The cost for a given level of AI performance halves every 3.5 months—a rate 10 times faster than Moore's Law. This exponential improvement means entrepreneurs should pursue ideas that seem financially or computationally unfeasible today, as they will likely become practical within 12-24 months.

OpenAI's CFO highlights a key dynamic: the cost of raw compute inputs (power, memory) is rising, but the cost to produce a unit of intelligence is falling dramatically, citing a 97% cost reduction from GPT-4 to 5.4. This deflationary curve is central to their financial modeling, allowing them to price future capacity and value creation more aggressively.

The falling cost of AI infrastructure, driven by competition and supply chain improvements, will make AI so affordable that its adoption and usage will explode. This isn't just incremental growth; it's a paradigm shift in accessibility and scale.

The cost for a given level of AI capability has decreased by a factor of 100 in just one year. This radical deflation in the price of intelligence requires a complete rethinking of business models and future strategies, as intelligence becomes an abundant, cheap commodity.

The cost of AI, priced in "tokens by the drink," is falling dramatically. All inputs are on a downward cost curve, leading to a hyper-deflationary effect on the price of intelligence. This, in turn, fuels massive demand elasticity as more use cases become economically viable.

Even for complex, multi-hour tasks requiring millions of tokens, current AI agents are at least an order of magnitude cheaper than paying a human with relevant expertise. This significant cost advantage suggests that economic viability will not be a near-term bottleneck for deploying AI on increasingly sophisticated tasks.

A radical improvement in compute efficiency won't just lower costs; it will trigger Jevons' paradox, where consumption increases by more than the price drops. Making AI compute 1000x cheaper will unlock currently unimaginable applications, creating a market far larger than linear projections and potentially the largest in human history.

Arvind Krishna forecasts a 1000x drop in AI compute costs over five years. This won't just come from better chips (a 10x gain). It will be compounded by new processor architectures (another 10x) and major software optimizations like model compression and quantization (a final 10x).

Countering the narrative of insurmountable training costs, Jensen Huang argues that architectural, algorithmic, and computing stack innovations are driving down AI costs far faster than Moore's Law. He predicts a billion-fold cost reduction for token generation within a decade.