Record-breaking AI deflation: the cost of intelligence has plummeted to nearly zero in 30 months
We are witnessing a unique phenomenon in the history of technology: the cost of running neural networks has dropped to nearly zero over the past 30 months. This is the fastest price deflation among all known technological cycles. For comparison, personal computer prices fell by 90% over 15 years, while language models have undergone the same decline in less than three years.
Why Cheap Intelligence is Reshaping the Market
This deflation is the driving force behind all current changes in the software industry. When the cost of a technology collapses, the premium for its scarcity disappears along with it. The economics of software companies were built on a different calculation—investors valued SaaS businesses based on the assumption that development would remain expensive. This scenario is collapsing five times faster than it did in the PC era.
Such rapid deflation splits the market in two: on one hand, it destroys the pricing power of developers; on the other, it creates enormous benefits for consumers. Companies that sell intelligence are losing their pricing power, while companies that apply it are receiving a gift. Every business simply needs to figure out which side of the graph it stands on.
How Much the US and China Are Spending
Deflation is accelerating amid record investments. US private investment in AI reached $285.9 billion in 2025, while in China it amounted to only $12.4 billion—these figures come from the Stanford AI Index 2026 report.
However, the formal 23-fold gap is misleading. China calculates expenses differently: the $12.4 billion figure includes only private investments, while Chinese AI companies have also received about $184 billion in government funding since 2000. Analysts at Societe Generale also do not believe in the 23-fold gap—China's actual AI spending, in their view, is much closer to that of the US. The authorities are simply hiding it from conventional methods of calculation.
US statistics distort the picture for another reason. The impressive $285.9 billion does not fuel a competitive industry as a whole but instead accumulates among a few companies: the number of mega-rounds of $1 billion or more has jumped from 15 to 28. OpenAI's $40 billion round is the most striking example, while Google has spent over $150 billion on AI infrastructure.
My expert analysis: The US and China are pouring hundreds of billions into AI, but the technology itself is becoming cheaper so quickly that the beneficiaries are not the creators but the users. Money is piling up among a few model developers, while cheap intelligence strengthens the rest of the economy—this is the main paradox of the current cycle. For the crypto industry, this means that projects capable of effectively applying increasingly affordable AI will gain a massive competitive advantage over those trying to build it from scratch.