning” models, called o3. We estimate that for an average cost of 30 cents per question, it could achieve a 75% score on GPQA Diamond, a multiple-choice exam covering PhD-level physics, chemistry, and biology.¹ Just under 18 months later, OpenAI released GPT-5.6 Luna. It scored just as well — for four hundredths of a penny per question ($0.0004). That is a 725-fold drop in the price of thought in under 18 months. It is like the sticker price on a new car falling from $50,000 to $69. No other general-purpose technology in history appears to have gotten so cheap so fast.

[...]

Our analysis comes with major caveats. AI companies may be expressly training their models for some benchmarks (“benchmaxxing”), so that improvement on the benchmarks outstrips improvement for real-world tasks. Even if they are not, doing well on a benchmark is not synonymous with useful work. Because we focus on the frontier — the absolute cheapest model capable of any given level of performance — we implicitly posit an AI user who relentlessly searches for the most cost-effective model for each task, when real users do not switch models so often, and therefore do not reap quite the same savings. Our data are incomplete and noisy: the timeframe is barely three years, and we do not include all combinations of AI model and benchmark. Prices drop differently for different models, benchmarks, time periods, and performance ranges, and there are many reasonable ways to average over this variegated experience. Overall, while we believe that our bottom-line numbers are reasonably representative of reality, they should not be read as exact.