Goldman Sachs data shows AI model costs fell as fast in three years as PC prices did in 15
An a16z analysis built on Goldman Sachs data shows large language model prices collapsing at a pace that took computers more than a decade to match
Goldman Sachs data shows that AI model prices have fallen about as much in three years as personal computer prices fell over 15.
That comparison is the core of a recent analysis by venture capital firm a16z. It suggests the AI industry is compressing a generation of tech deflation into a single product cycle.
The numbers behind the collapse
The a16z analysis tracks large language model pricing against a baseline index set at 100 in March 2023. By September 2026, the index had dropped to approximately 5.
That works out to a decline of approximately 95%. A service that cost a dollar at the start now costs about a nickel.
The benchmark the analysis uses is the personal-consumption-expenditures (PCE) price index for computers. The analysis says it took roughly 15 years for computer prices to fall by a similar amount. That decline happened during the ICT investment wave that began in the 1980s.
The analysis credits three forces for the speed of the AI price drop: intense competition among model developers, growing efficiency in proprietary models, and the arrival of cheaper open-source alternatives.
AI, tech, and the markets they move—in one daily briefing.
Daily. Free. Join 34,000+ readers across crypto, finance, and policy.
Token prices tell the same story, only louder
A separate gauge points in the same direction. The Silicon Data LLM Token Expenditure Index measures how much buyers spend per million tokens.
In August 2026, that index hit a record low of $0.97 per million tokens. That was a 29% fall from July.
It also put the index more than 50% below its peak of roughly $2.05, set in May 2026.
What cheaper AI means for the companies building it
The overcapacity risk lands most heavily on the biggest spenders. Meta, Microsoft, Amazon, and Alphabet are racing to adjust as the economics of AI shift under their feet.
Goldman’s warning boils down to this: if compute supply outruns demand, margins could come under pressure. The sustainability of those infrastructure investments could face real questions unless demand catches up with supply.
For buyers of AI services, the trend has been almost entirely favorable. Costs that would have looked absurdly low in March 2023 are now standard, and the Silicon Data index suggests the slide has not yet stopped.