AI COSTS SOAR AS TOKEN PRICES PLUMMET

The AI industry faces a fundamental economic squeeze. Token prices—the cost of processing AI queries—have collapsed as competition intensifies and models improve efficiency. Meanwhile, the hardware and infrastructure required to build these systems remains prohibitively expensive. This divergence creates a classic margin problem. Companies investing billions in GPUs and data centers generate revenue from cheaper and cheaper AI outputs. The math doesn't work for most players. The Hardware Reality Building competitive AI systems demands massive capital expenditure. Nvidia and other chip manufacturers can barely keep up with demand, yet prices for cutting-edge processors remain stubbornly high. Cloud providers and AI startups must commit enormous sums upfront, betting on future revenue that may not materialize at profitable levels. The Token Deflation Meanwhile, prices for AI tokens have dropped 80-90% in some cases over the past year. Increased competition from OpenAI, Anthropic, Google, and others has forced aggressive pricing. Larger models becoming more efficient further pressures rates. The Investor Question Equity investors face a puzzle: who survives this squeeze? The answer likely depends on scale. Large cloud providers with existing infrastructure and diverse revenue streams may absorb losses. Smaller AI-focused startups lack this cushion. Several scenarios emerge. Consolidation could reduce the number of viable competitors, allowing survivors to raise prices. Alternatively, commoditization continues and only infrastructure providers (chip makers, cloud giants) capture meaningful margins. A third possibility: breakthrough applications emerge that justify current spending. For now, the sector operates in uncomfortable territory. The cost of entry remains astronomical while the cost of output approaches commodity pricing. This sustainability question will dominate investor conversations through the coming quarters.