The price of intelligence has collapsed to zero in 30 months: a record deflation in the history of technology
The artificial intelligence market is experiencing a unique phenomenon: the cost of running neural networks has virtually dropped to zero over the past 30 months. This is the most rapid price deflation among all technology cycles we have observed in history. For comparison, personal computer prices fell by 90% over 15 years, while language models have undergone the same journey in less than three years.
Why Cheap Intelligence Changes the Game
It is this deflation that is currently driving all processes 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 completely different calculation: investors valued SaaS businesses based on the assumption that software development would remain expensive. But this scenario is unraveling five times faster than during the PC era.
Such rapid depreciation is splitting the market in two. On one hand, it destroys the pricing power of intelligence creators, and on the other, it creates enormous benefits for consumers. Companies that sell intelligence are losing control over prices, while companies that apply it are receiving a gift. Every business simply needs to figure out which side of the graph it is on.
How Much the US and China Are Spending Amid Deflation
Paradoxically, deflation is accelerating against a backdrop of record investments. US private investment in AI reached $285.9 billion in 2025, while in China it amounted to only $12.4 billion. The formal 23-fold gap is deceptive: analysts at Bull Theory rightly note that China calculates expenses differently. Chinese AI companies have also received about $184 billion through government channels since 2000.
Analysts at Societe Generale also do not believe in the 23-fold gap. In their view, China's real AI spending is much closer to that of the US—authorities are simply hiding it from conventional methods of calculation. Meanwhile, 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 from $1 billion jumped from 15 to 28, with OpenAI's $40 billion round being the most striking example. Google has spent over $150 billion on AI infrastructure.
My analysis: The US 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 is piling up with a few model developers, while cheap intelligence strengthens the rest of the economy. This is the main paradox of the current cycle: investors pay for infrastructure, while the market redistributes value in favor of consumers. In the cryptocurrency sphere, this effect will be even more pronounced, as decentralized networks and smart contracts directly benefit from the reduction in computing costs.