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In a post on X, Armstrong weighed in on the economics of AI while responding to comments from Delphi Ventures founding partner Tom Shaughnessy, who warned that the industry's current pricing structure may be unsustainable.
Armstrong argued that demand for AI remains "near infinite," predicting that roughly 80% of workloads could shift to models that are 99% cheaper within the next 12 to 18 months.
The remaining 20%, he said, will likely rely on cutting-edge systems for tasks requiring maximum performance, including scientific research and advanced AI agents.
"The limiting factor will be energy and compute, not better models," Armstrong wrote.
He compared AI model selection to consumer hardware markets, arguing that most users will not require top-tier intelligence in the same way most consumers do not purchase maxed-out gaming PCs.
Open-Source AI And Pricing Pressure Debate Intensifies
In his post, Shaughnessy said that AI companies may struggle to maintain margins as customers increasingly turn to cheaper alternatives.
He argued subscription pricing is masking the true cost of heavy AI usage and suggested businesses shifting toward APIs are discovering that metered pricing can quickly inflate costs.
Shaughnessy also pointed to open-source models and inference providers as a growing threat, arguing that lower-cost alternatives could limit the pricing power of major AI labs.
AI Costs Surge As Execs Warn Of Budget Strain
Previously, venture capitalist Chamath Palihapitiya also said that many companies are overpaying for premium AI models despite cheaper alternatives quickly narrowing the performance gap.
Earlier, Nvidia Corp.
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