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Morgan Stanley says open-weight AI could boost demand
The launch of Moonshot AI's Kimi K3 underscores the growing momentum behind open-weight AI models, which are increasingly narrowing the gap with frontier systems developed by U.S. rivals.
With Silicon Valley increasingly split between open and closed AI development, Wall Street is now debating whether cheaper models are a threat or a tailwind for the industry's biggest winners.
In a note on Monday, Morgan Stanley (MS) said growing adoption of open-weight models such as Kimi K3, Qwen and Llama should accelerate, rather than undermine, AI usage by making the technology cheaper and more accessible to enterprises.
The brokerage argued that lower costs are likely to fuel broader deployment of AI across companies, echoing the "Jevons Paradox" theory that higher efficiency ultimately drives greater consumption.
Jevons Paradox is the idea that when a technology becomes more efficient and cheaper to use, overall consumption often rises rather than falls because more people adopt it and use it more frequently.
MS noted that many companies already use a mix of open and closed models, with open-weight systems typically deployed for coding, document parsing and other specialized tasks that require speed, customization or lower operating costs. About 63% of enterprises surveyed by McKinsey use open-source models alongside proprietary alternatives, it said.
The brokerage outlined three potential industry outcomes: a world dominated by closed AI models, a hybrid ecosystem where open and closed models coexist, and a scenario where open-weight models achieve frontier-level performance and gain wider adoption.
Across all three possibilities, one winner stands out: Nvidia NVDA.O. Morgan Stanley said the chipmaker remains a key beneficiary as expanding AI adoption continues to drive demand for computing infrastructure regardless of which model architecture prevails.
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