The price of artificial intelligence has collapsed to zero in 30 months — a historic record of depreciation.
We are witnessing a unique phenomenon: the cost of operating neural networks has practically dropped to zero in just 30 months. This is the most rapid price deflation among all technological cycles I have ever analyzed. For comparison, personal computer prices fell by 90% over 15 years, while language models have gone through the same trajectory in less than three years.
This deflation is the key driver of 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 entirely different calculations: investors valued SaaS businesses based on the assumption that development would remain expensive. But this scenario is breaking down five times faster than in the PC era.
Such rapid deflation splits the market in two. On one hand, it destroys the pricing power of technology creators; on the other, it creates enormous benefits for consumers. Companies that sell intelligence lose control over prices, while those that apply it receive a gift from fate. Every business simply needs to figure out which side of the graph it is on.
The Investment Paradox: Who Really Wins
Deflation is accelerating amid record investments. U.S. private AI investments reached $285.9 billion in 2025, while in China they were only $12.4 billion. However, the formal 23-fold gap is deceptive. Chinese analysts calculate expenses differently: the $12.4 billion figure includes only private investments, while Chinese AI companies have received approximately $184 billion through government channels since 2000.
Societe Generale analysts also do not believe in the 23-fold gap. In their view, China's actual AI spending is much closer to that of the United States—the authorities are simply hiding it from conventional measurement methods. Meanwhile, U.S. statistics distort the picture for another reason: the impressive $285.9 billion does not fuel the entire competitive industry but instead concentrates among a few companies. The number of mega-rounds from $1 billion jumped from 15 to 28. OpenAI's $40 billion round became the most striking example, while Google spent over $150 billion on AI infrastructure.
My analysis: The U.S. and China are pouring hundreds of billions into AI, but the technology itself is becoming cheaper so quickly that the benefits go not to the creators, but to the users. Money accumulates among a few model developers, while cheap intelligence strengthens the rest of the economy—this is the main paradox of the current cycle. For crypto investors, this is a signal: projects that build a business on selling "expensive AI" are in the risk zone, while platforms using cheap intelligence for scaling are in the growth zone.