The per-token inference cost, the integration overhead, and the total compute bill for running AI workloads at scale are the variables enterprise procurement teams price before committing to any AI deployment. When that cost stack falls, the addressable market for AI products expands. Singapore's GIC, the sovereign wealth fund managing the city-state's foreign reserves, has publicly argued that Chinese AI models will materially cut those adoption costs, and the fund expects that compression to produce strong growth among Chinese AI companies.
The cost compression thesis
GIC's argument ties the falling cost of Chinese AI models directly to broader enterprise deployment. Lower model costs reduce the friction at the point of integration, which is where most AI pilots stall before reaching production. The fund has communicated this view publicly, though available reporting does not attach specific cost reduction figures to GIC's expectation.
The logic is recognizable from earlier technology cycles. When the foundational layer gets cheaper, the companies positioned to sell into a larger deployment base tend to benefit more than the underlying model providers absorb in margin compression.
Caution at the start-up tier
GIC's positive outlook on Chinese AI companies does not extend uniformly across the sector. The fund is specifically cautious on start-ups. In an environment where model costs are falling, that caution has a structural basis: the cost advantage that once allowed early-stage companies to attract price-sensitive customers shrinks when larger, established players can offer comparable inference at similar or lower prices. Distribution, existing enterprise relationships, and proprietary data access become the harder-to-replicate edges.
GIC's two-part view, bullish on established Chinese AI companies and cautious on start-ups, is where the actual positioning signal sits.