OpenAI's Monetization Puzzle Poses Systemic Economic Risk

OpenAI Chief Financial Officer Sarah Friar's recent public pitch for investor confidence reveals troubling gaps in the artificial intelligence pioneer's path to profitability – gaps that could have cascading effects throughout the global economy. Despite projecting $20+ billion revenue for 2025, Friar's central argument that increased compute spending directly drives monetization faces harsh scrutiny from financial analysts and economists.

Friar's presentation highlighted OpenAI's compute capacity explosion, growing from 0.2 gigawatts in 2023 to 1.9 GW in 2025 alongside claimed 10x revenue growth. "We firmly believe that more compute in these periods would have led to faster customer adoption and monetization," she stated, framing AI infrastructure investment as an automatic revenue generator. This logic underpins OpenAI's strategy to pursue increasingly capital-intensive projects in scientific research, drug discovery, and enterprise applications.

However, HSBC's 2025 analysis exposes fundamental flaws in this premise. The bank estimates OpenAI would require $207 billion in new financing by 2030 to sustain operations, noting that 95% of ChatGPT's 800 million users pay nothing despite generating 70% of current revenue. With subscription rates hovering near 8-10% and advertising models unproven, the disconnect between usage and monetization threatens OpenAI's survival absent perpetual investor subsidies.

The systemic risk emerges from OpenAI's embedded position in global tech ecosystems. Nvidia, Microsoft, Oracle, AMD, and CoreWeave maintain interdependent partnerships involving complex equity arrangements with OpenAI. As International Monetary Fund chief economist Pierre-Olivier Gourinchas recently warned, there are "reasons to be somewhat concerned about the risk of a market correction" if AI's promised productivity gains fail to materialize. This concern gains urgency given AI's disproportionate economic impact: the sector drove 40% of US GDP growth and 80% of US stock market gains in 2025 according to Financial Times analysis.

Friar's vision of "new economic models" emerging through licensing and outcome-based pricing mirrors early internet-era optimism but ignores critical differences. Unlike web services, generative AI faces:

1. Exponentially higher operational costs ($700,000 daily for ChatGPT alone)
2. Regulatory uncertainty across GDPR, CCPA and emerging AI governance frameworks
3. Enterprise hesitation due to accuracy gaps and intellectual property concerns
4. Consumer monetization resistance for tools perceived as public utilities

With the IMF forecasting AI-driven US growth to outpace G7 nations through 2027, the stakes extend beyond Silicon Valley. OpenAI's gamble represents a microcosm of the broader AI investment bubble – one where trillions in market valuation depend on unproven monetization theories. As Friar urges faith in emergent business models, global economies balance precariously on the assumption that intelligence can indeed be harnessed profitably, not merely expensively.