OpenAI cannot do the same without incurring unsustainable operational losses.
For the better part of two decades, Silicon Valley has operated on a specific, highly successful export model: the zero-marginal-cost subsidy. This “Western Playbook” allowed giants like Meta and Google to acquire billions of users in the Global South effectively for free, cross-subsidising them with revenue generated in the US and Europe.
However, the emergence of Generative AI has fundamentally broken the unit economics that made this strategy viable.
The dominance of Web 2.0 platforms—Instagram, WhatsApp, YouTube—was built on a simple technical reality: these are “retrieval” architectures. When a user in Mumbai opens Instagram, the server retrieves a cached image. The cost to Meta for delivering that feed is negligible, asymptotically approaching zero.
This allowed for a straightforward commercial trade: Meta could onboard 500 million Indian users, incur almost no infrastructure penalty, and monetise the data later. Even if the Average Revenue Per User (ARPU) in developing markets was low, the cost to serve them was even lower.
The Compute Drain
AI Reality: Every token burns cash. The free ride is over.
Generative AI reverses this logic. Large Language Models (LLMs) are not retrieval engines; they are generation engines. Every time a user queries ChatGPT, a GPU cluster must perform complex calculations to generate the response fresh. This incurs a linear cost in electricity and hardware depreciation—what is known in the industry as “inference cost.”
The math for 2025 is stark. OpenAI is currently facing a projected burn rate of $5–8 billion, largely because their free tier accounts for 95% of usage.
The Cost: A rural user engaging in 50 interactions a day generates a wholesale compute cost of approximately $2.50 per month.
The Ceiling: In markets like India or Nigeria, the maximum extraction via ads or subscriptions hovers between $0.20 and $0.35 per month.
When the cost of service exceeds the revenue ceiling by a factor of ten, the “freemium” model is no longer a growth strategy; it is a financial liability.
Western tech firms are beholden to public market margins and cannot afford to subsidise billions of users when the marginal cost is tangible. Consequently, the “Next Billion Users” will likely bypass the Western cloud ecosystem entirely.
Instead of relying on expensive APIs from OpenAI or Anthropic, developing markets are shifting toward a more pragmatic, capital-efficient stack:
• On-Device Inference: Running “distilled” models locally on smartphones eliminates cloud costs.
• Sovereign Utilities: Nations like Egypt and Pakistan are investing in state-owned GPU clusters, often utilizing hardware from non-Western vendors like Huawei.
• Chinese Open-Weights: Highly efficient models such as Qwen and DeepSeek-R1 offer reasoning capabilities comparable to GPT-4 but are optimised for lower-cost infrastructure.
The era of “growth at all costs” has hit a hard physical limit. The Western funnel, designed to capture global attention via free services, cannot sustain the energy and hardware demands of the AI age.
As a result, we are witnessing a bifurcation of the global technology stack. The West will retain a centralised, high-cost cloud model, while the Global South will default to a sovereign, edge-based architecture running on local silicon and Chinese weights—entirely outside the Western revenue loop.
